Open Access

Similarity-based gene detection: using COGs to find evolutionarily-conserved ORFs

BMC Bioinformatics20067:31

https://doi.org/10.1186/1471-2105-7-31

Received: 03 August 2005

Accepted: 19 January 2006

Published: 19 January 2006

Abstract

Background

Experimental verification of gene products has not kept pace with the rapid growth of microbial sequence information. However, existing annotations of gene locations contain sufficient information to screen for probable errors. Furthermore, comparisons among genomes become more informative as more genomes are examined. We studied all open reading frames (ORFs) of at least 30 codons from the genomes of 27 sequenced bacterial strains. We grouped the potential peptide sequences encoded from the ORFs by forming Clusters of Orthologous Groups (COGs). We used this grouping in order to find homologous relationships that would not be distinguishable from noise when using simple BLAST searches. Although COG analysis was initially developed to group annotated genes, we applied it to the task of grouping anonymous DNA sequences that may encode proteins.

Results

"Mixed COGs" of ORFs (clusters in which some sequences correspond to annotated genes and some do not) are attractive targets when seeking errors of gene predicion. Examination of mixed COGs reveals some situations in which genes appear to have been missed in current annotations and a smaller number of regions that appear to have been annotated as gene loci erroneously. This technique can also be used to detect potential pseudogenes or sequencing errors. Our method uses an adjustable parameter for degree of conservation among the studied genomes (stringency). We detail results for one level of stringency at which we found 83 potential genes which had not previously been identified, 60 potential pseudogenes, and 7 sequences with existing gene annotations that are probably incorrect.

Conclusion

Systematic study of sequence conservation offers a way to improve existing annotations by identifying potentially homologous regions where the annotation of the presence or absence of a gene is inconsistent among genomes.

Background

The rapidly growing amount of genomic sequence information necessitates tools for its annotation. Although predicting bacterial genes is in many ways simpler than predicting eukaryotic genes, it is clear that there remains room for improvement in the bacterial case. Several groups have undertaken efforts to re-annotate specific genomes [13], often finding a small but significant number of errors in existing annotation of gene loci. The presence of these errors has motivated the effort of some groups to systematically revise the gene annotations in public databases as a continuous process [4, 5].

Because technology for genome sequencing is much more mature than proteomic analysis, only a small fraction of annotated bacterial gene products have been detected as protein; most have been annotated using only computational methods. Although methods for detecting and identifying all proteins in a cell are being developed [3, 6, 7] and incorporated into annotations of newly-sequenced genomes [8], these techniques are currently limited by the ability to express all of the polypeptides in an organism and separate them into fractions with low enough complexity for analysis. It is still useful to refine our computational predictions so that we can make targeted searches for potential proteins.

Accuracy of gene identification is particularly important in studies of the gene content of a genome as a whole. Studies of phyletic patterns of gene presence [9], the extent of horizontal gene transfer among genomes, the entire set of protein structures encoded by a genome [10], and the components of a "minimal genome" [11, 12] are all predicated on an accurate catalog of the genes within an organism. Because these studies involve comparing the presence or absence of genes among several organisms, it is particularly important that all of the genes present be identified. Insights in these areas of study could impact our understanding of bacterial evolution physiology and pathogenicity. As an example, in the initial report of the Mycoplasma mobile genome sequence the correlation of presence or absence of certain genes with a presence or absence of a specific phenotypic characteristic (motility) among nine species was used to suggest genes which might confer that phenotype [8].

Methods for predicting protein-coding genes are often divided into intrinsic and extrinsic classes [13, 14]. Intrinsic methods only use evidence from within the primary sequence of a genome. This evidence may include i) the presence of a relatively long frame uninterrupted by a stop codon, ii) the statistical pattern of polynucleotide stretches that match the typical frequencies present in other coding regions of the organism and iii) the existence of appropriate non-coding control elements. It may be difficult to identify some small genes using the first two types of evidence; small genes can be difficult to distinguish from open reading frames that occur by chance, and in such short regions, sequence characteristics may be affected stochastically. Gene finding methods which use sequence characteristics or control elements often need to be tuned for the specific organism studied, and in many cases several statistical models of coding regions may need to be developed in a single organism [15].

Extrinsic methods use information from comparisons of genomes. These analyses originally used simple pairwise comparisons among potential protein-coding regions. Harrison et al. [16] examined ORFs of 15 or more codons in 65 microbial genome sequences, using BLAST E-value of less than 10-4 to indicate similarity suggestive of conserved function. Other searches use sequence alignments of a protein family as a query against all possible translations of the genome of interest. This alignment may be specified beforehand (e.g. using a Pfam protein family) or developed as part of the search as by PSI-BLAST [17]. Pair hidden Markov models use a pairwise sequence alignment coupled to a hidden Markov model to more precisely determine the amino termini of protein-coding genes [18]. Programs have been developed that use pairwise alignment of syntenic regions to predict gene structure in eukaryotes [19]. The ratio of synonymous to nonsynonymous substitutions between pairs of putative genes can be used to examine whether there is selection for protein-coding function, but this requires sequences from closely related organisms [20]. The majority of bacterial genome annotations have used intrinsic methods at least initially to predict the presence of genes. Extrinsic gene prediction methods serve as a useful complement to intrinsic methods because independent information is used to make the same prediction.

In this study we describe a systematic extension to examining similarity shared among several genomes using a modification of the analysis of Clusters of Orthologous Groups (COGs). COGs were developed to cluster annotated genes into functionally related groups in order to facilitate the transfer of functional annotations among organisms [21]. Here we use COGs to cluster open reading frames as a means of recognizing genes. An advantage of the COG analysis is that no explicit threshold for sequence similarity is used; genes that are missed in pairwise comparisons may be detected. Because the focus is on annotation of gene location rather than gene function, we are not concerned with finding genes that are strictly orthologous. Evidence of homology to another gene is sufficient to imply that a region is a gene, and we make no effort to avoid the joining of COGs which may occur due to a gene fusion [22]. The idea of stringency [23] of COGs expands upon the initial COG definition by requiring an adjustable of connectedness for grouping genes. As the number of studied organisms increases, the stringency can further filter some similarities which may have occurred by chance. ORFs that do not correspond to annotated genes but that nonetheless have conserved sequences present in several genomes are likely to be protein-coding genes that have been missed by current annotations.

A cluster of ORFs can be examined with regards to its multiple sequence alignment, the network of similarities among the ORFs, and the respective genomic contexts of the ORFs in the cluster. These characteristics of ORF clusters can be used to screen existing gene predictions for potential errors. The extrinsic nature of the use of COGs of ORFs is complementary to the intrinsic methods that have been used in producing the majority of gene loci annotations. Because our strategy has different strengths and weaknesses from the intrinsic methods, it may be expected to pick up some genes that other methods have missed.

In this study we examined open reading frames from the complete genome sequences of 27 bacteria (Table 1). We selected these genome sequences for the following reasons. M. genitalium and M. pneumoniae are model systems for defining the minimal cellular genome. Consequently, their proteins became the focus of research at the Berkeley Structural Genomics Center [10, 24]. Other members of the class Mollicutes provide a closely related set which may help identify genes specific to this class. The protein products of M. pneumoniae and M. mobile have been recently studied using high-throughput identification using multidimensional chromatography and mass spectrometry [3, 8]. We included other small-genome parasitic bacteria to study the extent to which gene content has convergently evolved among this group. We also included a more diverse selection of bacterial genome sequences from major phylogenetic groups to see how robust our strategy would be when examining distantly related organisms.
Table 1

Genomes included in this study

Accessiona

Name

Length (nt)

# of genes annotateda

# of ORFsb >30 aa

BA000004

Bacillus halodurans strain C-125

4202352

4066

73839

BSXX

Bacillus subtilis subsp. subtilis str. 168

4214630

4106

75310

AE000783

Borrelia burgdorferi str. B31

910724

850

10756

AE001273

Chlamydia trachomatis strain D/UW-3/CX

1042519

894

17211

AE001363

Chlamydophila pneumoniae CWL029

1230230

1052

19259

AE001437

Clostridium acetobutylicum strain ATCC 824

3940880

3672

48244

BA000016

Clostridium perfringens str. 13

3031430

2660

31417

U00096

Escherichia coli K12

4639221

4289

86919

AE005174

Escherichia coli O157:H7 EDL933

5528970

5349

102747

L42023

Haemophilus influenzae Rd KW20

1830138

1709

27756

AE001439

Helicobacter pylori J99

1643831

1491

21997

AL591824

Listeria monocytogenes

2944528

2855

45146

AE015450

Mycoplasma gallisepticum str. R

996422

726

13506

L43967

Mycoplasma genitalium strain G-37

580074

480

8058

AE017308

Mycoplasma mobile strain 163K

777079

633

10241

BX293980

Mycoplasma mycoides subsp. mycoides SC

1211703

1016

14127

BA000026

Mycoplasma penetrans strain HF-2

1358633

1037

17111

U00089

Mycoplasma pneumoniae strain M129

816394

688

13868

MPUABCTIP

Mycoplasma pulmonis (Sabin 1941) Freundt 1955

963879

782

13324

AE002098

Neisseria meningitidis serogroup B strain MC58

2272351

2025

42660

AE004091

Pseudomonas aeruginosa str. PAO1

6264403

5566

92461

RPXX

Rickettsia prowazekii da Rocha-Lima 1916

1111523

834

12029

STYPHCT18

Salmonella enterica subsp. enterica serovar Typhi

4809037

4600

90974

AE007317

Streptococcus pneumoniae str. R6

2038615

2043

31733

AE000520

Treponema pallidum subsp. pallidum str. Nichols

1138011

1031

21937

AF222894

Ureaplasma urealyticum biovar 2

751719

611

9173

AE003852

Vibrio cholerae serotype O1 biotype ElTor strain N16961

2961149

2736

53378

AE003853

Vibrio cholerae serotype O1 biotype ElTor strain N16961

1072315

1092

19506

aAccessions and annotated genes reference Genome Reviews version 25.0

We wanted to detect fragments of genes and genes which use start codons other than ATG, so we used a very general definition of an ORF: any frame of length at least 30 codons (90nucleotides) uninterrupted by stop codons. The software developed as part of this study, SPROCKET, can be used to detect probable errors in existing gene annotations.

Results

Gene discovery using conservation of potential peptide sequence

Extrinsic gene-finding methods are based on the duality that, given sufficient evolutionary distance, conserved sequences are likely to be functional and that functional sequences are likely to be conserved. The conserved regions may represent control elements or may encode functional RNA molecules or proteins. We examined the extent to which multi-species sequence conservation could be used to detect the presence of protein-coding genes. We considered two systems for classifying ORFs as genes or not-genes. The first system was based solely on existence of a COG containing the ORF. If we determined that an ORF was a member of a COG then we classified it as a gene, otherwise we classified it as not being a gene. Our second classification system used existing gene annotations for all genomes except the one containing the ORF to be classified, reducing the number of false positives. In this system we classified an ORF as a gene if we found it in a COG containing at least one ORF from another genome that was annotated as a gene. In both systems the COG stringency controlled the extent of conservation required for classification.

We compared these gene predictions to existing gene annotations using sensitivity/specificity analysis. Although there are errors in existing gene annotations (as discussed below) the current annotations represent the expert consensus. Sensitivity for a gene classification system is the number of correct gene predictions divided by the number of actual genes. The specificity is the number of true negative predictions divided by the total number of non-genes.

Sensitivity and specificity of gene predictions varied at different stringency levels of the COGs used in classification. The accuracies of both classification systems are summarized in Figure 1. Receiver-operator characteristic (ROC) plots show the true positive rate (sensitivity) along the vertical axis against the false positive rate (1-specificity) along the horizontal axis. An ideal test (when compared to an ideal "gold standard") would be represented by a point plotted in the upper-left corner (0,1). Figures 1A and 1B show the classifications based solely on COG membership and based on COG membership with an annotated gene from another organism, respectively. Each point plotted on this graph represents the sensitivity and specificity of classification at a given stringency. ROC curves are shown for a sample of seven of the genome sequences studied and for the pooled results for all of the genomes. The accuracy of tests for each genome sequence and pooled results for all of the genome sequences are summarized across COG stringencies by the area under the ROC plots (Figure 1C). The sensitivity and specificity values for all of the studied genomes were computed [see Additional file 1].
Figure 1

Gene prediction based on sequence conservation. (A) and (B) show receiver-operator characteristic curves summarizing the sensitivity and specificity of gene prediction based on COG membership when compared to the current gene annotations. In (A), an ORF is classified as a gene if it is conserved in a COG at a certain stringency; for (B), the ORF must be in a COG that contains at least one annotated gene from another species. Curves are produced by examining COGs at different stringencies. At stringency 2, tests are very sensitive but not very specific (points at upper right of each panel). As stringency increases, specificity increases and sensitivity decreases (indicated by arrow). For clarity, full ROC curves are shown for only seven of the organisms studied, and for the pooled result among all of the organisms studied. The plotting symbols and colors used in (A) and (B) are next to the organism names in (C). (C) shows the areas under the curves in (A) black bars and (B) grey bars. The ROC curve of a perfect test would enclose an area of 1, for a completely arbitrary test the area would be 0.5. The organisms in (C) are ordered by the area under the ROC curve in (B).

"Mixed" COGs

COGs formed from ORFs can also be used to mine existing gene location annotation for potential errors. COGs in which there are some ORFs that correspond to annotated genes and other ORFs that do not correspond to annotated genes represent potential anomalies in existing annotations. The number of these mixed COGs at different stringencies is shown in Figure 2. Also shown in Figure 2 are the number of COGs at each stringency level which contain only ORFs which correspond to annotated genes ("all matches") or which contain no ORFs which correspond to annotated genes ("no matches"). The initial increase in numbers of COGs when moving from stringency two (single-linkage clusters) to three is the result of larger, weakly connected COGs splitting into several smaller COGs. As stringency increases beyond three, the number of COGs in each group decreases exponentially. The "all matches" and "mixed" classes have similar connectedness structures in that the numbers of COGs in these classes decay at similar rates. The number of COGs in the "no matches" class drops more rapidly because the less well-conserved or connected sets of ORFs that may not be conserved due to protein-coding function are not present at higher stringencies.
Figure 2

Presence of annotated genes in COGs of ORFs. Open reading frames (ORFs) of at least 90 nucleotides between stop codons were used to construct COGs at varying stringencies as described in the methods. COGs were divided into one of three groups: "All members match annotated genes" – contain only ORFs which correspond to annotated genes, "No members match annotated genes" – contain no ORFs which match annotated genes, or "Mixed" – contain some ORFs that correspond to annotated genes and some that do not. The numbers of ORFs in COGs of each of these classes are plotted along the y-axis with a logarithmic scale.

Screening existing gene predictions for errors

Mixed COGs are attractive targets when looking for errors in existing gene predictions. We examined the genomic context and peptide sequence alignments of the mixed COGs of stringency six to explain the inconsistency of gene annotation within these COGs. In COGs where the majority of ORFs correspond to annotated genes, the remaining ORFs are likely to represent missed genes or pseudogenes. At stringency six there are 147 mixed COGs in which the majority of ORFs correspond to annotated genes (Table 2). At this stringency every member of a COG is in a bidirectional best-hit relationship with at least six other ORFs in the COG. These COGs contain 143 ORFs that are not associated with annotated genes. Some of the COGs contained multiple ORFs that did not correspond to annotated genes and some ORFs were members of multiple COGs.
Table 2

ORFs in Majority-annotated mixed COGs of stringency 6 that may represent missed genes

ORF COG ida

Organism

Genomic coordinatesb

Annotated gene(s) present in COGc

ORF COG ida

Organism

Genomic coordinatesb

Annotated gene(s) present in COGc

Potential genes missed in current annotations

Potential genes missed in current annotations (continued)

678

Bbur

117772-116825

cdsA

397

Nmen

340008-339358

coaE

314

Bhal

1503738-1503905

rpmG

871

Nmen

554238-552676

mucD/deg

314

Bsub

2477091-2476963

rpmG

723

Nmen

666433-665363

potA/cysA/malK

2346

Bsub

4202360-4202148

 

119

Nmen

690163-687386

trkH

1717

Cace

243535-242696

alx

1382

Nmen

1056138-1057340

hflX

1908

Cace

1395172-1395522

minE

464

Nmen

1147918-1149261

tilS

2064

Cace

2284461-2283778

 

464

Nmen

1179954-1181297

tilS

148

Cace

3287735-3286509

tufA

2743

Nmen

1400226-1401977

 

1840

Cace

3650828-3649308

 

978

Nmen

1484110-1486353

dnaX

659

Cace

3842459-3840768

plpB

635

Nmen

1527781-1528521

 

1551

EcoK12

311756-311598

rpmJ

1248

Nmen

1629570-1628017

pepA

148

EcoK12

3469408-3468167

tufA

2793

Nmen

1749455-1752016

gcvP

1551

EcoO157

344941-344783

rpmJ

618

Nmen

2119341-2120882

hrpB

2748

EcoO157

4240898-4240665

 

618

Nmen

2124720-2128169

hrpB

2531

Hinf

131970-132959

mltA

788

Nmen

2199859-2200686

folD

2319

Hinf

170676-169396

dcuB

2519

Paer

224101-225219

ald

2432

Hinf

235913-238519

 

1385

Paer

434829-433933

 

2947

Hinf

370735-372912

 

38

Paer

4143744-4142569

prfA

1098

Hpyl

315887-316504

dppC

2748

Sent

4247574-4247864

 

309

Lmon

640139-639558

bioY

192

Tpal

213049-213270

rpmD

2023

Mgen

180733-181020

 

653

Tpal

624206-625738

ptsP

994

Mmob

102995-102588

nusB

890

Tpal

946250-944889

comM

3131

Mmob

201807-201646

rpmG

946

Tpal

1032059-1031772

 

3175

Mmob

317659-317411

secG

39

Upar

3002-3886

hemK

3186

Mmob

449811-451241

 

142

Upar

3861-4427

 

3000

Mmyc

441031-441783

 

3131

Upar

725869-726024

rpmG

542

Mmyc

441031-441783

 

38

VchoI

709524-710558

prfA

199

Mmyc

830915-830742

rpmI

2932

VchoI

1045279-1044317

 

73

Mmyc

831148-830924

infA

2947

VchoI

1627856-1625871

 

182

Mmyc

836915-836712

rpsN

1246

VchoI

2869620-2871836

pulA/glgX

3175

Mmyc

973088-973423

secG

2793

VchoII

295059-292882

gcvP

3131

Mmyc

1089962-1090141

rpmG

2621

VchoII

299032-300000

gcvT

314

Mmyc

1089962-1090141

rpmG

2699

VchoII

406033-405167

sbp

1670

Mpen

2755-3009

 

2573

VchoII

987698-986424

aroF/aroG/aroH

3131

Mpen

1191375-1191163

rpmG

2340

VchoII

1026697-1023563

dhaS/aldA

879

Mpen

1226934-1226722

rpmI

    

199

Mpen

1317088-1316960

rpmI

Gene annotated in different framed

166

Mpen

1327926-1326898

rplV

1769

Bhal

251734-251429

nrdG

2023

Mpne

207436-207717

 

3183

Mpul

130854-130480

 

2090

Nmen

70930-70358

 

3175

Mpul

412829-413074

secG

148

Nmen

149590-150777

tufA

946

Rpro

433751-433479

 

2564

Nmen

238562-237666

 

363

Tpal

262583-262897

rpsT

2572

Nmen

299359-298070

phr

    

aThe identifiers for COGs are local to this study. They do not correspond to numbers in the NCBI COG database.

bCoordinates in which the first number is greater than the second indicate that the ORF is on the minus strand.

cA named annotated putative ortholog in another organism or paralog within the organism to the ORF listed.

dThese COGs may indicate both that the ORF listed is a missed gene and that the annotated

The potential amino acid sequences of 83 of these ORFs contain regions that have substantial similarity to the multiple-sequence alignment of the annotated genes that are also members of the COG (Table 2). We judge that these ORFs are likely to represent genes missed in current annotations. 5 of the 83 candidate genes involve instances where there were two ORFs with approximately equal length present in two different strands; however the opposite strand had previously been chosen as coding.

In 60 (of 143) cases comparison of gene lengths to ORF lengths indicates that the ORFs that are not annotated as genes may be pseudogenes (Table 3); most have frameshifts but some have nonsense mutations. 20 of these are annotated as pseudogenes in Genome Reviews 25.0 [4, 25]. It is possible that some of the apparent nonsense or frameshift mutations may be due to sequencing errors.
Table 3

ORFs in majority-annotated mixed COGs that do not appear to represent missed genes

ORF COG ida

Organism

Genomic coordinatesb

Annotated gene(s) present in COGc

ORF COG ida

Organism

Genomic coordinatesb

Annotated gene(s) present in COGc

Existing annotation of pseudogene

Frameshift 3' fragmentc

876

EcoK12

1488620-1487985

gap

1036

Bbur

21098-20445

queA

2340

Sent

4738725-4740071

dhaS/aldA

1750

Bhal

984866-983856

celB

2433

Sent

4745051-4743573

hsdB

1188

Bhal

1359362-1360555

recD

1895

Sent

3243737-3244861

fadH

2257

Bhal

3182850-3181696

ilvI/poxB/alsS

1399

Sent

461578-461874

 

88

Bsub

3671944-3672555

gtaB

653

Sent

2505700-2506824

ptsP

641

Hinf

1525427-1524561

thiI

1058

Sent

3413535-3416306

acrD/mdtC/mdtB

2031

Hinf

1719924-1718821

tldD

3088

Sent

4084807-4083605

 

2473

Mgal

431452-431778

fldA

815

Sent

1360931-1362226

rhlE

2309

Mgen

416785-416336

acpD

3104

Sent

4009730-4009993

 

975

Mmyc

57011-56760

recR

569

Sent

1969437-1970648

penA

686

Mmyc

690895-690356

rpsB

    

1319

Nmen

107757-109406

msbA

Annotated in GenomeReviews but with different stop

842

Nmen

1995043-1994876

 

928

Bsub

2500726-2499347

bfmBC

556

VchoI

553588-552383

dnaG

157

Cper

2751593-2751051

rplD

745

VchoI

555313-556182

gcp

999

EcoO157

3613249-3610595

alaS

106

VchoI

1087924-1089819

uvrB

107

Hinf

655042-654365

metI

2435

VchoI

2612949-2611972

 

589

Mpne

329463-331229

lepA

2807

VchoII

1060889-1060107

qseC

210

Mpul

150772-151668

grpE

    

743

Sent

2492196-2490763

gltX

Frameshift 5' fragmentc

534

Tpal

478406-478777

 

697

Bhal

3580443-3579682

csd

166

Upar

279005-279949

rplV

2029

Bsub

2304436-2305248

metA

    

2049

Bsub

3032201-3032512

 

Fragments around stop codons (nonsense)c

2

Cpne

383405-384037

recF

928

Bsub

2500726-2499347

bfmBC

462

Cpne

1088259-1088711

ispE

157

Cper

2751593-2751051

rplD

2769

EcoK12

3814680-3813886

rph

999

EcoO157

3613249-3610595

alaS

2257

EcoK12

3948538-3949566

ilvI/poxB/alsS

107

Hinf

655042-654365

metI

2433

Hinf

232074-232991

 

589

Mpne

329463-331229

lepA

3066

Hinf

1377365-1378063

dgt

210

Mpul

150772-151668

grpE

1075

Hinf

1477189-1476557

pstB

743

Sent

2492196-2490763

 

641

Hinf

1526028-1525285

thiI

    

2571

Nmen

292645-294051

 
    

220

Tpal

220772-221749

dnaJ

    

556

VchoI

554244-553561

dnaG

    

1826

VchoI

637551-638246

amt

    

42

VchoI

851189-849954

oadA

    

1082

VchoII

690599-690273

glpF

aThe identifers for COGs are local to this study. They do not correspond to numbers in the NCBI COG database.

bCoordinates in which the first number is greater than the second indicate that the ORF is on the minus strand.

cA named annotated putative ortholog in another organism or paralog within the organism to the ORF listed.

dThese categories represent probable pseudogenes or sequencing errors.

For COGs in which the majority of members do not correspond to annotated genes, the preponderance of evidence suggests that sequence conservation can be better explained by reasons other than protein coding of the ORF. At stringency 6 there are 12 of this type of COG among the organisms we studied, representing 11 distinct annotated genes (Table 4). The presence of a conserved ORF of length sufficient to be a potential gene may be explained by the presence of a gene in the opposite strand. This is particularly likely in species such as those of the Mollicutes class that do not use UGA as a stop codon. In these organisms, the exclusion of TAA and TAG in the actual coding frame can be associated with a relative shortage of TAn codons in the opposite strand, leading to ORFs of substantial length on the non-coding strand. This was the case for 7 of the 11, and we predict that the previous annotations are erroneous. Three of the annotated genes in majority-unannotated COGs overlap regions that appear to encode non-translated RNA genes. The constraints of the RNA genes may have reduced the probability of occurrence of stop codons in the region, which lead to ORFs of sufficient length that they have been annotated as hypothetical genes in previous annotations.
Table 4

Minority-annotated mixed COGs of stringency 6

aORF COG id

Organism

bGenomic coordinates

Annotated locus tag

Explanation for similarity

458

Bhal

2607307-2607975

BH2488

ambiguous--smc may be annotated as too long

2939

Lmon

2784312-2784674

MYPU_4520

opposite strand dnaG

3041

Mgen

400107-399841

MG320.1

opposite strand tRNA cluster

715

Mmob

474080-474634

MMOB3820

opposite strand tRNA cluster

1171

Mmyc

315687-315178

MSC_0275

opposite strand annotated gene

1172

Mmyc

315687-315178

MSC_0275

opposite strand annotated gene

3172

Mpul

547792-547565

LMO2711

opposite strand RNA-gene (scr) in Bhal, Bsub

169

Mpul

703396-704043

MYPU_5820

ribosomal protein in opposite strand

1148

Mpul

706478-707455

MYPU_5880

opposite strand ribosomal protein

1436

Spne

199207-198743

SPR0193

opposite strand ribosomal protein

400

Tpal

321084-317926

TP0304

region upstream of gene is opposite pyrG

625

Tpal

580802-581407

TP0539

opposite strand pgk

aCOG identifiers are local to this study.

bCoordinates in which the first number is greater than the second indicate that the ORF is on the minus strand.

Possible pseudogenes as listed in Table 3 can be re-sequenced to evaluate whether there is in fact an underlying sequencing error. Although this could be done on individual regions of a genome, the recent re-sequencing [26] of Mycoplasma genitalium strain G-37 [GenBank:AAGX00000000] when compared to the original sequence [NC_000908.1] is illustrative. There are no mixed COGs at stringency 6 which contain unnannotated ORFs from the original M. genitalium sequence, but there are four such COGs at stringency 4 (Table 5). One of these ORFs (in COG 4-3347) is highly similar to sequences from 12 other organisms. The other ORFs contain apparent frameshifts that are resolved in the new sequence.
Table 5

Mixed COGs containing ORFs from Mycoplasma genitalium that do not correspond to annotated genes

aGenome coordinates

Strand

bCOG id

Notes

180733-181020

+

4-3347

Homologous to genes in 12 other organisms, some annotated as N-utilization substance

237114-237299

-

4-1487

Deletion of 'C' at 237175 joins this to the gene (MG199) annotated at 236591-237084. Together the joined fragments are similar to ribonuclease genes. [GenBank:AAGX01000004.1]

416336-416785

-

4-3943

Deletion of 'G' at 416710 joins this to fragment at 416661-416939. Together the joined fragments are similar to acyl carrier protein diesterases. [GenBank:AAGX01000016.1]

290638-291003

+

4-8314

Insertion of 'T' at 290983 joins this fragment to the gene (MG243) annotated at 290922-291326. Together the joined fragments are similar to hypothetical genes in M. pneumonia, M. gallisepticum, and U. parvum . [GenBank:AAGX01000005.1]

aCoordinates and insertions/deletions refer to [GenBank:NC_000908.1]

bCOG identifiers are local to this study

Discussion

Peptide sequence similarity as a gene discovery technique

Although studies of intrinsic gene prediction report higher accuracies (for example, GeneMark is reported to have detected genes with sensitivity 98.3% and specificity 91.3% averaged over eight prokaryotes [27], this fact should be considered with the caveat that those algorithms or algorithms similar to them were used to produce the initial annotations. Because intrinsic methods require statistical models of coding sequence to be tuned to a specific organism, they may miss recently acquired genes for which selection has not yet altered the polynucleotide frequencies to match the new host organism. Intrinsic methods may also miss small genes for which there is insufficient nucleotide sequence to provide a statistically significant result. Most of the genes missed by our method are likely to be species specific among the organisms studied, (i.e. they are ORFans [28] within the context of the analyzed genomes). Our method may be more able to pick up genes that may have been horizontally transferred when compared to intrinsic methods.

A different choice of size cutoff for consideration of ORFs would affect the accuracy of our method because longer ORFs are more likely to represent genes but genes smaller than the cutoff would be excluded. An increase in ORF size cutoff would result in an increase in specificity with a corresponding decrease in sensitivity. The fact that we were able to detect some genes that had been missed in prior genome annotations is in part because we used a very small size cutoff for consideration of which ORFs may be genes. The newly detected genes we report are disproportionately small (27% are shorter than 100 codons, compared to 11% of annotated genes).

A comparison of Figures 1A and 1B shows that incorporating gene information from other organisms greatly increases the specificity (reducing false positives) of similarity-based gene identification, with a much smaller decrease in sensitivity. Classification using stringency 2 COGs (i.e. single linkage clusters) is highly sensitive but not very specific. Increasing stringency to three (the COGs as described by Tatusov et al. [21]) causes the largest difference in specificity. The difference in specificity between stringencies two and three is even more pronounced when incorporating gene information from the other organisms.

The two gene prediction systems shown in Figure 1 are in general most accurate for the small genomes (Mycoplasma genitalium, Haemophilus influenzae, other Mycoplasma species). The high accuracies for Mollicutes may be explained by the presence of many members of this class in the set of organisms studied. The genes for Escherichia coli strain K12 can also be relatively well predicted, perhaps due to the fact that it is so well studied. Classification of E. coli genes performs particularly well in the second classification system, when information about annotated genes from other genomes is included. This may be due to the fact that the characteristics of genes of E. coli have been used to find those other genes in other organisms.

Limitations of using sequence conservation for gene prediction

Genes with little conservation among the studied genomes or which are only present in a few genomes cannot be detected using sequence conservation, leading to false negatives. Sequence similarity can be due to reasons other than selection due to protein-coding function. This can lead to false positive gene predictions. Sequences that do not encode protein may be similar because: i) they are conserved for other reasons (non-coding control elements, RNA-genes) or ii) because there is insufficient evolutionary distance between a pair of studied species. The latter can be the case for pseudogenes – a region in one genome may contain a gene while the corresponding region in another genome may not actually code for protein but may not have accumulated enough mutations to prevent detection of homology.

Examples of gene prediction inconsistencies in COGs of ORFs

The ORFs in Mycoplasma penetrans from 1316960 to 1317088 on the minus strand and in Mycoplasma mycoides from 830742 to 830915 on the minus strand do not contain annotated genes. They are however members of a COG at stringency 6 (COG id 199 in Table 2) in which the majority of ORFs (25 of 27) correspond to annotated genes. The annotated members of the COG encode the 50S ribosomal protein L36. The sequence identities from the M. penetrans ORF to the annotated genes in the COG range from 39.0% to 83.3%. For the M. mycoides ORF the range is from 35.2% to 75.7%. Neither of the ORFs have any interruption in coding potential compared to the annotated genes in the COG. It is likely that these ORFs contain genes that were missed in the initial annotations of their organisms. The peptides they encode are less than 40 amino acids long, and this may account for the fact that the genes had not been previously detected.

The Vibrio cholerae ORF from 637551 to 638246 in the plus strand of chromosome I is present in a COG of stringency 6 (COG id 1826 in Table 3) in which 11 ORFs correspond to annotated genes. The V. cholerae ORF is only about 60% as long as the annotated genes, and the potential peptide sequence it encodes aligns to the amino terminal region of the annotated genes. Examination of the genomic context of the members of this COG reveals a nearby V. cholerae ORF from positions 638126 to 638788 which could encode peptide which would align the to carboxy terminus of the annotated genes of this COG. This suggests the presence of a frameshift or sequencing error in the region encompassing the two V. cholerae ORFs.

The secG gene annotated in M. genitalium, M. penetrans, M. pneumoniae and U. parvum has homologs in M. gallisepticum, M. mobile, M. mycoides and M. pulmonis . The M. genitalium homolog was detected by curators of the Genome Reviews database (it is not identified in the EMBL genome file). This group of homologs is present in COG id 3175 in Table 2. The M. gallisepticum homolog is identified as a potential gene, but given the annotation "unique hypothetical". The M. mobile and M. mycoides homologs are not identified as genes. In M. pulmonis, the ORF homologous to secG (genomic coordinates 412829 to 413074 on the forward strand) is not identified as a gene, but overlaps the locus MYPU_3500 which is on the opposite strand. It is likely that this is an error in the existing annotation – that the secG homolog is a real gene and the MYPU_3500 locus, while being slightly longer, does not actually encode a peptide sequence in the cell.

Detecting pseudogenes

Several groups have used computational techniques to find pseudogenes in prokaryotes. Intrinsic techniques are poorly suited for identifying pseudogenes. Pseudogenes may lack characteristics of protein-coding sequence in a particular organism because they have resulted from recent horizontal transfer [29] or because there is a lack of selective pressure to maintain the characteristics. As a result, extrinsic techniques are common when searching for pseudogenes. Liu et al. [29] sought pseudogenes using SwissProt entries as queries in FastX searches against genome sequences. They used a fixed similarity cutoff score of 0.01 to define significant matches. The lack of selective pressure and resulting genetic drift that makes it difficult to find pseudogenes by intrinsic methods can also make it difficult to find homology between a pseudogene and the gene from which it was derived in disparate genome sequences. Lerat and Ochman [30] considered sets of closely related organisms and used thresholds for inferred homology as strict as TBLASTN E-value < 10-15 and protein identity >79%. By using COGs we were able to detect much more distant homology. There were 232 COGs of stringency-6 that contained at least one best-hit with a BLASTP E-value > 1.0.

Potential extensions and modifications to the methods of this study

One obvious extension of this study is to apply the strategy described here to more genome sequences. The scalability of the methods described in this study are limited primarily by the initial BLASTP searches of translations of the ORFs. Because all pairwise comparisons are performed, this step scales as the square of the number of ORFs among the genome sequences studied, which can be approximated by the square of the number of genome sequences. As of June 2005, there are 211 bacterial and 21 archaeal genome sequences published [31], about 8.9 times the number of genome sequences analyzed here. The similarity search comparison step is time consuming but highly parallel. Because each similarity search is independent of the other searches, the process can be carried out among many CPUs. New genome sequences can be added to the study incrementally.

One shortcut that could mitigate scaling issues would be to form a standard set of COGs of the various stringencies among only the annotated genes. All of the ORFs in a test genome sequence could then be compared to the members of the standard COGs. The annotated genes of the genomes studied here are only 5.75% of the ORFs of 30 or more codons, greatly reducing the number of comparisons that would need to be performed. This shortcut has a few drawbacks: i) it will not be possible to detect genes which are not present in enough of the genomes in the standard set (depending on stringency) and ii) it will not be possible to find the 'minority-annotated' mixed COGs that can indicate over-prediction of genes.

The varying stringency COGs produced using the techniques of this study could also be used in other analyses. The multiple sequence alignments of COGs could be used to help define which of several potential translational start sites may be used in a given gene. Stringency-three COGs which contain members in many organisms have been used to indicate genes which may be essential on account of their pervasiveness. Higher stringency COGs show not only that corresponding elements are present in many organisms but also that between most or all of pairs of organisms the elements are best-hit pairs of each other.

Relationship to proteomic studies

Jaffe et al. [3] revisited the annotation of genes in Mycoplasma pneumoniae, incorporating evidence of peptides detected using multi-dimensional chromatography followed by analysis by mass-spectrometry. They detected evidence for 16 proteins which could not be associated with annotated genes. One of these (from 207448 to 207717 in the (+) strand) was present in COGs up to stringency 10. Two more (from 250021 to 250293 and from 415490 to 416032, both on the (+) strand) were present in COGs of stringency 3. The others were only present in COGs of stringency 2. The Jaffe et al. study was aided by the fact that M. pneumoniae has a simple lifestyle and grows in a relatively static natural environment. Although some change in M. pneumoniae gene expression is reported in response to heat shock [32], it is thought that most of its genes are expressed constitutively. In organisms with more complex niches or lifestyles that may involve growing in multiple hosts or environments, the shotgun proteomic approach will require exposing the organisms to multiple conditions in order to induce detectable expression of all proteins.

Conclusion

In this paper, we describe a method that can be used in combination with existing techniques for detecting protein-coding gene sequences in bacterial genomes. Our method is extrinsic an individual in that it incorporates pairwise sequence similarities among several genomes.

The methods we describe can also be used to screen existing gene predictions. The "mixed COGs", in which some open reading frames correspond to annotated genes and some do not, are attractive targets for further study. Such COGs exist even when requiring best-hit similarity pairings among many organisms, and we list the COGs of this type that exist at stringency six among 27 sequenced bacterial strains. The methods we describe can be used to generate hypotheses about the presence of specific genes that may have been missed in existing annotations. Such a hypothesis could be evaluated by a targeted search for the expected protein product based on predicted protein characteristics.

Methods

Sequence preparation, comparison and Best Hit determination

Sequences and existing annotations for genomes under study (Table 1) were obtained from the Genome Reviews database [4] version 25.0 [25]. The annotations in the Genome Review database include those from the EMBL sequence files with corresponding accessions as well as some genes that were identified on the basis of BLAST similarity to sequences in UniProt. We located open reading frames (ORFs) using the criterion of at least 90 nucleotides (30 codons) between in-frame stop codons (for codon usage tables appropriate to each organism – the included Mollicute species do not use UGA as a stop codon).

Sequence libraries composed of all of the ORFs in the sequences of included genomes were searched using each ORF as a query. These all-against-all searches were performed using WU-BLAST (BLASTP 2.0 MP-WashU [06-Apr-2005] [macosx-10.2-g4-ILP32F64 2005-04-06T17:46:37], BLOSUM62 similarity matrix, filtered with SEG, Smith-Waterman alignment used in the scoring phase). The top ten hits from each query to a library of ORFs from another organism were recorded provided the BLAST e-score was less than or equal to 20. There were 84009520 BLAST hits which met these criteria.

COG analysis

A modification of the concept of Clusters of Orthologous Groups (COGs) [21, 33, 34] which includes levels of stringency [23] was used to group similar sequences among organisms. The COGs formed are graphs with ORFs as vertices using the following procedure:
  1. 1.

    For each ORF, compare it to all ORFs in another organism, recording the best hit (BeT), provided that hit meets the loose stringency cutoff mentioned above (e-score less than 20).

     
  2. 2.

    If an ORF a has ORF b as its best hit in another organism and b has a as its best hit when the reciprocal similarity search is performed then a and b are said to have a "bidirectional" or "congruent" best-hit relationship. A pair of vertices will have edges connecting them if the ORFs they represent have a bidirectional best-hit relationship. For purposes of this step, ORFs the two strains of E. coli were not compared to each other.

     
  3. 3.

    For a given stringency n, cliques (complete graphs – wherein all nodes are connected to all other nodes) of size n are found. Cliques are joined when they share a sub-clique of size n-1 maximally-joined sets of cliques form a COG.

     
According to this formalization, the original COGs as described by Tatusov, et al. [21] are COGs of stringency three. They consist of triangles formed from congruent best-hit relationships which are clustered by shared edges. These triangles are cliques of size 3 and are referred to by Tatusov et al. as orthologous groups (OGs). Stringency-two COGs are equivalent to graphs clustered by single linkage. Each stringency-two COG is an individual connected component of the total graph. As stringency increases, poorly connected vertices drop out of COGs and COGs may split (Figure 3).
Figure 3

COGs at varying stringencies. The concept of stringency places a requirement of interconnectedness of elements of a COG. As stringency increases, COGs may split into smaller COGs and less-connected nodes are dropped. Each vertex represents a gene (as used in the initial definition of COGs) or an ORF (as used in this study). Edges represent bidirectional best-hit pairs. Dashed lines enclose elements of a single COG. Grayed vertices and edges do not participate in a COG at the given stringency. There is a single COG of stringency (2) containing all of the vertices in this graph because they are all transitively connected. Stringency (3) COGs are as described by Tatusov et al. [21]. An orthologous group of stringency 3 forms a triangle (such as {i, j, k }); orthologous groups of stringency (3) are clustered if they share two vertices (alternatively: if they share an edge). Stringency (4) OGs are clustered if they share three vertices. The orthologous groups {j, k, l, m } and {l, m, n, o } only share two vertices so they form two separate COGs. At stringency (5) only one orthologous group, and thus only one COG, remains.

Like the extended COGs used in the STRING database [35], the COGs in this study are 'non-supervised'; we have not performed any manual curation subsequent to COG production. A gene fusion may result in the merging of two disparate COGs. Although such an artifact affects the use of COGs in functional annotation, it is not problematic when using COGs for gene recognition so we made no effort to avoid these merging events. The BLAST searches resulted in 2649524 best-hit pairs involving 891039 ORFs. Only 38 best-hit pairs involved a similarity with E-value as high as 20, and none of these best-hit pairs held together a COG of stringency three or higher.

SPROCKET

The SPROCKET program (S ystem for P rotein R ecognition using O RF C OGs – a K nowledge E xtraction T ool) provides several ways of viewing and analyzing COG data of the type produced in this study (Figure 4). The front-end of the program is written in Java and runs on any platform with a version 1.4 or later Java virtual machine. Data for the program is stored in a relational database using a schema based on the BioSQL schema developed by the Open Bioinformatics Foundation [36] with extensions for the COG-specific information. Loading of initial sequences into the database was performed using portions of the BioJava project [37].
Figure 4

SPROCKET. The SPROCKET program was developed to facilitate the analysis performed in this study. For the members of a COG, a user can view the peptide sequence alignment (using CLUSTALW), a graph of the best-hit relationships and the genomic context.

A summary view of the COGs shows the number of members of a COG and how many of those members share a stop codon with an annotated gene. Individual COGs can be examined in more detail by seeing a list of their members, a force-directed graph layout showing best-hit relationships among these members, an alignment of the potential peptide sequences of the ORFs as produced by CLUSTALW [38] or a graphical view of the regions of the genomes around the ORFs which are in a COG.

We provide source code for producing high stringency COGs [see Additional file 2] and for browsing the database of COGs [see Additional file 3].

Availability and Requirements

Project name: SPROCKET

Project home page: http://groove.med.unc.edu/sprocket

Operating system(s): Platform independent

Programming language: C++ (COG construction), Java (COG viewing)

Other requirements: Java 1.4 or higher, PostgreSQL (only required if data stored locally)

License: GNU GPL (COG construction), BSD (COG viewing)

Declarations

Acknowledgements

This work was supported by a Public Health Service research grant subcontracted to UNC-CH (Clyde A. Hutchison III) from the Berkeley Structural Genomics Center (GM62412, Sung-Hou Kim). We thank Morgan Giddings for computational resources and Jack Snoeyink and Craig Falls for algorithmic insights into calculating high-stringency COGs.

Authors’ Affiliations

(1)
Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill
(2)
Department of Microbiology and Immunology, University of North Carolina at Chapel Hill
(3)
J. Craig Venter Institute

References

  1. Dandekar T, Huynen M, Regula JT, Ueberle B, Zimmermann CU, Andrade MA, Doerks T, Sanchez-Pulido L, Snel B, Suyama M, Yuan YP, Herrmann R, Bork P: Re-annotating the Mycoplasma pneumoniae genome sequence: adding value, function and reading frames. Nucleic Acids Res 2000, 28(17):3278–3288. 10.1093/nar/28.17.3278PubMed CentralView ArticlePubMedGoogle Scholar
  2. Iliopoulos I, Tsoka S, Andrade MA, Enright AJ, Carroll M, Poullet P, Promponas V, Liakopoulos T, Palaios G, Pasquier C, Hamodrakas S, Tamames J, Yagnik AT, Tramontano A, Devos D, Blaschke C, Valencia A, Brett D, Martin D, Leroy C, Rigoutsos I, Sander C, Ouzounis CA: Evaluation of annotation strategies using an entire genome sequence. Bioinformatics 2003, 19(6):717–726. 10.1093/bioinformatics/btg077View ArticlePubMedGoogle Scholar
  3. Jaffe JD, Berg HC, Church GM: Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics 2004, 4(1):59–77. 10.1002/pmic.200300511View ArticlePubMedGoogle Scholar
  4. Kersey P, Bower L, Morris L, Horne A, Petryszak R, Kanz C, Kanapin A, Das U, Michoud K, Phan I, Gattiker A, Kulikova T, Faruque N, Duggan K, McLaren P, Reimholz B, Duret L, Penel S, Reuter I, Apweiler R: Integr8 and Genome Reviews: integrated views of complete genomes and proteomes. Nucleic Acids Res 2005, 33(Database issue):D297–302. 10.1093/nar/gki039PubMed CentralView ArticlePubMedGoogle Scholar
  5. Pruitt KD, Tatusova T, Maglott DR: NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 2005, 33(Database issue):D501–4. 10.1093/nar/gki025PubMed CentralView ArticlePubMedGoogle Scholar
  6. Arthur JW, Wilkins MR: Using proteomics to mine genome sequences. J Proteome Res 2004, 3(3):393–402. 10.1021/pr034056eView ArticlePubMedGoogle Scholar
  7. Giddings MC, Shah AA, Gesteland R, Moore B: Genome-based peptide fingerprint scanning. Proc Natl Acad Sci U S A 2003, 100(1):20–25. 10.1073/pnas.0136893100PubMed CentralView ArticlePubMedGoogle Scholar
  8. Jaffe JD, Stange-Thomann N, Smith C, DeCaprio D, Fisher S, Butler J, Calvo S, Elkins T, FitzGerald MG, Hafez N, Kodira CD, Major J, Wang S, Wilkinson J, Nicol R, Nusbaum C, Birren B, Berg HC, Church GM: The complete genome and proteome of Mycoplasma mobile. Genome Res 2004, 14(8):1447–1461. 10.1101/gr.2674004PubMed CentralView ArticlePubMedGoogle Scholar
  9. Wu J, Kasif S, DeLisi C: Identification of functional links between genes using phylogenetic profiles. Bioinformatics 2003, 19(12):1524–1530. 10.1093/bioinformatics/btg187View ArticlePubMedGoogle Scholar
  10. Kim SH, Shin DH, Choi IG, Schulze-Gahmen U, Chen S, Kim R: Structure-based functional inference in structural genomics. J Struct Funct Genomics 2003, 4(2–3):129–135. 10.1023/A:1026200610644View ArticlePubMedGoogle Scholar
  11. Hutchison CA, Peterson SN, Gill SR, Cline RT, White O, Fraser CM, Smith HO, Venter JC: Global transposon mutagenesis and a minimal Mycoplasma genome. Science 1999, 286(5447):2165–2169. 10.1126/science.286.5447.2165View ArticlePubMedGoogle Scholar
  12. Mushegian AR, Koonin EV: A minimal gene set for cellular life derived by comparison of complete bacterial genomes. Proc Natl Acad Sci U S A 1996, 93(19):10268–10273. 10.1073/pnas.93.19.10268PubMed CentralView ArticlePubMedGoogle Scholar
  13. Fickett JW: Finding genes by computer: the state of the art. Trends Genet 1996, 12(8):316–320. 10.1016/0168-9525(96)10038-XView ArticlePubMedGoogle Scholar
  14. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, McKenney K, Sutton G, Fitzhugh W, Fields C, Gocayne JD, Scott J, Shirley R, Liu LI, Glodek A, Kelley JM, Weidman JF, Phillips CA, Spriggs T, Hedblom E, Cotton MD, Utterback TR, Hanna MC, Nguyn DT, Saudek DM, Brandon RC, Fine LD, Frichtman JL, Fuhrmann JL, Geoghagen NSM, Gnehm CL, McDonald LA, Small KV, Fraser CM, Smith HO, Venter JC: Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995, 269(5223):496–512.View ArticlePubMedGoogle Scholar
  15. Karlin S, Mrazek J, Campbell AM: Codon usages in different gene classes of the Escherichia coli genome. Mol Microbiol 1998, 29(6):1341–1355. 10.1046/j.1365-2958.1998.01008.xView ArticlePubMedGoogle Scholar
  16. Harrison PM, Carriero N, Liu Y, Gerstein M: A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs. J Mol Biol 2003, 333(5):885–892. 10.1016/j.jmb.2003.09.016View ArticlePubMedGoogle Scholar
  17. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25(17):3389–3402. 10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
  18. Pachter L, Alexandersson M, Cawley S: Applications of generalized pair hidden Markov models to alignment and gene finding problems. J Comput Biol 2002, 9(2):389–399. 10.1089/10665270252935520View ArticlePubMedGoogle Scholar
  19. Brent MR, Guigo R: Recent advances in gene structure prediction. Curr Opin Struct Biol 2004, 14(3):264–272. 10.1016/j.sbi.2004.05.007View ArticlePubMedGoogle Scholar
  20. Ochman H: Distinguishing the ORFs from the ELFs: short bacterial genes and the annotation of genomes. Trends Genet 2002, 18(7):335–337. 10.1016/S0168-9525(02)02668-9View ArticlePubMedGoogle Scholar
  21. Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science 1997, 278(5338):631–637. 10.1126/science.278.5338.631View ArticlePubMedGoogle Scholar
  22. Thomas JW, Touchman JW, Blakesley RW, Bouffard GG, Beckstrom-Sternberg SM, Margulies EH, Blanchette M, Siepel AC, Thomas PJ, McDowell JC, Maskeri B, Hansen NF, Schwartz MS, Weber RJ, Kent WJ, Karolchik D, Bruen TC, Bevan R, Cutler DJ, Schwartz S, Elnitski L, Idol JR, Prasad AB, Lee-Lin SQ, Maduro VV, Summers TJ, Portnoy ME, Dietrich NL, Akhter N, Ayele K, Benjamin B, Cariaga K, Brinkley CP, Brooks SY, Granite S, Guan X, Gupta J, Haghighi P, Ho SL, Huang MC, Karlins E, Laric PL, Legaspi R, Lim MJ, Maduro QL, Masiello CA, Mastrian SD, McCloskey JC, Pearson R, Stantripop S, Tiongson EE, Tran JT, Tsurgeon C, Vogt JL, Walker MA, Wetherby KD, Wiggins LS, Young AC, Zhang LH, Osoegawa K, Zhu B, Zhao B, Shu CL, De Jong PJ, Lawrence CE, Smit AF, Chakravarti A, Haussler D, Green P, Miller W, Green ED: Comparative analyses of multi-species sequences from targeted genomic regions. Nature 2003, 424(6950):788–793. 10.1038/nature01858View ArticlePubMedGoogle Scholar
  23. Montague MG, Hutchison CA: Gene content phylogeny of herpesviruses. Proc Natl Acad Sci U S A 2000, 97(10):5334–5339. 10.1073/pnas.97.10.5334PubMed CentralView ArticlePubMedGoogle Scholar
  24. BSGC: Berkeley Structural Genomics Center.[http://www.strgen.org]
  25. GenomeReviews: Genome Reviews.[http://www.ebi.ac.uk/GenomeReviews]
  26. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H, Makhijani VB, McDade KE, McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant R, Puc BP, Ronan MT, Roth GT, Sarkis GJ, Simons JF, Simpson JW, Srinivasan M, Tartaro KR, Tomasz A, Vogt KA, Volkmer GA, Wang SH, Wang Y, Weiner MP, Yu P, Begley RF, Rothberg JM: Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005, 437(7057):376–380.PubMed CentralPubMedGoogle Scholar
  27. Besemer J, Lomsadze A, Borodovsky M: GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res 2001, 29(12):2607–2618. 10.1093/nar/29.12.2607PubMed CentralView ArticlePubMedGoogle Scholar
  28. Siew N, Fischer D: Analysis of singleton ORFans in fully sequenced microbial genomes. Proteins 2003, 53(2):241–251. 10.1002/prot.10423View ArticlePubMedGoogle Scholar
  29. Liu Y, Harrison PM, Kunin V, Gerstein M: Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome Biol 2004, 5(9):R64. 10.1186/gb-2004-5-9-r64PubMed CentralView ArticlePubMedGoogle Scholar
  30. Lerat E, Ochman H: Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res 2005, 33(10):3125–3132. 10.1093/nar/gki631PubMed CentralView ArticlePubMedGoogle Scholar
  31. GOLD: GOLD: Genomes OnLine Database.[http://www.genomesonline.org]
  32. Weiner J, Zimmerman CU, Gohlmann HW, Herrmann R: Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures. Nucleic Acids Res 2003, 31(21):6306–6320. 10.1093/nar/gkg841PubMed CentralView ArticlePubMedGoogle Scholar
  33. Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV: The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 2001, 29(1):22–28. 10.1093/nar/29.1.22PubMed CentralView ArticlePubMedGoogle Scholar
  34. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA: The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003, 4(1):41. 10.1186/1471-2105-4-41PubMed CentralView ArticlePubMedGoogle Scholar
  35. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B: STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 2003, 31(1):258–261. 10.1093/nar/gkg034PubMed CentralView ArticlePubMedGoogle Scholar
  36. OBDA: OBDA Main Page.[http://obda.open-bio.org]
  37. Biojava: BioJava.[http://biojava.org]
  38. Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD: Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res 2003, 31(13):3497–3500. 10.1093/nar/gkg500PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Powell and Hutchison; licensee BioMed Central Ltd. 2006

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.