• Skip to primary navigation
  • Skip to main content
  • Skip to primary navigation
  • Skip to main content
Choose which site to search.
University of Arkansas for Medical Sciences Logo University of Arkansas for Medical Sciences
College of Medicine: Department of Biomedical Informatics
  • UAMS Health
  • Jobs
  • Giving
  • About Us
    • Employment
    • Access, Opportunity, and Advocacy
      • About DBMI-AOA
      • Current DBMI-AOA Committee Members
      • DBMI-AOA Resources
      • DBMI-AOA Committee Events
    • Links
    • News
    • Department Intranet
  • Faculty & Staff
    • Primary Faculty
    • Secondary Faculty
    • Adjunct Faculty
    • Staff
  • Education
    • Admission Information
    • Clinical Informatics Fellowship
      • Fellowship Overview
      • Training Sites
      • Faculty
      • Current Fellows
      • Welcome to Little Rock!
    • Graduate Programs
    • Current Course Offerings
    • DBMI FAQs
    • Research & Application Seminar
    • Recorded Sessions for CME Credit
    • Student Funding Opportunities
    • Graduate Students
  • Cores and Shared Resources
    • Arkansas Clinical Data Repository (AR-CDR)
    • Bioinformatics Collaborative Resource Center
    • INBRE
      • INBRE Bioinformatics Core Support Request Form
  • Research
    • Databases
    • Research Labs
      • Biomedical Ontologies Arkansas (BOAR)
    • Publications
  • Artificial Intelligence for Health
  1. University of Arkansas for Medical Sciences
  2. College of Medicine
  3. Department of Biomedical Informatics
  4. News
  5. Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics

Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics

https://doi.org/10.1016/j.ygeno.2017.03.001

Subazini Thankaswamy-Kosalai, Partho Sen, Intawat Nookaew

Abstract

Massive data produced due to the advent of next-generation sequencing (NGS) technology is widely used for biological researches and medical diagnosis. The crucial step in NGS analysis is read alignment or mapping which is computationally intensive and complex. The mapping bias tends to affect the downstream analysis, including detection of polymorphisms. In order to provide guidelines to the biologist for suitable selection of aligners; we have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial genomes. Two million simulated read pairs of various sizes (36 bp, 50 bp, 72 bp, 100 bp, 125 bp, 150 bp, 200 bp, 250 bp and 300 bp) were aligned. Specific alignment features such as sensitivity of mapping, percentage of properly paired reads, alignment time and effect of tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners showed high sensitivity towards long reads (> 100 bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36 bp, 50 bp, 72 bp) and long reads (> 100 bp) mappings; It also showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum. The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly higher. None of the aligners outperforms the others in the benchmark, however the aligners perform differently with genome characteristics. We expect that the results from this study will be useful for the end user to choose aligner, thus enhance the accuracy of read mapping.

Posted by Chris Lesher on July 17, 2017

Filed Under: Publications Tagged With: Algorithm, Aligners, Alignments, Genome, Intawat Nookaew, Mapping, Next-generation sequencing, NGS, Partho Sen, Reads, Subazini Thankaswamy-Kosalai

UAMS College of Medicine LogoUAMS College of MedicineUniversity of Arkansas for Medical Sciences
Mailing Address: 4301 West Markham Street, Little Rock, AR 72205
Phone: (501) 686-7000
  • Facebook
  • X
  • Instagram
  • YouTube
  • LinkedIn
  • Pinterest
  • Disclaimer
  • Terms of Use
  • Privacy Statement

© 2025 University of Arkansas for Medical Sciences