Software

Gene Expression Tools:

  1. Gene Expression Analysis System: GEXPAS


    Developed an integrated, user-friendly package for the analysis of gene expression data. The system allows extraction of differentially expressed genes, gene ontology analysis, pathways and transcription binding sites analysis. The package has a unique tool for the individual analysis of gene expression data to predict better candidate genes for personalized medicine. The package has a unique tool for predicting the potential biomarker candidates form cancer gene expression data. Contract mbhasin@bidmc.harvard.edu for using this package.

GEXPAS

  1. Integrated analysis gene and miRNA data:


    We have developed a bioinformatics package for integrated systems biology analysis of mRNA and miRNA high-throughput data to identify the microRNAs that are critical in the pathophysiology of diseases and are the best potential therapeutic targets. For the integration of miRNA and mRNA data, workflow perform the consensus prediction of miRNA target genes using TargetScan, PicTar and MiRanda algorithms to reduce the number of false positives. Contract mbhasin@bidmc.harvard.edu for using this package.
    mrna_miRNA

  1. Integrated analysis of multi-dimensional OMICS data:

    We have developed a bioinformatics workflow for integration of non-coding RNA, epigenetics, transcriptome, proteome and metabolomics data in system-level manners. The integration can be performed either using known biological frameworks (e.g. Pathways) or in data-driven manner on the basis of known genome and biological information. This tool identifies biological process or pathway that effected across multiple genome spaces, as these are critical for pathophysiology of disease.

Integration

Immunoinformatics Tools:

  1. MHCBN: MHCBN is a curated database consisting of detailed information about Major Histocompatibility Complex (MHC) binding, non-binding peptides and T-cell epitopes.
  1. nHLAPred: A method based on hybrid approach of artificial neural networks and quantitative matrices for prediction of MHC Class I restricted T cell epitopes.
  1. MMBPred: A quantitative matrix based method for the identification of mutations (type and position) required to enhance the MHC-peptide affinity.
  1. TAPPred: An cascade SVM based method for prediction of TAP binding affinity of peptides.
  1. HLADR4Pred: :An SVM based method for identifying HLA-DRB1*0401 binding peptides in an antigenic sequence.
  1. CTLPred: A quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) based method for direct prediction of CTL epitopes.
  1. Pcleavage: A method for prediction of 20S proteasomal cleavage sites using new machine learning classifiers.
  1. HLAPred: A server for predicting binders for MHC class I and II alleles and their mimicry analysis against various genomes.
  1. MHC2Pred: Prediction of promiscuous binders for 42 different MHC class II alleles.

 

Cellular Localization Prediction tools:

  1. ESLpred: A hybrid approach based method for prediction of subcellular localization of eukaryotic proteins.
  1. Nrpred: An SVM based method for classifying the nuclear receptors based on amino acid and dipeptide composition.
  1. GPCRpred: A web server for the prediction of G-protein coupled receptors using SVM.
  1. GPCRSclass: A web tool for recognition and classification of amine type of GPCRs.
  1. HSLpred: Allows predicting the subcellular localization of human proteins based on various type of residue composition of proteins using SVM technique.
  1. PSLpred: A method for subcellular localization proteins belongs to prokaryotic genomes.