Automatic detection of anchor points for multiple sequence alignment

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 us...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Florian Pitschi, Claudine Devauchelle, Eduardo Corel
• Keywords: BMCBI 2010, Multiple Local Similarities, Multiple Sequence Alignment, Partial Columns