Computational Biology and Bioinformatics Services and Consultations
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Consulting for the study design, including the selection of appropriate technologies and experimental parameters
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Grant proposal and manuscript preparation support
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Analysis of next generation sequencing data, including RNA-sequencing, ChIP-sequencing, whole exome-sequencing, ATAC-sequencing, whole genome re-sequencing, and long-read sequencing (e.g., Oxford Nanopore)
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Analysis of single-cell sequencing, spatial transcriptomics, TCR/BCR repertoire profiling, and targeted gene panels
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Various downstream and systems-level analyses of results from sequencing and microarray data
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Analysis of nCounter NanoString data
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Analysis of genomic editing (e.g., shRNA and CRISPR/Cas9 screening) data
Single-cell RNA-seq (scRNA-seq) data analysis services
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Basic scRNA-seq data analyses, including quality control, alignment, trimming, assembly, differentiation expression, and clustering
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Advanced scRNA-seq data analyses, including cell trajectory analysis, cell type prediction, and co-regulated gene module identification
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Interpretation of results
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Integrative analysis of single-cell multi-omics data
Spatial transcriptomics data analysis services
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Basic spatial transcriptomics data analyses, including quality control, tissue architecture identification, spatial variable gene identification, and differential expression analysis
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Advanced spatial transcriptomics data analyses, including deconvolution and cell-cell communication analyses
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Interpretation of results
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Integrative analysis of spatial multi-omics data
Immune profiling
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CyToF data analysis
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Analysis of immunome flow panels
Metabolomics data analysis
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LC-MS data preprocessing, including quality control assessment of raw chromatograms, peak calling, retention time alignment, and preliminary identification using MS2 data, if available
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Interpretation of liquid-chromatography untargeted metabolomics datasets, which include relative abundances of a broad spectrum of polar and non-polar small molecules (< 1500 Daltons)
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Statistical analysis to identify biologically relevant metabolites
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Pathway enrichment analysis of relevant metabolites
Metagenomic and metatranscriptomics services
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Metagenomic data analysis, including species/strain composition profiling, taxonomic analysis, abundance analysis, phylogenetic analysis, and whole-genome shotgun analysis
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Metatranscriptomic data analysis, including functional profiling, expression activity analysis,16S ribosomal RNA analysis, and whole-transcriptome shotgun analysis
Public domain and commercial data source mining services
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Obtaining multiple data sources, including dbGaP, GEO, ENCODE, TCGA, ICGC, GTEx, and ORIEN Avatar
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Implementing integrative analyses of these datasets in concert with locally generated data
Data management and sharing services
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Big data transfer
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Data storage management
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Data/code submission to repositories
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Data management and sharing policy development, along with relevant cost estimation
Analysis workflow development and standardization
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Reviewing best practices
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Defining standard operating procedures
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Adopting standard data analysis protocols
Novel bioinformatics tool development
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Custom software engineering when existing tools do not meet investigators’ needs
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Development of novel computational methods for genomic data analysis and integration
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Development of R and Python packages
Cloud computing and web development
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Development and management of interactive and dynamic web interfaces
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Development and management of cloud infrastructures
Training and Workshops
BISR faculty and staff organize short 1-2 day workshops and seminars focused on bioinformatics data analysis. The workshops cover various areas of bioinformatics and contain hands-on activities to help CCC researchers understand the process of data analysis and the interpretation of the results.
Massively Parallel Sequence Analysis
The sequence output from the Illumina HiSeq 4500. The sequencer output is useful to biologists when the BISR provides several standard data transformations. The BISR provides three classes of service for the sequence analysis: 1) running an automated pipeline from the sequencing machine to compile sequence reads and their quality assignments; 2) running best practice workflows for the analysis of RNA-sequencing, ChIP-sequencing, Exome-sequencing, and whole genome re-sequencing. Final output besides alignment results are gene expression quantification for RNA-sequencing, variant analysis for exome-sequencing and whole genome re-sequencing, and peak detection for ChIP-sequencing datasets; 3) performing downstream analysis including sample comparisons, pathway analysis and integration with private and publicly available datasets.
Analysis of Microarray Methods
The analysis of microarray methods includes mRNA (Affymetrix), SNP and microRNA. Microarray methods results are first analyzed by the Biostatistics Shared Resource to identify genes/probes that have significantly changed expression level in the assay. The BISR not only provides downstream analysis of identifying pathways enriched in the results, but it also helps with data presentation and results submission in public databases.
Publicly Available Datasets Analysis
The Gene Expression Omnibus (GEO) is one of the world’s largest databases in which primary data from nearly all published microarray experiments are stored. The BISR staff analyzes this publicly available data with the biologist.
The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) are data portals that allows access to results of thousands of deep sequencing projects. BISR staff implement and run custom workflows and integrate these publicly available datasets to generate new hypotheses or draw new conclusions.
Pathway and Network Analysis of Results from Sequencing and Microarray
Typical analysis of sequencing and microarray data results in set of genes that have altered gene expression or have functional mutations. BISR staff are expert users of the Ingenuity Pathway Analysis program, which enables the identification of biological pathways and networks that are enriched in results. The pathway analysis assists the biologist with navigating the large numbers of gene hits, and it can generate new hypotheses for the biologist to test in the laboratory.
Additionally, the BISR can provide advanced network analysis such as co-expression network analysis, gene regulatory and interaction network analysis, and dense network motif discovery.