Please use this identifier to cite or link to this item:
Title: TRAL : tandem repeat annotation library
Authors : Schaper, Elke
Korsunsky, Alexander
Pečerska, Jūlija
Messina, Antonio
Murri, Riccardo
Stockinger, Heinz
Zoller, Stefan
Xenarios, Ioannis
Anisimova, Maria
Published in : Bioinformatics
Volume(Issue) : 31
Issue : 18
Pages : 3051
Pages to: 3053
Publisher / Ed. Institution : Oxford University press
Issue Date: 2015
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Molecular sequence; Profile hidden Markov models; Tandem repeat; Bioinformatics
Subject (DDC) : 004: Computer science
572: Biochemistry
Abstract: Motivation: Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results. Results: We present TRAL, a tandem repeat annotation library that allows running and parsing of various detection outputs, clustering of redundant or overlapping annotations, several statistical frameworks for filtering false positive annotations, and importantly a tandem repeat annotation and refinement module based on circular profile hidden Markov models (cpHMMs). Using TRAL, we evaluated the performance of a multi-step tandem repeat annotation workflow on 547,085 sequences in UniProtKB/Swiss-Prot. The researcher can use these results to predict run-times for specific datasets, and to choose annotation complexity accordingly. Availability and implementation: TRAL is an open-source Python3 library and is available, together with documentation and tutorials via
Further description : Erworben im Rahmen der Schweizer Nationallizenzen (
Departement: Life Sciences und Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Publication type: Article in scientific Journal
DOI : 10.1093/bioinformatics/btv306
ISSN: 1367-4803
Restricted until : 2019-01-01
Appears in Collections:Publikationen Life Sciences und Facility Management

Files in This Item:
File Description SizeFormat 
  Until 2019-01-01
172.66 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.