Document Actions
Software
The Broad Institute/Dana Farber Cancer Institute
Principal Investigator: Todd Golub, M.D.
GenePattern
PI: Jill P. Mesirov, Ph.D.
Contact: gp-help@broadinstitute.org
Web Link: http://www.genepattern.org
GenePattern is an integrative genomics platform containing over 125 tools for analysis and visualization of gene expression, genomic variation, proteomic, and flow cytometric data. It provides extensive capabilities to create, edit, and share reproducible workflows. A point and click interface makes complex methodologies accessible to non-programmers, and programming interfaces are provided for statisticians and computational biologists.
Integrative Genomics Viewer (IGV)
PI: Jill P. Mesirov, Ph.D.
Contact: igv-help@broadinstitute.org
Web Link: http://www.broadinstitute.org/igv
IGV is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types including alignments for standard and short-read sequence data, microarrays, genomic variation, mutation, methylation and histone modification, and genomic annotations. Users can navigate quickly and easily from whole-genome to single base pair views as well as panning across a region of genomic interest. IGV supports the simultaneous viewing of hundreds to thousands of samples, providing extensive sorting, filtering, and selection capabilities.
Connectivity Map (CMAP)
PI: Todd Golub, M.D.
Contact: cmap-help@broadinstitute.org
Web Link: http://www.connectivitymap.org/cmap/
The Connectivity Map allows researchers to screen compounds against genomewide disease signatures, rather than a pre-selected set of target genes. Drugs are paired with diseases using sophisticated pattern-matching methods with a high level of resolution and specificity.
Gene Set Enrichment Analysis (GSEA)
PI: Jill P. Mesirov, Ph.D.
Contact: gsea@broadinstitute.org
Web Link: http://www.broadinstitute.org/gsea/
A computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).
GeneCruiser
PI: Jill P. Mesirov, Ph.D.
Contact: genecruiser@broad.mit.edu
Web Link: http://genecruiser.broadinstitute.org/genecruiser3/
GeneCruiser provides integrated access to genomic information available from public data sources. Users can convert between Affymetrix gene expression and SNP chip microarrays and corresponding annotations in UniGene, Entrez Gene, and other genomic resources.
Tumorscape
PI: Matthew Meyerson, Ph.D.
Contact: Michael Reich (michaelr@broadinstitute.org)
Web Link: http://www.tumorscape.org
Tumorscape facilitates the use and understanding of high resolution copy number data amassed from multiple cancer types.
Broad-Novartis Cell Line Encyclopedia
PI: Levi Garraway, Ph.D.
Contact: Michael Reich (michaelr@broadinstitute.org)
Web Link: http://www.broadinstitute.org/ccle
The Cancer Cell Line Encyclopedia (CCLE) project is a collaboration between the Broad Institute, the Novartis Institutes for Biomedical Research and the Genomics Institute of the Novartis Research Foundation to conduct a detailed genetic and pharmacologic characterization of a large panel of human cancer models, to develop integrated computational analyses that link distinct pharmacologic vulnerabilities to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. The CCLE provides public access analysis and visualization of DNA copy number, mRNA expression and mutation data for about 1000 cell lines.
Gene-E
PI: Todd Golub, M.D.
Contact: Michael Reich (michaelr@broadinstitute.org)
Web Link: http://www.broadinstitute.org/cancer/software/GENE-E/
Gene-E allows the rapid visual exploration of datasets derived from RNAi and chemical screens.
Columbia University
Principal Investigator: Andrea Califano, Ph.D.
Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE)
Contact: Mukesh Bansal, Ph.D., Columbia University
Web Link: http://wiki.c2b2.columbia.edu/califanolab/index.php/Software/ARACNE
ARACNE is an algorithm for inferring transcriptionl gene regulatory networks from a set of microarray experiments. The method uses mutual information to identify genes that are co-expressed and then applies the data processing inequality to filter out interactions that are likely to be indirect.
MINDY2
Contact: Mukesh Bansal, Ph.D., Columbia University
Web Link: http://wiki.c2b2.columbia.edu/califanolab/index.php/Software/MINDY2?
Given a transcription factor of interest, MINDY uses a large set of gene expression profile data to identify potential post-transcriptional modulators of the transcription factor's activity. MINDY is based on a three-way statistical interaction model that captures the post-transcriptional regulatory event where the ability of a transcription factor to activate/repress its target genes is monotonically controlled by a potential modulator gene.
geWorkbench
Contact: Kenneth Smith, Ph.D., Columbia University
Web Link: http://wiki.c2b2.columbia.edu/workbench/index.php/Home
geWorkbench is a bioinformatics platfrom that provides users with an integrated suite of genomics tools. It is built on an open-source, extensible architecture that promotes interoperability and simplifies the development of new as well as the incorporation of pre-existing components. The resulting system provides seamless access to a multitude of both local and remote data and computational services through an integrated environment that offers a unified user experience. Over 70 data analysis and visualization components have been developed for the framework, covering a wide range of genomics domains including gene expression, sequence, structure, and network data.
Mark-Us
Contact: Markus Fischer, Ph.D., Columbia University
Web Link: http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Mark-Us
Mark-Us is a web server to assist the assessment of the biochemical function for a given protein structure. Mark-Us identifies related protein structures and sequences, detects protein cavities, and calculates the surface electrostatic potentials and amino acid conservation profile. The results can be browsed by an interactive web interface that allows to integrate Gene Ontology terms, UniProt features, and the Enzyme Classification.
MotifREDUCE
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://bussemaker.bio.columbia.edu/software/REDUCE/
An algorithm that builds a motif-based multivariate linear model. REDUCE is an acronym that stands for Regulatory Element Detection Using Correlation with Expression. Based on a simple model for transcriptional regulation by independently acting transcription factors (Bussemaker et al, 2001), REDUCE makes it possible to discover regulatory motifs based on a single microarray experiment. MotifREDUCE is a robust and efficient reimplementation of the original REDUCE algorithm. Required inputs are (i) a genome-wide set of measurements (mRNA expression log-ratios or ChIP fold-enrichments) and (ii) a nucleotide sequence associated with each measurement (e.g., upstream promoter sequence). Output are (i) a set of cis-regulatory oligonucleotide motifs, and (ii) the corresponding regression coefficients.
MatrixREDUCE
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://bussemaker.bio.columbia.edu/software/REDUCE/
A sophisticated algorithm that builds a multivariate linear model based on weight matrices (Foat et al., 2005, 2006). Required inputs are the same as for MotifREDUCE: (i) a genome-wide set of measurements (mRNA expression log-ratios or ChIP fold-enrichments) and (ii) a nucleotide sequence associated with each measurement (e.g., upstream promoter sequence). Outputs include (i) the binding specificity, in the form of a position-specific affinity matrix (PSAM), and (ii) the condition-specific concentration/activity for each of a set of trans-acting factors (TF).
Transfactivity
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://bussemaker.bio.columbia.edu/software/REDUCE/
Fit a multivariate linear model to one or more genome-wide sets of measurements. In contrast to MotifREDUCE/MatrixREDUCE, motifs/PSAMs are not inferred from the data, as in, but instead are provided as inputs. This is useful for inferring changes in the (hidden) regulatory activity of one or more TFs of known binding specificity. Transfactivity is a contraction of "trans-factor" and "activity".
LogoGenerator
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://bussemaker.bio.columbia.edu/software/REDUCE/
A versatile and robust command-line tool that generates logo images in a variety of styles (raw data, frequency, conventional bit information, or affinity logo in ΔΔG). The input can be a PSAM or a multiple sequence alignment file in either FASTA or flat format. The output logo image is in EPS format and is converted to PNG format by default for display in a web page (as from HTMLSummary), using the widely and freely available tool GhostScript tool gs. Other supported image formats include PDF, JPEG, and GIF (further utilizing the convert utility program from ImageMagick).
T-profiler
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://www.t-profiler.org/
One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, only gene expression data from Saccharomyces cerevisiae and Candida albicans are supported.
SA-CLR
Contact: Wei-Yi Cheng, Ph.D., Columbia University
Web Link: http://www.ee.columbia.edu/~anastas/saclr
SA-CLR is a gene regulatory interaction inference algorithm for gene expression datasets. Based on CLR, the method also uses the information-theoretic measure of synergy to assign confidence scores to potential transcription factor - target gene pairs. The algorithm can also incorporate gene perturbations and time series information to refine the predictions.
Georgetown University
Principal Investigator: Robert Clarke, Ph.D.
JigCell
Contact: Subha Madhavan (sm696@georgetown.edu)
Web Link: http://jigcell.cs.vt.edu/
Biological reaction-based modeling and simulation tool.
CNSuite
Contact: Subha Madhavan (sm696@georgetown.edu)
Web Link: http://www.cbil.ece.vt.edu/software.htm
Analytical tool for gene copy number change analysis.
Differential dependence Networks (DDN)
Contact: Subha Madhavan (sm696@georgetown.edu)
Web Link: http://www.cbil.ece.vt.edu/software.htm
An analytical tool for detecting significant topological changes of biological network between different conditions.
PUB-SVM
Contact: Subha Madhavan (sm696@georgetown.edu)
Web Link: http://www.cbil.ece.vt.edu/software.htm
An analytical tool for multiclass gene selection and predictive classifications.
Massachusetts Institute of Technology (MIT)
Principal Investigator: Doug Lauffenburger, Ph.D.
NetPhorest
Contact: rune.linding@gmail.com
Web Link: http://netphorest.info
NetPhorest integrates in vitro kinase and phosphopeptide-binding domain specificity assays with publically accessible known in vivo substrate lists in order to generate substrate specificity descriptions for individual proteins as well as protein families.
NetworKIN
Contact: rune.linding@gmail.com
Web Link: http://networkin.info
NetworKIN predicts the kinase responsible for sites of protein phosphorylation using motif analysis to identify the likely kinase family, and then protein-protein interaction network analysis to identify the most likely individual kinase.
phoMSVal
Contact: sampsa.hautaniemi@helsinki.fi
Web Link: http://csbi.ltdk.helsinki.fi/phomsval/
phoMSVal is an open-source platform developed for managing MS/MS data and automatically validating identified phosphopeptides.
PTMScout
Contact: ptmscout_admin@mit.edu
Web Link: http://ptmscout.mit.edu
PTMScout is a tool for viewing and analyzing high-throughput post-translational modification proteomic data, with a particular eye towards novel hypothesis generation. External data from Gene Ontology, PFAM, and Scansite are integrated with user data automatically, as well as expression data in a large number of tissue types and cell lines. Additionally, all post-translational proteomic mass spectrometry datasets from the White Lab at MIT are banked here, and other users may include their data on request.
Scansite
Contact: Mike Yaffe, Ph.D. (myaffe@mit.edu)
Web Link: http://scansite.mit.edu
Scansite contains substrate specificity matrices determined by in vitro peptide library experiments for a number of kinases and phosphopeptide-binding domains. These are used to predict likely interactors with putative substrate sequences of interest.
Memorial Sloan-Kettering Cancer Center
Principal Investigator: Chris Sander, Ph.D.
ProteinKeys
Contact: Anil Korkut (akorkut@cbio.mskcc.org)
Web Link: http://www.proteinkeys.org/proteinkeys
ProteinKeys projects known functional residues in homologs on your protein of interest: SNPs, disease-associated mutations, binding and phosphorylation sites, etc.
NetBox
Contact: Anil Korkut (akorkut@cbio.mskcc.org)
Web Link: http://cbio.mskcc.org/tools/netbox/netbox.tar.gz
NetBox is a Java-based software tool for performing network analysis on human interaction networks.
MiRanda
Contact: Anil Korkut (akorkut@cbio.mskcc.org)
Web Link: http://www.microrna.org/microrna/getDownloads.do
miRanda is an algorithm for finding genomic targets for microRNAs.
Methodist Hospital Research Institute
Principal Investigator: Stephen Wong, Ph.D.
Software: NeuriteIQ
PI: Stephen T. Wong
Institution: Methodist Hospital Research Institute
Contact email: stwong@tmhs.org
Web Link to Software: http://www.cbi-tmhs.org/NeuriteIQ/index.htm
Software description:
In Alzheimer’s Disease (AD), neurite degeneration is observed in the areas responsible for higher cognitive functions. Loss of neuronal projections in AD can be modeled in vitro in primary mouse cortical neurons treated with the amyloid beta (Aβ) peptide, the primary cause of neurodegeneration in AD. High Content Screening is used to identify compounds specifically suppressing amyloid-induced neurite damage and loss. We developed an integrative image bioinformatics tool, NeuriteIQ to help identify compound hits for treatment of AD and other neurodegenerative diseases.
NeuriteIQ can be used for quantitative, reproducible and accurate interpretation of automatic fluorescence microscopy images, in particular, for the labeling and measurement of neurites. It had been evaluated using datasets from Professor Junying Yuan 's Laboratory at Harvard NeuroDiscovery Center, formally known as the HCNR (Harvard Center for Neurodegeneration and Repair), Harvard Medical School; also in collaboration with Professor Alexei Degterev at School of Graduate Biomedical Sciences, Tufts University.
Software: NeuronIQ
PI: Stephen T. Wong
Contact email: stwong@tmhs.org
Web Link to Software: http://www.cbi-tmhs.org/Neuroniq/index.htm
Software description:
Confocal and two-photon microscopy can image a neuronal specimen in 3D and produce rich information about the fine structures, such as neuronal dendrites and spines, in high resolution. Manual analysis is slow, and many time-lapse experiments are limited to small scale manual analysis. The bottleneck to efficiently and effectively exploit the data is the lack of a computer-based system to track, analyze, quantitate, and store neuron images. We proposed to develop a computational neurobiology system, t-NeuronlQ (time-lapse Neuron image Quantitator) to track, analyze, and store neuronal dendrite and spine changes in time-lapse 3D optical microscopy images. The system will automatically and semi-automatically track and analyze dendrites and spines in a time-lapse manner. We developed software packages, called NeuronIQ for segmenting and tracking spine and dendrites in 2D maximum projection neuron images, volumetric 3D images, and time-lapse 3D neuron images. The 2D NeuronIQ had been evaluated using datasets from Dr. Yong Kim in Rockefeller University. We are currently conducting an extensive evaluation of the 3D NeuronIQ and t-NeuronIQ by collaborating with Dr. Kim and Dr. Thomas A. Blanpied, Department of Physiology, University of Maryland School of Medicine.
Dynamic Cell Image Quantitator (DCellIQ)
Contact: Stephen Wong, Ph.D.
Web Link: http://www.cbi-tmhs.org/Dcelliq/index.html
The software aims to provide an automated pipeline for quantitative, reproducible and accurate interpretation of cell dynamic behaviors using time-lapse cellular images. DCellIQ is an automated tool with several parameters being set before the processing. Thus, it is well suitable for batch processing large image dataset with little human interference. This work was done in large part while at the Harvard Center for Neurodegeneration and Repair.
Genomic Cell Image Quantitator (GCellIQ)
Contact: Stephen Wong, Ph.D.
Web Link: http://www.cbi-tmhs.org/GCellIQ/
The software aims to provide an automated pipeline for processing large volumes of digital images generated from genome-wide, high-content screening (HCS), including those by RNA interference (RNAi) or other small molecular screenings. GCellIQ is an automated tool with several parameters being set before the processing. Thus, it is well suitable for batch processing large image dataset with little human interference. The bulk of the development effort was done in close collaboration with Perrimon's Laboratory at Howard Hughes Medical Institute, Harvard Medical School.
The Ohio State University
Principal Investigator: Tim H-M Huang, Ph.D.
QUEST
Contact: Kun Huang, Ph.D. (Kun.Huang@osumc.edu)
Web Link: https://bisr.osumc.edu/QUEST/Public/Login.aspx?ReturnUrl=%2fQUEST%2fStartPage.aspx
This is a next-generation sequencing data management and query tool.
W-ChIPMotifs
Contact: Kun Huang, Ph.D. (Kun.Huang@osumc.edu)
Web Link: http://motif.bmi.ohio-state.edu/ChIPMotifs/
A web application tool for de novo motif discovery from ChIP-based high throughput data.
MyTrack
Contact: Kun Huang, Ph.D. (tools@sagebase.org
Web Link: http://www.sagebase.org/research/tools.php
Key Driver Analysis (KDA) is an analysis tool, as both an R package and Cytoscape plugin, for identifying key regulators of a gene regulatory network. It takes as input a gene network N (directed or undirected) and a gene set (module) G. The gene set is any subset of genes from the network N (e.g. pathway, module, ontology), permitting focus on a particular biological context.
St. Elizabeth’s Medical Center/Tufts University
Principal Investigator: Lynn Hlatky, Ph.D.
SUMO
Contact: Julia Fox, Ph.D. (julia.l.fox.phd@gmail.com)
Web Link: http://angiogenesis.dkfz.de/oncoexpress/software/index.htm
A gene expression and data analysis tool.
Cell-Type Tumor Contribution Model?
Contact: Julia Fox, Ph.D. (julia.l.fox.phd@gmail.com)
Web Link: http://www.cancer-systems-biology.org/index.html?/ccsb/memberpages/heiko/abm.html
An agent-based model of early tumor growth dynamics.
Stanford University School of Medicine
Principal Investigator: Sylvia Plevritis, Ph.D.
Spectral Analysis for Class Discovery and Classification (SPACC)
Contact: Peng Qiu, Ph.D. (PQiu@mdanderson.org)
Web Link: http://icbp.stanford.edu/software/SPACC/
SPACC is a classifier that can perform both class discovery and classification. The algorithm is implemented in Matlab 7, with a Graphic User Interface on top of it, designed and written by Peng Qiu.
Fast Calculation of Pairwise Mutual Information (FastPairMI)
Contact: Peng Qiu, Ph.D. (PQiu@mdanderson.org)
Web Link: http://icbp.stanford.edu/software/FastPairMI/
Fast calculation of pairwise mutual information from gene expression microarrays for network reconstruction.
Nonlinear Models Using Dirichlet Product Mixtures
Contact: Babak Shahbaba, Ph.D. (babaks@uci.edu
Web Link: gunnar@math.stanford.edu)
Web Link: http://comptop.stanford.edu/programs/
A computational method for extracting simple descriptions of high dimensional data sets in the form of simplicial complexes.
Lirnet
Contact: Daphne Koller, Ph.D. (koller@cs.stanford.edu)
Web Link: http://dags.stanford.edu/lirnet/
Module network reconstruction
Penalized Multivariate Analysis (PMA)
Contact: Daniela Witten, Ph.D. (dwitten@uwashington.edu)
Web Link: http://cran.r-project.org/web/packages/PMA/index.html
Performs Penalized Multivariate Analysis
Correlate
Contact: Rob Tibshirani,Ph.D. (tibs@stat.stanford.edu)
Web Link: http://www-stat.stanford.edu/~tibs/Correlate/index.html
Correlate is an Excel plug-in that performs sparse canonical correlation analysis.
Vanderbilt University Medical Center
Principal Investigator: Vito Quaranta, M.D.
Mathematical Models
Contact: Alexander "Sandy" Anderson, Ph.D. (alexander.anderson@moffitt.org)
Web Link: http://vicbc.vanderbilt.edu/ccsb/math_models
Various mathematical models developed by the Center for Cancer Systems Biology at Vanderbilt University.
iTumor
Contact: Jerome Jourquin, Ph.D. (jerome.jourquin@vanderbilt.edu)
Web Link: http://vicbc.vanderbilt.edu/ccsb/itumor_front_page
iTumor is a limited user interface for visualizing some of our mathematical model simulations.
