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Bioinformatics Tools: Integrative Analysis Tools

This guide contains a curated set of resources and tools that will help you with your research data analysis. It also includes those medical library workshops available at Yale University on many of these bioinformatics tools.

Analysis Suite

Use BIOBASE to predict protein attributes. The BIOBASE Knowledge Library™ (BKL) is a collection of gene-regulation and protein oriented scientific databases created from the peer-reviewed scientific literature. The TRANSFAC®, and PROTEOME™ products are included in BIOBASE. 


Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes.


Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols.


Web-based Gene Set Analysis Toolkit (WebGestalt) was designed for functional genomic, proteomic and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated.


The Molecular Signatures Databases (MSigDB) contains gene sets from various sources. This web site is designed to help you find gene sets and explore their annotations to gain further insight into the biology behind the gene sets.


The Database for Annotation, Visualization and Integrated Discovery (DAVID ) provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.


SNPsnap enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on

  • Minor allele frequency
  • Number of SNPs in linkage disequilibrium (LD buddies)
  • Distance to nearest gene
  • Gene density

SNP-based Pathway Enrichment Analysis for Genome-wide Association Study (SSEA) this tool allows a pathway analysis by combining evidences of association over multiple SNPs within a gene and multiple genes within a pathway.

Biomedical Sciences Research Support

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Rolando Garcia-Milian
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Contact:
Yale Medical Library, Bioinformatics Support Hub, 333 Cedar St, Room L-103, New Haven CT 06520
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Subjects: Medicine