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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
IVA: Accurate de novo assembly of RNA virus genomes
Bioinformatics, Volume 31, No. 14, Year 2015
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Description
Motivation: An accurate genome assembly from short read sequencing data is critical for downstream analysis, for example allowing investigation of variants within a sequenced population. However, assembling sequencing data from virus samples, especially RNA viruses, into a genome sequence is challenging due to the combination of viral population diversity and extremely uneven read depth caused by amplification bias in the inevitable reverse transcription and polymerase chain reaction amplification process of current methods. Results: We developed a new de novo assembler called IVA (Iterative Virus Assembler) designed specifically for read pairs sequenced at highly variable depth from RNA virus samples. We tested IVA on datasets from 140 sequenced samples from human immunodeficiency virus-1 or influenzavirus- infected people and demonstrated that IVA outperforms all other virus de novo assemblers. © The Author 2015. Published by Oxford University Press.
Authors & Co-Authors
Hunt, Martin
United Kingdom, Hinxton
Wellcome Sanger Institute
Gall, Astrid
United Kingdom, Hinxton
Wellcome Sanger Institute
Ong, Swee Hoe
United Kingdom, Hinxton
Wellcome Sanger Institute
Brener, Jacqui
United Kingdom, Oxford
University of Oxford Medical Sciences Division
Ferns, Bridget
United Kingdom, London
University College London
Goulder, Philip Jeremy Renshaw
United Kingdom, Oxford
University of Oxford Medical Sciences Division
Nastouli, Eleni
United Kingdom, London
University College London Hospitals Nhs Foundation Trust
Keane, Jacqueline A.
United Kingdom, Hinxton
Wellcome Sanger Institute
Kellam, P.
United Kingdom, Hinxton
Wellcome Sanger Institute
United Kingdom, London
University College London
Otto, Thomas Dan
United Kingdom, Hinxton
Wellcome Sanger Institute
Statistics
Citations: 135
Authors: 10
Affiliations: 4
Identifiers
Doi:
10.1093/bioinformatics/btv120
ISSN:
13674803
Study Design
Cross Sectional Study