Useful links to data bases and analysis software

1. MHC epitopes data bases and epitope prediction software  The IEDB hosts tools to assist in the prediction and analysis of B cell and T cell epitopes (has exhaustive list of tools)

For a list of T cell epitopes in cancer, see also NetMHCIIpan 3.0 server predicts binding of peptides to MHC class II molecules  NetMHCII 2.2 server predicts binding of peptides to HLA-DR, HLA-DQ, HLA-DP and mouse MHC class II alleles using articial neuron networks.

Predictions can be obtained for 14 HLA-DR alleles covering the 9 HLA-DR supertypes, six HLA-DQ, six HLA-DP, and two mouse H2 class II alleles.

Class I predictions. ANNs have been trained for 78 different Human MHC (HLA) alleles representing all 12 HLA A and B Supertypes. Furthermore 41 animal (Monkey, Cattle, Pig, and Mouse) allele predictions are available.

2. Flow cytometry Resources


They have some publically available flow and Cytof datasets that may be usefulfor people to peruse:

Mass Cytometry Resource The newly redesigned Fluidigm Cytobank resource provides mass cytometry researchers with access to demo datasets, protocols, analyses, and featured articles discussing mass cytometry topics.

Nolan Lab | Signaling-Based (Fluorescence & Mass) Cytometry Resource: This resource was developed to facilitate dissemination of protocols and materials used in the lab as well as provide access to published articles linked to the underlying data and analysis via Cytobank Reports.

BD FACSelect Intracellular Flow Cytometry Resource BD FACSelect Intracellular Flow Cytometry Resource will help you navigate buffer choices to select theright combination for intracellular and surface marker experiments.

3. Microarray data sets 

Immgen   The Immunological genome project. Microarray dissection of gene expression in innate and adaptive immune cells of the mouse under carefully standardized conditions.

Interferome  Data base of IFN regulated genes

4.  Disease/Gene association tools


gene/variant prioritization tool of the BIER (the Team of BioInformatic for Rare Diseases). This interactive tool allows finding genes affected by deleterious variants that segregate along family pedigrees, case-controls or sporadic samples.

Manteia: a predictive data mining system for vertebrate genes and its applications to human genetic diseases

5. Analysis tools/network analysis for gene expression data

Biomet Toolbox

The BioMet ToolBox Version 2.0 (1) is a web-based resource for exploiting the capabilites of metabolic networks described in genome scale models using flux analysis and random sampling, powered by RAVEN, gene set analysis and basic microarray analysis using PIANO, thereby providing an integrated analysis to identify coregulated subnetwork structures within the metabolic network and also for identifying statistically significant gene sets enabling biological interpretation.


Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high-dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations.


NetVenn is a network-based web platform for the comparison and analysis of gene lists by combining Venn diagram and gene set enrichment. The tool can integrate gene sets with differentially expressed gene on interaction network. Power graph analysis is available for the gene lists network.

Network analyst:

Network analyst is designed to support integrative analysis of gene expression data through statistical, visual and network-based approaches: Data inputs: one or more gene/protein lists with optional fold changes; one or more gene expression tables from microarray or RNAseq experiments. Statistical analysis: for single data – paired comparisons, time series, common reference, and nested comparisons; for integrating multiple data sets – p values, fold changes, effect sizes, vote counts, and direct merge.

6. Chip-Seq and transcription factor data bases and analysis tools 

BloodChIP: database of comparative genome- wide transcription factor binding profiles in human blood cells

CR Cistrome:

a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse

DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data


ChIP-exo allows for precise mapping of protein-DNA interactions. MACE is a bioinformatics tool dedicated to analyze ChIP-exo data that operates in 4 major steps: 1) Sequencing depth normalization and nucleotide composition bias correction. 2) Signal consolidation and noise reduction using Shannon?s entropy. 3) Single base resolution border detection using Chebyshev Inequality. 4) Border matching using Gale-Shapley’s stable matching algorithm.


CHOP CHOP CHOPCHOP is a web tool for selecting the optimum target sites for CRISPR/Cas9- or TALEN-directed mutagenesis.

Addgene  – quick guide, plasmids, and additional resources

Zhang lab  Identifies gRNA target sequences from an input sequence and checks for off-target binding. Currently supports: Drosophila, Arabidopsis, zebrafish, C. elegans, mouse, human, rat, rabbit, pig, possum, chicken, dog, mosquito, and stickleback

8. siRNA target prediction


A data base of cooperating miRNAs/ The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency.

9. Other

topPTM:  A data based of experimentally verified post translational modifications

LenVarDB: Database of length variant protein domains

Metacyc: database of experimentally elucidated metabolic pathways from all domains of life

Pubchem  Biological activities of small molecules:

Thanks to Bhargavi Duvvuri, Eleanor Fish, Cynthia Guidos and Naoto Hirano  for advice on useful links