This project addresses two major bioinformatics problems: the development of better software for finding genes in eukaryotic genome sequences, and the development of genome assemblers for large shotgun sequencing projects. The gene finding project will pursue two tracks: first, we will continue to improve our Generalized Hidden Markov Model and our Pair Hidden Markov Model gene finders, training them for new species as new genomes appear, and enhancing their capabilities to use related species as a guide to gene finding in a new species. Second, we will develop a new eukaryotic annotation pipeline, which will integrate the results from a wide range of sources, including gene finders, protein sequence alignments, cDNA and EST alignments, and other sequence features. This pipeline will be used to predict comprehensive gene sets for multiple species, focusing especially on species for which the available annotation is incomplete or outdated. The pipeline will also be available as a service to annotate genomes for other groups. The assembler project will include several major efforts. First, we will continue to build on our successful open source assembler project, AMOS, adding new modules to allow inter-operation with other assembly packages. Second, we will develop new assemblers that can handle pyrosequencing data, low coverage genome projects, and sequences collected from complex mixtures of species. Third, we will provide assembly services to genome sequencing centers and other collaborators, helping them to assemble genomes using the latest available assembly tools. These will include new sequencing projects as well as genomes that, although already sequenced, can be re-assembled more accurately using improved assembly software. For all of the software development projects, we will continue our practice of making all our source code freely available to investigators in the scientific research community worldwide.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM006845-11
Application #
7858163
Study Section
Special Emphasis Panel (ZLM1-ZH-S (J2))
Program Officer
Ye, Jane
Project Start
2007-06-01
Project End
2011-09-20
Budget Start
2010-06-01
Budget End
2011-09-20
Support Year
11
Fiscal Year
2010
Total Cost
$608,513
Indirect Cost
Name
University of Maryland College Park
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
790934285
City
College Park
State
MD
Country
United States
Zip Code
20742
Magoc, Tanja; Wood, Derrick; Salzberg, Steven L (2013) EDGE-pro: Estimated Degree of Gene Expression in Prokaryotic Genomes. Evol Bioinform Online 9:127-36
Schatz, Michael C; Phillippy, Adam M; Sommer, Daniel D et al. (2013) Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies. Brief Bioinform 14:213-24
Salzberg, Steven L; Phillippy, Adam M; Zimin, Aleksey et al. (2012) GAGE: A critical evaluation of genome assemblies and assembly algorithms. Genome Res 22:557-67
Walenz, Brian; Florea, Liliana (2011) Sim4db and Leaff: utilities for fast batch spliced alignment and sequence indexing. Bioinformatics 27:1869-70
Lipman, David; Flicek, Paul; Salzberg, Steven et al. (2011) Closure of the NCBI SRA and implications for the long-term future of genomics data storage. Genome Biol 12:402
Shulaev, Vladimir; Sargent, Daniel J; Crowhurst, Ross N et al. (2011) The genome of woodland strawberry (Fragaria vesca). Nat Genet 43:109-16
Mago?, Tanja; Salzberg, Steven L (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957-63
Florea, Liliana; Souvorov, Alexander; Kalbfleisch, Theodore S et al. (2011) Genome assembly has a major impact on gene content: a comparison of annotation in two Bos taurus assemblies. PLoS One 6:e21400
Angiuoli, Samuel V; Salzberg, Steven L (2011) Mugsy: fast multiple alignment of closely related whole genomes. Bioinformatics 27:334-42
Brady, Arthur; Salzberg, Steven (2011) PhymmBL expanded: confidence scores, custom databases, parallelization and more. Nat Methods 8:367

Showing the most recent 10 out of 112 publications