Tag: T-cell

Scirpy v0.10.1 – Scanpy Extension for analyzing Single-cell T-cell Receptor-sequencing data

Scirpy v0.10.1

:: DESCRIPTION

Scirpy is a scalable python-toolkit to analyse T cell receptor (TCR) or B cell receptor (BCR) repertoires from single-cell RNA sequencing (scRNA-seq) data. It seamlessly integrates with the popular scanpy library and provides various modules for data import, analysis and visualization.

::DEVELOPER

the Institute of Bioinformatics, Innsbruck Medical University

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

Scirpy

:: MORE INFORMATION

Citation

Sturm G, Szabo T, Fotakis G, Haider M, Rieder D, Trajanoski Z, Finotello F.
Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data.
Bioinformatics. 2020 Sep 15;36(18):4817-4818. doi: 10.1093/bioinformatics/btaa611. PMID: 32614448; PMCID: PMC7751015.

Decombinator 4.0.3 – Tool for Analysing T cell Receptor Sequences

Decombinator 4.0.3

:: DESCRIPTION

Decombinator is a tool for the fast, efficient analysis of T cell receptor (TcR) repertoire samples, designed to be accessible to those with no previous programming experience. It is based on the Aho-Corasick algorithm which uses a finite state automaton (FSA) to quickly assign a specific V and J gene segment. From these assignments, it is then able to determine the number of germline V and J deletions and the string of contiguous nucleotides which lie between the 3′ end of the V gene segment and the 5′ end of the J gene segment. These 5 variables form the identier which uniquely categorises each distinct TcR sequence.

::DEVELOPER

UCL Innate2Adaptive

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Decombinator

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Mar 1;29(5):542-50. doi: 10.1093/bioinformatics/btt004. Epub 2013 Jan 9.
Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine.
Thomas N, Heather J, Ndifon W, Shawe-Taylor J, Chain B.

TepiTool – Computational Prediction of T Cell Epitope Candidates

TepiTool

:: DESCRIPTION

The Tepitool provides prediction of peptides binding to MHC class I and class II molecules.

::DEVELOPER

the IEDB Solutions Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 TepiTool

:: MORE INFORMATION

Citation

TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates.
Paul S, Sidney J, Sette A, Peters B.
Curr Protoc Immunol. 2016 Aug 1;114:18.19.1-18.19.24. doi: 10.1002/cpim.12.

tcR 2.2.4 – T Cell Receptor Repertoire Advanced data analysis

tcR 2.2.4

:: DESCRIPTION

tcR is a platform designed for TCR repertoire data analysis in R after preprocessing data with CDR3 extraction and gene alignment software tools such as MiTCR, ImmunoSEQ and MiGEC.

::DEVELOPER

Laboratory of Comparative and Functional Genomic

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows
  • R

:: DOWNLOAD

 tcR

:: MORE INFORMATION

Citation:

tcR: an R package for T cell receptor repertoire advanced data analysis.
Nazarov VI, Pogorelyy MV, Komech EA, Zvyagin IV, Bolotin DA, Shugay M, Chudakov DM, Lebedev YB, Mamedov IZ.
BMC Bioinformatics. 2015 May 28;16:175. doi: 10.1186/s12859-015-0613-1.

BJTEpitope – T-cell Epitope Prediction for the MHC class I allele HLA-A*0201

BJTEpitope

:: DESCRIPTION

BJTEpitope is a software for T-cell epitope prediction for the MHC class I allele HLA-A*0201

::DEVELOPER

Center of Computational Biology, Beijing Institute of Basic Medical Sciences

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

  BJTEpitope

:: MORE INFORMATION

PREDIVAC – CD4+ T-cell Epitope Prediction for Vaccine Design

PREDIVAC

:: DESCRIPTION

Predivac implements a method for prediction of CD4+ T-cell epitopes based on the specificity-determining residues (SDRs) approach. SDRs are small set of structurally conserved amino acids in the peptide:protein interaction interface that are responsable for specific recognition events.

::DEVELOPER

Kobe Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

   NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 Feb 14;14(1):52. [Epub ahead of print]
PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity.
Oyarzún P, Ellis JJ, Bodén M, Kobe BT.

FRED 1.0 – Framework for T-cell Epitope Detection

FRED 1.0

:: DESCRIPTION

FRED is a framework for T-cell epitope detection that offers consistent, easy, and simultaneous access to well established prediction methods for MHC binding and antigen processing. FRED can handle polymorphic proteins and offers analysis tools to combine, benchmark, or compare different methods. It is implemented in Python in a modular way and can easily be extended by user defined methods.

DEVELOPER

the Kohlbacher lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows with cygwin / MacOsX
  • Python

:: DOWNLOAD

 FRED

:: MORE INFORMATION

Citation:

Feldhahn, M, Dönnes, P, Thiel, P, and Kohlbacher, O (2009).
FRED – A Framework for T-cell Epitope Detection
Bioinformatics, 25(20):2758-9.

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