Alignment-free genetic sequence comparisons: A review of recent approaches by word analysis

Oliver Bonham-Carter, Joe Steele, Dhundy Bastola

Research output: Contribution to journalArticle

76 Scopus citations

Abstract

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events.New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies.We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base - base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression.

Original languageEnglish (US)
Pages (from-to)890-905
Number of pages16
JournalBriefings in bioinformatics
Volume15
Issue number6
DOIs
StatePublished - Aug 2 2013

Keywords

  • Alignment-free
  • Information theory
  • Sequence-alignment
  • Word-analysis

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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