The Bioinformatics Shared Resource (BSR) provides essential services and technical support for all aspects of bioinformatics for CSHL Cancer Center members. The pervasive need for Bioinformatics in the genomic era for all aspects of biological research makes it an essential tool for cancer researchers. Over the past five years, a total of 21 Cancer Center members (57% of members) used the bioinformatics programming and analysis services of the BSR, accounting for 93% of its total use. This has contributed to 60 publications over this time period. The goal of the BSR is to give Cancer Center members access to state-of-the-art bioinformatics expertise and support. This includes consulting with Cancer Center members to find the best available bioinformatics software for their particular projects, as well as developing new tools and techniques for Cancer Center members whose projects push the boundaries of what is currently available. Next-generation sequencing technology has revolutionized the scientific questions that can be addressed, leading to a pressing need for both improved statistical analysis tools and standardized analysis protocols. The large volumes of data created have also necessitated an increased need for both basic and advanced bioinformatics support. The BSR has taken a proactive role in building the computational infrastructure required to support these large- scale genomics projects. During the past five years, the BSR developed novel computational tools for sequencing data analysis, established standard pipelines for sequencing data analysis, built powerful computational servers to support data analysis for the CSHL cancer community, and trained the students and postdoctoral scholars at CSHL to use these new computational tools. One major goal is to continually upgrade and improve these computational support structures to ensure the long-term sustainability of sequencing data analysis at the CSHL Cancer Center. The second major goal is to contribute computational infrastructure that remains at the forefront of cancer research. Specifically, during the next five year period, the BSR intends to develop novel computational tools to address the newest questions at the forefront of cancer research, including the development of software to support single-cell sequencing studies and to aid in the design of CRISPR guide RNAs for functional genomics studies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
2P30CA045508-29
Application #
9151078
Study Section
Subcommittee I - Transistion to Independence (NCI)
Project Start
Project End
Budget Start
2016-08-01
Budget End
2017-07-31
Support Year
29
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Cold Spring Harbor Laboratory
Department
Type
DUNS #
065968786
City
Cold Spring Harbor
State
NY
Country
United States
Zip Code
11724
Kumar, Vijay; Rosenbaum, Julie; Wang, Zihua et al. (2018) Partial bisulfite conversion for unique template sequencing. Nucleic Acids Res 46:e10
Lee, Je H (2018) Tracing single-cell histories. Science 359:521-522
Alexander, Joan; Kendall, Jude; McIndoo, Jean et al. (2018) Utility of Single-Cell Genomics in Diagnostic Evaluation of Prostate Cancer. Cancer Res 78:348-358
Huang, Yu-Han; Klingbeil, Olaf; He, Xue-Yan et al. (2018) POU2F3 is a master regulator of a tuft cell-like variant of small cell lung cancer. Genes Dev 32:915-928
Tiriac, Hervé; Belleau, Pascal; Engle, Dannielle D et al. (2018) Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov 8:1112-1129
Naguib, Adam; Mathew, Grinu; Reczek, Colleen R et al. (2018) Mitochondrial Complex I Inhibitors Expose a Vulnerability for Selective Killing of Pten-Null Cells. Cell Rep 23:58-67
Forcier, Talitha L; Ayaz, Andalus; Gill, Manraj S et al. (2018) Measuring cis-regulatory energetics in living cells using allelic manifolds. Elife 7:
Bhagwat, Anand S; Lu, Bin; Vakoc, Christopher R (2018) Enhancer dysfunction in leukemia. Blood 131:1795-1804
Aberle, M R; Burkhart, R A; Tiriac, H et al. (2018) Patient-derived organoid models help define personalized management of gastrointestinal cancer. Br J Surg 105:e48-e60
Chen, Wei-Chia; Tareen, Ammar; Kinney, Justin B (2018) Density Estimation on Small Data Sets. Phys Rev Lett 121:160605

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