define general annotation

Anxiety Disorder. (2013). NIMH Website. Retrieved from: http://www.nimh.nih.gov/health... This is a comprehensive listing of anxiety-related disorders with descriptions of each disorder and narratives from those who have coped with the symptoms. The site discusses how sufferers can get help and what resources are available. There is information about research currently underway to help with these disorders.The National Institute of Mental Health is a renowned organization committed to the education of individuals on mental health issues as well as research and dissemination of information pertaining to all aspects of mental health. This site is a useful tool to understand anxiety disorders and how they affect those suffering from them. Dimeff, Linda, Koerner, Kelly, and Linehand, Marsha. Dialectical Behavior Therapy in Clinical Practice: Applications across Disorders and Settings. Guilford Press. 2007. Dialectical Behavior Therapy, initially created as a means of treatment for those with bipolar disorder who showed suicidal tendencies, is now a more generalized method of treatment, established as effective for many psychological disorders. This book outlines the method and its increased usage. Guilford Press is a publisher of many reputable books, both scholarly and in the self-help genre, that relate to psychology and psychiatry. The authors are highly knowledgeable in their field of practice making the source highly reliable Magnitude of placebo response and drug-placebo differences across psychiatric disorders. (2004). Psychological Medicine. Retrieved from http://journals.cambridge.org/... This article discusses the usage and effectiveness of various drugs in treatment for myriad psychiatric disorders, including anxiety. Six different disorders were studied using placebos to study the effects Published by Cambridge Press, a respected and renowned publication, this scholarly article is highly informative, and the data considered reliable Self Help Publications. (2013). Anxiety and Depression Association of America. Retrieved from http://www.adaa.org/finding-he... This site is a useful tool to find resources to help those dealing with anxiety-related issues, no matter what the disorder. It is useful for various age ranges, giving information for adults as well as how to help teens or young children. Furthermore, the list offers some informative texts that would be helpful to those whose family members, friends, or other loved ones are trying to cope with anxiety-related disorders. Composed by a reputable organization, the Anxiety and Depression Association of America, this list is a useful means of locating print resources to learn more about anxiety and how to help oneself, or others. Some treatment methods are discussed in detail in some publications, as well, helping researchers and others to better understand some of the specifics of treatment options.

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An annotation serves as a brief summary of the law and the facts of a case and demonstrates how a particular law enacted by Congress or a state legislature is interpreted and applied. Annotations usually follow the text of the statute they interpret in annotated statutes.

ANNOTATION, civil law. The designation of a place of deportation. Dig. 32, 1, 3 or the summoning of an, absentee. Dig. lib. 5. 2. In another sense, annotations were the answers of the prince to questions put to him by private persons respecting some doubtful point of law. See Rescript.

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To Work with General Annotations

Use the General Annotation commands to add dimensions, hole notes, and surface texture symbols to the 3D model.

What's New: 2021.2 , 2023 .

Before you begin, set the units in Document Settings, Standards tab, and then select the desired standard in the Annotations, Active Standard drop-list.

Add or edit dimensions

Change the required dimension values and then click OK.

Extract existing dimensions

Right-click a feature in the browser and select Show Dimensions.

If the model dimension includes tolerance information, it is included in the promoted dimension.

Add or edit hole/thread notes

Holes and threads annotated by a hole/thread note cross-highlight when you select the leader or the browser node.

Change the required values and then click OK.

Add or edit surface texture symbols

Surface Texture annotations are created with a single-segment leader by default. To create more leader segments, start the command, right-click, and click Single-Segment Leader to unselect the option.

Add or edit datum targets

Optionally, before you start the command create 2D sketch points or place Work Points.

Change the required values in the property panel and then click OK.

an·no·ta·tion

An•no•ta•tion.

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Koonin EV, Galperin MY. Sequence - Evolution - Function: Computational Approaches in Comparative Genomics. Boston: Kluwer Academic; 2003.

Cover of Sequence - Evolution - Function

Sequence - Evolution - Function: Computational Approaches in Comparative Genomics.

Chapter 5 genome annotation and analysis.

In the preceding chapter, we gave a brief overview of the methods that are commonly used for identification of protein-coding genes and analysis of protein sequences. Here, we turn to one of the main subjects of this book, namely, how these methods are applied to the task of primary analysis of genomes, which often goes under the name of “genome annotation”. Many researchers still view genome annotation as a notoriously unreliable and inaccurate process. There are excellent reasons for this opinion: genome annotation produces a considerable number of errors and some outright ridiculous “identifications” (see 3.1.3 and further discussion in this chapter). These errors are highly visible, even when the error rate is quite low: because of the large numbers of genes in most genomes, the errors are also rather numerous. Some of the problems and challenges faced by genome annotation are an issue of quantity turning into quality: an analysis that can be easily and reliably done by a qualified researcher for one or ten protein sequences becomes difficult and error-prone for the same scientist and much more so for an automated tool when the task is scaled up to 10,000 sequences. We discuss here the performance of manual, automated, and mixed approaches in genome annotation and ways to avoid some common pitfalls. Mostly, however, we concentrate in this chapter on the so-called context methods of genome analysis, which are the recent excitement in the annotation field. These approaches go beyond individual genes and explicitly take advantage of genome comparison.

5.1. Methods, Approaches and Results in Genome Annotation

5.1.1. genome annotation: data flow and performance.

What is genome annotation? Of course, there hardly can be any exact definition but, for the purpose of this discussion, it might be useful to define annotation as a subfield in the general field of genome analysis, which includes more or less anything that can be done with genome sequences by computational means. In simple, operational terms, annotation may be defined as the part of genome analysis that is customarily performed before a genome sequence is deposited in GenBank and described in a published paper. We say “customarily” because the annotations available through GenBank and particularly the types of analysis reported in the literature for different genomes vary widely. For instance, the reports on the human genome sequence [ 488 , 870 ] clearly include a considerable amount of information that goes beyond typical genome annotation. The “unit” of genome annotation is the description of an individual gene and its protein (or RNA) product, and the focal point of each such record is the function assigned to the gene product. The record may also include a brief description of the evidence for this assigned function, e.g. percent identity with a functionally characterized homolog or the boundaries of domains detected in a domain database search, but there is no room for any details of the analysis.

Figure 5.1 shows a rough schematic of the data flow in genome annotation, starting with the finished sequence; we leave finishing of the sequence out of this scheme but indicate the possibility of feedback resulting in correction of sequencing errors. Of these procedures, which must be integrated for predicting gene functions, statistical gene prediction and search of general-purpose databases for sequence similarity are central in the sense that this is done comprehensively as part of any genome project. The contribution of the other approaches in the scheme in Figure 5.1 , particularly specialized database search, including domain databases, such as Pfam, SMART, and CDD (see 3.2.2 ), and genome-oriented databases, such as COGs, KEGG, or WIT (see 3.4 ), and genomic context analysis, varies greatly from project to project. So far, these relatively new methods and resources remain ancillary to traditional database search in genome annotation, but we argue further in this chapter that they can and probably will transform the annotation process in the nearest future.

A generalized flow chart of genome annotation. FB: feedback from gene identification for correction of sequencing errors, primarily frameshifts. General database search: searching sequence databases (typically, NCBI NR) for sequence similarity, usually (more...)

Before we consider several aspects of genome annotation, it may be instructive to assess its brutto performance, i.e. the fraction of the genes in a genome, to which a specific function is assigned. Table 5.1 lists such data for several genomes sequenced in 2001 and annotated using relatively up-to-date methods. This comparison shows notable differences between the levels of annotation of different genomes. Some genomes simply come practically unannotated, such as, for example, Sulfolobus tokodaii , which is a crenarchaeon closely related to S. solfataricus , and represented in the COGs to the same extent as the latter species. In most genomes, however, functional prediction has been made for the majority of the genes, from 54% to 79% of the protein-coding genes. Obviously, these differences depend both on the taxonomic position of the species in question (e.g. it is likely that for Crenarchaea, whose biology is in general poorly understood, the fraction of genes for which functional prediction is feasible will be lower than for bacteria of the well-characterized Bacillus - Clostridium group, such as C. acetobutylicum or L. lactis ) and on the methods and practices of genome annotators.

Microbial genome annotation 2001.

Even in better-characterized genomes, for hundreds of genes (those encoding “conserved hypothetical” and “hypothetical” proteins), there is no functional prediction whatsoever. Furthermore, among those proteins that formally belong to the annotated category, a substantial fraction of the predictions are only general and are in need of major refinement. Some of these problems can be solved only through experiment, but the above numbers show beyond doubt that there is ample room for improvement in computational annotation itself; further in this chapter, we touch upon some of the possible directions.

Genome annotation necessarily involves some level of automation. No one is going to manually paste each of several thousand-protein sequences encoded in a genome into the BLAST window, hit the button, and wait for the results to appear on screen. For annotation to be practicable at all, software is necessary to run such routine tasks in a batch mode and also to organize the results from different programs in a convenient form, and each genome project employs one or another set of tools to achieve this. After that point, however, genome annotation is still mostly “manual” (or, better, “expert”) because decisions on how to assign gene functions are made by humans (supposedly, experts). Several attempts have been made to push automation beyond straightforward data processing and to allow a program to actually make all the decisions. We briefly discuss some of the automated systems for genome annotation in the next section.

5.1.2. Automation of genome annotation

Terry Gaasterland and Christoph Sensen once estimated that annotating genomic sequence by hand would require as much as one year per person per one megabase [ 253 ]. We now believe, on the basis of our own experience of genome annotation (e.g. [ 622 , 779 , 805 ]), that this estimate is exaggerated perhaps by a factor of 5 or 6. Nevertheless, there is no doubt that genome annotation has become the limiting step in most genome projects. Besides, humans are supposed to be inconsistent and error-prone. Hence the incentives for automating as much of the annotation process as possible.

The GeneQuiz ( http://www.sander.ebi.ac.uk/genequiz/ ) project was the first automatic system for genome analysis, which performed similarity searches followed by automatic evaluation of results and generation of functional annotation by an expert system based on a set of several predefined rules [ 749 ]. Several other similar systems have been created since then, but GeneQuiz remains the only such tool that is open to the general public [ 350 ].

GeneQuiz runs automated database searches and sequence analysis by taking a protein sequence and comparing it against a non-redundant protein database, generated by automated cross-linking and cross-referencing of PDB, SWISS-PROT , PIR, PROSITE, and TrEMBL databases, with the addition of human, mouse, fruit fly, zebrafish, and Anopheles gambiae protein sets obtained from the Ensemble project ( http://www.ensembl.org ) and a C. elegans protein set ( http://www.sanger.ac.uk/Projects/C_elegans/wormpep ). This comparison is done by running BLAST and FASTA programs and is used to identify the cases with high similarity, where function can be predicted. Additionally, searches for PROSITE patterns are performed. Predictions are also made for coiled-coil regions using COILS2 [ 533 ], transmembrane segments using PHDhtm [ 715 ], and secondary structure elements using PHDsec [ 718 ]. The system further clusters proteins from the analyzed genome by sequence similarity [ 822 ] and constructs multiple alignments. The results are presented in a table that contains information on the best hits (including gene names, database identifiers, and links to the corresponding databases), predictions for secondary structure, coiled-coil regions, etc. and a reliability score for each item. The functional assignment is then made automatically on the basis of the functions of the homologs found in the database. At this level, functional assignments are qualified as clear or as ambiguous.

The effectiveness and accuracy of such fully automated system have been the subject of a rather heated discussion but still remain uncertain. While the authors originally estimated the accuracy of their functional assignments to be 95% or better [ 638 , 749 ], others reported that only 8 of 21 new functional predictions for M. genitalium proteins made by GeneQuiz could be fully corroborated [ 466 ]. A similar discrepancy between the functional predictions made by the GeneQuiz team [ 31 ] and those obtained by mostly manual annotation [ 466 ] was reported for the proteins encoded in the M. jannaschii genome ([ 264 ], see http://www.bioinfo.de/isb/1998/01/0007 ). It appeared that GeneQuiz analysis suffered from the usual pitfalls of sequence similarity searches (see 3.1.3 , the next section and [ 99 , 104 , 264 ]).

PEDANT, MAGPIE, ERGO, IMAGENE

While GeneQuiz seems to be the only fully automated genome annotation tool that is open to the public for new genome analysis, there have been reports of similar systems developed by other genome annotation groups. These include Dmitrij Frishman's PEDANT ( http://pedant.gsf.de , [ 245 , 248 ], Terry Gaasterland's MAGPIE and its sister programs ( http://genomes.rockefeller.edu , [ 252 , 253 ]), Ross Overbeek's ERGO ( http://ergo.integratedgenomics.com/ERGO , [ 642 , 643 ]), Alan Viari's Imagene ( http://wwwabi.snv.jussieu.fr/research , [ 561 ]), and some others. Although none of these systems is freely available to outside users, many of the genome annotation results they produced are accessible on the web and can be used to judge the performance.

The PEDANT web site contains by far the most information open to the public and can be used as a good reference point for automated genome analyses (see also 2.4 ).

In addition to completely automated systems, some tools that greatly facilitate and accelerate manual genome annotation are worth a mention. System for Easy Analysis of Lots of Sequences (SEALS), developed by Roland Walker at the NCBI is, for obvious reasons, the one most familiar to the authors of this book (available for downloading at http://iubio.bio.indiana.edu:7780/archive/00000466/ , [ 878 ]). The SEALS package consists of ~50 simple, UNIX-based tools (written in PERL), which follow consistent syntax and semantics. SEALS combines software for retrieving sequence information, scripting database searches with BLAST , viewing and parsing search outputs, searching for protein sequence motifs using regular expressions, and predicting protein structural features and motifs. Typically, using SEALS, a genome analyst first looks for structural features of proteins, such as signal peptides (predicted by SignalP), transmembrane domains (predicted by PHDhtm), coiled-coil domains (predicted by COILS2), and large non-globular domains (predicted using SEG ). Once these regions are identified and masked, database searches are run in a batch mode using the chosen method, e.g. PSI-BLAST. The outputs can be presented in a variety of formats, of which filtering with taxonomic queries implemented in the SEALS script TAX_COLLECTOR is among the most useful. SEALS has been extensively used in the comparative studies of bacterial, archaeal, and eukaryotic genomes (e.g. [ 52 , 55 , 540 ].

5.1.3. Accuracy of genome annotation, sources of errors, and some thoughts on possible improvements

Benchmarking the accuracy of genome annotation is extremely hard. It has been shown on numerous occasions that more advanced methods for sequence comparison, such as gapped BLAST and subsequently PSI-BLAST, sometimes used in combination with threading, as well as various forms of motif analysis and careful manual integration of the results produced by all these approaches, substantially improve detection of homologs (e.g. [ 168 , 401 , 434 , 466 , 585 ]). At the end, however, genome annotation is not about detection of homologs but rather about functional prediction, and here, the problem of a standard of truth is formidable. By definition, functional annotation (more precisely, functional prediction) deals with proteins whose functions are unknown, and the rate of experimental testing of predictions is extremely slow. We believe that it is possible to design an objective test of the accuracy of genome annotation in the following manner. The protein set encoded in a newly sequenced genome is analyzed, and specific active centers and other functionally important sites are predicted for as many proteins as possible. When a new, preferably phylogenetically distant genome becomes available, orthologs of the proteins from the first genome are identified, and the conservation of the predicted functional sites is assessed. Lack of conservation would count as an error; this is, of course, a harsh test that would give the low bound of accuracy because: first, functional site prediction may be partly wrong but the function of the protein still would be predicted correctly; and second, some active sites might be disrupted in the new genome. In this way, the accuracy of the prediction could be assessed quantitatively and, in principle, even a “tournament” analogous to the CASP competition in protein structure prediction [ 869 ] could be arranged.

However, so far, evaluation of the accuracy of genome annotation has been largely limited to the assessments of consistency of annotations of the same genome generated by different groups and various “sanity checks” and expert judgments. Steven Brenner published an interesting comparison of three independent annotations [ 242 , 467 , 639 ] of the smallest of the sequenced bacterial genomes, Mycoplasma genitalium [ 116 ]. Without attempting to determine which annotation was “better”, he manually examined all conflicting annotations, eliminating trivial semantic differences and counting the apparent irreconcilable ones as errors (in at least one of the annotations). His conclusion was that there was an at least 8% error rate among the 340 genes annotated by at least two of the three groups. In a similar exercise that we have done on the basis of the COG database, we found that of 786 COGs that did not include paralogs (the number for the end of 1999), members of 194 had conflicting annotations in GenBank [ 267 ]. This suggests, more pessimistically, an annotation error rate of at least 25% using the same criterion as applied by Brenner. Clearly, even the lower of these estimates represents a serious problem for genome annotation, bringing up the specter of error catastrophe [ 89 , 104 ]. We first briefly discuss the most common sources of errors and then some ideas regarding the ways out. Manual and automated genome annotation encounter the same typical problems, which we already mentioned in the discussion of the reliability of sequence database records (see 3.1.3 ). Inevitably, even partial automation of the annotation process tends to increase the likelihood of all these types of errors.

In order to examine various kinds of errors that are common in genome annotation, it is convenient to re-examine four cases of discrepancies in the annotation of M. genitalium proteins that were specifically highlighted in the aforecited article of Steven Brenner ( Table 5.2 ). Although one of the authors was involved in one of the compared annotations, we think we can be completely impartial in the spirit of Brenner's article, especially since six years have passed, an eternity for genomics.

Different types of errors in genome annotation.

The protein MG302 was not annotated in the original genome publication by Fraser and colleagues and was assigned conflicting annotations by the other two groups. Ouzounis and coworkers notably characterized this protein as a “mitochondrial 60S ribosomal protein L2”, whereas Koonin and coworkers annotated it is as a permease, perhaps specific for glycerol-3-phosphate. A database search performed in 2002 leaves no doubt whatsoever that the protein is a permease; this is, of course, readily supported by transmembrane segment prediction. However, the glycerol-3-phosphate specificity is not supported at all. Instead, these searches, particularly the CDD search, unequivocally pointed to a relationship between MG302 and a family of cobalt transporters. Nevertheless, since the similarity between MG302 and the cobalt transporters is not particularly strong and transporters switch their specificity with relative ease during evolution, caution is due, and the annotation as “probable Co transporter” seems most appropriate. This single case nicely covers several common problems of genome annotation. The most benign but also apparently most widespread of these is overprediction or, more precisely, overly specific prediction . Even with the methods available in 1996 (ungapped BLAST , FASTA , various alignment methods, and transmembrane segment prediction), the conclusion that MG302 was a permease was quite firm. However, glycerol-3-phosphate permease turned up as the most similar functionally characterized protein just by chance (Co 2+ transporters had not been characterized at the time). Transferring functional information from this unreliable best hit, however tentatively, was a typical error of overprediction; the appropriate annotation at the time would have been, simply, “predicted permease”. The annotation of MG302 as “mitochondrial 60S ribosomal protein L2” is, of course, much more conspicuous. At face value, this does not even pass a “reality check”: there certainly can be no mitochondria and no 60S ribosomes in mycoplasmas.

Such semantic snafus are pretty common in genome annotation, especially those that are either produced fully automatically or manually but non-critically (e.g. the “discovery” of head morphogenesis in bacteria mentioned in Chapter 3 ). However, these are probably the least serious annotation errors.

Let us just assume that the authors of this annotation meant “homolog of mitochondrial 60S ribosomal protein L2”. What is worse: the search result that presumably gave rise to this annotation is impossible to reproduce at this time, at least not without detailed research, which we are not willing to undertake. It is most likely that this blatantly wrong annotation was due to a spurious database hit to a ribosomal protein that was not critically assessed. It is not clear, in this particular case, how could this spurious hit pass the significance threshold, but in general, this happens most often because of the lack of proper filtering for low complexity (or alternative approaches, such as composition-based statistics, which are available in 2002 but had not been developed in 1996; see Chapter 4 ). Alternatively or additionally, the problem might lie in non-critical transfer of annotation from an unreliable database record , i.e. a low-complexity sequence erroneously labeled as a ribosomal protein. Notably, our re-analysis shows that the annotations assigned by each of the three groups were not completely correct: one was an outright error; another one involved overprediction; and the third one, an underprediction. Although less notorious than false predictions (false-positives, in statistical terms), lack of prediction, where a confident one is feasible with available methods, is still an error (a false-negative).

The case of the MG225 protein is quite similar except that there was no clear false prediction involved. Once again, the original genome project gave no annotation (a false-negative), whereas one of the remaining groups annotated the protein as “histidine permease”, and the other one stopped at an “amino acid permease” annotation without proposing specificity. Today's searches support the latter decision because no convincing, specific relationship between this protein and transporters for any particular amino acid could be detected (in fact, given the small repertoire of transporters in mycoplasmas, this one might have a broad specificity). Notably, both MG302 and MG225 remain “hypothetical proteins” in GenBank to this day, although closely related orthologs from M. pneumoniae are correctly annotated as permeases [ 168 ].

The MG085 protein was annotated as an oxidoreductase (of different families) in the original genome report and by Ouzounis and coworkers, whereas Koonin and coworkers predicted that it was an ATP(GTP?)-utilizing enzyme on the basis of the conservation of the P-loop motif in this protein and its homologs. In 2002, database searches immediately identify this protein as HPr kinase (this annotation is now correctly assigned to MG085 in GenBank ), a regulator of the sugar phosphotransferase system, which indeed is a P-loop-containing, ATP-utilizing enzyme [ 723 ]. Back in 1996, this was the only informative annotation that could be derived for this protein; HPr kinase genes had not been identified at the time. Once again, the specific source of the oxidoreductase assignments is hard to determine; spurious hits, non-critical use of incorrect database annotations, or a combination thereof must have caused this.

The case of MG448 is of particular interest. This protein was annotated as “pilin repressor” or simply PilB protein by Fraser and coworkers and Ouzounis and coworkers and, somewhat cryptically, as “chaperone-like protein” by Koonin and coworkers. This protein remains “hypothetical” in GenBank but became a peptide methionine sulfoxide reductase (PMSR) in SWISS-PROT . A database search detects highly significantly similarity with numerous proteins that are annotated primarily as PMSR and, in some cases, as PilB-related repressors. In reality, this protein is indeed a recently characterized, distinct form of PMSR, MsrB [ 476 , 526 ], which is evolutionarily unrelated to, but is often associated with, the classic PMSR, MsrA, either as part of a multidomain protein or as a separate gene in the same operon [ 267 ]. These fusions resulted in the annotation of MG448 as PMSR, which, ironically, turned out to be correct, but mostly (except for the recently updated SWISS-PROT description), for a wrong reason, because it was the MsrA domain that was recognized in the fusion proteins. Furthermore, in several bacteria, these two domains are fused to a third, thioredoxin domain. The three-domain protein of Neisseria gonorrhoeae has been characterized as a regulator of pili operon expression, and this is what caused the annotation of MG448 as PilB, which was reproduced by two groups. This annotation is outright wrong and does not even pass a “reality check” because there are no pili in mycoplasmas (parenthetically, latest reports appear to indicate that even the original functional characterization of the Neisseria protein was erroneous [ 776 ]).

Unrecognized multidomain architecture of either the analyzed protein or its homologs or both is a common cause of erroneous annotation. The “chaperone-like protein” annotation was based on the notion that the PMSR function could be interpreted as a form of chaperone action, and accordingly, the associated domain was also likely to have a chaperone-like activity. In retrospect, this looks like overprediction combined with insufficient information included in the annotation. A straightforward annotation of MG448 as a PMSR-associated domain, perhaps with an extra prediction of redox activity on the basis of conservation of cysteines in this domain, the way it has been done in a subsequent publication [ 267 ], would have been appropriate. We revisit this interesting set of proteins when discussing context analysis in Section 5.2 .

While considering only four proteins with contradictory annotations, we encountered all the main sources of systematic error in genome annotation. We list them here again, more or less in the order of decreasing severity, as we see it: (i) spurious database hits, often caused by low-complexity regions in the query or the database sequence; (ii) non-critical transfer of functional prediction from an unreliable database record; (iii) incorrect interpretation (lack of recognition) of multidomain architecture of the query and/database sequences; (iv) overly specific functional prediction; and (v) underprediction.

We believe that this brief discussion highlights more general problems beyond these specific causes of errors. Even the apparently correct database annotations are insufficiently informative. Typically, the records do not include the evidence behind the prediction or include only minimal data that may be hard to interpret, such as E-values of the hits to particular domains. In this situation, any complicated case will not be represented adequately (e.g. the PMSR-associated domain discussed above). In addition, there is no controlled vocabulary for genome annotation, which creates numerous semantic problems, although an attempt to correct this situation is being undertaken in the form of the Genome Ontology project [ 60 , 513 ].

The above discussion shows that the general state of genome annotation is far from being satisfactory. What can be done to improve it? In his paper on genome annotation errors, Steven Brenner noted that, “to prevent errors from spreading out of control, database curation by the scientific community will be essential.” [ 116 ]. Curation, however, implies that databases other than GenBank will have to be employed because GenBank, by definition, is an archival database ( Chapter 3 ). It appears that the future and, to some degree, already the present of genome annotation lies in specialized databases that actually function as annotation tools. The beginnings of such tools can be seen in databases like KEGG, WIT, and COGs, complemented by tools for domain identification, such as CDD and SMART (see Chapters 3 and 4 ).

Conceptually, the advantage of this approach may be viewed as reduction and structuring of the search space for genome annotation. Thus, when using COGs, a genome analyst compares each protein sequence not to the unstructured set of more than a million proteins (the NR database) but instead to a collection of ~5,000 mostly well-characterized protein sets classified by orthology, which is the appropriate level of granularity for functional assignment. Already genome annotation today is starting to change through the use of the new generation of databases and tools. However, smooth integration of these and development of new, richer formats for annotation are things of the future. In the next subsection, we turn to a specific example to illustrate how the use of COGs helps genome annotation.

5.1.4. A case study on genome annotation: the crenarchaeon Aeropyrum pernix

Aeropyrum pernix was the first representative of the Crenarchaeota (one of the two major branches of archaea; see Chapter 6 ) and the first aerobic archaeon whose genome has been sequenced [ 427 ]. A. pernix was reported to encode 2,694 putative proteins in a 1.67-Mbase genome. Of these, 633 proteins were assigned a specific or general function in the original report on the basis of sequence comparison to proteins in the GenBank , SWISS-PROT , EMBL, PIR, and Owl databases. Given the intrinsic interest of the first crenarchaeal genome and also because of the unexpectedly low fraction of predicted genes that were assigned functions in the original report, A. pernix was chosen for a pilot annotation project centered around the COG database [ 605 ].

Figure 5.2 (see the color plates) shows the protocol employed for the COG-based genome annotation. This procedure was not limited to straightforward COGNITOR analysis but also explicitly drew from the phyletic patterns. Whenever A. pernix was unexpectedly not represented in a COG (e.g. a COG that included all other archaeal species), additional analysis was undertaken. To identify possible diverged COG members from A. pernix, PSI- BLAST searches were run with multiple members of the respective COGs, and to detect COG members that could have been missed in the original genome annotation, the translated sequence of the A. pernix genome was searched using TBLASTN. Conversely, unexpected occurrence of A. pernix proteins in COGs that did not have any other archaeal members were examined case by case to detect likely HGT events and novel functions in the crenarchaeal genome.

Protocol of genome annotation using the COG database.

Proteins were assigned to COGs through two rounds of automated comparison using COGNITOR, each followed by curation, that is, manual checking of the assignments. The first round attempts to assign proteins to existing COGs; typically, >90% of the assignments are made in this step. The second round serves two purposes: first, to assign paralogs, that might have been missed in the first round, to existing COGs; and, second, to create new COGs from unassigned proteins.

The results of COG assignment for A. pernix are shown in Table 5.3 . Manual curation of the automatic assignments revealed a false-positive rate of less than 2% (23 of 1123 proteins). Even if the less severe errors, when a protein was transferred from one related COG to another, are taken into account, the false-positive rate was 4%, which is not negligible but substantially lower than the estimates cited above for more standard genome annotation methods. The number of identified false-negatives was even lower, but in this case, of course, it is not possible to determine how many proteins remain unassigned. It is further notable that the great majority of assigned proteins belonged to pre-existing COGs, which facilitates a (nearly) automatic annotation.

Assignment of predicted Aeropyrum pernix proteins to COGs.

Altogether, 1,102 A. pernix proteins were assigned to COGs. Some of these proteins ( 154 ) were members of functionally uncharacterized COGs. Subtracting these, annotation has been added to 315 proteins, which is an increase of about 50% compared to the original annotation. These newly annotated A. pernix proteins included, among others, the key glycolytic enzymes glucose-6-phosphate isomerase (APE0768, COG0166) and triose phosphate isomerase (APE1538, COG0149), and the pyrimidine biosynthetic enzymes orotidine-5′-phosphate decarboxylase (APE2348, COG0284), uridylate kinase (APE0401, COG0528), cytidylate kinase (APE0978, COG1102), and thymidylate kinase (APE2090, COG0125). Similarly, important functions in DNA replication and repair were confidently assigned to a considerable number of A. pernix proteins, which, in the original annotation, were described as “hypothetical”. Examples include the bacterial-type DNA primase (COG0358), the large subunit of the archaeal-eukaryotic-type primase (COG2219), a second ATP-dependent DNA ligase (COG1423), three paralogous photolyases (COG1533), and several helicases and nucleases of different specificities.

The case of the large subunit of the archaeal-eukaryotic primase is particularly illustrative of the contribution of different types of inference to genome annotation. COGNITOR failed to assign an A. pernix protein to the respective COG (COG2219). However, given the ubiquity of this subunit in euryarchaea and eukaryotes and the presence of a readily detectable small primase subunit in A. pernix (COG1467), a more detailed analysis was undertaken by running PSI- BLAST searches against the NR database with all members of COG2219 as queries. When the A. fulgidus primase sequence (AF0336) was used to initiate the search, the A. pernix counterpart (APE0667) was indeed detected at a statistically significant level.

An interesting case of re-annotation of a protein with a critical function, which also led to more general conclusions, is the archaeal uracil DNA glycosylase (UDG; COG1573). The members of this COG were originally annotated (and still remain so labeled in GenBank ) as a “DNA polymerase homologous protein” (APE0427 from A. pernix ) or as a “DNA polymerase, bacteriophage type” (AF2277 from A. fulgidus ) or as a hypothetical protein. However, UDG activity has been experimentally demonstrated for the COG1573 members from T. maritima and A. fulgidus [ 740 , 741 ]. The reason for the erroneous annotation of these proteins as DNA polymerases is already well familiar to us: independent fusion of the uracil DNA glycosylase with DNA polymerases was detected in bacteriophage SPO1 and in Yersinia pestis [ 44 ]. Although these fusions hampered the correct annotation in the original analysis of the archaeal genomes, they seem to be functionally informative, suggesting that this type of UDG functions in conjunction with the replicative DNA polymerase.

The 1,102 COG members from A. pernix comprise 41% of the total number of predicted genes. This percentage was significantly lower than the average fraction of COG members (72%) for the other archaeal species. It seems most likely that this was due to an overestimate of the total number of ORFs in the genome. Many of the A. pernix ORFs with no similarity to proteins in sequence databases (1,538, or 57.1%) overlap with ORFs from conserved families, including COG members. On the basis of the average representation of all genomes in the COGs (67%) and the average for the other archaea (72%), one could estimate the total number of A. pernix proteins to be between 1,550 and 1,700. This range is also consistent with the size of the A. pernix genome (1.67 Mb), given the gene density of about one gene per kilobase, which is typical of bacteria and archaea. More conservatively, 849 ORFs, originally annotated as probable protein-coding genes, significantly overlapped with COG members and could be confidently eliminated, which brings the total number of protein-coding genes in A. pernix to a maximum of 1,873. Unfortunately, the spurious ORFs still remain in the NR database, polluting it and potentially even leading to the emergence of ghost “protein” families once new, related genomes are sequenced. Evidence has been presented that spurious “proteins” have been produced by other microbial genome products also [ 777 ], although probably not on the same scale as A. pernix . This regrettable pollution emphasizes the value of specialized, curated databases that are free of apparitions.

Despite this overrepresentation of ORFs in A. pernix , we nonetheless added 28 previously unidentified ORFs that were detected by searching the genome sequence translated in all six frames for possible members of COGs with unexpected phyletic patterns. These newly detected genes represent conserved protein families, including functionally indispensable proteins, such as chorismate mutase (APE0563a, COG1605), translation initiation factor IF-1 (APE_IF-1, COG0361), and seven ribosomal proteins (APE_rpl21E, COG2139; APE_rps14, COG0199; APE_rpl29, COG0255; APE_rplX, COG2157; APE_rpl39E, COG2167; APE_rpl34E, COG2174; APE_rps27AE, COG1998).

This pilot analysis, while falling far short of the goal of comprehensive genome annotation, highlights some advantages of specialized comparative-genomic databases as annotation tools. In this particular case, the original annotation probably had been overly conservative, which partly accounts for the large increase in the functional prediction rate. However, the employed protocol is general and, with modifications and addition of some extra procedures, has been used in primary genome analysis [ 622 , 779 ]. In other genome projects, the WIT system has been employed in a conceptually similar manner [ 179 , 418 ]. As shown above, this type of analysis yields reasonable accuracy of annotation, even when applied in a fully automated mode ( Table 5.3 ). However, additional expert contribution, particularly in the form of context analysis discussed in the next section, adds substantial value to genome annotation.

5.2. Genome Context Analysis and Functional Prediction

All the preceding discussion in this chapter centered on prediction of the functions of proteins encoded in sequenced genomes by extrapolating from the functions of their experimentally characterized homologs. The success of this approach depends on the sensitivity and selectivity of the methods that are used for detecting sequence similarity (see Chapter 4 ) and on the employed rules of inference (see 5.1 ). There is no doubt that homology analysis remains the central methodology of genomics, i.e. the one that produces the bulk of useful information. However, a group of recently developed approaches in comparative genomics goes beyond sequence or structure comparison. These methods have become collectively and, we think, aptly known as genome context analysis [ 267 , 368 , 369 , 372 ]. The notion of “context” here includes all types of associations between genes and proteins in the same or in different genomes that may point to functional interactions and justify a verdict of “guilt by association” [ 36 ]: if gene A is involved in function X and we obtain evidence that gene B functionally associates with A, then B is also involved in X. More specifically, context in comparative genomics pertains to phyletic profiles of protein families, domain fusions in multidomain proteins, gene adjacency in genomes, and expression patterns. Indeed, genes whose products are involved in closely related functions (e.g. form different subunits of a multisubunit enzyme or participate in the same pathway) should all be either present or absent in a certain set of genomes (i.e. have similar if not identical phyletic patterns) and should be coordinately expressed (i.e. are expected to be encoded in the same operon or at least to have similar expression patterns). This simple logic gives us a potentially powerful way to assign genes that have no experimentally characterized homologs to particular pathways or cellular systems. Although context methods usually provide only rather general predictions, they represent a new and important development in genomics that explicitly takes advantage of the rapidly growing collection of sequenced genomes.

5.2.1. Phyletic patterns (profiles)

Genes coding for proteins that function in the same cellular system or pathway tend to have similar phyletic patterns [ 259 , 828 ]. Numerous examples for a variety of metabolic pathways are given in Chapter 7 . These observations led to the suggestion that this trend could be used in the reverse direction, i.e. to deduce functions of uncharacterized genes [ 665 ]. However attractive this idea might be, the real-life phyletic patterns are heavily affected by such major evolutionary phenomena as partial redundancy in gene functions, non-orthologous gene displacement, and lineage-specific gene loss. As a result, there are thousands different phyletic patterns in the COGs, most of them represented only once or twice. Moreover, examination of a variety of multi-component systems and biochemical pathways ( http://www.ncbi.nlm.nih.gov/cgi-bin/COG/palox?sys=all ) shows that, despite the tendency of the components of the same complex or pathway to have similar patterns, there is not even one pathway in which all members show exactly the same pattern. Even the principal metabolic pathways, such as glycolysis, TCA cycle, and purine and pyrimidine biosynthesis, show considerable variability of phyletic patterns due to non-orthologous gene displacement ([ 265 , 270 , 370 ], see Chapter 7 ).

Because of this variability, the predictive power of the observation that two genes have the same phyletic pattern is, in and by itself, limited. However, when supported by other lines of evidence, such observations prove useful. Somewhat counterintuitively, the universal pattern is one of the most strongly indicative of gene function: among the 63 universal COGs, at least 56 consist of proteins involved in translation. The functions of those few proteins in the universal set that remain uncharacterized can be predicted with considerable confidence through combination of this phyletic pattern with other lines of evidence. For example, the uncharacterized protein YchF, which belongs to the universal set (COG0012), is predicted by sequence analysis to be a GTPase; in addition, this protein contains a C-terminal RNA-binding TGS domain [ 909 ]. Taken together with the ubiquity of this protein and with the fact that, in phylogenetic trees, the archaeal members of the COG clearly cluster with eukaryotic ones, this strongly suggests that YchF is an uncharacterized, universal translation factor [ 267 ]. This is supported by the juxtaposition of the ychF gene with the gene for peptidyl-tRNA hydrolase ( pth ) in numerous proteobacteria. The discussion of this protein made us run ahead of ourselves and invoke other context methods, which are considered in the next subsections, namely, analysis of domain fusions and gene juxtaposition. This situation is quite typical: context methods are at their best when they complement one another. Although statistical significance estimates for a combination of context methods do not currently seem feasible, in a case like YchF, the evidence appears to be, for all practical purposes, irrefutable.

There is more to phyletic pattern analysis then prediction based on identical or similar patterns. Guilt by association can be established also through identification of sets of genes that are co-eliminated in a given lineage; this approach exploits the widespread phenomenon of lineage-specific gene loss. A systematic analysis of the set of genes that have been co-eliminated in the yeast S. cerevisiae after its divergence from the common ancestor with S. pombe led to the prediction that a particular group of proteins, including one that contained a helicase and a duplicated RNAse III domain, was involved in post-transcriptional gene silencing [ 55 ]. This protein turned out to be the now famous dicer nuclease, which indeed has a central role in silencing [ 365 , 436 ].

On many occasions, non-orthologous gene displacement manifests in complementary , rather than identical or similar, phyletic patterns, like we have seen for phosphoglycerate mutase in 2.2.6 . The complementarity is rarely perfect because of partial functional redundancy: some organisms, particularly those with larger genomes, often encode more than one protein to perform the same function. This can be illustrated by the case of the recently discovered new type of fructose-1,6-bisphosphate aldolase, referred to as FbaB or DhnA [ 257 ]. The two well-known variants of this enzyme, class I (Schiff-base forming, metal-independent) and class II (metal-dependent), have long been considered to be unrelated (analogous) enzymes until structural comparisons revealed their underlying similarity (see Figure 1.9) [ 95 , 187 , 257 , 549 ]. These enzymes are generally limited in their phyletic distribution to eukaryotes (class I) and bacteria (class II); some bacteria, however, have both variants and yeast has the bacterial (class II) form of the enzyme [ 549 ]:

Sequencing of archaeal genomes revealed the absence of either form of the fructose-1,6-bisphosphate aldolase. The same was the case with chlamydiae, which were predicted to have a third form of this enzyme [ 412 , 805 ]. Indeed, investigation of the metal-independent fructose-1,6-bisphosphate aldolase activity in E. coli led to the discovery of another metal-independent Schiff-base-forming variant [ 844 ] whose sequence, however, was more closely related to those of class II enzymes than to typical class I enzymes [ 257 ]. Highly conserved homologs of this new, third form of fructose-1,6-bisphosphate aldolase were found in chlamydial and archaeal genomes:

As with phosphoglycerate mutase, combining these phyletic patterns shows almost perfect complementarity, with aldolase missing only in Rickettsia , which does not encode any glycolytic enzymes, and in Thermoplasma , which appears to rely exclusively on the Entner-Doudoroff pathway (see 7.1.1 ):

Other interesting examples of complementary phylogenetic patterns include lysyl-tRNA synthetases, pyridoxine biosynthesis proteins PdxA and PdxZ [ 256 ], thymidylate synthases [ 267 ], and many others. The case of thymidylate synthases is particularly remarkable. Thymidylate synthase is a strictly essential enzyme of DNA precursor biosynthesis, and its apparent absence in several bacterial and archaeal species became a major puzzle as their genome sequences were reported.

The alternative thymidylate synthase was predicted [ 267 ] on the basis of a phyletic pattern that was nearly complementary (with just one case of redundancy) to that of the classic thymidylate synthase (ThyA) and the report that the homolog of the COG1351 proteins from Dictyostelium complemented thymidylate synthase deficiency [ 206 ]. Just before this book went to print, a new issue of Science reported the confirmation of this prediction: not only was it shown that the COG1351 member from H. pylori had thymidylate synthase activity, but also the structure of this proteins has been solved and turned out to be unrelated to that of ThyA [ 589 , 598 ].

5.2.2. Gene (domain) fusions: “guilt by association”

It is fairly common that functionally interacting proteins that are encoded by separate genes in some organisms are fused in a single polypeptide chain in others. This has been confirmed by statistical analysis that demonstrated general functional coherence of fused domains [ 930 ]. The advantages of a multidomain architecture are that this organization facilitates functional complex assembly and may also allow reaction intermediate channeling [ 546 ].

The basic assumption in the analysis of domain fusions is that a fusion will be fixed during evolution only when it provides a selective advantage to the organism in the form of improved functional interaction between proteins. Thus, finding fused proteins (domains) in one species suggests that they might interact, physically or at least functionally, in other species. In and by itself, this notion is trivial and has been employed for predicting protein and domain functions on an anecdotal basis for years (see [ 100 ], just as an example). However, with the rapid growth of the sequence information, the applicability of this approach widened and two independent groups proposed, in well-publicized papers, that analysis of domain fusions could be a general method for systematic and, moreover, automatic, prediction of protein functions [ 213 , 546 ]. In one of these studies [ 546 ], domain fusions are referred to as “Rosetta Stone” proteins – clues to deciphering the functions of their component domains, and this memorable name stuck to the whole approach. (The Rosetta Stone metaphor is quite loose: the notorious stone used by François Champollion to decipher the Egyptian hieroglyphs and now on public display in the British Museum, is a tri-lingua, i.e. a monument that has on it the same text in three different languages. There is nothing exactly like that about domain fusions, it is just possible to say vaguely that the “language” of domain fusions is translated into the “language” of functional interactions. The “guilt by association” simile [ 36 ] seems much more apt if less glamorous).

In his comment on the “Rosetta Stone” excitement, Russell Doolittle pointed out that cases that establish a link between two well-known domains or those that link two unknown domains are not likely to lead to any scientific breakthroughs [ 188 ]. Only those “Rosetta Stone” proteins, in which an unknown domain is linked to a previously characterized one, can be used to infer the function(s) of the uncharacterized domain. Analysis of domain fusions in complete microbial genomes indicates that they are a complex mixture of informative, uninformative and potentially misleading cases, which certainly provide many clues to functions of uncharacterized domains. However, interpretations stemming from domain fusion seem to require case-by-case examination by human experts and, most of the time, become really useful only when combined with other lines of evidence.

One of the advantages of the guilt by association approach is that, at least in principle, it allows transitive closure, i.e. expansion of functional associations between transitively connected components. In other words, detection of domain combinations AB, BC, and CD suggests that domains A, B, C and D form a functional network. This approach has been successfully applied to the analysis of prokaryotic signal-transduction systems, resulting in the prediction of several new signaling domains. Participation of these domains in signaling cascades has been originally proposed solely on the basis of their conserved domain architectures and subsequently confirmed experimentally [ 269 ].

In Figure 5.3 , we illustrate the “guilt by association” approach using the peptide methionine sulfoxide reductase example discussed in the previous section as a case of annotation complicated by domain fusion. As in the examples above, the logic of the analysis does not allow us to use domain fusions only; we also have to invoke phyletic patterns and organization of genes in the genome.

A Rosetta Stone case: domain fusions and gene clusters that involve peptide methionine sulfoxide reductases.

In most organisms, protein methionine sulfoxide reductase A (MsrA) is a small, single-domain protein. However, in H. influenzae , H. pylori and T. pallidum, it is fused with another, highly conserved domain (MsrB) that is found as a distinct protein in all other organisms that encode MsrA. In other words, the two fusion components show the same phyletic patterns:

In B. subtilis , the genes for MsrA and MsrB are not fused, but are adjacent and may form an operon. In contrast, in T. pallidum , MsrA and MsrB are fused, but in reverse order, compared to H. influenzae and H. pylori ( Figure 5.3 ). The H. influenzae and H. pylori “Rosetta Stone” proteins are most closely related to each other, but the one from T. pallidum does not show particularly strong similarity to any of them, suggesting two independent fusion events in these two lineages.

In Neisseria and Fusobacterium , a third, thioredoxin-like domain joins the MsrAB fusion ( Figure 5.3 ). In H. influenzae , the ortholog of this predicted thioredoxin is encoded two genes upstream of MsrAB. The gene in between encodes a conserved integral membrane protein, designated CcdA for its requirement for cytochrome c biogenesis in B. subtilis . Its ortholog is encoded next to MsrAB in H. pylori and next to thioredoxin in several other genomes ( Figure 5.3 ).

Combining all this evidence from the guilt by association approach, gene adjacency data, phyletic profiles, and sequence analysis, it has been predicted that the MsrA, MsrB and thioredoxin form an enzymatic complex, which catalyzes a cascade of redox reactions and is associated with the bacterial membrane via CcdA. However, this is probably not the only complex in which MsrAB is involved, because not all genomes that have this gene pair also encode CcdA ( Figure 5.3 ). Since the publication of this prediction, it has been largely confirmed by the demonstration that MsrB is a second, distinct, thioredoxin-dependent peptide methionine sulfoxide reductase, which cooperates with MsrA in the defense of bacterial cells against reactive oxygen species [ 316 , 526 , 776 ]. However, the CcdA connection remains to be investigated.

This case study demonstrates both the considerable potential of domain fusion analysis as a tool for protein function prediction, particularly when combined with other context-based and homology-based approaches, and potential problems. One could be tempted to extend the small network of domains shown in Figure 5.3 by including other domains that form fusions (or are encoded by adjacent genes) with the thioredoxin domain. It appears, however, that such an extension would have been ill-advised. Firstly, orthologous relationships among thioredoxins are ambiguous, and secondly, although thioredoxins are not among the most “promiscuous” domains, the variety of their “guilt by association” links still is sufficiently large to make any predictions regarding potential functional connections between the respective domains and MsrAB dubious at best. These two issues, identification of orthologs and “promiscuity” characteristic of certain domains, are the principal problems encountered by the “guilt by association” approach. Domain fusions often are found only within a specialized, narrow group of orthologous protein domains, and translating their functional interaction into a general prediction for the respective domains is likely to be grossly misleading. A relatively small number of “promiscuous” domains, particularly those involved in signal transduction and different forms of regulation (e.g. CBS, PAS, GAF domains), combine with a variety of other domains that otherwise have nothing in common and therefore significantly increase the number of false-positives among the Rosetta Stone predictions. Although it is possible to simply exclude the worst known offenders from any Rosetta Stone analysis [ 546 ], other domains also have the potential of showing “illicit” behavior and compromising the results. Manual detection of such cases is relatively straightforward, but automation of this process may be complicated.

5.2.3. Gene clusters and genomic neighborhoods

As already mentioned in Chapter 2 , comparisons of complete bacterial genomes have revealed the lack of large-scale conservation of the gene order even between relatively close species, such as E. coli and H. influenzae [ 595 , 829 ] or E. coli and P. aeruginosa ( Figure 2.6B ). Although these pairs of genomes have numerous similar strings of adjacent genes (most of them predicted operons), comparisons of more distantly related bacterial and archaeal genomes have shown that, at large phylogenetic distances, even most of the operons are extensively rearranged [ 461 , 884 ]. The few operons that are conserved across distantly related genomes typically encode physically interacting proteins, such as ribosomal proteins or subunits of the H-ATPase and ABC-type transporter complexes [ 169 , 385 , 461 , 595 ].

It should be noted that only a relatively small number of operons have been identified experimentally, primarily in well-characterized bacteria, such as E. coli and B. subtilis [ 363 , 732 ]. However, analysis of gene strings that are conserved in bacterial and archaeal genome strongly suggested that the great majority of them do form operons [ 916 ]. This conclusion was based on the following principal arguments: (i) as shown by Monte Carlo simulations, the likelihood that identical strings of more than two genes are found by chance in more than two genomes is extremely low; (ii) most of those conserved strings that include characterized genes either are known operons or include functionally linked genes and can be predicted to form operons; (iii) typical conserved gene strings include 2 to 4 genes, which is the characteristic size of operons; (iv) conserved gene strings that include genes from adjacent, independent operons are extremely rare; (v) nearly all conserved gene strings consist of genes that are transcribed in the same direction [ 916 ]. As a result, one can usually assume that conserved gene strings are co-regulated, i.e. form operons, even if they contain additional promoters.

Pairwise genome comparisons showed that, on average, ~10% of the genes in each genome belong to gene strings that are conserved in at least one of the other available genomes [ 385 , 916 ]. These numbers vary widely from <5% for the cyanobacterium Synechocystis sp. to 23–24% in T. maritima and M. genitalium ; the fraction of genes that belonged to predicted operons in the archaeal genomes was only slightly lower than that in bacterial genomes [ 916 ].

These observations indicate that conserved gene strings are under stabilizing selection that prevents their disruption. For functionally related genes (e.g. those encoding proteins that function in the same pathway or multimeric complex), this selective pressure probably comes from the necessity to synchronize their expression. This conclusion holds even in the face of the “selfish operon” hypothesis, which posits that operons survive during evolution because they are disseminated via HGT [ 494 , 495 ]. We believe that the selfish operon hypothesis seems to put the cart ahead of the horse: operons certainly do spread via HGT, but their transfer leads to fixation more often than transfer of individual genes because of the selective advantage conferred to the recipient by the acquired operon. In contrast, for functionally unrelated genes, there would be no selection towards coexpression. Therefore, an observation of similar operons found in phylogenetically distant species can be considered an indication of a potential functional relationship between the corresponding genes, even if these genes are scattered in other genomes. Because of the simplicity and elegance of this approach to functional analysis of complete genomes, there are several web sites that offer slightly different approaches to delineation of the conserved gene strings.

The operon comparison tool in the WIT database ( http://wit.mcs.anl.gov ), the first of the genome context-based tools, was developed by Ross Overbeek in 1998 [ 640 , 641 ]. This tool identifies conserved gene strings by searching for pairs of homologous proteins that are encoded by genes located no more than 300 bp apart on the same DNA strand in each of the analyzed genomes. Each of these pairs is then assigned a score based on the evolutionary distance between the respective species on the rRNA-based phylogenetic tree. It is expected that chance occurrence of pairs of homologous genes in distantly related species is less likely than in closely related ones, so such pairs are more likely to be functionally relevant. Homologous genes are defined as bidirectional best hits in all-against-all BLAST comparisons, which is similar to the method used in constructing the COG database [ 828 ].

Because the number of potential gene linkages grows exponentially with the number of the analyzed genomes [ 640 ], the sensitivity of methods based on the detection of conserved gene strings can be significantly improved by taking into consideration even unfinished genome sequences. For this reason, WIT and ERGO databases include many incomplete genome sequences from the DOE Joint Genome Institute and other sequencing centers. This approach was used in the successful reconstruction of several known metabolic pathways and led to the correct prediction of candidate genes for some previously uncharacterized metabolic enzymes [ 82 , 171 , 641 ]. Unfortunately, while this book was in preparation, the ERGO database has been closed for the public, while WIT was still missing some of the useful functionality. We will therefore illustrate the use of the method by exploiting a somewhat similar tool in the COG database.

The COG database ( http://www.ncbi.nlm.nih.gov/COG ) allows a simple and straightforward search for conserved operons. Because all proteins in the same COG are presumed to be orthologs, the “ Genome context” view, available from each COG page, shows the genes that encode members of the given COG together with the surrounding genes. Genes whose products belong to the same COG are identically colored. This provides for easy identification of sets of COGs that tend to be clustered in genomes. Of course, this tool only works for the genes whose products belong to COGs, so the relationships between genes that are found in only two complete genomes and hence do not belong to any COG would be missed. An exhaustive matching of the co-localization of genes encoding members of the same two COGs allowed new functional predictions for almost 90 COGs, which comprised ~4% of the total set [ 469 , 916 ].

For a practical example of the use of this method, let us consider the search for the archaeal shikimate kinase, the enzyme that is not homologous to the bacterial shikimate kinase (AroK) and hence was not found by traditional sequence similarity searches [ 171 ]. Reconstruction of the aromatic amino acids biosynthesis pathway in archaea showed that genomes of A. fulgidus , M. jannaschii , and M. thermoautotrophicum encoded orthologs of bacterial enzymes for all but three reactions of this pathway ([ 540 ], see Figure 7.6 ).

Two of these missing enzymes catalyze first and second reactions of the pathway, indicating that aromatic acids biosynthesis in (most) archaea uses different precursors than in bacteria, whereas the third reaction, phosphorylation of shikimate, was attributed to a non-orthologous kinase, encoded only in archaea [ 540 ]. Daugherty and coworkers made a list of the genes involved in aromatic amino acid biosynthesis in archaea and looked for potential neighbors of the aroE gene whose product, shikimate dehydrogenase, catalyzes the reaction immediately preceding the phosphorylation of shikimate ( Figure 7.6 ). In P. abyssi genome, the aroE gene (PAB0300) was followed by an uncharacterized gene (PAB0301) encoding a predicted kinase, which is distantly related to homoserine kinases. This was also the case in A. pernix and T. acidophilum genomes, where the PAB0301-like gene (COG1685, Figure 5.4 ) was found sandwiched between the aroE gene and the aroA gene, whose product catalyzes the next step of the pathway after shikimate phosphorylation [ 171 ]. Genes encoding PAB0301 orthologs (COG1685) were also found in other archaeal genomes, but not in any of the bacterial genomes that contain the typical aroK gene ( Figure 5.4 ). Given this connection, Daugherty et al. expressed MJ1440, the COG1685 member from M. jannaschii and demonstrated that it indeed had shikimate kinase activity [ 171 ].

Genome context of COG1685 “Archaeal shikimate kinase”. Each line corresponds to an individual genome: aful, Archaeoglobus fulgidus ; hbsp, Halobacterium sp.; mjan, Methanococcus jannaschii ; mthe, Methanobacterium thermoautotrophicum ; pyro, (more...)

The Search Tool for Recurring Instances of Neighbouring Genes (STRING, http://www.bork.embl-heidelberg.de/STRING ), developed by Peer Bork and colleagues, is based on a similar approach [ 788 ]. Gene clusters are defined by STRING the same way as in WIT, namely as strings of genes on the same strand located no more than 300 bp from each other. Orthologs are identified as bidirectional best hits using Smith-Waterman comparisons. The STRING search starts from a single protein sequence that can be entered as a FASTA file or just by its gene name in the complete genome. The sequence entered in FASTA format is compared against the database of all proteins encoded in complete genomes so that the user could choose one of the best hits for further examination. Like COGs, STRING contains information only on completely sequenced genomes. The default option in STRING further reduces the number of analyzed genomes by eliminating closely related ones (this option can be switched off by the user). Additionally, STRING features a useful tool that allows the user to perform an “iterative” analysis of gene neighborhoods. After the nearest neighbors of a gene in question are identified, the next “iteration” of STRING would look for their neighbors and record if any of these were found previously. If no new neighbors are found, STRING reports that the search has “converged”. If this does not happen even after five consequent search cycles, the program would just tabulate how many times was each particular gene found in the output. Combined with impressive graphics, this approach makes STRING a fast and convenient tool to search for consistent gene associations in complete genomes.

In contrast to the tools described above, identification of gene strings in the KEGG database ( http://www.genome.ad.jp/kegg-bin/mk_genome_cmp_html ) is geared toward an analysis of the operon conservation. It allows one to find all genes in any two selected complete genomes whose products are sufficiently similar to each other and are separated by no more than five genes. The user can specify the desired degree of similarity between the proteins in terms of the minimal pairwise BLAST score (or maximal Evalue), the minimal length of the alignment, and the type of BLAST hits (bidirectional or unidirectional hits, or just any hits with the specified BLAST score). The user can also specify maximum allowable distances between the genes in either organism, limiting it to any number of genes from zero to five. This option allows one to retrieve much more distant gene pairs than those detected by the ERGO tool. The downside of this richness is that unless one uses fairly strict criteria for protein similarity and the intergenic distances, he or she will end up with dozens or even hundreds of reported gene pairs, few of which would have predictive power. Nonetheless, a sensible use of this tool can bring some very interesting results [ 268 ].

Genome context tools in genome annotation

To evaluate the power of gene order-based methods for making functional predictions, we have isolated those cases where a substantial functional prediction did not appear possible without explicit use of gene adjacency information [ 916 ]. In spite of the inherent subjectivity of such assessments, the result was instructive: such unique predictions were made for ~90 genes (more precisely, COGs) or ~4% of all COGs analyzed. Given that, as noted above, homology-based approaches already allow functional predictions for a majority of the genes in each sequenced prokaryotic genome, this places gene-string analysis in the position of an important accessory methodology in the hierarchy of genome annotation approaches. Other genome context-based methods may also be useful but are clearly less powerful. This is, of course, a pessimistic assessment because more subtle changes in prediction for gene already annotated by homology-based methods were not taken into account.

These limitations notwithstanding, some of the predictions made on the basis of gene order conservation combined with homology information seem to be exceptionally important. Perhaps the most straightforward case is the prediction of the archaeal exosome, a complex of RNAses, RNA-binding proteins and helicases that mediates processing and 3’->5’ degradation of a variety of RNA species [ 469 ]. This finding was made by examination of archaeal genome alignments, which led to the detection of a large superoperon, which, in its complete form, consists of 15 genes. This full complement of co-localized genes, however, is present in only one species, M. thermoautotrophicum , whereas, in all other archaea, the superoperon is partially disrupted and, in some cases, certain genes have been lost altogether. Remarkably, the predicted exosomal superoperon also includes genes for proteasome subunits. According to the logic outlined above, this points to a hitherto unknown functional and possibly even physical association between the proteasome and the exosome, the machines for controlled degradation of RNA and proteins, respectively.

Gene order-based functional prediction seems to be impossible for eukaryotes because of the apparent lack of clustering of functionally linked genes. However, several operons that have been identified in C. elegans [ 645 , 894 , 944 ] comprise the first exceptions to this rule and suggest that gene order analysis could be eventually used for eukaryotes, too. Besides, the above prediction of proteasome-exosome association might potentially extend to eukaryotes, offering yet another example of the use of prokaryotic genome comparisons for understanding the eukaryotic cell.

Given the fluidity of gene order in prokaryotes, detection of subtle conservation patterns requires fairly sophisticated computational procedures that search for gene neighborhoods , sets of genes that tend to cluster together in multiple genomes, but do not necessarily show extensive conservation of exact gene order [ 447 , 491 , 640 , 641 , 709 ]. One of the interesting findings that have been made possible through these approaches is the prediction of a new DNA repair system in archaeal and bacterial hyperthemophiles [ 541 ]. As shown in Figure 5.5 (see color plates), the gene neighborhood predicted to encode this system forms a complex patchwork, with very few conserved gene strings. However, the overall conservation of the neighborhood is obvious (once the analysis is completed and the results are summarized as in Figure 5.5 ) and statistically significant [ 541 , 709 ]. In an already familiar theme, prediction of this repair system involved a combination of genomic neighborhood detection with fairly complicated protein sequence analysis and structure prediction. One of the notable findings was the identification of a novel family of predicted DNA polymerases (COG1353). Finally, this is where we encounter, once again, COG1518, the protein family already discussed in 4.5 . When we first analyzed those proteins, we were inclined to predict that they were novel enzymes, perhaps with a hydrolytic activity. Context analysis allows us to make a much more specific prediction: these proteins mostly likely are nucleases involved in DNA repair.

Predicted DNA repair system in hyperthermophiles. The pink boxes show optimal growth temperatures for each of the analyzed species ( A. aeolicus, T. maritima, A. fulgidus, M. thermoautotrophicum, M. jannaschii ). The genes are not drawn to scale; arrows (more...)

5.3. Conclusions and Outlook

In this chapter, we discussed both traditional methods for genome annotation based on homology detection and newer approaches united under the umbrella of genome context analysis. We noted that, although functions can be predicted, at some level of precision, for a substantial majority of genes in each sequenced prokaryotic genome, current annotations are replete with inaccuracies, inconsistencies and incompleteness. This should not be construed as any kind of implicit criticism of those researchers who are involved in genome annotation: the task is objectively hard and is getting progressively more difficult with the growth of databases (and accumulation of inconsistencies). Fortunately, we believe that the remedy is already at hand (see 3.1.3 ). Specialized databases, designed as genome annotation tools, seem to be capable of dramatically improving the situation, if not solving the annotation problem completely. Prototypes of such databases already exist and function and their extensive growth in the near future seems assured.

The context-based methods of genome annotation are quite new: the development of these approaches started only after multiple genome sequences became available. These approaches have a lot of appeal because they are, indeed, true genomic methods based on the notion that the genome (and, especially, many compared genomes) is much more than the sum of its parts. The results produced by these methods are often very intuitive and even visually appealing as in gene string analysis. Objectively, however, these methods yield considerably less information on gene function than homology-based methods, at least for the foreseeable future. Nevertheless, different genome context approaches substantially complement each other and homology-based methods. In fact, homology-based and context-based methods often produce different and complementary types of functional predictions. The former tend to predict biochemical functions (activities), whereas the latter result in biological predictions, such as involvement of a gene in a particular cellular process (e.g. DNA repair in the example above), even if the exact activity cannot be predicted.

We would like to end this chapter on an upbeat note by stating, in large part on the basis of personal experience, that genome annotation is not a routine, mundane activity as it might seem to an outside observer. On the contrary, this is exciting research, somewhat akin to detective work, which has the potential of teasing out deep mysteries of life from genome sequences.

5.4. Further Reading

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Understanding & Interacting with a Text

Annotations, definition and purpose.

Annotating literally means taking notes within the text as you read. As you annotate, you may combine a number of reading strategies—predicting, questioning, dealing with patterns and main ideas, analyzing information—as you physically respond to a text by recording your thoughts. Annotating may occur on a first or second reading of the text, depending on the text’s difficulty or length. You may annotate in different formats, either in the margins of the text or in a separate notepad or document. The main thing to remember is that annotation is at the core of active reading. By reading carefully and pausing to reflect upon, mark up, and add notes to a text as you read, you can greatly improve your understanding of that text.

Think of annotating a text in terms of having a conversation with the author in real time. You wouldn’t sit passively while the author talked at you. You wouldn’t be able to get clarification or ask questions. Your thought processes would probably close down and you would not engage in thinking about larger meanings related to the topic. Conversation works best when people are active participants. Annotation is a form of active involvement with a text.

Reasons to Annotate

There are a number of reasons to annotate a text:

The following video offers a brief, clear example of annotating a text.

What to Annotate

You’ll find that you’re annotating differently in different texts, depending on your background knowledge of the topic, your own ease with reading the text, and the type of text, among other variables. There’s no single formula for annotating a text. Instead, there are different types of annotations that you may make, depending on the particular text.

View the following video, which reviews reading strategies for approximately the first three minutes and then moves into a comprehensive discussion of the types of things to annotate in non-fiction texts.

How to Annotate

Make sure to annotate through writing. Do not – do not – simply highlight or underline existing words in the text. While your annotations may start with a few underlined words or sentences, you should always complete your thoughts through a written annotation that identifies why you underlined those words (e.g., key ideas, your own reaction to something, etc.). The pitfall of highlighting is that readers tend to do it too much, and then have to go back to the original text and re-read most of it. By writing annotations in your own words, you’ve already moved to a higher level in your conversation with the text.

If you don’t want to write in a margin of a book or article, use sticky notes for your annotations. If the text is in electronic form, then the format itself may have built-in annotation tools, or write in a Word document which allows you to paste sentences and passages that you want to annotate.

You may also want to create your own system of symbols to mark certain things such as main idea (*), linkage to ideas in another text (+), confusing information that needs to be researched further (!), or similar idea (=). The symbols and marks should make sense to you, and you should apply them consistently from text to text, so that they become an easy shorthand for annotation. However, annotations should not consist of symbols only; you need to include words to remember why you marked the text in that particular place.

Above all, be selective about what to mark; if you end up annotating most of a page or even most of a paragraph, nothing will stand out, and you will have defeated the purpose of annotating.

Here’s one brief example of annotation:

Sample Annotation

What follows is a sample annotation of the first few paragraphs of an article from CNN, “One quarter of giant panda habitat lost in Sichuan quake,” July 29, 2009. Sample annotations are in color.

“The earthquake in Sichuan, southwestern China, last May left around 69,000 people dead and 15 million people displaced. Now ecologists have assessed the earthquake’s impact on biodiversity look this word up and the habitat for some of the last existing wild giant pandas.

According to the report published in “Frontiers in Ecology and the Environment,” 23 percent of the pandas’ habitat in the study area was destroyed, and fragmentation of the remaining habitat could hinder panda reproduction. How was this data gathered? Do we know that fragmentation will hinder panda reproduction?

The Sichuan region is designated as a global hotspot for biodiversity, according to Conservation International. Home to more than 12,000 species of plants and 1.122 species of vertebrates, the area includes more than half of the habitat for the Earth’s wild giant panda population, said study author Weihua Xu of the Chinese Academy of Sciences in Beijing.” So can we assume that having so much of the pandas/ habitat destroyed will impact other species here?

Link to two additional examples of what and how to annotate

Summary: Annotation = Making Connections

The video below offers a review of reading concepts in the first part, focused on the concept of reading as connecting with a text. From approximately mid-way to the end, the video offers a good extended example and discussion of annotating a text.

Note: if you want to try annotating an article and find the one in the video difficult to read, you may want to practice on a similar article about the same topic, “ Tinker V. Des Moines Independent Community School District: Kelly Shackelford on Symbolic Speech ” on the blog of the U.S. Supreme Court.

Read the paragraphs from “ Cultural Relativism ” that deal with the sociological perspective. Annotate the paragraphs with insights, questions, and thoughts that occur to you as you read.

Organizing Your Social Sciences Research Assignments

An annotated bibliography is a list of cited resources related to a particular topic or arranged thematically that include a brief descriptive or evaluative summary. The annotated bibliography can be arranged chronologically by date of publication or alphabetically by author, with citations to print and/or digital materials, such as, books, newspaper articles, journal articles, dissertations, government documents, pamphlets, web sites, etc., multimedia sources like films and audio recordings, or documents and materials preserved in archival collections.

Harner, James L. On Compiling an Annotated Bibliography . 2nd edition. New York: Modern Language Association, 2000.

Importance of a Good Annotated Bibliography

In lieu of writing a formal research paper or in preparation for a larger writing project, your professor may ask you to develop an annotated bibliography. An annotated bibliography may be assigned for a number of reasons, including :

On a broader level, writing an annotated bibliography can lay the foundation for conducting a larger research project. It serves as a method to evaluate what research has been conducted and where your proposed study may fit within it. By critically analyzing and synthesizing the contents of a variety of sources, you can begin to evaluate what the key issues are in relation to the research problem and, by so doing, gain a better perspective about the deliberations taking place among scholars. As a result of this analysis, you are better prepared to develop your own point of view and contributions to the literature.

In summary, creating a good annotated bibliography...

In addition, writing an annotated bibliography helps you develop skills related to critically reading and identifying the key points of a research study and to effectively synthesize the content in a way that helps the reader determine its validity and usefulness in relation to the research problem or topic of investigation.

NOTE: Do not confuse annotating source materials in the social sciences with the act of annotating source materials in the arts and humanities. Rather than encompassing forms of synopsis and critical analysis, an annotation assignment in arts and humanities courses refers to the systematic interpretation of literary texts, art works, musical scores, performances, and other forms of communication for the purpose of clarifying and encouraging analytical thinking about what the author(s) have written or created. They are assigned to encourage students to actively engage with the text or creative object.

Annotated Bibliographies. The Writing Center. University of North Carolina; Annotated Bibliographies. The Writing Lab and The OWL. Purdue University; Annotated Bibliography. The Waldin Writing Center. Waldin University; Hartley, James. Academic Writing and Publishing: A Practical Guide . (New York: Routledge, 2008), p. 127-128; Writing an Annotated Bibliography. Assignment Structures and Samples Research and Learning Online, Monash University; Kalir, Remi H. and Antero Garcia. Annotation . Essential Knowledge Series. Cambridge, MA: MIT Press, 2021.

Structure and Writing Style

I. Types

NOTE: There are a variety of strategies you can use to critically evaluate a source based on its content, purpose, and format. A description of these strategies can be found here .

II. Choosing Sources for Your Bibliography

There are two good strategies to begin identifying possible sources for your bibliography--one that looks back into the literature and one that projects forward based on tracking citations.

Your method for selecting which sources to annotate depends on the purpose of the assignment and the research problem you are investigating . For example, if the research problem is to compare the social factors that led to protests in Egypt with the social factors that led to protests against the government of the Philippines in the 1980's, you should consider including non-U.S., historical, and, if possible, foreign language sources in your bibliography.

NOTE: Appropriate sources to include can be anything that you believe has value in understanding the research problem . Be creative in thinking about possible sources, including non-textual items, such as, films, maps, photographs, and audio recordings, or archival documents and primary source materials, such as, diaries, government documents, collections of personal correspondence, meeting minutes, and official memorandums. Consult with a librarian if you're not sure how to locate these types of materials for your bibliography.

III. Strategies to Define the Scope of your Bibliography

It is important that the scope of sources cited and summarized in your bibliography are well-defined and sufficiently narrow in coverage to ensure that you're not overwhelmed by the number of potential items to consider including. Many of the general strategies used to narrow a topic for a research paper are the same that you can apply to framing the scope of what to include in an annotated bibliography. Examples include:

IV. Assessing the Relevance and Value of Sources All the items included in your bibliography should reflect the source's contribution to understanding the research problem . In order to determine how you will use the source or define its contribution, you will need to critically evaluate the quality of the central argument within the source or, in the case of including non-textual items, determine how the source contributes to understanding the research problem [e.g., if the topic is outreach strategies to homeless populations, a film that profiles the life of a homeless person]. Specific elements to assess a research study include an item’s overall value in relation to other sources on the topic, its limitations, its effectiveness in defining the research problem, the methodology used, the quality of the evidence, and the strength of the author’s conclusions and/or recommendations. With this in mind, determining whether a source should be included in your bibliography depends on how you think about and answer the following questions related to its content:

V. Format and Content

The format of an annotated bibliography can differ depending on its purpose and the nature of the assignment. Contents may be listed alphabetically by author, arranged chronologically by publication date, or arranged under headings that list different types of sources [i.e., books, articles, government documents, research reports, etc.]. If the bibliography includes a lot of sources, items may also be subdivided thematically, by time periods of coverage or publication, or by source type. If you are unsure, ask your professor for specific guidelines in terms of length, focus, and the type of annotation you are to write. Note that most professors assign annotated bibliographies that only need to be arranged alphabetically by author.

Introduction Your bibliography should include an introduction that describes the research problem or topic being covered, including any limits placed on items to be included [e.g., only material published in the last ten years], explains the method used to identify possible sources [such as databases you searched or methods used to identify sources], the rationale for selecting the sources, and, if appropriate, an explanation stating why specific types of some sources were deliberately excluded. The introduction's length depends, in general, on the complexity of the topic and the variety of sources included.

Citation This first part of your entry contains the bibliographic information written in a standard documentation style , such as, MLA, Chicago, or APA. Ask your professor what style is most appropriate, and be consistent! If your professor does not have a preferred citation style, choose the type you are most familiar with or that is used predominantly within your major or area of study.

Annotation The second part of your entry should summarize, in paragraph form, the content of the source. What you say about the source is dictated by the type of annotation you are asked to write [see above]. In most cases, however, your annotation should describe the content and provide critical commentary that evaluates the source and its relationship to the topic.

In general, the annotation should include one to three sentences about the item in the following order : (1) an introduction of the item; (2) a brief description of what the study was intended to achieve and the research methods used to gather information; ( 3) the scope of study [i.e., limits and boundaries of the research related to sample size, area of concern, targeted groups examined, or extent of focus on the problem]; (4) a statement about the study's usefulness in relation to your research and the topic; (5) a note concerning any limitations found in the study; (6) a summary of any recommendations or further research offered by the author(s); and, (7) a critical statement that elucidates how the source clarifies your topic or pertains to the research problem.

Things to think critically about when writing the annotation include:

Length An annotation can vary in length from a few sentences to more than a page, single-spaced. However, they are normally about 300 words--the length of a standard paragraph. The length also depends on the purpose of the annotated bibliography [critical assessments are generally lengthier than descriptive annotations] and the type of source [e.g., books generally require a more detailed annotation than a magazine article]. If you are just writing summaries of your sources, the annotations may not be very long. However, if you are writing an extensive analysis of each source, you'll need to devote more space.

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In English, many past and present participles of verbs can be used as adjectives. Some of these examples may show the adjective use.

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Add annotated to one of your lists below, or create a new one.

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Refine the Object Page with Annotations

Prerequisites

Whenever your unique suffix for creating objects is needed, the object names within this tutorial are named with suffix “######”. For the screenshots the suffix “000100” was used.

Clicking on any item of the list report will show the object page for this item. The object page currently shows only some standard buttons and does not contain any further fields or actions. In this step you will add annotations to show a title and a subtitle in the object page header.

Open the metadata extensions for the Travel view ZC_FE_TRAVEL_###### . In the previous tutorial Refine the List Report with Additional Annotations you already defined header information in step 3. Now you will enhance the annotation @UI.headerInfo with a title and a description property.

Choose Save and Activate .

Refresh the app preview. You will see an object page containing a title and a subtitle in the header and an empty content section.

In this step you will add some key information to the object page header using data points.

As in the steps before, open the metadata extensions file for the Travel view ZC_FE_TRAVEL_###### .

Add the @UI.facet annotation with two objects of type #DATAPOINT_REFERENCE .

Annotating properties TotalPrice and OverallStatus with @UI.datapoint using the targetQualifier from the facet definition in step 1 will assign the properties to the header facet accordingly.

Refresh the app preview. The two new data points show up in the object page header. The labels are taken from property title , the color of Status from property criticality of the datapoint annotations .

In this step you will add a section to the content area of the object page. The section will contain a form with three data fields.

Open the metadata extensions file for the Travel view ZC_FE_TRAVEL_###### and enter the facet annotations that define the section General Information as a collection facet, using the type Collection . Add a second facet as a child of General Information with facet type #IDENTIFICATION_REFERENCE to create a form with title General . Add the code from line 8 to line 21 to your existing UI facet definition.

Add a new property Description and annotate this and the properties AgencyID and CustomerID with @UI.Identification to position these fields under General .

Refresh the app preview. The new form General is shown in section General Information containing the three fields.

A section in an object page is ...

A field group contains one or more data fields inside a UI container. In this step you define two field groups in the section General Information .

Open the metadata extensions file for the Travel view ZC_FE_TRAVEL_###### .

First, define a field group for the beginning and end date of a travel item and for the prices. The facet type for a field group is #FIELDGROUP_REFERENCE . Add the code from line 8 to line 25 to the end of the @UI.facet section.

Annotate the properties BeginDate and EndDate with @UI.fieldGroup . Make sure you use the same field group qualifier DatesGroup but different positions in each annotation. Apply the same for the properties BookingFee and TotalPrice using field group annotations with qualifier PricesGroup .

Refresh the app preview. There are two additional field groups showing price and date information.

In this step you will add a new section that contains a table with booking information. This requires access to another entity Booking via an association and an additional metadata extensions file.

Open the metadata extensions file for the Travel view ZC_FE_TRAVEL_###### . In the facet annotation block, add a new facet Booking with type #LINEITEM_REFERENCE . Add the code from line 7 to line 14 to the end of the @UI.facet section. Choose Save and Activate .

The property targetElement: _Booking references the association to the booking table that will be shown in the booking section. You can look up the definition of Booking in the projection view of Travel ZC_FE_TRAVEL_###### .

In the project explorer open the folder Data Definitions , right-click on projection view ZC_FE_BOOKING_###### and create a new metadata extensions file from the context menu. Enter ZC_FE_BOOKING_###### as name and Metadata Extension for Booking view as description.

Choose Next and then Finish .

In the metadata extensions file ZC_FE_BOOKING_###### use @UI.lineItem annotations to add some fields from the Booking view ZC_FE_BOOKING_###### Projection View for Booking to the bookings table. Replace the content of ZC_FE_BOOKING_###### by the following code:

Refresh the app preview. The booking table is now displayed in the new Bookings section of the object page.

Instead of showing IDs for the fields Customer ID and Airline ID, one would preferably show descriptions or names.

This will be made possible by using specific annotations which are implemented within the projection view ZC_FE_BOOKING_###### . Therefore, open the projection view which contains the root view definitions for the booking entity.

Add the @ObjectModel and @EndUserText annotations to the fields as shown in the coding fragments below.

Annotation @EndUserText.label defines the column label for the related fields. Using annotation @ObjectModel.text.element controls the source of the content shown for the related field. Fields CarrierID and CustomerID will get their content through the corresponding association.

Refresh the app preview. The booking table is now displayed in the new Bookings section of the object page with descriptions for Customer and Airline .

In this step you will add the airline logo in a new column at the beginning of the booking table.

To achieve this, open the metadata extensions file ZC_FE_BOOKING_###### and add the following code lines to the annotation structure.

Refresh the app preview. The booking table is now displayed with the airline logo in the first column.

What determines the position of a column in the booking table?

Java New Features

Java 9 features, java 8 features, java 7 features, java 4/5 features.

Example to specify annoation for a class

Example to specify annotation for a class, methods or fields.

@Retention annotation is used to specify to what level annotation will be available.

Example to specify the RetentionPolicy

Example of custom annotation: creating, applying and accessing annotation.

Let's see the simple example of creating, applying and accessing annotation.

File: Test.java

How built-in annotaions are used in real scenario?

In real scenario, java programmer only need to apply annotation. He/She doesn't need to create and access annotation. Creating and Accessing annotation is performed by the implementation provider. On behalf of the annotation, java compiler or JVM performs some additional operations.

By default, annotations are not inherited to subclasses. The @Inherited annotation marks the annotation to be inherited to subclasses.

@Documented

The @Documented Marks the annotation for inclusion in the documentation.

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Jackson Annotations for JSON (Part 4): General Annotations

In this final series post about jackson annotations, let's look at some general use annotations that are handy to have at the ready..

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Jackson is a suite of data-processing tools for Java comprising of three components:

Streaming (jackson-core) defines low-level streaming APIs and includes JSON-specific implementations.

Annotations (jackson-annotations) contains standard Jackson annotations.

Databind (jackson-databind) implements data-binding (and object serialization) support on the streaming package. This package depends on both the streaming and annotations packages.

In this series of articles, I will explain data binding Java objects to JSON using Jackson annotations. I will take up each of the Jackson annotations and explain, with code snippets, how to use them. Each annotation usage is accompanied with proper test cases.

If you want to catch up on what's happened so far, read:

Part 1: Serialization and Deserialization

Part 2: Serialization

Part 3: Deserialization

General Annotations

The general annotations are:

@JsonProperty

@jsonformat, @jsonunwrapped, @jsonmanagedreference and @jsonbackreference, @jsonidentityinfo, @jsonfilter.

The @JsonProperty annotation is used to map property names with JSON keys during serialization and deserialization. By default, if you try to serialize a POJO, the generated JSON will have keys mapped to the fields of the POJO. If you want to override this behavior, you can use the @JsonProperty annotation on the fields. It takes a String attribute that specifies the name that should be mapped to the field during serialization.

You can also use this annotation during deserialization when the property names of the JSON and the field names of the Java object do not match.

Let us consider an example Java class that uses the @JsonProperty annotation.

PropertyDemoBean.java

The test code to test the @JsonProperty annotation is:

The @JsonFormat annotation is used to tell Jackson that the format in which the value for a field is serialized. It specifies the format using the JsonFormat.Shape enum.

Let us consider an example Java class that uses the @JsonFormat annotation to modify the Date and Time format of an activeDate field.

FormatDemoBean.java

The test code to test the @JsonFormat annotation is:

The @JsonUnwrapped annotation unwraps the values during serialization and deserialization. It helps in rendering the values of a composed class as if they belonged to the parent class. Let us consider an example of Java class that uses the @JsonUnwrapped annotation.

UnwrappedDemoBean.java

In this example, the Address class is inside the UnwrappedDemoBean class. Without the @JsonUnwrapped annotation, the serialized Java object would be similar to this:

Let us see what happens when you use the @JsonUnwrapped annotation.

The test code to test the @JsonUnwrapped annotation is:

As you can see, the Address object is unwrapped and is displayed as the properties of the parent classUnwrappedDemoBean.

The @JsonView annotation is used to include or exclude a property dynamically during serialization and deserialization. It tells the view in which the properties are rendered. Let us consider an example Java class that uses the @JsonView annotation with Public and Internal views.

ViewDemoBean.java

The test code to test the @JsonView annotation is:

As you can see in the test code, you need to configure the ObjectMapper to include which type of view must be used for writing the JSON from the Java object using the writerWithView() method.

The @JsonManagedReference and @JsonBackReference annotation are used to create JSON structures that have a bidirectional relationship. Without this annotation, you get an error like this:

Let us consider an example Java class that uses the @JsonManagedReference and @JsonBackReference annotations:

ManagedReferenceDemoBean.java

Backreferencedemobean.java.

The test code to test both @JsonManagedReference and @JsonBackReference annotations is:

@BackReferenceDemoBean and @ManagedReferenceDemoBean Test Output

As you can see, the field marked with @JsonManagedReference is the forward reference which will be included during serialization. The field marked with @JsonBackReference is the back reference and is usually omitted during serialization.

The @JsonIdentityInfo tells Jackson to perform serialization or deserialization using the identity of the object. This annotation works similar to the @JsonManagedReference and @JsonBackReference annotations with the difference that @JsonIdentityInfo includes the back reference object.

Let us consider an example where the IdentityInfoEmployeeDemoBean has a bidirectional relationship withIdentityInfoManagerDemoBean using the @JsonIdentityInfo annotation.

IdentityInfoEmployeeDemoBean.java

Identityinfomanagerdemobean.java.

The test code to test the @JsonIdentityInfo annotation is:

As you can see, the output gives the information about the employee with his manager details. It also provides the additional information about the employees under the manager.

The @JsonFilter annotation is used to tell Jackson to use a custom defined filter to serialize the Java object. To define your filter, you need to use the FilterProvider class. This provider gets the actual filter instance to use. The filter is then configured by assigning the FilterProvider to ObjectMapper.

Let us consider an example of Java class that uses the @JsonFilter annotation.

FilterDemoBean.java

The test code to test the @JsonFilter annotation is:

The output of running the test in IntelliJ is:

You can download the source code of this post from here .

Published at DZone with permission of John Thompson , DZone MVB . See the original article here.

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Annotation Definition & Meaning
annotation noun an· no· ta· tion ˌa-nə-ˈtā-shən 1 : a note added (as to a statute) by way of comment or explanation often furnishing summaries of relevant court decisions 2 capitalized : an informational and descriptive note or essay (as about a case or legal issue) especially in American Law Reports More from Merriam-Webster on annotation
Annotation Definition & Meaning
An annotation is a note or comment added to a text to provide explanation or criticism about a particular part of it. Annotation can also refer to the act of annotating —adding annotations. Annotations are often added to scholarly articles or to literary works that are being analyzed.
Annotation Definition & Meaning
1 [count] : a note added to a text, book, drawing, etc., as a comment or explanation Without the annotations, the diagram would be hard to understand. 2 [noncount] : the act of adding notes or comments to something : the act of annotating something the author's annotation of the diagram
Annotated Bibliographies
An annotation is a summary and/or evaluation. Therefore, an annotated bibliography includes a summary and/or evaluation of each of the sources. Depending on your project or the assignment, your annotations may do one or more of the following. Summarize: Some annotations merely summarize the source. What are the main arguments?
Annotation
An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. [1] Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation .
Annotation Examples Simply Explained
Annotations ensure that you understand what is happening in a text when you come back to it, or provide others with valuable information about the text. Why Use Annotations? Annotations are used in order to add notes or more information about a topic as well as to explain content listed on a page or at the end of a publication.
Annotation Examples & Techniques
The term annotation refers to the actual notes one has written during the process of annotating. This process of annotating is used to help readers think through a piece of text, whether it be...
Annotation legal definition of Annotation
Annotation: A note, summary, or commentary on some section of a book or a statute that is intended to explain or illustrate its meaning. An annotation serves as a brief summary of the law and the facts of a case and demonstrates how a particular law enacted by Congress or a state legislature is interpreted and applied. Annotations usually ...
How to define general enumeration in annotation?
i want create a annotation @Documented @Retention(RUNTIME) @Target({METHOD, FIELD, ANNOTATION_TYPE, CONSTRUCTOR, PARAMETER, TYPE_USE}) public @interface Digits { ErrorCode baseCode(); } How should I define a generic, containing class that implements ErrorCode the interface
Declaring an Annotation Type (The Java™ Tutorials
The annotation type definition looks similar to an interface definition where the keyword interface is preceded by the at sign ( @) (@ = AT, as in annotation type). Annotation types are a form of interface, which will be covered in a later lesson. For the moment, you do not need to understand interfaces.
ANNOTATION
a short explanation or note added to a text or image, or the act of adding short explanations or notes: The annotation of literary texts makes them more accessible. The revised edition of the book includes many useful annotations. computing, language specialized
To Work with General Annotations
Use the General Annotation commands to add dimensions, hole notes, and surface texture symbols to the 3D model. What's New: 2021.2, 2023. Before you begin, set the units in Document Settings, Standards tab, and then select the desired standard in the Annotations, Active Standard drop-list. Add or edit dimensions
Annotation
an•no•ta•tion (ˌæn əˈteɪ ʃən) n. 1. a critical or explanatory note added to a text. 2. the act of annotating. 3. note (def. 1). [1425-75] Random House Kernerman Webster's College Dictionary, © 2010 K Dictionaries Ltd. Copyright 2005, 1997, 1991 by Random House, Inc. All rights reserved. annotation
Genome Annotation and Analysis
In simple, operational terms, annotation may be defined as the part of genome analysis that is customarily performed before a genome sequence is deposited in GenBank and described in a published paper.
Annotations
Definition and Purpose. Annotating literally means taking notes within the text as you read. As you annotate, you may combine a number of reading strategies—predicting, questioning, dealing with patterns and main ideas, analyzing information—as you physically respond to a text by recording your thoughts. Annotating may occur on a first or ...
Annotate Definition & Meaning
: to make or furnish annotations (see annotation sense 1) for (something, such as a literary work or subject) annotated his translation of Dante's Divine Comedy annotative ˈa-nə-ˌtā-tiv adjective annotator ˈa-nə-ˌtā-tər noun Example Sentences He annotated the text at several places.
Annotated Bibliography
In general, the annotation should include one to three sentences about the item in the following order: (1) an introduction of the item; (2) a brief description of what the study was intended to achieve and the research methods used to gather information; (3) the scope of study [i.e., limits and boundaries of the research related to sample size ...
ANNOTATED
annotate verb [ T ] formal uk / ˈæn.ə.teɪt / us / ˈæn.ə.teɪt / [ often passive ] to add a short explanation or opinion to a text or image: Annotated editions of Shakespeare's plays help readers to understand old words. an annotated bibliography / manuscript / edition
Refine the Object Page with Annotations
In this step you will add a section to the content area of the object page. The section will contain a form with three data fields. Open the metadata extensions file for the Travel view ZC_FE_TRAVEL_##### and enter the facet annotations that define the section General Information as a collection facet, using the type Collection.Add a second facet as a child of General Information with facet ...
Java Annotations
Java Annotation is a tag that represents the metadata i.e. attached with class, interface, methods or fields to indicate some additional information which can be used by java compiler and JVM. Annotations in Java are used to provide additional information, so it is an alternative option for XML and Java marker interfaces.
Jackson Annotations for JSON (Part 4): General Annotations
The general annotations are: @JsonProperty @JsonFormat @JsonUnwrapped @JsonView @JsonManagedReference and @JsonBackReference @JsonIdentityInfo @JsonFilter @JsonProperty The @JsonProperty...
PDF Week 6
Dimensions and Annotations After creating drawing views, you can annotate those views with dimensions, hole and thread notes, centerlines, and symbols. Production-ready drawings also typically include revision tables and tags. While traditional annotation methods can be quite tedious, you can quickly and easily include these elements in