About this item:

141 Views | 91 Downloads

Author Notes:

Contact: borodovsky@gatech.edu

Authors would like to thank Alex Lomsadze and Chengwei Luo for valuable discussions.

Conflict of Interest: none declared.

Subjects:

Research Funding:

This work was supported in part by NIH grant HG00783 to M.B.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Technology
  • Physical Sciences
  • Biochemical Research Methods
  • Biotechnology & Applied Microbiology
  • Computer Science, Interdisciplinary Applications
  • Mathematical & Computational Biology
  • Statistics & Probability
  • Biochemistry & Molecular Biology
  • Computer Science
  • Mathematics
  • BIOCHEMICAL RESEARCH METHODS
  • BIOTECHNOLOGY & APPLIED MICROBIOLOGY
  • MATHEMATICAL & COMPUTATIONAL BIOLOGY
  • GENE
  • IDENTIFICATION
  • PREDICTION

Journal Title:

Bioinformatics

Volume:

Volume 29, Number 1

Publisher:

, Pages 114-116

Type of Work:

Article | Final Publisher PDF

Abstract:

Frameshift (FS) prediction is important for analysis and biological interpretation of metagenomic sequences. Since a genomic context of a short metagenomic sequence is rarely known, there is not enough data available to estimate parameters of species-specific statistical models of protein-coding and non-coding regions. The challenge of ab initio FS detection is, therefore, two fold: (i) to find a way to infer necessary model parameters and (ii) to identify positions of frameshifts (if any). Here we describe a new tool, MetaGeneTack, which uses a heuristic method to estimate parameters of sequence models used in the FS detection algorithm. It is shown on multiple test sets that the MetaGeneTack FS detection performance is comparable or better than the one of earlier developed program FragGeneScan.

Copyright information:

© The Author(s) 2012. Published by Oxford University Press.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/).

Creative Commons License

Export to EndNote