The Bioinformatics Shared Resource provides cutting-edge computational and systems biology support to the Institute and its NCI-designated Cancer Center. We specialize in omics data analysis, multi-omics data integration, network and pathway analysis, and machine learning. Different levels of data analyses are provided, based on the complexity of researchers’ data sets. This may include automated pipeline-based analyses, customized deep data mining, development and application of machine learning models, and hypothesis driven in-silico drug discovery. Our focus is to help researchers put data into biological contexts across various disease areas, and to create testable hypotheses and understandable biological processes.
We also provide Bioinformatics classes and training for the entire Cancer Center and Sanford Burnham Prebys community. Both internal and external customers are charged at assigned hourly rates.
The areas we focus on include:
- Data mining of Next Generation Sequencing (NGS) data sets, including:
- RNA-Seq, ChIP-Seq, ATAC-Seq, DNA Methylation etc.
- Data integration of transcriptomics, genomics, proteomics, and epigenomics data sets
- Network and pathway analyses using customized algorithms and commercially available software, including Regulattice, Ingenuity Pathway Analysis, Metacore, GSEA etc.
- Machine learning applications and implementation
- High Throughput Screening data analysis
- In silico drug discovery, biomarker identification, and toxicogenomics
- Training and consultation on bioinformatics
- Grant and letter-of-support writing services
We are looking for a talented candidate for a Bioinformatics Specialist position in this challenging computational team. The successful candidate should have strong genomics, computational and statistical skills, great communication and interpersonal skills, and be able to independently solve scientific projects.
We also welcome undergraduate or graduate interns for short term computational biology projects.
Please contact Dr. Jun Yin for more information.
Assistance is provided in the following areas:
- Systems Biology Support: Generating and Analyzing networks and pathways to produce testable hypotheses, to discover mechanisms of drug action and new drug targets.
- Next-Generation Sequencing Data Analysis: From Reads to Biology (RNA-Seq, ChiP-Seq, ATAC-Seq, DNA-Seq, exome sequencing, targeted resequencing, SNP and indel detection, single cell analysis and more)
- Proteomics, Microarray, RNA-seq, and related analysis
- Biostatistics: experiment design, power analysis, estimation of the minimum number of animals required etc. For advanced statistical support we have a consulting agreement with PhD level statistician
- Integrative analysis of multi-omics data
- Grant Preparation Assistance
- Bioinformatics classes, tutorials and trainings
- Results reports sent to the customer include description of the analysis, data tables, figures and power point presentation with analysis details
We have advanced hardware and large collection of software to solve your research problems. The software collections include commercially licensed software suites, including Ingenuity Pathway Analysis (IPA), Omicsoft Array Suite, Oncomine, NextBio, MetaCore; and open source software and databases such as Cytoscape, Broad Institute Genome Analysis ToolKit (GATK), GSEA; and customized algorithm and pipeline development using R Bioconductor, Perl and Python. We actively use data from public databases (GEO, TCGA, UCSC Cancer Genome Browser, CCLE, and others) for biomarker discovery, survival analysis and predictive modeling.
We have constructed automated computational pipelines using best-practices for RNA-Seq, ChIP-Seq, ATAC-Seq etc. We are also efficient in analyzing CRISPR, miCLIP, DNA Methylation, Single Cell Sequencing data sets. We have strong industrial experience in drug discovery, high-throughput screening, biomarker discovery, and toxicogenomics.
Our Regulattice pipeline for advanced machine learning in identifying actionable cancer drivers has been updated to accommodate additional functional and biological validation. RNA-Seq and clinical data from twenty-three major cancer cohorts from TCGA have been analyzed using our Viper/Regulattice protocol and made publicly available (regulattice.sbpdiscovery.org username: demo password: demo1).
To discuss your project needs, please call (858)795-5200 x5058 or email jyin@SBPdiscovery.org.
|Bioinformatics Custom Data Analysis/Application||hour||$50||$62.50||$67.50||$131.50|
|Statistics data Analysis||hour||$50||$62.50||$67.50||$131.50|
Jun Yin, Ph.D.
Director, Bioinformatics Shared Resource
(858)795-5200 ext. 5085
Email Jun Yin
Dr. Jun Yin is specialized in using integrative genomics, network analysis and human genetics information to enhance target discovery, biomarker identification and toxicogenomics. Prior to joining SBP, Dr. Yin was a Senior Scientist and Computational Biology Team Lead at Amgen Inc. Dr. Yin's previous work contributed to the FDA approved melanoma therapy, IMLYGIC, osteoporosis treatment, EVENITY, and early target discovery for Bispecific T-cell Engager (BiTE) caner immunotherapy platform and other drug modalities. In his early career Dr. Yin received Postdoctoral training at Yale University on genomics and epigenomics research, and Ph.D. in Systems Biology and Bioinformatics from University College Dublin, Ireland. He has published several papers in the top scientific journals, including Science, Cell, Nature Communications and PNAS.
Andrew P. Hodges, Ph.D.
Bioinformatics Project Manager
(858)646-3100 ext. 4223
Email Andrew Hodges
Dr. Andrew Hodges implements existing and novel systems biology and machine learning approaches for target and drug discovery in cancer and other diseases. He provides bioinformatics data analyses, machine learning, pathway analysis and functional annotation, statistical simulations and synthetic network design, teaching and training, heterogeneous dataset integration, and biomarker/drug signature identification all applied to fundamental and translational research Dr. Hodges received his M.S and Ph.D. in Bioinformatics from the University of Michigan Medical School for development of a novel BN+1 machine learning algorithm applied to pathway augmentation in prokaryotic and eukaryotic biological systems. His postdoctoral work at SBP focused on combinatorial drug discovery in lung and related cancers. Dr. Hodges then joined the Godzik laboratory (at SBP) as a research programmer for developing novel bioinformatics databases and infrastructure as part of the CHAVI consortium team prior to joining our SBP Bioinformatics Shared Resource. His scientific publications span a breadth of scientific and biomedical focuses in specialized journals such as Nucleic Acids Research, Journal of Clinical Endocrinology & Metabolism, PLoS One, AIDS, and Cancer Research.
Please call (858)795-5200x5085 or use the button below to send us an email.