Repository: Freie Universit├Ąt Berlin, Math Department

IMSEQ - a fast and error aware approach to immunogenetic sequence analysis

Kuchenbecker, L. and Nienen, M. and Hecht, J. and Neumann, A. U. and Babel, N. and Reinert, K. and Robinson, P. N. (2015) IMSEQ - a fast and error aware approach to immunogenetic sequence analysis. Bioinformatics, 31 (18). pp. 2963-2971. ISSN 1367-4803

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btv309

Abstract

Motivation: Recombined T and B cell receptor repertoires are increasingly being studied using next generation sequencing (NGS) in order to interrogate the repertoire composition as well as changes in the distribution of receptor clones under different physiological and disease states. This type of analysis requires efficient and unambiguous clonotype assignment to a large number of NGS read sequences, including the identification of the incorporated V and J gene segments and the CDR3 sequence. Current tools have deficits with respect to performance, accuracy and documentation of their underlying algorithms and usage. Results: We present IMSEQ, a method to derive clonotype repertoires from next generation sequencing data with sophisticated routines for handling errors stemming from PCR and sequencing artefacts. The application can handle different kinds of input data originating from single- or paired-end sequencing in different configurations and is generic regarding the species and gene of interest. We have carefully evaluated our method with simulated and real world data and show that IMSEQ is superior to other tools with respect to its clonotyping as well as standalone error correction and runtime performance. Availability: IMSEQ was implemented in C++ using the SeqAn library for efficient sequence analysis. It is freely available under the GPLv2 open source license and can be downloaded at www.imtools.org.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:1551
Deposited By: Anja Kasseckert
Deposited On:02 Jun 2015 12:53
Last Modified:25 Feb 2016 11:24

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