The Bioinformatics Core provides analytical, computational, and technical services for all aspects of genomics and bioinformatics research performed by Salk Cancer Center members. In the era of big data in biology, bioinformatics analysis is crucial for deriving biological insights from large and noisy high-throughput data. The goal of the Core has been to give Cancer Center members access to state-of-the-art bioinformatics expertise and support. Staff in the Core assist Cancer Center members with integrative analysis of sequencing datasets, provide software and tools that Cancer Center members can use to perform their own analyses, provide training in software use, and develop customized tools and pipelines for cutting-edge analyses. The Bioinformatics Core provides standard, best practice data analysis for commonly requested analyses such as: variant calling from whole exome and whole genome sequencing data, annotation of genomic variants, identification and annotation of peaks from ChIP-Seq, Clip-Seq, DamID, or 4C experiments, motif analysis from these peaks, identification of differentially expressed genes from RNA-Seq and small RNA-Seq datasets, identification and annotation of interactions from HiC datasets, identification of novel splice isoforms from RNA- Seq datasets, reanalysis of published or generated microarray datasets, integration of diverse genomics datasets (e.g., identifying correlations between differential ChIP-Seq and differential RNA-Seq datasets) and pathway/overrepresentation analysis of analyzed genomic datasets. As needed, they also develop new tools and techniques for Cancer Center members whose projects push the boundaries of research, generate publication quality visualizations of analyses, and help prepare scientific manuscripts and grants containing bioinformatics or genomics components.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
5P30CA014195-48
Application #
10114234
Study Section
Subcommittee I - Transistion to Independence (NCI)
Project Start
1996-12-31
Project End
2024-01-31
Budget Start
2021-02-01
Budget End
2022-01-31
Support Year
48
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Salk Institute for Biological Studies
Department
Type
DUNS #
078731668
City
La Jolla
State
CA
Country
United States
Zip Code
92037
Zarrinpar, Amir; Chaix, Amandine; Xu, Zhenjiang Z et al. (2018) Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism. Nat Commun 9:2872
Ramaswamy, Suvasini; Tonnu, Nina; Menon, Tushar et al. (2018) Autologous and Heterologous Cell Therapy for Hemophilia B toward Functional Restoration of Factor IX. Cell Rep 23:1565-1580
Hsu, Cynthia L; Lee, Elian X; Gordon, Kara L et al. (2018) MAP4K3 mediates amino acid-dependent regulation of autophagy via phosphorylation of TFEB. Nat Commun 9:942
Sonntag, Tim; Vaughan, Joan M; Montminy, Marc (2018) 14-3-3 proteins mediate inhibitory effects of cAMP on salt-inducible kinases (SIKs). FEBS J 285:467-480
Herzig, Sébastien; Shaw, Reuben J (2018) AMPK: guardian of metabolism and mitochondrial homeostasis. Nat Rev Mol Cell Biol 19:121-135
Sweeney, Lora B; Bikoff, Jay B; Gabitto, Mariano I et al. (2018) Origin and Segmental Diversity of Spinal Inhibitory Interneurons. Neuron 97:341-355.e3
Hartmann, Phillipp; Hochrath, Katrin; Horvath, Angela et al. (2018) Modulation of the intestinal bile acid/farnesoid X receptor/fibroblast growth factor 15 axis improves alcoholic liver disease in mice. Hepatology 67:2150-2166
Glustrom, Leslie W; Lyon, Kenneth R; Paschini, Margherita et al. (2018) Single-stranded telomere-binding protein employs a dual rheostat for binding affinity and specificity that drives function. Proc Natl Acad Sci U S A 115:10315-10320
Giraddi, Rajshekhar R; Chung, Chi-Yeh; Heinz, Richard E et al. (2018) Single-Cell Transcriptomes Distinguish Stem Cell State Changes and Lineage Specification Programs in Early Mammary Gland Development. Cell Rep 24:1653-1666.e7
Ma, Jiao; Saghatelian, Alan; Shokhirev, Maxim Nikolaievich (2018) The influence of transcript assembly on the proteogenomics discovery of microproteins. PLoS One 13:e0194518

Showing the most recent 10 out of 457 publications