Format

Send to

Choose Destination

See 1 citation found by title matching your search:

See comment in PubMed Commons below
Bioinformatics. 2006 Dec 1;22(23):2870-5. Epub 2006 Oct 26.

Adding sequence context to a Markov background model improves the identification of regulatory elements.

Author information

  • 1National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA.

Abstract

MOTIVATION:

Many computational methods for identifying regulatory elements use a likelihood ratio between motif and background models. Often, the methods use a background model of independent bases. At least two different Markov background models have been proposed with the aim of increasing the accuracy of predicting regulatory elements. Both Markov background models suffer theoretical drawbacks, so this article develops a third, context-dependent Markov background model from fundamental statistical principles.

RESULTS:

Datasets containing known regulatory elements in eukaryotes provided a basis for comparing the predictive accuracies of the different background models. Non-parametric statistical tests indicated that Markov models of order 3 constituted a statistically significant improvement over the background model of independent bases. Our model performed slightly better than the previous Markov background models. We also found that for discriminating between the predictive accuracies of competing background models, the correlation coefficient is a more sensitive measure than the performance coefficient.

AVAILABILITY:

Our C++ program is available at ftp://ftp.ncbi.nih.gov/pub/spouge/papers/archive/AGLAM/2006-07-19

PMID:
17068091
DOI:
10.1093/bioinformatics/btl528
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center