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Edward G. Miner Library

Bioinformatics, Biostatistics and Molecular Biology: Making Sense of Your Gene, Protein, Metabolite or Drug List: Secondary Analysis

Making Sense of Your Gene, Protein or Metabolite List: Secondary Analysis

Broad Institute Toolkit

The Broad Institute of MIT and Harvard is home to a variety of research and tools and methods development projects for work in genomics and bioinformatics.  Among these, they host software developed at the Institute and data sets from Institute publications.

Bioinformatics Basics Literature

From Nature Methods special issue on Next-Gen Sequencing (Volume 6 No 11s):

"Next-generation gap", J.D. McPherson, Nature Methods 6, S2 - S5 (2009)

"Sense from sequence reads: methods for alignment and assembly", P. Flicek & E. Birney, Nature Methods 6, S6 - S12 (2009)

"Computation for ChIP-seq and RNA-seq studies", S. Pepke, B. Wold & A. Mortazavi, Nature Methods 6, S22 - S32 (2009)

 From Nature Methods special issue on Visualization (March 2010, Volume 7, No 3s):

"Visualization of omics data for systems biology", N. Gehlenborg et al., Nature Methods 7, S56 - S68 (2010)

From Nature Biotechnology:

"What is principle components analysis?", M. Ringnér, Nature Biotechnology 26, 303 - 304 (2008)

Visualization and Pattern Identification Tools

Clustering Tools

  • Morpheus

    • ​Performs hierarchical clustering and a few other pattern identification workflows on uploaded data or publicly available data from The Cancer Genome Atlas Project or the Cancer Cell Line Encyclopedia

  • MultiExperiment Viewer (MeV)

    • Cloud-based application supporting analysis, visualization and stratification of large genomic data

    • Can perform differential expression analysis using RNASeq raw count data, directly search and pull TCGA and GEO gene expression and sample attribute data in addition to private data for analysis, and perform complex cohort stratification using sophisticated regular expression, facet filter, and set operations

  • Bioconductor and the R Project for Statistical Computing

    • R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. 

    • Bioconductor runs under R and provides tools for analysis and comprehension of high-dimensional data.

Pathway Visualization

Statistics and Visualization in One Package

JMP Pro Screenshots 

Now available for academic use in Miner Library's Computing Center or purchase a license for use on your own or your laboratory's desktop or laptop computer (see below for details).

From the JMP Website:  "Built with scientists and engineers in mind, JMP Pro statistical analysis software from SAS provides all the superior visual data access and manipulation, interactivity, comprehensive analyses and extensibility that are the hallmarks of JMP, plus a multitude of additional techniques."

Please follow these links for more information about JMP licensing, including discounted pricing through a joint effort of Miner Library and the University IT Tech Store.

RNA-seq Analysis in R (GEPA Workshop, 2015-2016 Academic Year)