Automatic detection of anchor points for multiple sequence alignment.

Abstract:

BACKGROUND:determining beforehand specific positions to align (anchor points) has proved valuable for the accuracy of automated multiple sequence alignment (MSA) software. This feature can be used manually to include biological expertise, or automatically, usually by pairwise similarity searches. Multiple local similarities are be expected to be more adequate, as more biologically relevant. However, even good multiple local similarities can prove incompatible with the ordering of an alignment. RESULTS:we use a recently developed algorithm to detect multiple local similarities, which returns subsets of positions in the sequences sharing similar contexts of appearence. In this paper, we describe first how to get, with the help of this method, subsets of positions that could form partial columns in an alignment. We introduce next a graph-theoretic algorithm to detect (and remove) positions in the partial columns that are inconsistent with a multiple alignment. Partial columns can be used, for the time being, as guide only by a few MSA programs: ClustalW 2.0, DIALIGN 2 and T-Coffee. We perform tests on the effect of introducing these columns on the popular benchmark BAliBASE 3. CONCLUSIONS:we show that the inclusion of our partial alignment columns, as anchor points, improve on the whole the accuracy of the aligner ClustalW on the benchmark BAliBASE 3.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Pitschi F,Devauchelle C,Corel E

doi

10.1186/1471-2105-11-445

subject

Has Abstract

pub_date

2010-09-02 00:00:00

pages

445

issn

1471-2105

pii

1471-2105-11-445

journal_volume

11

pub_type

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