Rapid advances in genome sequencing techniques and throughput are providing scientists with increasingly detailed views of individual genomes, furthering our understanding of genetic variation in a wide array of organisms, of human population history and of the biology of Mendelian disorders and complex traits. Experiments that were until recently restricted to very large genome centers, such as the resequencing of human genomes, can now be carried out by a wide range of investigators. While these technological advances will enable many new discoveries in human and model organism genetics, they also pose formidable computational challenges. RFA-HG-10-018, entitled """"""""Informatics Tools for High-Throughput Sequence Data Analysis"""""""", is intended to fund further development of existing software to ensure that any biological or biomedical research laboratory can benefit from advances in sequencing technologies. We have developed specialized, state-of-the-art tools for the processing and analysis of next generation sequence data. Our tools encompass many key steps in sequence data analysis, ranging from quality control, to read mapping, to the identification, genotyping and annotation of many classes of sequence variation, to downstream association analyses that seek to connect identified variants with organismal phenotypes. These tools have been used to support analysis of several large, challenging datasets including not only data from the 1000 Genomes Project but also >1000 whole genomes and >2500 exomes sequenced in medical sequencing projects. Here, we propose to develop these tools into easy-to-use, portable, well-documented packages and complete pipelines that facilitate biomedical research in a wide variety of settings. A key component of the proposal is the deployment of these tools in the Galaxy cloud, where they will be accessible to investigators without direct access to a local high-throughput computing and data storage facility.

Public Health Relevance

We are developing computer software to discover and interpret genetic differences between individual human genomes from DNA sequencing data. We are starting with existing computer programs and turning them into stable software packages that can be readily used by any biological laboratory. These methods will enhance the study of human genetic variability and the understanding of heritable human diseases.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01HG006513-04S1
Application #
8923483
Study Section
Special Emphasis Panel (ZHG1-HGR-M (O3))
Program Officer
Sofia, Heidi J
Project Start
2012-02-01
Project End
2015-12-31
Budget Start
2014-07-01
Budget End
2014-12-31
Support Year
4
Fiscal Year
2014
Total Cost
$76,646
Indirect Cost
$25,206
Name
University of Utah
Department
Genetics
Type
Schools of Medicine
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
Chiang, Charleston W K; Marcus, Joseph H; Sidore, Carlo et al. (2018) Genomic history of the Sardinian population. Nat Genet 50:1426-1434
Than, Hein; Qiao, Yi; Huang, Xiaomeng et al. (2018) Ongoing clonal evolution in chronic myelomonocytic leukemia on hypomethylating agents: a computational perspective. Leukemia 32:2049-2054
Ostrander, Betsy E P; Butterfield, Russell J; Pedersen, Brent S et al. (2018) Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy. NPJ Genom Med 3:22
Steri, Maristella; Orrù, Valeria; Idda, M Laura et al. (2017) Overexpression of the Cytokine BAFF and Autoimmunity Risk. N Engl J Med 376:1615-1626
Ward, Alistair; Karren, Mary A; Di Sera, Tonya et al. (2017) Rapid clinical diagnostic variant investigation of genomic patient sequencing data with iobio web tools. J Clin Transl Sci 1:381-386
van den Berg, Marten E; Warren, Helen R; Cabrera, Claudia P et al. (2017) Discovery of novel heart rate-associated loci using the Exome Chip. Hum Mol Genet 26:2346-2363
Khorashad, J S; Tantravahi, S K; Yan, D et al. (2016) Rapid conversion of chronic myeloid leukemia to chronic myelomonocytic leukemia in a patient on imatinib therapy. Leukemia 30:2275-2279
Danjou, Fabrice; Zoledziewska, Magdalena; Sidore, Carlo et al. (2015) Genome-wide association analyses based on whole-genome sequencing in Sardinia provide insights into regulation of hemoglobin levels. Nat Genet 47:1264-71
Flickinger, Matthew; Jun, Goo; Abecasis, Gonçalo R et al. (2015) Correcting for Sample Contamination in Genotype Calling of DNA Sequence Data. Am J Hum Genet 97:284-90
Lo, Yancy; Kang, Hyun M; Nelson, Matthew R et al. (2015) Comparing variant calling algorithms for target-exon sequencing in a large sample. BMC Bioinformatics 16:75

Showing the most recent 10 out of 62 publications