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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG006677-14
Application #
8530261
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Bonazzi, Vivien
Project Start
1999-09-01
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
14
Fiscal Year
2013
Total Cost
$575,512
Indirect Cost
$192,844
Name
Johns Hopkins University
Department
Genetics
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Zimin, Aleksey V; Stevens, Kristian A; Crepeau, Marc W et al. (2017) An improved assembly of the loblolly pine mega-genome using long-read single-molecule sequencing. Gigascience 6:1-4
Jeffares, Daniel C; Jolly, Clemency; Hoti, Mimoza et al. (2017) Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat Commun 8:14061
Zimin, Aleksey V; Puiu, Daniela; Hall, Richard et al. (2017) The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum. Gigascience 6:1-7
Vurture, Gregory W; Sedlazeck, Fritz J; Nattestad, Maria et al. (2017) GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33:2202-2204
Kandathil, Abraham J; Breitwieser, Florian P; Sachithanandham, Jaiprasath et al. (2017) Presence of Human Hepegivirus-1 in a Cohort of People Who Inject Drugs. Ann Intern Med 167:1-7
Feigin, Michael E; Garvin, Tyler; Bailey, Peter et al. (2017) Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma. Nat Genet 49:825-833
Luo, Ruibang; Zimin, Aleksey; Workman, Rachael et al. (2017) First Draft Genome Sequence of the Pathogenic Fungus Lomentospora prolificans (Formerly Scedosporium prolificans). G3 (Bethesda) 7:3831-3836
Canzar, Stefan; Salzberg, Steven L (2017) Short Read Mapping: An Algorithmic Tour. Proc IEEE Inst Electr Electron Eng 105:436-458
Zimin, Aleksey V; Puiu, Daniela; Luo, Ming-Cheng et al. (2017) Hybrid assembly of the large and highly repetitive genome of Aegilops tauschii, a progenitor of bread wheat, with the MaSuRCA mega-reads algorithm. Genome Res 27:787-792
Vij, Shubha; Kuhl, Heiner; Kuznetsova, Inna S et al. (2016) Chromosomal-Level Assembly of the Asian Seabass Genome Using Long Sequence Reads and Multi-layered Scaffolding. PLoS Genet 12:e1005954

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