The facility transforms raw data into biological knowledge. We provide Systems Biology analysis of datasets generated in the Shared Resources (e.g., proteomics and next-generation sequencing data) as well as comparison with and analysis of other available datasets. Three members of the Core (two with Ph.D. and one B.S.) assist researchers with advanced bioinformatics and biostatistics analysis helping to put data into biological context across various disease areas to create testable hypotheses and understand biology of the process. The facility also assists in database development, data management for large projects and implementing specific bioinformatic tools and pipelines.
The bulk of support includes connecting functional genomic data with pathways and networks, relating gene/protein expression and disease state and consultations on statistical aspects of the research with our team statistician. We also provide Bioinformatics classes and training for the entire Cancer Center community. Both internal and external customers are charged per hour.
Examples of recent systems biology/network building projects:
- Cross-cancer comparison of cancer progression drivers
- Network Analysis of Co-Expressed Metabolic Genes
- Study of interaction of pancreatic cancer with stroma from transcriptomics and metabolomicsDesign of activating oligonucleotides to up-regulate gene expression in blood vessels
- Classification of glioblastoma and medulloblastoma samples based on RNA-seq data
Examples of recent database-related projects include:
- Infrastructure development, website design, and data analysis assistance to the SBP Proteomics Core
- Regulattice, pipeline and a web site for advanced machine learning in identifying actionable cancer drivers
- bNAber, Database of Broadly Neutralizing HIV-1 Antibodies, PluriTest website for high-throughput pluripotency assessment via global gene expression analysis
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, DNA-seq, exome sequencing, targeted resequencing, SNP and indel detection and more)
- 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.
- Microarray and Proteomics Data Analysis, Integrative analysis of multi-omics data
- Grant Preparation Assistance
- Website and Database Development
- Bioinformatics Classes, tutorials and trainings
- The results 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 includs R/Bioconductor, GenomeStudio, GenePattern, Cytoscape, Ingenuity Pathway Analysis (IPA), Oncomine, NextBio, Thomson Reuters MetaCore, Broad Institute Genome Analysis ToolKit (GATK), Partek NGS tools and more. 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 expanded our MetaCore license with Oncology Module, which has hundreds of prebuilt cancer maps and to MetaDrug module, which predicts possible targets of small molecules and finds biological pathways affected by small molecules. We have experience in CRISPR, CHIP-seq, miCLIP, Single Cell Sequencing data analysis.
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 (http://regulattice.sbpdiscovery.org username: demo password: demo1). Novel drivers of cancer and disease progression have been identified using our new tools and shared with the community. New features such as survival analysis, advanced integrative -omics comparisons (for multi-omics datasets), and domain-based enrichments have been tested in specific human cancer and metabolic disease studies to further prioritize and validate predictions.
To discuss your project needs, please call (858)646-3100 x4058 or email firstname.lastname@example.org.
|Bioinformatics Custom Data Analysis/Application||hour||$38||$47.50||$51.30||$99.94|
|Statistics data Analysis||hour||$38||$47.50||$51.30||$99.94|
Andrew P. Hodges, Ph.D.
(858)646-3100 ext. 4058
Email Andrew Hodges
Dr. Hodges implements existing and novel systems biology and machine learning approaches for target and drug discovery in cancer and other diseases. He provides pathway analysis and functional annotation, statistical simulations and synthetic network design, and heterogeneous dataset integration all applied to fundamental and translational research. Andrew incorporates advanced technologies (R/Bioconductor, Cytoscape, NextBio, GenePattern, AracNe/MI, BN+1, etc.) for validating existing and generating new hypotheses. He will be assisting researchers with database mining, network analyses, and selection of the most promising biomarkers and drug targets.
Vicky Guo (Xiaohui Guo), B.S.
Part-time Support Programmer
(858)646-3100 ext. 4223
Email Xiaohui (Vicky) Guo
Vicky Guo, B.S., involved in testing, adapting and running public domain bioinformatics tools. Vicky modified software tools and run new analysis pipelines on PIs data. She will modify software tools to tailor PIs needs. Implemented CHIP-seq, miCLIP, RNA-seq and other pipelines.
Jun Yin, Ph.D.
Building 10, Room 2406
Please call (858)646-3100x5085 or use the button below to send us an email.