In silico Platform for designing genome-based
Personalized immunotherapy or Vaccine against Cancer


We are grateful to researchers for their direct and indirect help in the development of this database. In this database we used information from various resources/databases as well as we used number of software/web services for developing this database. We are grateful to the developers of these databases/software/resources.

Name of Resource/Database/Software Reference or Publications
CCLE: Pharmacological data of 24 drugs tested against 503 cancer cell lines has been taken from CCLE along with the mutational data of drug targets. We acknowledge the developers of CCLE.

Barretina, J. et. al. (2012), The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. 483: 603-607. [PMID: 22460905]

CanProVar: CanProVar stores the single amino acid alterations including both germline and somatic variations in the human proteome, especially those related to the genesis or development of human cancer based on the published literatures. We have analysed the CanProVar dataset for most deleterious mutations, to select the cancer vaccine candidates.

Jing Li, Dexter T Duncan, Bing Zhang. CanProVar: a human cancer proteome variation database. Hum Mutat, 31(3):219-228, 2010. [PMID: 2005275]

BLAST: For fast sequence comparison of drug targets with the user-defined sequence (protein/nucleotide), BLAST software has been used. We acknowledge the BLAST developers.

BLAST : Altschul S.F., Gish W., Miller W., Myers E.W. and Lipman D.J. (1990) Basic local alignment search tool. J. Mol. Biol. 215: 403-410. [PMID:2231712]

1000 Genome Project:1000 Genome project is a magnificient study for several types of variations present in human populations by sequencing 1000 genomes.

A map of human genome variation from population-scale sequencing (2010); 1000 Genomes Project Consortium [PMID:20981092]

RefSeq:NCBI Reference Sequence Database.

A comprehensive, integrated, non-redundant, well-annotated set of reference sequences including genomic, transcript, and protein.

CTLPred: CTL epitope prediction .

Bhasin,M. and Raghava, G.P.S. (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine,22,3195-3201. [PMID:15297074].

ProPred1: MHC I prediction .

ProPred1: prediction of promiscuous MHC Class-I binding sites. [PMID:12761064].

nHLAPred: MHC I prediction .

Bhasin M, Raghava GP.; A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes;J Biosci. 2007 [PMID:17426378].

PrePred: MHC II prediction .

Singh H, Raghava GP.; ProPred: prediction of HLA-DR binding sites; Bioinformatics. 2001 [PMID:11751237].

LBTope: B cell epitope prediction .

Singh H, Ansari HR, Raghava GP.; Improved method for linear B-cell epitope prediction using antigen's primary sequence; PLoS One. 2013[PMID:23667458].


Wang K, Li M, Hakonarson H.; ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data; Nucleic Acids Res. 2010[PMID:23667458].

CANCERTOPE    |     Raghava's Group    |     IMTECH    |     CSIR    |     CRDD    |     GPSR Package