research-article

WHAM: A High-Throughput Sequence Alignment Method

Authors:

Jignesh M. Patel,

Allison TerrellAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 37, Issue 4

Article No.: 28, Pages 1 - 39

https://doi.org/10.1145/2389241.2389247

Published: 01 December 2012 Publication History

Abstract

Over the last decade, the cost of producing genomic sequences has dropped dramatically due to the current so-called next-generation sequencing methods. However, these next-generation sequencing methods are critically dependent on fast and sophisticated data processing methods for aligning a set of query sequences to a reference genome using rich string matching models. The focus of this work is on the design, development and evaluation of a data processing system for this crucial “short read alignment” problem. Our system, called WHAM, employs hash-based indexing methods and bitwise operations for sequence alignments. It allows rich match models and it is significantly faster than the existing state-of-the-art methods. In addition, its relative speedup over the existing method is poised to increase in the future in which read sequence lengths will increase.

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Index Terms

WHAM: A High-Throughput Sequence Alignment Method
1. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information retrieval
    1. Information retrieval query processing

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cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 37, Issue 4

December 2012

345 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/2389241

Issue’s Table of Contents

Copyright © 2012 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 December 2012

Accepted: 01 August 2012

Revised: 01 August 2012

Received: 01 October 2011

Published in TODS Volume 37, Issue 4

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Peng TYin LXiong YXie FJi CYang ZPan QLi MDeng XDong JWu J(2023) Retracted: Maternal traditional Chinese medicine exposure and risk of congenital malformations: a multicenter prospective cohort study Acta Obstetricia et Gynecologica Scandinavica10.1111/aogs.14553102:6(735-743)Online publication date: 19-Apr-2023
https://doi.org/10.1111/aogs.14553
Marques DCampos RSantander-Jimenez SMatveev ZSousa LIlic A(2022)Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00023(146-156)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00023
Panyukov VKiselev SOzoline O(2020)Unique k-mers as Strain-Specific Barcodes for Phylogenetic Analysis and Natural Microbiome ProfilingInternational Journal of Molecular Sciences10.3390/ijms2103094421:3(944)Online publication date: 31-Jan-2020
https://doi.org/10.3390/ijms21030944
Khoo POoi KWatson S(2019)Effectiveness of pharmaceutical interventions for meibomian gland dysfunction: An evidence‐based review of clinical trialsClinical & Experimental Ophthalmology10.1111/ceo.1346047:5(658-668)Online publication date: 18-Feb-2019
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Zhao BZhao JWang XWolf T(2019)RuleTailor: Optimizing Flow Table Updates in OpenFlow Switches With Rule TransformationsIEEE Transactions on Network and Service Management10.1109/TNSM.2019.294721716:4(1581-1594)Online publication date: Dec-2019
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Pan TFlick PJain CLiu YAluru S(2019)KmerindIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.276082916:4(1117-1131)Online publication date: 1-Jul-2019
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Guo DYuan EHu XWu X(2018)Co-occurrence pattern mining based on a biological approximation scoring matrixPattern Analysis & Applications10.5555/3288219.328822721:4(977-996)Online publication date: 1-Nov-2018
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Roy ADiao YEvani UAbhyankar AHowarth CLe Priol RBloom TChirkova RYang JSuciu D(2017)Massively Parallel Processing of Whole Genome Sequence DataProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064048(187-202)Online publication date: 9-May-2017
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