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Sihaela Salzberg

dblp:65/4704 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genome annotation › gene prediction
comparative gene finding
0.112005
Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005
Bioinformatics and computational biology › genome annotation
gene prediction
0.112005
Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005
Bioinformatics and computational biology › genome annotation › gene prediction
homology-based gene prediction
0.112005
Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005
Bioinformatics and computational biology › phylogenetics › phylogenetic inference › statistical alignment
pair hidden markov model
0.112005
Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

generalized pair hidden markov model · 0.1
YearPublicationVenuePosition
2005 Efficient implementation of a generalized pair hidden Markov model for comparative gene finding
abstract
MOTIVATION: The increased availability of genome sequences of closely related organisms has generated much interest in utilizing homology to improve the accuracy of gene prediction programs. Generalized pair hidden Markov models (GPHMMs) have been proposed as one means to address this need. However, all GPHMM implementations currently available are either closed-source or the details of their operation are not fully described in the literature, leaving a significant hurdle for others wishing to advance the state of the art in GPHMM design. RESULTS: We have developed an open-source GPHMM gene finder, TWAIN, which performs very well on two related Aspergillus species, A.fumigatus and A.nidulans, finding 89% of the exons and predicting 74% of the gene models exactly correctly in a test set of 147 conserved gene pairs. We describe the implementation of this GPHMM and we explicitly address the assumptions and limitations of the system. We suggest possible ways of relaxing those assumptions to improve the utility of the system without sacrificing efficiency beyond what is practical. AVAILABILITY: Available at http://www.tigr.org/software/pirate/twain/twain.html under the open-source Artistic License.
William H. Majoros, Mihaela Pertea, Sihaela Salzberg
Bioinform.3