These days, more and more scientists are diving into genome sequencing projects, urged by fast and cheap next-generation sequencing technologies. Only to discover that they are quickly drowning in an unfathomable sea of sequence data and gasping for help from experts to make biological sense of this ensuing disaster. Bioinformaticians and genome annotators to the rescue!

Microbial genome annotation involves primarily identifying the genes (or actually the open reading frames: ORFs) encrypted in the DNA sequence and deducing functionality of the encoded protein and RNA products (Fig. 1). First, a gene finder such as Glimmer (Delcher et al., 1999) or GeneMark (Lukashin and Borodovsky, 1998) is applied to the genome DNA sequence, producing a set of predicted protein-coding genes. These programs are quite accurate, though not perfect. The next step is to take the set of predictions and search for hits against one or more protein and/or protein domain databases using blast (Altschul et al., 1997), HMMer (Eddy, 1998) or other programs. For each gene that has a significant match, the blast output together with the annotation of the hit can be used to assign a name and function to the protein. The accuracy of this step depends not only on the annotation software, but also on the quality of the annotations already in the reference database.


Figure 1. A generalised flow chart of genome annotation. Statistical gene prediction: use of methods like GeneMark or Glimmer to predict protein-coding genes. General database search: searching sequence databases (typically, NCBI NR) for sequence similarity, usually using blast. Specialized database search: searching domain databases (such as Pfam, SMART and CDD), for conserved domains, genome-oriented databases (such as COGs), for identification of orthologous relationship and refined functional prediction, metabolic databases (such as KEGG) for metabolic pathway reconstruction and other database searches. Prediction of structural features: prediction of signal peptide, transmembrane segments, coiled domain and other features in putative protein functions.

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Genome sequences deposited in NCBI/GenBank, EMBL and DDBJ databases (which mirror each other) are annotated by the submitting groups, who each use their own methods, criteria and thoroughness. This leads to a large diversity in annotation completeness and accuracy. Many of the first genomes published had very limited or no functional annotation, simply because there was very little genomic information in these reference databases to compare with. Most public genome annotation remains static for years, and many annotations have never been changed since their initial publication. Over the years, annotation updates may have been maintained by the submitters, but they are generally only stored in local databases such as GenProtEC/EcoGene for Escherichia coli K12 (Rudd, 2000), Genolist/Bactilist for Bacillus subtilis 168 (Lechat et al., 2008) and SGD for Saccharomyces cerevisiae (Christie et al., 2004).

Since gene functional annotation relies heavily on sequence similarity searching techniques with protein sequence databases, automatically annotated entries based on blast hits to NCBI databases can quickly become outdated. In the mean time, downstream sciences, such as comparative genomics, proteomics, transcriptomics and metabolomics, have rapidly increased our knowledge of many gene products. It is critical therefore, that genome annotations are frequently updated if the information they contain is to remain accurate, relevant and useful.


  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

Re-annotation is defined as the process of updating a previously annotated genome. Automated annotation pipelines combine many different algorithms for gene calling and protein function analysis. In some cases this is followed by manual expert curation, albeit less and less these days, which involves including experimental evidence, and using more sophisticated bioinformatics analysis, such as operon predictions, comparative genome analysis, regulatory motifs prediction, metabolic pathway reconstruction and a lot of common (biochemical) sense. Automated methods save time and resources, but will not incorporate the maximum information available from expert curators, leading to incomplete or even false designations. By contrast, manual annotation is costly and time-consuming. However, manual re-annotation of genomes can significantly reduce the propagation of annotation errors and thus reduce the time spent on flawed research. Hence, there is a need for a research community-wide review and regular update of genome interpretations.

Re-annotations can be published in literature or made available on websites. Examples of published re-annotated genomes are unfortunately rare compared with the rapidly increasing number of sequenced genomes. A first overview of re-annotated genomes was made by (Ouzounis and Karp, 2002). In Table 1 we list some more recently re-annotated microbial genomes. In the latest cases, next-generation technologies have been used for re-sequencing of the original strain prior to re-annotation. Exemplary is the re-sequencing and re-annotation of B. subtilis 168 (Barbe et al., 2009), published 12 years after the original genome paper (Kunst et al., 1997). About 2000 sequence differences were revealed, mainly single nucleotide polymorphisms (SNPs), allowing correction of some frameshifts and variation of amino acid residues prior to re-annotation (Table 1).

Table 1.  Selection of re-annotated microbial genomes.
GenomeRe-sequencingDeleted genesNew genesCorrected genesbOriginal publicationPublication
  • a.

    Includes new pseudogenes.

  • b.

    Includes corrected pseudogenes, but not genes with SNPs leading to only amino acid changes.

 Saccharomyces cerevisiaeNo3703461996Wood et al. (2001)
 Aspergillus nidulansNo 6404942005Wortman et al. (2009)
 Bacillus subtilis 168454 pyro, Solexa 171a3261997Barbe et al. (2009)
 Campylobacter jejuni NCTC11168No   2000Gundogdu et al. (2007)
 Escherichia coli CFT073No6082994352002Luo et al. (2009)
 Mycobacterium tuberculosis H37RvNo1082601998Camus et al. (2002)
 Zymomonas mobilis ZM4454 pyro27148a5392005Yang et al. (2009)

Standardized (re)-annotation databases

  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

Many (re)annotation databases exist (see Table 2 for an overview), of which a few are general: DDBJ, EMBL, Pedant and NCBI GenBank. The ERGO resource is the only commercial database. Some of these databases contain manually curated and standardized gene functions (e.g. ERGO, RefSeq and Genome Reviews). Many of these databases contain gene functions compiled from various sources (e.g. GIB, GOLD, CMR, Genome Reviews, IMG, RefSeq, the SEED and ERGO).

Table 2.  Genome (re-)annotation databases.
NCBI GenbankNational Institutes of Health, USAAn annotated collection of all publicly available DNA sequences et al. (2009)
DDBJDDBJ (DNA Data Bank of Japan)General nucleotide database
EMBLEMBL-EBINucleotide sequence database
Entrez Genome ProjectNational Institutes of Health, USACollection of complete and incomplete genome sequences
ERGOIntegrated Genomics, USAA systems-biology informatics toolkit for comparative genomics Commercial licenseOverbeek et al. (2003)
Genome ReviewsEMBL-EBIUp-to-date, standardised and comprehensively annotated complete genomes et al. (2006)
RefSeqNational Institutes of Health, USAA curated non-redundant sequence database et al. (2009)
The SEEDFellowship for integration of genomes (FIG)Subsystems approach to genome annotation et al. (2005)
IMGDOE Joint Genome Institute, USAIntegrated microbial genomes databasehttp://img.jgi.doe.govMarkowitz et al. (2006); Markowitz et al. (2010)
Microbes OnlineVirtual Institute for Microbial Stress and SurvivalAn integrated portal for comparative and functional genomics et al. (2010)
CMRJ. Craig Venter Institute (JCVI)Comprehensive Microbial Resource: display information on all of the publicly available, complete prokaryotic genomes et al. (2010)
GOLDDOE Joint Genome Institute, USAGenomes On Line Database et al. (2010)
Genome information broker (GIB)DDBJ (DNA Data Bank of Japan)Database of microbial genomes and some comparative genomic tools et al. (2002)
Genome AtlasCBS, Technical University of DenmarkDNA structural atlases for complete microbial genomes and Ussery (2004)
PedantMunich Information Center for Protein Sequences (MIPS)PEDANT 3 database: a Protein Extraction, Description and ANalysis Toolhttp://pedant.gsf.deRiley et al. (2005)
REGANORCeBiTec, GermanyGene prediction server and database Note: site offlineLinke et al. (2006)
BacMapUniversity of Alberta, CanadaAn interactive picture atlas of annotated bacterial genomes et al. (2005)
MOSAICINRA, FranceDatabase dedicated to the comparative genomics of bacterial strains at the intra-species level et al. (2008)
InterProEMBL-EBIIntegrative protein signature database et al. (2009)
PfamSanger Institute, UKProtein families and domains database et al. (2010)
SMARTEMBL, GermanyProtein domain architecture database et al. (2009)
Gene Ontology Annotation (GOA)The Gene OntologyGO controlled vocabulary of biological processes and et al. (2009)
TIGRFAMsJ. Craig Venter Institute (JCVI)Assignment of molecular function and biological process Free to use hidden markov modelsSelengut et al. (2007)
Pseudogene.OrgYale Gerstein GroupA comprehensive database and comparison platform for pseudogene annotationhttp://pseudogene.orgLiu et al. (2004); Karro et al. (2007)
ExPASy ENZYMESwiss Institute for Bioinformatics (SIB)Enzyme nomenclature database (2000)
MetaCycSRI International, USADatabase of metabolic pathways and enzymes et al. (2010)
KEGGKyoto Encyclopedia for Genes and Genomes: Kanehisa LaboratoriesA bioinformatics resource for linking genomes to life and the environment et al. (2008)

Many of the previous databases make use of annotation information from InterPro protein domains, Gene Ontologies (GO; controlled vocabulary of cellular functions), and TIGRFAMs (also part of Manatee, used in IGS/JCVI annotation services). The database can be used to determine whether a gene in a given genome could be a pseudogene (non-functional).

Microbes adapt to their environment by modulating parts of their metabolic and gene regulatory networks. Metabolic networks consist of gene products (enzymes) that catalyse chemical reactions where metabolic compounds are (re)used. The Enzyme Commission (EC) number is a way of classifying enzyme activity, using a nomenclature with specific numbers that are organized hierarchically to indicate the catalysed chemical reaction (ExPASy). Both the KEGG and MetaCyc databases describe the relation of gene products to metabolic pathways. In addition to (curated) annotation information, a few databases also offer bioinformatics and/or visualisation tools for comparative genomics, e.g. MOSAIC, CMR, the Seed, ERGO, GIB, xBASE, MicrobesOnline and BacMap.

(Re)-annotation pipelines

  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

Many of the afore-mentioned databases contain annotation information that is generated by gene annotation pipelines. Table 3 lists annotation pipelines that are either offered as a service or that can be downloaded and installed locally. Locally running pipelines (AGMIAL, DIYA, Restauro-G, GenVar, SABIA, MAGPIE and GenDB) have the advantage that data can be kept confidential and that the annotation process is run on local hardware, ensuring reproducible annotation times. On-line services (IGS, IMG, JCVI, IGS, RAST, xBASE, BASys) have the advantage of simplicity and little time investment. Curation of the annotation results requires constant user interaction to view the genes in context of different annotation information. The JCVI and IGS services both use the (formerly known as TIGR) Manatee pipeline, which also uses the TIGRFAMs to detect functional domains in protein sequences. They offer the user the possibility to view and alter annotations in the respective databases they use. Similar functionality is offered by MAGE (which uses the MicroScope database) (Fig. 2), IMG-ER (uses the IMG data model as basis) and RAST (based on the Seed). The commercially available Pedant-Pro pipeline is based on the Pedant annotation pipeline with various enhancements. Usability of the MiGAP and ATCUG annotation pipelines could not be judged by us due to unavailable software (ATCUG) or website language in Japanese (MiGAP). The Taverna work-flow system allows to link different web services, and has the advantage that it can be adapted by experienced bioinformaticians. Assigning genes to metabolic pathways can be done using the KAAS service (Table 3), which annotates gene products by assigning EC numbers based on amino acid similarity to gene products with known EC numbers.

Table 3.  Genome (re-)annotation pipelines.
IGSUniversity of MarylandA FREE resource for genomics researchers and educators bringing advanced bioinformatics tools to the lab bench and the classroom Free serviceNone
JCVI annotation serviceJ. Craig Venter Institute (JCVI)Free to use genome annotation service Free to use annotation serviceNone
MiGAPDatabase Center for Life Sciences (DBCLS)Microbial Genome Annotation Pipeline (MiGAP) for diverse users Note: site is in Japanese
MaGe/MicroScopeGENOSCOPEMagnifying Genomes: microbial genome annotation system Free serviceVallenet et al. (2006); Vallenet et al. (2009)
BASysUniversity of Alberta, CanadaA web server for bacterial genome annotation Free to useVan Domselaar et al. (2005)
RASTFellowship for Integration of Genomes (FIG)The RAST Server: Rapid Annotations using Subsystems Technology based on the Seed Free to use serviceAziz et al. (2008)
xBASEUniversity of Birmingham, UKBacterial genome annotation service Free to use serviceChaudhuri et al. (2008)
IMG ERJoint Genome Institute (JGI)A system for microbial genome annotation expert review and curation Free serviceMarkowitz et al. (2009)
GenVarVirginia Bioinformatics InstituteBacterial gene annotation and comparative genomics pipeline Free for non-commercial useYu et al. (2007)
Pedant-ProBiomaxGenome analysis package for comprehensive analysis of DNA and protein sequences Commercial licenseFrishman et al. (2001)
AGMIALINRA, FranceAn annotation strategy for prokaryote genomes as a distributed system Open source licenseBryson et al. (2006)
GenDBCeBiTec, GermanyBacterial annotation system Free to use, stand-alone softwareMeyer et al. (2003)
DIYADIY Genomics ConsortiumA bacterial annotation pipeline for any genomics lab Free to use, stand-alone softwareStewart et al. (2009)
SABIALNCC, BrazilBacterial annotation system Free to use, stand-alone softwareAlmeida et al. (2004)
MAGPIEGenome Prairie Project, CanadaGenome annotation system Free to use, stand-alone softwareGaasterland and Sensen (1996)
Restauro-GInstitute for Advanced Biosciences, Keio UniversityA Rapid Genome Re-Annotation System for Comparative Genomics Software distributed under the GNU public licenseTamaki et al. (2007)
ATUCG systemUniversidade Federal do Rio Grande do Sul, BrasilAgent-based environment for automatic annotation of GenomesNone Software should be requested at authorsNascimento and Bazzan (2005)
Taverna: annotation of genomesUniversity of ManchesterInteractive genome annotation pipeline. et al. (2006)
KAASKyoto Encyclopedia for Genes and Genomes (KEGG)KEGG automated annotation service for metabolic pathways Free to use serviceMoriya et al. (2007)

Figure 2. Simplified prokaryotic genome database (PkGDB) relational model composed of three main components: sequence and annotation data (in green), annotation management (in blue) and functional predictions (in purple). Sequences and annotations come from public databanks, sequencing centres and specialized databases focused on model organisms. For genomes of interest, a (re)-annotation process is performed using AMIGene (Bocs et al., 2003) and leads to the creation of new ‘Genomic Objects’. Each ‘Genomic Object’ and associated functional prediction results are stored in the PkGDB. The database architecture supports integration of automatic and manual annotations, and management of a history of annotations and sequence updates. Reproduced from Vallenet and colleagues (2006).

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Once gene annotations have been determined, they can be checked for inaccurate or missing gene annotations using MICheck. Hsiao and colleagues (2010) describe an algorithm for policing gene annotations, which looks for genes with poor genomic correlations with their network neighbours, and are likely to represent annotation errors. They applied their approach to identify misannotations of B. subtilis. The Artemis generic visualisation tool can be used for manual editing of annotation (Rutherford et al., 2000). Prior to submission of a DNA sequence and annotation to the NCBI genome database, the NCBI Sequin service ( also facilitates checking gene annotations, making sure that certain standards and formats are used.

Comparison of automatic annotation pipelines

  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

Genome annotations are accumulating rapidly and most genome centres depend heavily on automated annotation systems. But rarely has their output been systematically compared to determine accuracy and inherent errors. (Bakke and colleagues (2009) compared the automatic genome annotation services IMG, RAST and JCVI, and found considerable differences in gene calls (Fig. 3), features and ease of use. Each service provided multiple unique start sites and gene product calls as well as mistakes. They argue that the most efficient way to substantially decrease annotation error is to compare results from multiple annotation services. Aggregating data and displaying discrepancies between annotations should resolve many possible errors including false positives, uncalled genes, genes without a predicted function, incorrectly predicted functions and incorrect start sites. To accomplish multi-annotation comparison, information must be interchangeable between annotation services, and software should be built to connect annotations in a manner that promotes easy human review. Tools that cross-query annotations and provide side-by-side comparisons that include genomic context and multiple functional annotations will aid the user and decrease the amount of time required to make an accurate correction, i.e. to decrease manual curation time.


Figure 3. Venn diagram of comparison of gene prediction in Halorhabdus utahensis using the RAST, IMG and JCVI automated annotation services. The diagram shows the number of predicted protein-coding genes that share start site and stop site with the other annotations. Overlapping regions indicate genes having exact matches between annotations. Adapted from Bakke and colleagues (2009).

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  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

Clearly, standardization of ORF calling and annotation (and re-annotation of published genomes) is of utmost importance. A few standard operating procedures for genome annotation have already been proposed in recent years (Angiuoli et al., 2008; Mavromatis et al., 2009). Still, we are a long way from achieving that goal, and it is unlikely we will ever be able to weed out all the incorrect gene calls and inherited annotations that are abundant in present genome databases. The contents of NCBI GenBank can only be changed by the original submitters, and that rarely happens. So be aware that a blast search against GenBank may retrieve very outdated or incorrectly inherited annotations. It is wiser to blast against curated genome databases, but there are so many to choose from (Table 2), and we clearly need tools to compare annotations from different curated databases.

Re-annotation of genomes is a never-ending process, and any current genome annotation is only a snap-shot. New information emerges almost every day from re-sequencing, experimentation (e.g. transcriptomics, proteomics, phenotypic tests, gene knock-outs), comparative genomics, etc. Salzberg (2007) has proposed that a ‘genome wiki’ might provide just the solution we need for genome annotation. A wiki would allow the community of experts to work out the best name for each gene, to indicate uncertainty where appropriate, to include experimental evidence, to discuss alternative annotations, and to continuously update annotations. Although wikis will not (and should not) supplant well-curated model-organism databases, for the majority of species they might represent our best chance for creating accurate, up-to-date genome annotation.

And if you are really serious about updating your annotations, don't forget to re-sequence your original strains using next-generation sequencing, at least if you can still find them in your freezer!


  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References

This project was carried out within the research programmes of the Kluyver Centre for Genomics of Industrial Fermentation and the Netherlands Bioinformatics Centre, which are part of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.


  1. Top of page
  2. Re-annotation
  3. Standardized (re)-annotation databases
  4. (Re)-annotation pipelines
  5. Comparison of automatic annotation pipelines
  6. Future
  7. Acknowledgements
  8. References
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