Browsing by Author "Pertea, Mihaela"
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Item Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments(Genome Biology, 2008-01) Haas, Brian U.; Salzberg, Steven L.; Zhu, Wei; Pertea, Mihaela; Allen, Jonathan E.; Orvis, Joshua; White, Owen; Buell, C Robin; Wortman, Jennifer REVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.Item Between a chicken and a grape: estimating the number of human genes(Springer Nature, 2010-05-05) Pertea, Mihaela; Salzberg, Steven LMany people expected the question 'How many genes in the human genome?' to be resolved with the publication of the genome sequence in 2001, but estimates continue to fluctuate.Item A computational survey of candidate exonic splicing enhancer motifs in the model plant Arabidopsis thaliana(Springer Nature, 2007-05-21) Pertea, Mihaela; Mount, Stephen M; Salzberg, Steven LAlgorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites. We have developed a new computational technique to identify significantly conserved motifs involved in splice site regulation. First, 84 putative exonic splicing enhancer hexamers are identified in Arabidopsis thaliana. Then a Gibbs sampling program called ELPH was used to locate conserved motifs represented by these hexamers in exonic regions near splice sites in confirmed genes. Oligomers containing 35 of these motifs have been shown experimentally to induce significant inclusion of A. thaliana exons. Second, integration of our regulatory motifs into two different splice site recognition programs significantly improved the ability of the software to correctly predict splice sites in a large database of confirmed genes. We have released GeneSplicerESE, the improved splice site recognition code, as open source software. Our results show that the use of the ESE motifs consistently improves splice site prediction accuracy.Item Detection of lineage-specific evolutionary changes among primate species(2011-07-04) Pertea, Mihaela; Pertea, Geo M; Salzberg, Steven LBackground: Comparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection. Results: We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection. Conclusions: DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.Item Efficient decoding algorithms for generalized hidden Markov model gene finders(BMC Bioinformatics, 2005-01-24) Majoros, William H.; Pertea, Mihaela; Delcher, Arthur L.; Salzberg, Steven L.Background: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. Results: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. Conclusions: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.Item JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions(Genome Biology, 2006-08-07) Allen, Jonathan E.; Majoros, William H.; Pertea, Mihaela; Salzberg, Steven L.Background: Predicting complete protein-coding genes in human DNA remains a significant challenge. Though a number of promising approaches have been investigated, an ideal suite of tools has yet to emerge that can provide near perfect levels of sensitivity and specificity at the level of whole genes. As an incremental step in this direction, it is hoped that controlled gene finding experiments in the ENCODE regions will provide a more accurate view of the relative benefits of different strategies for modeling and predicting gene structures. Results: Here we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy. Conclusions: While in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based ‘combiner’ program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research.