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METHODS Comparative Gene Prediction in Human and Mouse1Grup de Recerca en Informàtica Biomèdica. Institut Municipal d'Investigació Medica / Universitat Pompeu Fabra / Centre de Regulació Genòmica 08003 Barcelona, Catalonia, Spain; 2GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA; 3Freie Universität Berlin and Berlin Center for Genome Based Bioinformatics (BCB), 14195 Berlin, Germany; 4AstraZeneca R&D Boston, Waltham, Massachusetts 02451, USA
The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.
After the genome sequence of an organism has been obtained, the very first next step is to compile a complete and accurate catalog of the genes encoded in this sequence. For higher eukaryotic organisms, however, the accuracy of currently available gene prediction methods to perform such a task is limited (Guigó et al. 2000  ; Rogic et al. 2001; Guigó and Wiehe 2003). The
increasing availability of genome sequences from different organisms,
however, has lead to the development of new computational gene finding
methods that use sequence conservation to help identifying coding
exons, and improve the accuracy of the predictions (Fig.
1; Crollius et al. 2000; Wiehe et al. 2000;
Miller 2001; Rinner and Morgenstern 2002). Indeed, three such
comparative gene prediction programs, SLAM (Pachter et al. 2002), SGP2,
and TWINSCAN (Korf et al. 2001) have been used for the comparative
analysis of the human and mouse genomes. These analyses lead to more
accurate gene predictions, and to the verification of previously
unconfirmed genes. In this paper, we describe the program SGP2. Typical
computational ab initio gene prediction methods rely on the
identification of suitable splicing sites, start and stop codons along
the query sequence, and the computation of some measure of coding
likelihood to predict and score candidate exons, and delineate gene
structures (see Claverie 1997; Burge and Karlin 1998; Haussler 1998;
Zhang 2002 and references therein for reviews on computational gene
finding).
Similarity between the query sequence and known coding sequences (amino acid or cDNA) can also be used to infer gene structures. When the query sequence encodes a protein for which a close homolog exists, a special type of alignment can be used between the DNA sequence and the target protein/cDNA sequence, in which gaps in the target sequence corresponding to introns in the query sequence must be compatible with potential splicing signals. This is the approach in GENEWISE (Birney and Durbin 1997 ) and PROCRUSTES (Gelfand et al. 1996).
Alternatively, the results of searching the query sequence against a
database of known coding sequences, using for instance BLASTX (Altschul
et al. 1990, 1997; Gish and States 1993), can be incorporated more or
less ad hoc into the scoring schema of an ab initio gene prediction
method. The program GENOMESCAN (Yeh et al. 2001), which incorporates
BLASTX search results into the predictions by the GENSCAN program
(Burge and Karlin 1997), is an example of a recent development in that
direction. Recently developed comparative gene prediction programs further exploit sequence similarity. Instead of comparing anonymous genomic sequences to known coding sequences, anonymous genomic sequences are compared to anonymous genomic sequences from the same or different organisms, under the assumption that regions conserved in the sequence will tend to correspond to coding exons from homologous genes. The approach taken by the different programs to exploit this idea differs notably.
In one such approach (Blayo et al. 2002
In a different approach, the programs SLAM (Pachter et al. 2002
A third class of programs adopt a more heuristic approach, and separate
clearly gene prediction from sequence alignment. The programs ROSSETA
(Batzoglou et al. 2000 In the next section, we describe the algorithmic details of SGP2, and its implementation. We also describe the sequence sets used to benchmark SGP2 accuracy. Results based on these data sets indicate that SGP2 is an improvement over pure ab initio gene prediction programs, even when the informant genome is only in shotgun form. We have found that 3x coverage will generally suffice to achieve maximum accuracy. Finally, we describe the application of SGP2 to the comparative analysis of the human and mouse genomes.
SGP2 SGP2 is a method to predict genes in a target genome sequence using the sequence of a second informant or reference genome. Essentially, SGP2 is a framework to integrate the ab initio gene prediction program GENEID (Guigó et al. 1992 ; Parra et al. 2000) with the sequence similarity search
program TBLASTX. The approach is conceptually similar to that used
in TWINSCAN to incorporate BLASTN searches into GENSCAN.
GENEID is a genefinder that predicts and scores all potential coding
exons along a query sequence. Scores of exons are computed as
log-likelihood ratios, which are a function of the splice sites
defining the exon, and of the coding bias in composition of the exon
sequence as measured by a Markov Model of order five (Borodovsky and
McIninch 1993 When using an informant genome sequence to predict genes in a target genome sequence, ideally we would like to incorporate into the scores of the candidate exons predicted along the target sequence, the score of the optimal alignment at the amino acid level between the target exon sequence and the counterpart homologous exon in the informant genome sequence. If a substitution matrix, for instance from the BLOSUM family, is used to score the alignment, the resulting score can also be assumed to be a log-likelihood ratio: informally, the ratio between the likelihood of the alignment when the amino acid sequences code for functionally related proteins, and the likelihood of the alignment, otherwise. In principle, this score could be added to the GENEID score for the exon. TBLASTX provides an appropriate shortcut to often find a good enough approximation to such an optimal alignment, and infer the corresponding score: The optimal alignment can be assumed to correspond to the maximal scoring high-scoring segment pairs (HSP) overlapping the exon. However, when dealing in particular with the informant genome sequence in fragmentary shotgun form, often different regions of a candidate exon sequence will align optimally to different informant genome sequences. Thus, in the approach used here, we identify the optimal HSPs covering each fraction of the exon, and compute separately the contribution of each HSP into the score of the exon. In the next section, we describe in detail how this computation is performed.
Scoring of Candidate Exons
We compute st(e) in the following way. Let h1···hq be the set of HSPs found by TBLASTX after comparing the query sequence S against a database of DNA sequences (Fig. 2A).
First, we find the maximum scoring projection of the HSPs onto the query sequence. We simply register the maximum score among the scores of all HSPs covering each position, and then partition the query sequence in equally maximally scoring segments (bounded by dotted lines in Fig. 2A) x1···xr, with scores sp(x1)···sp(xr) (Fig. 2B).
Then, for each predicted exon e (Fig. 2C), we find
Xe, the set of maximally scoring segments
overlapping e
 b denotes the overlap between sequence segments
a and b, and  means no overlap. We compute
st(e)in the following way:
That is, each exon gets the score of the maximally scoring HSPs along the exon sequence proportional to the fraction of the HSP covering the exon. In other words, st(e) is the integral of the maximum scoring projection function within the exon interval. Once the scores s have been computed for all predicted exons in the sequence S, gene prediction proceeds as usual in GENEID: The gene structure is assembled maximizing the sum of scores of the assembled exons.
Running SGP2 In all the analyses reported here, we used BLOSUM62 as the amino acid substitution matrix, but changed the penalty for aligning any residue to a stop codon to –500. This helps to get rid of a large fraction of HSPs in noncoding regions. Because of TBLASTX limitations, large query sequences may need to be split in fragments before the search, and the results reconstructed afterwards. Results of TBLASTX search are then parsed to obtain the maximum scoring projection of the HSPs onto the query sequence. The parsing includes discarding all HSPs below a given bit score cutoff, subtracting this value from the score of the remaining HSPs, weighting the resulting score by w (see above), and collapsing the HSPs in to the maximum scoring projections. In all analyses described here, the bit score cutoff was set to 50, and w to 0.20. These values were chosen to optimize the gene predictions in sequence sets of known homologous human and mouse genomic sequences (see the Results section). The maximum scoring projection is given to GENEID in general feature format (GFF; R. Durbin and D. Haussler, http://www.sanger.ac.uk/Software/GFF/). GENEID uses it to rescore the exons predicted along the query sequence as explained, and assembles the corresponding optimal gene structure. GENEID was already designed to incorporate external information into the gene predictions, and no changes were required in the program to accommodate it into the SGP2 context, only a small adjustment in the parameter file to cope with the change in scale of the exon scores. We have written a simple PERL script which, given a query DNA sequence and the results of the TBLASTX search, performs all the components of the SGP2 analysis transparently: the parsing of the TBLASTX search results, and the GENEID predictions. In the case wherein both the query and the informant sequence are single genomic fragments, the gene predictions can be obtained in both sequences (without the need for a second TBLASTX search). The script, as well as the individual components, can be found at http://www1.imim.es/software/sgp2/. GENEID has essentially no limits to the length of the input sequence, and deals well with chromosome size sequences. Limits to the length of the input query sequence that can be analyzed by SGP2 are, thus, those imposed by TBLASTX. GENEID is quite fast; given the parsed TBLASTX results, it takes 6 h to reannotate the whole human genome in a MOSIX cluster containing four PCs (PentiumIII Dual 500 Mhz processors).
Accelerating TBLASTX Searches
Sequence Data Sets
To test the accuracy of SGP2, we used the data set constructed by
Batzoglou et al. (2000)
Finally, we tested SGP2 in the complete sequence of human chromosome 22
(Dunham et al. 1999
Mouse and Human Genome Sequences
Evaluating Accuracy
At the gene level, a gene is correctly predicted if all of the coding
exons are identified, every intron–exon boundary is correct, and all
of the exons are included in the proper gene. In addition, we compute
the missed genes (MGs), real genes for which none of its exons are
overlapped by a predicted gene, and the wrong genes (WGs), predictions
for which none of the exons are overlapped by a real gene. In general,
gene finders predict the initial and terminal exons very poorly. This
often leads to so-called chimeric predictions—one predicted gene
encompassing more than one real gene—or to split predictions—one real
gene split in multiple predicted genes. Reese et al. (2000)
Benchmarking SGP2 We evaluated the accuracy of SGP2 using a number of different data sets. The lack of a gold standard of gene prediction makes it difficult to get accurate assessments from any single data set. We primarily used three data sets as described earlier.
To benchmark SGP2, we constructed BLAST databases from the mouse and
human sections of SCIMOG and MIT, and each mouse/human sequence to the
entire human/mouse database, respectively. This enabled us to predict
genes in both the mouse and human databases. The results from comparing
SGP2, GENSCAN, and ROSSETA accuracy values in this case are taken from
Batzoglou et al. (2000)
The accuracy of SGP2 is comparable to that of ROSSETA, and is significantly higher than that of GENSCAN. SGP2 also improves substantially over a simple TBLASTX search. The relative low specificity of the TBLASTX search—even after the large penalties for stop codons—reflects the fact that a substantial fraction of the conservation between the human and mouse genomes extends into the noncoding regions (Mouse Genome Sequencing Consortium 2002 ). At the
nucleotide level, SGP2 accuracy is almost equal in the MIT data set and
the SCIMOG data set (even though the SGP2 was trained on SCIMOG).
The accuracy at the exact exon level, however, decreases, in particular
when prediction of multiple genes is allowed. This is a problem
inherited from GENEID, which tends to replace short initial and
terminal exons with longer internal exons.
Accuracy of SGP2 as a Function of the Coverage of the Mouse Genome
Accuracy of SGP2 in Human Chromosome 22 Human chromosome 22 was the first human chromosome fully sequenced (Dunham et al. 1999 ), and it is quite the best annotated thus far, due
to a number of experimental followups (Das et al. 2001; Shoemaker et
al. 2001). Therefore, it provides an excellent data set to validate any
gene prediction technology. Human chromosome 22 was searched using
TBLASTX against the masked whole-genome assembly from the mouse genome
(MGSCv3). The HSPs in chromosomal coordinates resulting from the
TBLASTX search were used in GENEID to perform SGP2 gene prediction.
Although the HSPs had been computed on the masked sequence, in this
case the SGP2 predictions were obtained on the unmasked one. SGP2
predicted 729 genes on human chromosome 22. Table
4 shows the comparative accuracy of the
SGP2, GENSCAN, GENOMESCAN, and pure ab initio GENEID predictions
(without TBLASTX data). GENSCAN predictions on the masked sequence were
taken from the USCS genome browser http://genome.cse.ucsc.edu/.
GENOMESCAN predictions were obtained from
ftp://ftp.ncbi.nih.gov/genomes/H_sapiens/build28_chr_genomescan.gtf.gz.
Pure ab initio GENEID predictions were obtained on the masked sequence,
and can also be downloaded from
http://www1.imim.es/genepredictions/.
Although SGP2 is not more sensitive than GENSCAN, it appears to be more specific (as it utilizes the mouse genome). Fifty percent of the GENSCAN-predicted exons do not overlap annotated chromosome 22 exons; this number is only 31% for SGP2. Overall, SGP2 appears to be more accurate than GENSCAN in human chromosome 22: GENSCAN's CC at the nucleotide level is 0.64, whereas that of SGP2 is 0.73. Although accuracy decreases for both programs when going from single-gene sequences (Tables 1, 2) to an entire chromosome, SGP2 retains more accuracy. GENSCAN overall shows higher sensitivity than SGP2, but there were 45 real genes not predicted by GENSCAN on human chromosome 22, and SGP2 was able to predict, at least partially, 15 of them. This suggests that SGP2 and GENSCAN may play complementary roles. GENOMESCAN, on the other hand, did not appear to be superior to GENSCAN in human chromosome 22. Mouse matches (TBLASTX HSPs) covered 11% of the human chromosome 22. Though they covered 85% of the coding nucleotides, 74% of the HSPs fell outside annotated coding regions. This illustrates the difficulties of using genome sequence conservation even at the protein level between human and mouse genomes to infer coding genes.
Prediction of Genes in the Human and Mouse Genomes
Overall, 23,913 of the human predictions and 24,203 of the mouse predictions overlapped ENSEMBL genes, whereas 95% of the mouse and 93% of the human ENSEMBL genes were among the genes predicted by SGP2. Of the remaining putative novel 20,570 mouse SGP2 genes and 20,193 human SGP2 genes, 10,456 mouse and 9,006 human predictions were found to be similar at P < 10–6 to a prediction in the counterpart genome. Of these, 5,960 and 4,909 have multiple exons and are longer than 300 bp. A significant fraction of these putative homologous predictions are likely to correspond to real genes (Guigó et al. 2003 ). The predictions are
interactively accessible through the USCS genome browser
(http://genome.cse.ucsc.edu/) and through the DAS server at ENSEMBL
(http://www.ensembl.org, under "DAS sources"). The complete set of
prediction files is available at http://www1.imim.es/genepredictions/.
Speeding Up TBLASTX Searches Accuracy in Tables 1 and 2 was computed using default TBLASTX parameters. Table 5 shows the comparative accuracy of TBLASTX and SGP2 predictions, under the default and the speed-up configuration of TBLASTX parameters on the SCIMOG data set. The sensitivity of speed-up TBLASTX searches drops from 0.89 to 0.72, but specificity increases slightly. SGP2 is more robust, and it compensates for some of the sensitivity lost in the TBLASTX search. Overall accuracy for SGP2, as measured by the CC, drops only from 0.95 to 0.93.
Predictions on human chromosome 22 and the whole human and mouse genomes have been obtained with this speed-up configuration of parameters.
We have described the program SGP2 for comparative gene finding, and presented the results of its application to the human and mouse genome sequences. Results in controlled benchmark sequence data sets indicate that, by including information from genome sequence conservation, predictions by SGP2 appear to be more accurate than those obtained by pure ab initio programs, exemplified here by GENSCAN and GENEID. Although there is not a significant gain in sensitivity, the specificity of the predictions appears to increase substantially, and a smaller number of false positive exons are predicted.
Indeed, one the major obstacles towards the completion of the catalog
of human (mammalian) genes is our inability to assess the reliability
of the large number of computational gene predictions that have not
been verified experimentally. Whereas the ENSEMBL pipeline produces
about 25,000 human and mouse genes, the NCBI annotation pipeline
predicts almost 50,000 genes in mouse, and the program
GENOMESCAN predicts close to 55,000 genes in this species.
Although a large fraction of the ENSEMBL genes correspond to
computational predictions without experimental verification, the method
is quite conservative, and recent experiments suggest that essentially
all ENSEMBL genes are indeed real (Guigó et al.
2003
In this regard, the results presented here demonstrate that through the
comparison of the human and mouse genomes using SGP2 (or another
available comparative gene prediction tool), the false-positive rate
can be reduced significantly, and the catalog of mammalian genes better
defined. SGP2 predicts a few thousand candidate genes not in ENSEMBL
that we believe are worth verifying experimentally. Indeed, the
experimental verification of a subset of these provides evidence of at
least 1000 previously nonconfirmed genes (Guigó et
al. 2003
The predictions by SGP2 obtained here are, of course, still far from
definitively setting this catalog. For one thing, the mouse may be too
close a species to human: A large fraction of the sequence has been
conserved between the genomes of these two species. Indeed, most
sequence conservation between human and mouse does not correspond
to coding exons (Mouse Genome Sequencing Consortium 2002 SGP2 is flexible enough so that it can be easily accommodated to analyze species other than human and mouse. The fact that it can deal with shotgun data at any level of coverage means that as the sequence of a new genome starts becoming available, it can be used to improve the annotation of other already existing genomes. Particularly relevant in this context is a feature of SGP2 (and GENEID) that we have not explored here. SGP2 can produce predictions on top of preexisting annotations. For instance, we could have given to SGP2 the location and exonic coordinates (in GFF format) of known REFSEQ genes (or ENSEMBL), and SGP2 would have predicted genes only outside the boundaries of these genes of already well known exonic structure. Preliminary results indicate that this approach improves gene prediction outside of the preassumed genes, and reduces the rate of chimeric predictions (i.e., predictions encompassing multiple genes). Moreover, we believe that SGP2 can be substantially improved. The flexibility of the SGP2/GENEID framework makes it quite easy to integrate additional information that can contribute to the accuracy of the predictions: synonymous versus nonsynonymous substitution rates in the alignments by TBLASTX, conservation of the splice signals in the informant genome, amino acid substitution matrices specific to the phylogenetic distance between the species compared, etc. In this regard, the reasons to use the default BLOSUM62 matrix are not obvious. Given the expected sequence similarity between mouse–human orthologs, BLOSUM80 appears to be a better choice. However, we intended to also detect divergent families. Towards that end, the superiority of BLOSUM80 is less clear. We have compared TBLASTX search results on human chromosome 22 against the whole mouse genome. Whereas the HSPs resulting from the BLOSUM62 search cover 84% of the chromosome 22 coding nucleotides, BLOSUM80 HSPs cover 88% of them. However, BLOSUM80 is much less specific than BLOSUM62: 60% of the nucleotides in the BLOSUM62 HSPs fall outside coding regions, compared to 88% for BLOSUM80. It is thus clear that the optimal matrix or combination of matrices for comparative gene-finding using TBLASTX requires further investigation. Although a large fraction of the human genome sequence has been known for more than a year, the exact number of human genes and their precise definition remain unknown. Gene specification in higher eukaryotic sequences is the result of the complex interplay of sequence signals encoded in the primary DNA sequence, which is only partially understood. Without an exhaustive catalog of human genes, however, the promises of genome research in medicine and technology cannot be completely fulfilled. The work presented here, in which it is shown that human–mouse comparisons can contribute to the completion of the mammalian (human) gene catalog, underscores the importance of the comparisons of the genomes of different organisms to fully understand the phenomenon of life, and in particular to deciphering the mechanism, central to life, by means of which the genome DNA sequence specifies the amino acid sequence of the proteins.
http://www.sanger.ac.uk/Software/formats/GFF/; GFF format description page. http://genome.cse.ucsc.edu/goldenPath/22dec2001/; Human genome sequence goldenpath from Dec. 22, 2001 (hg10) equivalent to NCBI28 build. http://genome.cse.ucsc.edu/goldenPath/mmFeb2002/; Mouse genome sequence goldenpath from Feb. 2002 (mm2) equivalent to MGSCv3.
http://www.cs.columbia.edu/ ftp://ftp.ncbi.nih.gov/genomes/H_sapiens/build28_chr_genomescan.gtf.gz; Genomescan predictions from NCBI. http://genome.cse.ucsc.edu/goldenPath/mmFeb2002/database/ensGene.txt.gz; Mouse ENSEMBL annotations file. http://blast.wustl.edu; Washington University BLAST Archives http://genome.cse.ucsc.edu/goldenPath/22dec2001/database/ensGene.txt.gz; Human ENSEMBL annotations file. http://genome.cse.ucsc.edu; UCSC genome browser. http://www.ensembl.org; ENSEMBL genome browser. http://www1.imim.es/genepredictions/; GENEID and SGP2 full data predictions. http://www1.imim.es/software/sgp2/; SGP2 home page. http://www1.imim.es/datasets/sgp2002/; SGP2 training data sets page.
We thank the Mouse Genome Sequencing Consortium for providing the mouse genome sequence as well as support throughout the analysis process. We especially thank Francisco Câmara for arranging the data listed in the gene-prediction page on our group Web site, and for setting up and taking care of our DAS server. We also thank Ian Korf for inspiring discussions regarding the parameters to use in the TBLASTX search. We thank Enrique Blanco, Sergi Castellano, and Moisés Burset for helpful discussions and constant encouragement. This work was supported by a grant from Plan Nacional de I+D (BIO2000-1358-C02-02), Ministerio de Ciencia y Tecnologia (Spain), and from a fellowship to J.F.A. from the Instituto de Salud Carlos III (99/9345). The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
5 Corresponding author.E-MAIL rguigo{at}imim.es; FAX 34 93 224-0875.  Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.871403.
Received November 4, 2002; accepted in revised format November 15, 2002. 13:108-117 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00  CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati What's this?
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