Ballast: Blast postprocessing based on locally conserved segments

Motivation: Blast programs are very efficient in finding relatively strong similarities but some very distantly related sequences are given a very hig h Expect value and are ranked very low in Blast results. We have developed Ballast, a program to predict local maximum segments (LMSs-i.e. sequence se gments conserved relatively to their flanking regions)from a single Blast d atabase search and to highlight these divergent homologues. The TBlastN dat abase searches can also be processed with the help of information from a jo int BlastP search. Results: We have applied the Ballast algorithm to BlastP searches performed with sequences belonging to well described dispersed families (aminoacyl-t RNA synthetases; helicases) against the SwissProt 38 database. We show that Ballast is able to build an appropriate conservation profile and that LMSs are predicted that are consistent with the signatures and motifs described in. the literature. Furthermore, by comparing the Blast, PsiBlast and Ball ast results obtained on a well defined database of structurally related seq uences, we show that the LMSs provide a scoring scheme that can concentrate on top ranking distant homologues better than Blast Using the graphical us er interface available on the Web, specific LMSs may be selected to detect divergent homologues sharing the corresponding properties with the query se quence without requiring any additional database search.