Research Bioinformatics Core Laboratory

The Bioinformatics Core Laboratory provides computational expertise and resources for the design and analyses of high-throughput assays and for comparisons and integration of Texas Children's Hospital data with data derived from external knowledge bases. The Bioinformatics Core Laboratory will provide state-of-the-art, secure, regulatory-compliant and customized solutions to address clinical and research needs at Texas Children's Hospital.

Facilities

The Bioinformatics Core Laboratory occupies a 600 sq. ft. office in the 12th floor of the Texas Children's Hospital Feigin Center, including six workstations and a projector. It employs three bioinformaticians with degrees (2 PhDs and one MS) in Computer Science, Mathematics, Physics, Genetics, and Biomedical Informatics.

Faculty and staff within the core have contributed experience in high-throughput experiment design and in various methods for the analysis of molecular and clinical profiles. Faculty and staff within the core have also contributed to over 50 published manuscripts including publications in Cell, Cancer Cell, Genome Research, Nature Communications, and Science Cell Signaling.

The laboratory personnel has also advised on predictive biomarker discovery for both research and clinical applications.

Instrumentation

Computing. 600-core cluster with Infiniband. QDR QSFP interconnect all nodes Storage/head node with 40 TB RAID for working storage, 32 TB of shared Lustre-style distributed scratch space, 16 cores, 2.6/3.3ghz (2x E5-2670, Sandy Bridge)

Core Services

• Power estimates and study design including platform and data volume choice
• Quality control, normalization, testing for systematic technical errors
• DNA sequencing analysis, including variant calling
• RNA sequencing analysis, including expression and differential expression
• Analyzing ChIP and epigenetics data
• Gene-set enrichment and pathway analyses
• Sample comparison
• Integrative analysis across omics, including: RNA, DNA data, functional data
• Statistical cross validation with published data sets
• Obtaining, storing and reanalyzing public data
• Integration of external data sets with analyzed omics data to identify common and unique features
• Integrating external case-control studies, including population stratification
• Applying lessons learned from large-scale molecular data, such as TARGET and TCGA data, to locally-produced data and vice versa
• Domain-specific analyses, including genetic tests and rare variant grouping and burden tests
• Prediction of functional variants using molecular profiling of multiple tumors
• Prediction of functional variants using integrative analysis of multiple assays and clinical data
• Inference of clonal composition of tumors based on their molecular profiles
• Prediction of off-target effects in CRISPR assays
• Presentation of results
• Protein expression analysis - Identify differentially expressed genes