Using ESTs to improve the accuracy of de novo gene prediction
© Wei and Brent; licensee BioMed Central Ltd. 2006
Received: 28 March 2006
Accepted: 03 July 2006
Published: 03 July 2006
ESTs are a tremendous resource for determining the exon-intron structures of genes, but even extensive EST sequencing tends to leave many exons and genes untouched. Gene prediction systems based exclusively on EST alignments miss these exons and genes, leading to poor sensitivity. De novo gene prediction systems, which ignore ESTs in favor of genomic sequence, can predict such "untouched" exons, but they are less accurate when predicting exons to which ESTs align. TWINSCAN is the most accurate de novo gene finder available for nematodes and N-SCAN is the most accurate for mammals, as measured by exact CDS gene prediction and exact exon prediction.
TWINSCAN_EST is a new system that successfully combines EST alignments with TWINSCAN. On the whole C. elegans genome TWINSCAN_EST shows 14% improvement in sensitivity and 13% in specificity in predicting exact gene structures compared to TWINSCAN without EST alignments. Not only are the structures revealed by EST alignments predicted correctly, but these also constrain the predictions without alignments, improving their accuracy. For the human genome, we used the same approach with N-SCAN, creating N-SCAN_EST. On the whole genome, N-SCAN_EST produced a 6% improvement in sensitivity and 1% in specificity of exact gene structure predictions compared to N-SCAN.
TWINSCAN_EST and N-SCAN_EST are more accurate than TWINSCAN and N-SCAN, while retaining their ability to discover novel genes to which no ESTs align. Thus, we recommend using the EST versions of these programs to annotate any genome for which EST information is available.
TWINSCAN_EST and N-SCAN_EST are part of the TWINSCAN open source software package http://genes.cse.wustl.edu/distribution/download_TS.html.
There are two major computational approaches to determining the exon-intron structures of genes: expression-based and de novo. Expression-based systems predict that a genomic nucleotide is exonic only if a transcript from it, or from a homologous gene (or a corresponding protein), has been sequenced. This approach can accurately predict genes whose transcripts have been sequenced and those that are highly similar to sequenced transcripts. However, its accuracy on genes that are not highly similar to sequenced transcripts is much lower [1, 2]. This is a significant limitation, since sequencing cDNA libraries typically produces complete cDNA sequences from only about 50–60% of the genes in a genome. When genes that are partially covered by ESTs are included, that number may rise to 70–85%, depending on the depth of library sequencing and the complexity of the organism. Genes that are expressed at a low level or in a small number of tissues tend not to be sequenced even after sequencing libraries very deeply [3, 4].
De novo gene prediction systems employ statistical models to predict gene structures using the sequences of one or more genomes as their only inputs. No cDNA sequences or other expression data are needed, so de novo methods can predict completely novel genes. However, they ignore the cDNA sequences that are available. As a result, they tend to be less accurate than expression-based methods on genes for which full-length cDNAs are available.
There is a long history of efforts to use databases of expressed sequences (ESTs, mRNAs, their conceptual translations, and experimental protein sequences) to enhance the accuracy of prediction systems that are based primarily on de novo methods. Studies that present quantitative evaluations of the effects of using ESTs alone, without using amino acid sequences from homologous genes, have reported mixed results [5–7]. Using a HMM-based de novo predictor, HMMGene, Krogh  reported no improvement in predictions for Drosophila melanogaster. Using GENIE, another HMM-based de novo predictor, Reese and colleagues reported a modest increase in sensitivity accompanied by a smaller decrease in specificity, also on Drosophila . The best results were reported by Howe et al. . Using GAZE, a generic evidence-combination framework, they obtained an increase in both the sensitivity and specificity of predictions by GeneFinder (P. Greene, unpublished) on Caenorhabditis elegans. Synthesizing these studies, it seems that better results were achieved by using a more stringent cutoff for similarity between the EST and the genome (93% identity for HMMGene, 95% for GENIE and GAZE). Better results were also achieved by using alignments created by EST_GENOME , a program designed to align ESTs with proper introns bounded by GT-AG (GAZE), rather than alignments created by BLASTN and then "fixed up" to make proper exons and introns (HMMGene and GENIE). Finally, better results were achieved on C. elegans, which has short introns and relatively less alternative splicing, than on D. melanogaster.
Another approach is to derive gene structures from a weighted combination of ESTs with multiple gene predictions, often including predictions from systems like ENSEMBL that use cDNA and protein alignments. This approach is exemplified by EuGene , Combiner , and its descendent JIGSAW . However, with the exception of JIGSAW, none of the work described so far includes evaluations on mammalian genomes, which have long introns, many pseudogenes, and extensive alternative splicing. The JIGSAW publication includes evaluation on selected genes and regions from the human genome, but not on entire chromosomes.
The more successful of the methods outlined above work in part by boosting the scores of predicted introns that match intron gaps in EST alignments. For GENIE, the boost is large, "effectively constraining the system to ensure that the introns were correctly annotated according to the EST/cDNA evidence" . For GAZE, the boost is a function of the EST alignment score: (%identity – 95) × length . In neither case, however, is the EST scoring system trained automatically (Howe et al. reported that the automatic training method they tried did not work very well). Recently, several papers have reported success in training parameters for use of EST alignments, including EuGene , Combiner , and JIGSAW .
In this paper, we report on a new approach to integrating information from EST alignments with an HMM-based, de novo gene predictor. Rather than using fixed score boosts for compatible predictions, our method learns the degree to which a particular set of EST alignments is predictive of correct gene structure. This predictive power depends on the quality and quantity of the ESTs, the degree of alternative splicing, the alignment method, and the pre-processing method for filtering out questionable alignments. When used in combination with our state-of-the-art gene prediction programs, TWINSCAN and N-SCAN, this system can be automatically retrained to work well on both C. elegans and human. Furthermore, accuracy on genes or parts of genes without aligned ESTs is not compromised. On the contrary, genes without ESTs are predicted more accurately as a result of the constraints imposed by ESTs aligned to neighboring genes.
Model for exploiting EST alignments
The EST sequence can be exploited by any HMM-based gene predictor. Each state of the HMM is required to emit both a target genome sequence and the corresponding ESTseq. When TWINSCAN uses ESTseq it emits ESTseq symbols, target genome bases, and conservation sequence symbols. Similarly, N-SCAN [14, 15] emits ESTseq symbols together with columns of multi-genome alignments. All states must have probability models for the emission of ESTseq symbols, so these symbols can influence the likelihoods of functional annotations such as splice donor and acceptor, exon, intron, translation initiation and termination site, and so on. For example, the likelihood of emitting the I symbol from intron states should be greater than the likelihood of emitting I from exon states. Parameters for these models are estimated from examples of known gene structures together with their ESTseqs. See Methods for the ESTseq models we used in each HMM state.
Accuracy evaluation: C. elegans
Results for deletion experiment. The first column is for TWINSCAN 2.03 and the remaining 3 columns are for TWINSCAN_EST. The second column is for the TWINSCAN_EST performance with empty ESTseq, i.e., all bases in ESTseqs are 'N's. For the third and fourth column, 10% of genes in the annotation were set to "N"s. The third column is for TWINSCAN_EST's performance on the 10% of genes with masked ESTseqs and the last column is for the 90% of genes with unmasked ESTseqs. Results show that EST alignments improve the prediction accuracy and do not compromise the capability to predict novel genes where EST alignments do not exist (column 2). Specificities are based on predictions that overlap with annotations by at least 1 bp.
10% with ESTseq masked
90% with ESTseq unmasked
The previous experiment in which all ESTs were deleted from the database may yield an overly pessimistic assessment of TWINSCAN_EST's accuracy on novel genes with no aligned ESTs. It is possible that the presence of EST alignments for some genes may improve the accuracy of TWINSCAN_EST on the neighboring genes even when those neighboring genes have no aligned ESTs. The intuition is that certain kinds of mistakes, such as incorrectly splitting a gene with an EST and joining part of it to a neighbor without an EST, will become much less common. To test whether such indirect benefits actually exist, we did a partial EST deletion experiment. All fully confirmed WS130 genes were divided into 10 groups at random, each containing about 10% of the fully confirmed genes. One group of fully confirmed genes was selected, its ESTseq was masked with "N", and TWINSCAN_EST was run on the entire genome. These steps were repeated 10 times. Each time, the ESTseq for a different 10% of the confirmed genes was masked, so that the ESTseq for each confirmed gene was masked in exactly one repetition. We then computed the average accuracy statistics over the 10 runs for both the masked and unmasked genes. Results are shown in Table 1. The gene sensitivity of TWINSCAN_EST on the genes with masked ESTseq was 2.4% higher than TWINSCAN 2.03 and the specificity was 1.9% higher. In addition, exon and gene accuracy were higher than TWINSCAN_EST with blank EST sequence, indicating that the presence of ESTs for other genes did indeed improve the accuracy of genes with no ESTs.
The previous experiments show TWINSCAN_EST's accuracy on genes with or without aligned ESTs. In practice, many genes are partially covered by ESTs. To investigate the effect of partial EST coverage, we did the following experiment. ESTseqs were generated as in the TWINSCAN_EST experiment for Figure 2. The ESTseq for each fully confirmed WS130 gene was then N-masked over a contiguous, randomly chosen 50% of its genomic extent (see Methods). The predictions were evaluated on all the confirmed genes. The gene sensitivity was 69%, which is about halfway between the gene sensitivity of TWINSCAN 2.03 (61%) and TWINSCAN_EST without ESTseq masking (75%). The gene specificity is 67%, which is about two-thirds of the way from that of TWINSCAN 2.03 (59%) to that of TWINSCAN_EST without ESTseq masking (71%).
Accuracy evaluation: human
While this paper was in revision, a paper was published describing AUGUSTUS+, a new, trainable system capable of combining evidence from EST alignments with de novo gene prediction . We compared the accuracy of N-SCAN_EST and AUGUSTUS+ by running them on human chromosome 22 using the same EST alignments (see Methods). Comparing the results to aligned RefSeq genes, N-SCAN_EST's sensitivity and specificity for predicting the exact ORFs were 47% and 24%, respectively. The comparable numbers for AUGUSTUS+ using the same EST alignments were 38% and 19%, respectively.
Impact of training EST parameters
Impact on an annotation pipeline using full length cDNA sequences
A complete pipeline for predicting exon-intron structures must give precedence to full length cDNA alignments over all other sources of evidence. The degree to which such a pipeline relies on ESTs and de novo gene prediction depends on how extensive is the set of available full length cDNAs. For example, we recently built a system in which the first stage is aligning full-ORF cDNA sequences to their native locus using our new cDNA-genome aligner, Pairagon . The CDS GenBank annotations of the cDNA sequences were used to convert these alignments into gene structures. Where there is no full-length cDNA to align, we used N-SCAN_EST together with ESTseq created from BLAT alignments. This system was independently evaluated on the human ENCODE regions as part of the recent EGASP community evaluation [22, 23] and found to be comparable in accuracy to the ENSEMBL pipeline (slightly better by most measures).
It can be trained to take advantage of the statistical characteristics of specific sets of EST alignments.
It substantially improves the accuracy (both sensitivity and specificity) of gene prediction on genes that have aligned ESTs.
It improves accuracy on genes that do not have aligned ESTs when they are interspersed with genes that do.
It predicts genes at least as accurately as the pure-HMM-based predictors when no ESTs align to the target genome.
Thus, the use of EST information comes at no cost. TWINSCAN_EST and N-SCAN_EST have the key benefit of a de novo gene finder – namely, the ability to find completely novel genes without sequence similarity to known genes – yet they are more accurate on genes for which EST information is available. Compared to other de novo gene finders, TWINSCAN is the most accurate program available for nematodes . Likewise, N-SCAN is the most accurate de novo predictor available for mammals as measured by exact CDS gene prediction and exact exon prediction [15, 18, 24]. Other programs are either more specific but less sensitive (EXONIPHY)  or more sensitive but less specific (AUGUSTUS-dual, Stanke, unpublished) in predicting individual coding nucleotides. Thus, we would recommend using the EST versions of these programs on any genome for which there is EST information.
We also showed that combining N-SCAN_EST with a state-of-the-art system for aligning full length cDNAs yields a pipeline whose exon-prediction accuracy shows relatively little dependence on the number of available cDNA sequences. Thus, low cost EST sequencing can be substituted for expensive sequencing of full length cDNAs with limited accuracy reduction.
The real goal of gene prediction is not to find known genes but to find novel genes that can be verified experimentally. N-SCAN_EST has proven very useful in this regard. As part of an ongoing project we are using RT-PCR and sequencing to obtain novel human cDNA sequences. In these experiments, we target predicted introns with at least one splice site that is not in a region previously known to be transcribed – that is, not in an intron or exon defined by the alignment of any human mRNA or EST. By targeting predictions from N-SCAN_EST, we have verified more than a thousand novel introns. Thus, in addition to its application for annotating genomes with few full length cDNAs, N-SCAN_EST is also useful for well-studied genomes like that of Homo sapiens.
TWINSCAN_EST and N-SCAN_EST are more accurate than TWINSCAN and N-SCAN, while retaining their ability to discover novel genes to which no ESTs align. Thus, we recommend using the EST versions of these programs to annotate any genome for which EST information is available.
ESTseq models for each state
In our implementation, the ESTseq models are homogeneous Markov chains for UTR, intron, and coding states, and position-specific Markov chains (sometimes called WAMs) for donor and acceptor site models.
Procedure for masking 50% of the ESTseq for each gene
Let [0, 1] stand for a gene region. A random number a in the range [0, 1] is generated, then all ESTseq bases in the region [a, a+0.5] were masked with "N" if a< = 0.5 or bases in region [a, 1] U [0, a-0.5] were masked with "N" if a>0.5. As a result, at least 50% of each gene region was not covered with any EST alignment. TWINSCAN_EST was then run on the entire genome with these masked ESTseqs.
The C. elegans genome sequence version WS130 was downloaded from the WormBase website [26–28]. The C. briggsae genome sequence version cb25.agp8 was downloaded from the Sanger Institute . Approximately 300,000 C. elegans ESTs were downloaded from dbEST (1/20/2005 version) [19, 30]. The genome sequence for the GAZE dataset was downloaded from the GAZE website . The informant database (C. briggsae) and EST database for the GAZE dataset were the same as for the whole C. elegans genome WS130.
Human ESTs were downloaded from dbEST on January 20th 2005. The informant database for TWINSCAN is the mouse genome  Build 33 (mm5 on the UCSC browser). Other informant datasets for N-SCAN include mouse, rat  (UCSC rn3) and chicken  (UCSC Galgal2) genomes [14, 15].
For worm datasets, conservation sequences were generated from WU-BLAST  alignments of the whole C. elegans genome against the C. briggsae genome. First, C. briggsae sequences longer than 150 kb were cut into 150 kb sequences with 20 kb overlap, and then the Blast database was generated from all sequences after they had been masked by NSEG with default parameters. BLASTN parameters were "M = 1 N = -1 Q = 5 R = 1 B = 10000 V = 100 lcfilter filter = seg filter = dust topcomboN = 1".
The human chromosomes were split into 1 Mb fragments first, and then conservation sequence was constructed for each fragment.
C. elegans ESTs were aligned to WS130 by using stand alone version 25 of BLAT . ESTseqs were generated using only those EST alignments in which the number of matches was at least 95% of the length of the entire EST, including unaligned portions. These alignments were projected onto genomic sequence to generate ESTseq as shown in Figure 1. For the GAZE dataset, similar procedures were done.
Human ESTs were aligned to the whole human genome by BLAT. An alignment was included only if its number of matches was at least 98% of the length of the entire EST. Those selected alignments were projected to the genomic sequence to generate ESTseqs as shown in Figure 1. The ESTseq of each chromosome was then split into 1 Mb fragments corresponding to the genomic sequences.
ESTseq parameter estimation
ESTseq parameter estimation is similar to conservation sequence parameter estimation. Given ESTseqs and the corresponding gene structures, distinct sets of parameters are estimated for the coding regions (excluding translation initiation and termination signals), UTRs, intron states, donor and acceptor splice site signals, and translation initiation and termination signals. For TWINSCAN_EST on C. elegans, 1st-order Markov chains were used for coding, UTR, intron states, and the translation initiation and termination signals. A 43-base-long, 2nd-order WAM was used for acceptor splice site signals and 9-base-long, 2nd-order WAM was used for donor splice site signals. Regions between 1000 bases and 150 bases upstream of the start of translation and downstream of the stop of translation were used as intergenic regions. Intergenic regions' ESTseqs were used as the null model for each state.
For N-SCAN_EST on human, the single 5' UTR state in TWINSCAN is replaced by four 5' UTR states. Those states are: a) unspliced UTR from transcription start site (TSS) to the translation start site; b) initial noncoding exon (from the TSS to the splice donor); c) internal noncoding exon (from acceptor to donor) and d) the noncoding segment of the exon from acceptor splice site to the start of translation [see 14 for details]. 5th-order Markov models were used for all ESTseq models except the acceptor and donor splice site models, which were the same as for worm.
When 5th order models are used for the worm data, as for human, all accuracies are within a fraction of a percent of those reported in this paper.
For training and evaluation purpose, human RefSeq mRNAs excluding the predicted XM_ accessions [37–39], aligned to human genome Build 35/hg17 were downloaded from the UCSC genome browser . The RefSeq annotation was then cleaned by removing genes with in frame stop codons. There were 17,798 transcripts remaining, 17,120 of which contain UTR annotations. In order to estimate the ESTseq parameters, single-gene ESTseqs were cut out from the whole chromosome ESTseq with an additional 1000 bases on each end as intergenic regions. Parameters were estimated from these single-gene ESTseqs and the corresponding gene structures.
N-SCAN_EST and Augustus+ comparison on human chromosome 22
In order to do a fair comparison to AUGUSTUS+, BLAT alignments of all spliced human ESTs on human chromosome 22 (Build 35/hg17) were downloaded from the spliced human EST track in the UCSC genome browser  on March 12th, 2006. These EST alignments were input into both Augustus+ and N-SCAN_EST. EST parameters for N-SCAN_EST were estimated from the cleaned RefSeq annotations on chromosome 1, 2, 20 and 21. EST Parameters for Augustus+ were estimated by its author from chromosome 21.
For the WS130 dataset, TWINSCAN_EST's performance was tested by 8-fold cross validation. The whole genome was split into fragments of about 500 kb. Each fragment was randomly assigned to one of the eight groups. TWINSCAN_EST was trained on fully confirmed genes from seven of the eight groups and run on the fragments from the eighth group to avoid training and testing on the same data set. For TWINSCAN_EST on the GAZE data set, no cross validation was applied. Parameters were estimated from all fully confirmed genes of WS130.
We are extremely grateful to Kevin Howe, Victor Solovyev, and Mario Stanke for their help in evaluating their respective programs: GAZE_est, FGENESH, and AUGUSTUS+. We also thank Marijke van Baren for help with her pseudogene detection software, Michael Stevens for primer design and sequence analysis, Samuel Gross for help with his N-SCAN program and Randall Brown for his help in manuscript proofreading. This work was supported by grant HG02278 from the National Human Genome Research Institute to M.R.B.
- Birney E, Clamp M, Durbin R: GeneWise and Genomewise. Genome Res 2004, 14(5):988–995. 10.1101/gr.1865504PubMed CentralView ArticlePubMedGoogle Scholar
- Brent MR: Genome annotation past, present and future:How to define an ORF at each locus. Genome Res 2005, 15: 1777–1786. 10.1101/gr.3866105View ArticlePubMedGoogle Scholar
- Guigó R, Dermitzakis ET, Agarwal P, Ponting C, Parra G, Reymond A, Abril JF, Keibler E, Lyle R, Ucla C, Antonarakis SE, Brent MR: Comparison of mouse and human genomes followed by experimental verification yields an estimated 1,019 additional genes. Proc Natl Acad Sci U S A 2003, 100: 1140–1145. 10.1073/pnas.0337561100PubMed CentralView ArticlePubMedGoogle Scholar
- The MGC Project Team: The Status, Quality, and Expansion of the NIH Full-Length cDNA Project: The Mammalian Gene Collection (MGC). Genome Res 2004, 14(10b):2121–2127. 10.1101/gr.2596504View ArticleGoogle Scholar
- Howe KL, Chothia T, Durbin R: GAZE: a generic framework for the integration of gene-prediction data by dynamic programming. Genome Res 2002, 12(9):1418–1427. 10.1101/gr.149502PubMed CentralView ArticlePubMedGoogle Scholar
- Reese MG, Kulp D, Tammana H, Haussler D: Genie--gene finding in Drosophila melanogaster. Genome Res 2000, 10(4):529–538. 10.1101/gr.10.4.529PubMed CentralView ArticlePubMedGoogle Scholar
- Krogh A: Using database matches with for HMMGene for automated gene detection in Drosophila. Genome Res 2000, 10(4):523–528. 10.1101/gr.10.4.523PubMed CentralView ArticlePubMedGoogle Scholar
- Mott R: EST_GENOME: a program to align spliced DNA sequences to unspliced genomic DNA. Comput Appl Biosci 1997, 13(4):477–478.PubMedGoogle Scholar
- Foissac S, Schiex T: Integrating alternative splicing detection into gene prediction. BMC Bioinformatics 2005, 6(1):25. 10.1186/1471-2105-6-25PubMed CentralView ArticlePubMedGoogle Scholar
- Allen JE, Pertea M, Salzberg SL: Computational gene prediction using multiple sources of evidence. Genome Res 2004, 14(1):142–148. 10.1101/gr.1562804PubMed CentralView ArticlePubMedGoogle Scholar
- Allen JE, Salzberg SL: JIGSAW: integration of multiple sources of evidence for gene prediction. Bioinformatics 2005, 21(18):3596–3603. 10.1093/bioinformatics/bti609View ArticlePubMedGoogle Scholar
- Korf I, Flicek P, Duan D, Brent MR: Integrating genomic homology into gene structure prediction. Bioinformatics 2001, 17 Suppl 1: S140–8.View ArticlePubMedGoogle Scholar
- Flicek P, Keibler E, Hu P, Korf I, Brent MR: Leveraging the mouse genome for gene prediction in human: From whole-genome shotgun reads to a global synteny map. Genome Res 2003, 13: 46–54. 10.1101/gr.830003PubMed CentralView ArticlePubMedGoogle Scholar
- Brown RH, Gross SS, Brent MR: Begin at the beginning: predicting genes with 5' UTRs. Genome Res 2005, 15(5):742–747. 10.1101/gr.3696205PubMed CentralView ArticlePubMedGoogle Scholar
- Gross SS, Brent MR: Using Multiple Alignments To Improve Gene Prediction: Boston. ; 2005:in press.View ArticleGoogle Scholar
- Salamov AA, Solovyev VV: Ab initio gene finding in Drosophila genomic DNA. Genome Res 2000, 10(4):516–522. 10.1101/gr.10.4.516PubMed CentralView ArticlePubMedGoogle Scholar
- Solovyev VV: Finding genes by computer: probabilistic and discriminative approaches. In Current Topics in Computational Biology. Edited by: Jiang T, Smith T, Xu Y, Zhang M. Cambridge, MA, The MIT Press; 2002:365–402.Google Scholar
- Wei C, Lamesch P, Arumugam M, Rosenberg J, Hu P, Vidal M, Brent MR: Closing in on the C. elegans ORFeome by Cloning TWINSCAN predictions. Genome Res 2005, 15: 577–582. 10.1101/gr.3329005PubMed CentralView ArticlePubMedGoogle Scholar
- Stanke M, Schoffmann O, Morgenstern B, Waack S: Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics 2006, 7: 62. 10.1186/1471-2105-7-62PubMed CentralView ArticlePubMedGoogle Scholar
- Pairagon software[http://genes.cs.wustl.edu/BrentLab/MB-Lab-Software.html]
- Guigo R, Reese MG: EGASP: collaboration through competition to find human genes. Nat Methods 2005, 2(8):575–577. 10.1038/nmeth0805-575View ArticlePubMedGoogle Scholar
- Manimozhiyan Arumugam CWRHBMRB: Pairagon+N-SCAN_EST: A Model-based Gene Annotation Pipeline. BMC Genome Biology, in press.Google Scholar
- Siepel AC, Haussler D: Computational Identification of Evolutionarily Conserved Exons: San Diego, CA. ACM; 2004.View ArticleGoogle Scholar
- Stein L, Sternberg P, Durbin R, Thierry-Mieg J, Spieth J: WormBase: network access to the genome and biology of Caenorhabditis elegans. Nucleic Acids Res 2001, 29(1):82–86. 10.1093/nar/29.1.82PubMed CentralView ArticlePubMedGoogle Scholar
- Harris TW, Lee R, Schwarz E, Bradnam K, Lawson D, Chen W, Blasier D, Kenny E, Cunningham F, Kishore R, Chan J, Muller HM, Petcherski A, Thorisson G, Day A, Bieri T, Rogers A, Chen CK, Spieth J, Sternberg P, Durbin R, Stein LD: WormBase: a cross-species database for comparative genomics. Nucleic Acids Res 2003, 31(1):133–137. 10.1093/nar/gkg053PubMed CentralView ArticlePubMedGoogle Scholar
- Harris TW, Chen N, Cunningham F, Tello-Ruiz M, Antoshechkin I, Bastiani C, Bieri T, Blasiar D, Bradnam K, Chan J, Chen CK, Chen WJ, Davis P, Kenny E, Kishore R, Lawson D, Lee R, Muller HM, Nakamura C, Ozersky P, Petcherski A, Rogers A, Sabo A, Schwarz EM, Van Auken K, Wang Q, Durbin R, Spieth J, Sternberg PW, Stein LD: WormBase: a multi-species resource for nematode biology and genomics. Nucleic Acids Res 2004, 32 Database issue: D411–7. 10.1093/nar/gkh066View ArticleGoogle Scholar
- WormBase for C.Briggsae[ftp://ftp.sanger.ac.uk/pub/wormbase/cbriggsae/cb25.agp8]
- Boguski MS, Lowe TM, Tolstoshev CM: dbEST--database for "expressed sequence tags". Nat Genet 1993, 4(4):332–333. 10.1038/ng0893-332View ArticlePubMedGoogle Scholar
- GAZE dataset[http://www.sanger.ac.uk/Software/analysis/GAZE]
- Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O'Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES: Initial sequencing and comparative analysis of the mouse genome. Nature 2002, 420(6915):520–562. 10.1038/nature01262View ArticlePubMedGoogle Scholar
- Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE, Okwuonu G, Hines S, Lewis L, DeRamo C, Delgado O, Dugan-Rocha S, Miner G, Morgan M, Hawes A, Gill R, Celera, Holt RA, Adams MD, Amanatides PG, Baden-Tillson H, Barnstead M, Chin S, Evans CA, Ferriera S, Fosler C, Glodek A, Gu Z, Jennings D, Kraft CL, Nguyen T, Pfannkoch CM, Sitter C, Sutton GG, Venter JC, Woodage T, Smith D, Lee HM, Gustafson E, Cahill P, Kana A, Doucette-Stamm L, Weinstock K, Fechtel K, Weiss RB, Dunn DM, Green ED, Blakesley RW, Bouffard GG, De Jong PJ, Osoegawa K, Zhu B, Marra M, Schein J, Bosdet I, Fjell C, Jones S, Krzywinski M, Mathewson C, Siddiqui A, Wye N, McPherson J, Zhao S, Fraser CM, Shetty J, Shatsman S, Geer K, Chen Y, Abramzon S, Nierman WC, Havlak PH, Chen R, James Durbin K, Egan A, Ren Y, Song XZ, Li B, Liu Y, Qin X, Cawley S, Cooney AJ, D'Souza LM, Martin K, Qian Wu J, Gonzalez-Garay ML, Jackson AR, Kalafus KJ, McLeod MP, Milosavljevic A, Virk D, Volkov A, Wheeler DA, Zhang Z, Bailey JA, Eichler EE, Tuzun E, Birney E, Mongin E, Ureta-Vidal A, Woodwark C, Zdobnov E, Bork P, Suyama M, Torrents D, Alexandersson M, Trask BJ, Young JM, Huang H, Wang H, Xing H, Daniels S, Gietzen D, Schmidt J, Stevens K, Vitt U, Wingrove J, Camara F, Mar Alba M, Abril JF, Guigo R, Smit A, Dubchak I, Rubin EM, Couronne O, Poliakov A, Hubner N, Ganten D, Goesele C, Hummel O, Kreitler T, Lee YA, Monti J, Schulz H, Zimdahl H, Himmelbauer H, Lehrach H, Jacob HJ, Bromberg S, Gullings-Handley J, Jensen-Seaman MI, Kwitek AE, Lazar J, Pasko D, Tonellato PJ, Twigger S, Ponting CP, Duarte JM, Rice S, Goodstadt L, Beatson SA, Emes RD, Winter EE, Webber C, Brandt P, Nyakatura G, Adetobi M, Chiaromonte F, Elnitski L, Eswara P, Hardison RC, Hou M, Kolbe D, Makova K, Miller W, Nekrutenko A, Riemer C, Schwartz S, Taylor J, Yang S, Zhang Y, Lindpaintner K, Andrews TD, Caccamo M, Clamp M, Clarke L, Curwen V, Durbin R, Eyras E, Searle SM, Cooper GM, Batzoglou S, Brudno M, Sidow A, Stone EA, Craig Venter J, Payseur BA, Bourque G, Lopez-Otin C, Puente XS, Chakrabarti K, Chatterji S, Dewey C, Pachter L, Bray N, Yap VB, Caspi A, Tesler G, Pevzner PA, Haussler D, Roskin KM, Baertsch R, Clawson H, Furey TS, Hinrichs AS, Karolchik D, Kent WJ, Rosenbloom KR, Trumbower H, Weirauch M, Cooper DN, Stenson PD, Ma B, Brent M, Arumugam M, Shteynberg D, Copley RR, Taylor MS, Riethman H, Mudunuri U, Peterson J, Guyer M, Felsenfeld A, Old S, Mockrin S, Collins F: Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 2004, 428(6982):493–521. 10.1038/nature02426View ArticlePubMedGoogle Scholar
- Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MA, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF, Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C, Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN, Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, Smith SM, Wallis JW, Yang SP, Romanov MN, Rondelli CM, Paton B, Smith J, Morrice D, Daniels L, Tempest HG, Robertson L, Masabanda JS, Griffin DK, Vignal A, Fillon V, Jacobbson L, Kerje S, Andersson L, Crooijmans RP, Aerts J, van der Poel JJ, Ellegren H, Caldwell RB, Hubbard SJ, Grafham DV, Kierzek AM, McLaren SR, Overton IM, Arakawa H, Beattie KJ, Bezzubov Y, Boardman PE, Bonfield JK, Croning MD, Davies RM, Francis MD, Humphray SJ, Scott CE, Taylor RG, Tickle C, Brown WR, Rogers J, Buerstedde JM, Wilson SA, Stubbs L, Ovcharenko I, Gordon L, Lucas S, Miller MM, Inoko H, Shiina T, Kaufman J, Salomonsen J, Skjoedt K, Wong GK, Wang J, Liu B, Yu J, Yang H, Nefedov M, Koriabine M, Dejong PJ, Goodstadt L, Webber C, Dickens NJ, Letunic I, Suyama M, Torrents D, von Mering C, Zdobnov EM, Makova K, Nekrutenko A, Elnitski L, Eswara P, King DC, Yang S, Tyekucheva S, Radakrishnan A, Harris RS, Chiaromonte F, Taylor J, He J, Rijnkels M, Griffiths-Jones S, Ureta-Vidal A, Hoffman MM, Severin J, Searle SM, Law AS, Speed D, Waddington D, Cheng Z, Tuzun E, Eichler E, Bao Z, Flicek P, Shteynberg DD, Brent MR, Bye JM, Huckle EJ, Chatterji S, Dewey C, Pachter L, Kouranov A, Mourelatos Z, Hatzigeorgiou AG, Paterson AH, Ivarie R, Brandstrom M, Axelsson E, Backstrom N, Berlin S, Webster MT, Pourquie O, Reymond A, Ucla C, Antonarakis SE, Long M, Emerson JJ, Betran E, Dupanloup I, Kaessmann H, Hinrichs AS, Bejerano G, Furey TS, Harte RA, Raney B, Siepel A, Kent WJ, Haussler D, Eyras E, Castelo R, Abril JF, Castellano S, Camara F, Parra G, Guigo R, Bourque G, Tesler G, Pevzner PA, Smit A, Fulton LA, Mardis ER, Wilson RK: Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 2004, 432(7018):695–716. 10.1038/nature03154View ArticleGoogle Scholar
- WU-BLAST software[http://blast.wustl.edu]
- Kent WJ: BLAT--the BLAST-like alignment tool. Genome Res 2002, 12(4):656–664. 10.1101/gr.229202. Article published online before March 2002PubMed CentralView ArticlePubMedGoogle Scholar
- Pruitt KD, Katz KS, Sicotte H, Maglott DR: Introducing RefSeq and LocusLink: curated human genome resources at the NCBI. Trends Genet 2000, 16(1):44–47. 10.1016/S0168-9525(99)01882-XView ArticlePubMedGoogle Scholar
- Maglott DR, Katz KS, Sicotte H, Pruitt KD: NCBI's LocusLink and RefSeq. Nucleic Acids Res 2000, 28(1):126–128. 10.1093/nar/28.1.126PubMed CentralView ArticlePubMedGoogle Scholar
- Pruitt KD, Maglott DR: RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res 2001, 29(1):137–140. 10.1093/nar/29.1.137PubMed CentralView ArticlePubMedGoogle Scholar
- UCSC genome browser[http://genome.ucsc.edu]
- van Baren MJ, Brent MR: Iterative gene prediction and pseudogene removal improves genome annotation. Genome Res 2006, 16(5):678–685. 10.1101/gr.4766206PubMed CentralView ArticlePubMedGoogle Scholar
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