New developments in DNA sequencing technology have spurred a tremendous increase in the use of sequencing to answer fundamental questions in biology and medicine. Whole- genome sequencing is being used to study cancer, to discover disease-causing gene variants in patient genomes, and to study human genetic diversity. Numerous WGS projects are being launched for species whose genomes have not yet been sequenced. Sequencing of messenger RNA through RNA-seq has led to an explosion of projects to characterize transcribed genes in multiple cell types and in many species, and simultaneously to discover new genes and new splice variants of known genes. These sequencing-based studies generate enormous amounts of data, which in turn require sophisticated, efficient, and innovative new algorithms that will make it possible to assemble these genomes and identify their gene content. We propose to develop new cloud-computing based assembly algorithms to assemble genomes from short reads generated by the latest sequencing technologies. In parallel, we will continue to improve our existing assemblers, extending them to handle new and diverse data types, including "3rd-generation" sequences. We will also reach out to outside groups to help them assemble novel species, modifying our software as needed and continuing to push the limits of assembly technology. One of the most exciting recent technology developments in the gene finding arena is RNA- seq, a new protocol for capturing and sequencing the mRNA in a cell. This technique is well on its way to replacing both conventional EST sequencing as a method for capturing transcribed protein-coding genes, and microarray hybridization experiments for measuring transcript levels. We propose to develop new algorithms to take advantage of the flood of new RNA-seq data that has begun to appear. We have already developed two new algorithms, TopHat and Cufflinks, for RNA-seq analysis, which are the first to be able to discover previously unknown splice sites and isoforms. These tools, enhanced with new features to handle a wider variety of sequence data, form the basis of our plans to develop integrated gene finders that can identify novel genes, novel isoforms of known genes, and fusion genes, and to include these methods in a genome annotation pipeline.

Public Health Relevance

Many biomedical researchers are now using large-scale DNA sequencing to study human disease and to understand human biology. The analysis of these new types of sequence data requires highly sophisticated software that can assemble millions or billions of DNA fragments to reconstruct a genome, and that can then identify genes in the assembled sequence. This project will develop new algorithms and software that will help researchers use the latest DNA sequencing technology to sequence, assemble, and find genes in human genomes as well as the genomes of many other species.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Bonazzi, Vivien
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Johns Hopkins University
Schools of Medicine
United States
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Salzberg, Steven L; Pertea, Mihaela; Fahrner, Jill A et al. (2014) DIAMUND: direct comparison of genomes to detect mutations. Hum Mutat 35:283-8
Wood, Derrick E; Salzberg, Steven L (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15:R46
Martinson, Vincent G; Magoc, Tanja; Koch, Hauke et al. (2014) Genomic features of a bumble bee symbiont reflect its host environment. Appl Environ Microbiol 80:3793-803
Narzisi, Giuseppe; O'Rawe, Jason A; Iossifov, Ivan et al. (2014) Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat Methods 11:1033-6
Magoc, Tanja; Pabinger, Stephan; Canzar, Stefan et al. (2013) GAGE-B: an evaluation of genome assemblers for bacterial organisms. Bioinformatics 29:1718-25
Maron, Lyza G; Guimaraes, Claudia T; Kirst, Matias et al. (2013) Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc Natl Acad Sci U S A 110:5241-6
Zimin, Aleksey V; Marcais, Guillaume; Puiu, Daniela et al. (2013) The MaSuRCA genome assembler. Bioinformatics 29:2669-77
Andreotti, Sandro; Reinert, Knut; Canzar, Stefan (2013) The duplication-loss small phylogeny problem: from cherries to trees. J Comput Biol 20:643-59
Kim, Daehwan; Pertea, Geo; Trapnell, Cole et al. (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14:R36
Bradnam, Keith R; Fass, Joseph N; Alexandrov, Anton et al. (2013) Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. Gigascience 2:10

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