Published online 8 May 2009 Nucleic Acids Research, 2009, Vol. 37, Web Server issue W53–W56 doi:10.1093/nar/gkp301 SIB-BLAST: a web server for improved delineation of true and false positives in PSI-BLAST searches Marianne M. Lee 1 , Michael K. Chan1,2,3,* and Ralf Bundschuh1,2,4,* 1The Ohio State Biophysics Program, 2Department of Biochemistry, 3Department of Chemistry and 4Department of Physics, Ohio State University, Columbus, OH 43210-1117, USA Received January 28, 2009; Revised April 6, 2009; Accepted April 15, 2009 ABSTRACT A SIB-BLAST web server (http://sib-blast.osc.edu) has been established for investigators to use the SimpleIsBeautiful (SIB) algorithm for sequence- based homology detection. SIB was developed to overcome the model corruption frequently observed in the later iterations of PSI-BLAST searches. The algorithm compares resultant hits from the second iteration to the final iteration of a PSI-BLAST search, calculates the figure of merit for each ‘overlapped’ hit and re-ranks the hits according to their figure of merit. By validating hits generated from the last pro- file against hits from the first profile when the model is least corrupted, the true and false positives are better delineated, which in turn, improves the accuracy of iterative PSI-BLAST searches. Notably, this improvement to PSI-BLAST comes at minimal computational cost as SIB-BLAST utilizes existing results already produced in a PSI-BLAST search. INTRODUCTION Bioinformatics tools, in particular, those utilizing sequence comparison methods to search for homologs in order to obtain clues regarding functional and evolution- ary relationships of uncharacterized proteins, have become an integral part of today’s laboratory research. BLAST (1) is arguably the most ubiquitous sequence alignment tool for uncovering close homologs. PSI- BLAST (2,3), an extended version of BLAST, is similarly popular, and also more sensitive in detecting the harder- to-ﬁnd distant homologs due to its iterative and proﬁle- based search approach (4,5). However, in PSI-BLAST searches it is commonly observed that non-homologous proteins are incorporated into the proﬁles of the later iterations, leading to model corruption and meaningless results. For this reason, PSI-BLAST developers recom- mend users to conﬁne their iterative search to no more than ﬁve or six rounds (3). Numerous homology detection algorithms with diﬀer- ent strategies to improve the discrimination of true and false positives have been developed. These include the state-of-the-art programs: SAM (6–10) and HMMER (7,11), which perform better than PSI-BLAST in remote homology detection (12), but are far less popular than PSI-BLAST due to their expensive computational costs. Thus, there is a trade-oﬀ between improved perfor- mance and computational eﬃciency. In light of these con- siderations, we have developed a novel algorithm, SimpleIsBeautiful (SIB) (13), that overcomes the model corruption problem in PSI-BLAST searches while at the same time, preserving PSI-BLAST’s computational eﬃ- ciency. By benchmarking resultant hits from the last itera- tion, where the algorithm has identiﬁed the most distant homologs, against resultant hits from the second iteration, when the proﬁle is the least corrupted (since it is com- prised mostly of close homologs), our SIB algorithm showed improved discrimination between true and false positives over standard PSI-BLAST searches. A direct performance comparison between the SIB algorithm and PSI-BLAST based on the same test set (Aravind dataset) (14) conﬁrmed that SIB outperforms PSI-BLAST in terms of both speciﬁcity and sensitivity. Further performance comparison of SIB against another state-of-the-art sequence alignment algorithm SAM-T2K (8,10) using the same Aravind dataset revealed that SIB exhibits a com- parable performance, but at a much lower computational costs. The SIB algorithm has previously been made available as a downloadable awk script at http://bioserv.mps.ohio- state.edu/SimpleIsBeautiful. The script takes the outputs from a PSI-BLAST search (second iteration and ﬁnal iteration), compares the two lists of hits and re-ranks the merged list of hits based on each hit’s corresponding ﬁgure of merit (FOM) (13)—a numeric representation analogous to PSI-BLAST’s E-value. Several requests have since been made for an implementation of a web interface of the SIB algorithm. In this article, we present the SIB-BLAST web server that performs PSI-BLAST searches, runs the latest version of the SIB algorithm, *To whom correspondence should be addressed. Tel: +1 614 688 3978; Fax: +1 614 292 7557; Email: bundschuh@mps.ohio-state.edu Correspondence may also be addressed to Michael K. Chan. Tel: +1 614 292 8375; Fax: +1 614 292 6773; Email: chan@chemistry.ohio-state.edu. 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.