MSigDB collections

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This page provides detailed descriptions of all collections of gene sets in MSigDB.

To learn about changes and other information specific for a particular release of MSigDB, please refer to the corresponding Release_Notes.

H: Hallmarks

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C1: positional gene sets

Genes from the same genomic location (chromosome or cytogenetic band) are grouped in a gene set. Cytogenetic annotations are from three sources:

1. Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC)
2. UniGene
3. Affymetrix microarray annotations

We merged the relevant annotations from these resources and derived a single cytogenetic band location for every gene symbol. These were then grouped into sets. Decimals in cytogenetic bands were ignored. For example, 5q31.1 was considered 5q31. Therefore, genes annotated as 5q31.2 and those annotated as 5q31.3 were both placed in the same set, 5q31.

When there were conflicts, the UniGene entry was used.

These sets are helpful in identifying effects related to chromosomal deletions or amplifications, dosage compensation, epigenetic silencing, and other regional effects.

C2: curated gene sets

Gene sets collected from various sources such as online pathway databases, scientific publications and personal contributions from domain experts.

CGP: chemical and genetic perturbations

• Sets curated from biomedical literature

  Over the past few years, microarray studies have identified signatures of several important biological and clinical states (e.g. cancer metastasis, stem cell characteristics, drug resistance). These gene sets are valuable biological results. Unfortunately, because gene sets are typically published as tables in a paper, the important biological findings they represent are not easily accessible to computational tools. Our first goal was to convert published gene sets into an electronic form. Towards this we compiled a list of microarray articles with published gene expression signatures. From each article, we extracted one or more gene sets from tables in the main text or supplementary information. Notably, our focus was on capturing the identity (e.g. gene symbol, GenBank accession) of all members in a gene set rather than on relationships between individual genes.

  A number of these gene sets come in pairs: an xxx_UP (xxx_DN) gene set representing genes induced (repressed) by the perturbation.

  Names of CGP sets start with the last name of the first author of the source publication. The majority of CGP sets were curated from publications and include links to the PubMed citation, the exact source of the set (e.g., Table 1) and links to the corresponding raw data in GEO or ArrayExpress repositories. When the set involves a genetic perturbation, brief description includes a link to the gene's entry in the NCBI Entrez Gene database. When the set involves a chemical perturbation, brief description includes a link to the chemical's entry in the NCBI [ PubChem Compound] database.

  • curated by the MSigDB curation team
  • contributed by the L2L database

  These sets came from the L2L database of published microarray gene expression data (described in Newman and Weiner) and were kindly shared with MSigDB. These sets list John Newman as the contributor.

• Sets contributed by Dr. Chi Dang from the MYC Target Gene Database.
• Individual gene set compilations contributed by MSigDB collaborators. These sets usually are not based on any specific publication.

CP: canonical pathways

Gene sets from the pathway databases. Usually, these gene sets are canonical representations of a biological process compiled by domain experts. These gene sets were either extracted from the online sources by MSigDB curation team or were contributed by teams of pathway databases in collaboration with MSigDB curation team.

• BioCarta
• KEGG
• Pathway Interaction Database (PID)
• Reactome
• SigmaAldrich
• Signaling Gateway
• Signal Transduction KE (STKE)
• SuperArray

C3: motif gene sets

Gene sets group genes by cis-regulatory motifs. The motifs are catalogued in Xie et al. and represent known or putative conserved regulatory elements in promoters and 3’-UTR regions. These sets make it possible to link changes in a genomic experiment to a conserved, putative cis-regulatory elements.

Transcription factor targets (TFT)

These sets share upstream cis-regulatory motifs which can function as potential transcripton factor binding sites. We used two approaches to generate these gene sets.

Motif gene sets of ‘conserved instances’ consist of the inferred target genes for each motif m of 174 upstream motifs highly conserved among five mammalian species (H. sapiens, M. musculus, R. norvegicus and C. lupus familiaris). The motifs are catalogued in Xie, et al. (2005, Nature 434, 338–345) and represent potential transcription factor binding sites. Each motif gene set consists of all human genes whose promoters (defined as regions -2kb to +2kb around transcription start site) contained at least one conserved instance of motif m. If the motif’s sequence matched a transcription factor binding site documented in v7.4 TRANSFAC database, then we appended the name of the TRANSFAC binding matrix to the motif sequence in the gene name, e.g.: MOTIFSEQ_FOO, where MOTIFSEQ is the sequence of motif m and FOO is the TRANSFAC matrix name (e.g., V$MIF1_01). The set’s full description in this case is the TRANSFAC entry for the matching matrix. If the motif’s sequence matched no transcription factor binding site from TRANSFAC v.7.4, then we named the set as MOTIFSEQ_UNKNOWN where MOTIFSEQ is the consensus sequence of motif m.

We also extracted 460 mammalian transcriptional regulatory motifs from v7.4 TRANSFAC database. We then generated the motif gene sets consisting of the inferred target genes for each motif. Every such set consists of human genes whose promoters (defined as regions -2kb to +2kb around transcription start site) contain at least one instance of the motif. We named these sets by the corresponding TRANSFAC matrix identifiers, e.g., V$MIF1_01. The set’s full description is the TRANSFAC entry for the matching matrix, in a format described here.

microRNA Targets (MIR)

These gene sets consist of the inferred target gene for each motif m of 221 3'-UTR motifs highly conserved among five mammalian species (H. sapiens, M. musculus, R. norvegicus and C. lupus familiaris). The motifs are catalogued catalogued in Xie, et al. 2005, Nature 434, 338–345 and represent potential microRNA binding sites. Each motif gene set consists of all genes whose 3’-UTR contained at least one conserved instance of motif m.

C4: computational gene sets

Gene sets defined by mining large collections of cancer-oriented genes.

CGN: Cancer Gene Neighborhoods

These sets are defined by expression neighborhoods centered on cancer-related genes. This collection has originally been described in Subramanian, Tamayo et al. 2005.

Starting with a list of 380 cancer associated genes curated from internal resources and a published cancer gene database [REF], Subramanian, Tamayo et al. 2005 mined four expression compendia datasets for correlated gene sets. Using the profile of a given gene as a template, Subramanian and Tamayo ordered every other gene in the data set by its Pearson correlation coefficient. A cutoff of R ≤ 0.85 was then applied to extract correlated genes. The calculation of neighborhoods was done independently in each data set. In this way, a given oncogene may have up to four "types" of neighborhoods according to the correlation present in each compendium. Neighborhoods with &lg; 25 genes at this threshold were omitted yielding the final 427 sets.

The names of these gene sets start with a code indicating the corresponding expression compendium followed by the symbol of the cancer associated gene.

The compendia and their codes are listed below:

• GNF2 Novartis normal tissue compendium
• CAR Novartis carcinoma compendium
• GCM Global cancer map
• MORF GSEA PNAS 2005

An internal compendium of gene expression data sets, including many of Broad Institute's Cancer Program in-house Affymetrix HG-U95 cancer samples (1,693 in all) from a variety of cancer projects representing many different tissue types, mainly primary tumors, such as prostate, breast, lung, lymphoma, leukemia, etc.

CM: Cancer Modules

Gene sets are identical to the modules described in Segal et al. Starting with 2,849 gene sets from a variety of resources such as KEGG, Gene Ontology, and others, the authors mined a large compendium of cancer related microarray data and identified 456 transcriptionally co-regulated modules. Two sets with fewer than 10 NCBI human Entrez Gene IDs have been deprecated since v3.0 MSigDB.

The names of these sets start with MODULE_ followed by the number of module according to the contributor's notes. Gene set pages contain external links to further details about these sets.

C5: GO gene sets

Gene sets in this new collection are derived from the controlled vocabulary of the Gene Ontology (GO) project.

The Gene Ontology project is a collaborative effort to develop and use ontologies to support biologically meaningful annotation of genes and their products. Each entry in GO (a GO term) has a unique numerical identifier of the form GO:nnnnnnn, and a term name. Each term is also assigned to one of the three ontologies, molecular function, cellular component or biological process. The ontologies are structured as directed acyclic graphs that represent a network in which a child (i.e., more specialized) term can have one or more parents (i.e., less specialized) terms. Every GO term must obey the true path rule: if the child term describes a gene product, then all its parent terms must also apply to that gene product. Annotation is the process of assigning GO terms to gene products. A GO annotation consists of a GO term associated with a specific reference that describes the work or analysis upon which the association between a specific GO term and gene product is based. Each annotation must also include an evidence code to indicate how the annotation to a particular term is supported. More details about evidence codes are here.

Outline of the procedure

All sets are based on associations of GO terms to human genes. Genes annotated with the same GO term make the corresponding GO term gene set.

The input files are:

• gene2go (downloaded on January 22, 2008)

This file reports GO terms that have been associated with genes in NCBI Entrez Gene. It is generated by processing the gene_association file on the GO FTP site and comparing the DB_Object_ID to annotation in NCBI Entrez Gene, as also reported in gene_info.gz. The file is available here. It is a tab delimited plain text file with one tax_id / gene_id / evidence_code per line.

• gene_ontology_edit_obo(downloaded on January 25, 2008)

There are three sub-collections according to three key biological domains in GO.

CC: GO Cellular component.

Gene sets derived from the Cellular Component (CC) ontology. This ontology describes the location of a gene product, within cellular structures and within macromolecular complexes.

MF: GO Molecular function.

Gene sets are derived from the Molecular Function (MF) ontology. This ontology describes what a gene product does at the biochemical level. It describes only what is done without specifying where or when the event actually occurs or its broader context.

BP: GO Biological process.

Gene sets derived from the Biological Process (BP) ontology. This ontology describes a broad biological objective to which the gene product contributes. A process is accomplished via one or more ordered assemblies of functions. It often involves transformation in the sense that something goes into a process and something different comes out of it.

C6: oncogenetic signatures

Gene sets represent signatures of cellular pathways which are often dis-regulated in cancer. The majority of signatures were generated directly from microarray data from NCBI GEO or from in house unpublished expression profiling experiments which involved perturbation of known cancer genes. In addition, a small number of oncogenic signatures was curated from scientific publications.

C7: immunologic signatures

Gene sets that represent cell states and perturbations within the immune system. The signatures were generated by manual curation of published studies in human and mouse immunology. For each study, pairwise comparisons of relevant classes were made and genes ranked by mutual information. Gene sets correspond to top or bottom ranking genes (FDR < 0.25 or maximum of 200 genes) for each comparison. This resource is generated as part of the Human Immunology Project Consortium (HIPC).