Genome (re‐)annotation and open‐source annotation pipelines

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.

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.

Re‐annotation

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.

Genome	Re‐sequencing	Deleted genes	New genes	Corrected genesb	Original publication	Publication
Eukaryotes
Saccharomyces cerevisiae	No	370	3	46	1996	Wood et al. (2001)
Aspergillus nidulans	No		640	494	2005	Wortman et al. (2009)
Prokaryotes
Bacillus subtilis 168	454 pyro, Solexa		171a	326	1997	Barbe et al. (2009)
Campylobacter jejuni NCTC11168	No				2000	Gundogdu et al. (2007)
Escherichia coli CFT073	No	608	299	435	2002	Luo et al. (2009)
Mycobacterium tuberculosis H37Rv	No	10	82	60	1998	Camus et al. (2002)
Zymomonas mobilis ZM4	454 pyro	271	48a	539	2005	Yang et al. (2009)

^a.Includes new pseudogenes.

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

Standardized (re)‐annotation databases

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.

Database	Organization	Description	Access/distribution	Reference
NCBI Genbank	National Institutes of Health, USA	An annotated collection of all publicly available DNA sequences	http://www.ncbi.nlm.nih.gov/Genbank	Benson et al. (2009)
DDBJ	DDBJ (DNA Data Bank of Japan)	General nucleotide database	http://www.ddbj.nig.ac.jp/	None
EMBL	EMBL‐EBI	Nucleotide sequence database	http://www.ebi.ac.uk/embl/	None
Entrez Genome Project	National Institutes of Health, USA	Collection of complete and incomplete genome sequences	http://www.ncbi.nlm.nih.gov/sites/entrez?db=genomeprj	None
ERGO	Integrated Genomics, USA	A systems‐biology informatics toolkit for comparative genomics	http://www.integratedgenomics.com/ergo.html Commercial license	Overbeek et al. (2003)
Genome Reviews	EMBL‐EBI	Up‐to‐date, standardised and comprehensively annotated complete genomes	http://www.ebi.ac.uk/GenomeReviews/	Sterk et al. (2006)
RefSeq	National Institutes of Health, USA	A curated non‐redundant sequence database	http://www.ncbi.nih.gov/RefSeq/	Pruitt et al. (2009)
The SEED	Fellowship for Integration of Genomes (FIG)	Subsystems approach to genome annotation	http://www.theseed.org/wiki/index.php/Main_Page	Overbeek et al. (2005)
IMG	DOE Joint Genome Institute, USA	Integrated microbial genomes database	http://img.jgi.doe.gov	Markowitz et al. (2006); Markowitz et al. (2010)
Microbes Online	Virtual Institute for Microbial Stress and Survival	An integrated portal for comparative and functional genomics	http://www.microbesonline.org/	Dehal et al. (2010)
CMR	J. Craig Venter Institute (JCVI)	Comprehensive Microbial Resource: display information on all of the publicly available, complete prokaryotic genomes	http://cmr.jcvi.org/tigr‐scripts/CMR/CmrHomePage.cgi	Davidsen et al. (2010)
GOLD	DOE Joint Genome Institute, USA	Genomes On Line Database	http://www.genomesonline.org/	Liolios et al. (2010)
Genome information broker (GIB)	DDBJ (DNA Data Bank of Japan)	Database of microbial genomes and some comparative genomic tools	http://gib.genes.nig.ac.jp/	Fumoto et al. (2002)
Genome Atlas	CBS, Technical University of Denmark	DNA structural atlases for complete microbial genomes	http://www.cbs.dtu.dk/services/GenomeAtlas/	Hallin and Ussery (2004)
Pedant	Munich Information Center for Protein Sequences (MIPS)	PEDANT 3 database: a Protein Extraction, Description and ANalysis Tool	http://pedant.gsf.de	Riley et al. (2005)
REGANOR	CeBiTec, Germany	Gene prediction server and database	https://www.cebitec.uni‐bielefeld.de/groups/brf/software/reganor/cgi‐bin/reganor_upload.cgi Note: site offline	Linke et al. (2006)
BacMap	University of Alberta, Canada	An interactive picture atlas of annotated bacterial genomes	http://wishart.biology.ualberta.ca/BacMap/	Stothard et al. (2005)
MOSAIC	INRA, France	Database dedicated to the comparative genomics of bacterial strains at the intra‐species level	http://genome.jouy.inra.fr/mosaic/	Chiapello et al. (2008)
InterPro	EMBL‐EBI	Integrative protein signature database	http://www.ebi.ac.uk/interpro/	Hunter et al. (2009)
Pfam	Sanger Institute, UK	Protein families and domains database	http://pfam.sanger.ac.uk/	Finn et al. (2010)
SMART	EMBL, Germany	Protein domain architecture database	http://smart.embl‐heidelberg.de/	Letunic et al. (2009)
Gene Ontology Annotation (GOA)	The Gene Ontology	GO controlled vocabulary of biological processes	http://www.geneontology.org/GO.tools.annotation.shtml and http://www.ebi.ac.uk/GOA/	Barrell et al. (2009)
TIGRFAMs	J. Craig Venter Institute (JCVI)	Assignment of molecular function and biological process	http://www.jcvi.org/cms/research/projects/tigrfams/overview/ Free to use hidden markov models	Selengut et al. (2007)
Pseudogene.Org	Yale Gerstein Group	A comprehensive database and comparison platform for pseudogene annotation	http://pseudogene.org	Liu et al. (2004); Karro et al. (2007)
ExPASy ENZYME	Swiss Institute for Bioinformatics (SIB)	Enzyme nomenclature database	http://www.expasy.ch/enzyme/	Bairoch (2000)
MetaCyc	SRI International, USA	Database of metabolic pathways and enzymes	http://metacyc.org/	Caspi et al. (2010)
KEGG	Kyoto Encyclopedia for Genes and Genomes: Kanehisa Laboratories	A bioinformatics resource for linking genomes to life and the environment	http://www.genome.jp/kegg/	Okuda 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 pseudogene.org 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

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.

Pipeline	Organization	Description	Access/distribution	Reference
IGS	University of Maryland	A FREE resource for genomics researchers and educators bringing advanced bioinformatics tools to the lab bench and the classroom	http://ae.igs.umaryland.edu/cgi/index.cgi Free service	None
JCVI annotation service	J. Craig Venter Institute (JCVI)	Free to use genome annotation service	http://www.jcvi.org/cms/research/projects/annotation‐service/overview/ Free to use annotation service	None
MiGAP	Database Center for Life Sciences (DBCLS)	Microbial Genome Annotation Pipeline (MiGAP) for diverse users	http://migap.lifesciencedb.jp/ Note: site is in Japanese	http://www.jsbi.org/modules/journal1/index.php/GIW09/Poster/GIW09S001.pdf
MaGe/MicroScope	GENOSCOPE	Magnifying Genomes: microbial genome annotation system	http://www.genoscope.cns.fr/agc/mage Free service	Vallenet et al. (2006); Vallenet et al. (2009)
BASys	University of Alberta, Canada	A web server for bacterial genome annotation	http://wishart.biology.ualberta.ca/basys/ Free to use	Van Domselaar et al. (2005)
RAST	Fellowship for Integration of Genomes (FIG)	The RAST Server: Rapid Annotations using Subsystems Technology based on the Seed	http://rast.nmpdr.org/ Free to use service	Aziz et al. (2008)
xBASE	University of Birmingham, UK	Bacterial genome annotation service	http://xbase.ac.uk/annotation/ Free to use service	Chaudhuri et al. (2008)
IMG ER	Joint Genome Institute (JGI)	A system for microbial genome annotation expert review and curation	http://img.jgi.doe.gov/er Free service	Markowitz et al. (2009)
GenVar	Virginia Bioinformatics Institute	Bacterial gene annotation and comparative genomics pipeline	http://patric.vbi.vt.edu/downloads/software/GenVar Free for non‐commercial use	Yu et al. (2007)
Pedant‐Pro	Biomax	Genome analysis package for comprehensive analysis of DNA and protein sequences	http://www.biomax.de/products/pedantpro.php Commercial license	Frishman et al. (2001)
AGMIAL	INRA, France	An annotation strategy for prokaryote genomes as a distributed system	http://genome.jouy.inra.fr/agmial/ Open source license	Bryson et al. (2006)
GenDB	CeBiTec, Germany	Bacterial annotation system	http://www.cebitec.uni‐bielefeld.de/groups/brf/software/gendb_info/ Free to use, stand‐alone software	Meyer et al. (2003)
DIYA	DIY Genomics Consortium	A bacterial annotation pipeline for any genomics lab	https://sourceforge.net/projects/diyg/ Free to use, stand‐alone software	Stewart et al. (2009)
SABIA	LNCC, Brazil	Bacterial annotation system	http://www.sabia.lncc.br/ Free to use, stand‐alone software	Almeida et al. (2004)
MAGPIE	Genome Prairie Project, Canada	Genome annotation system	http://magpie.ucalgary.ca/ Free to use, stand‐alone software	Gaasterland and Sensen (1996)
Restauro‐G	Institute for Advanced Biosciences, Keio University	A Rapid Genome Re‐Annotation System for Comparative Genomics	http://restauro‐g.iab.keio.ac.jp/ Software distributed under the GNU public license	Tamaki et al. (2007)
ATUCG system	Universidade Federal do Rio Grande do Sul, Brasil	Agent‐based environment for automatic annotation of Genomes	None Software should be requested at authors	Nascimento and Bazzan (2005)
Taverna: annotation of genomes	University of Manchester	Interactive genome annotation pipeline.	http://www.taverna.org.uk/introduction/taverna‐in‐use/annotation/annotation‐of‐genomes/	Hull et al. (2006)
KAAS	Kyoto Encyclopedia for Genes and Genomes (KEGG)	KEGG automated annotation service for metabolic pathways	http://www.genome.jp/tools/kaas/ Free to use service	Moriya 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).

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 (http://www.ncbi.nlm.nih.gov/projects/Sequin/) also facilitates checking gene annotations, making sure that certain standards and formats are used.

Comparison of automatic annotation pipelines

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).

Future

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!