On subset seeds for protein alignment
Abstract
We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets. We then perform a comparative analysis of seeds built over those alphabets and compare them with the standard BLASTP seeding method [2], [3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seeds is less expressive (but less costly to implement) than the cumulative principle used in BLASTP and vector seeds, our seeds show a similar or even better performance than BLASTP on Bernoulli models of proteins compatible with the common BLOSUM62 matrix. Finally, we perform a large-scale benchmarking of our seeds against several main databases of protein alignments. Here again, the results show a comparable or better performance of our seeds vs. BLASTP.
Cite
@article{arxiv.0901.3198,
title = {On subset seeds for protein alignment},
author = {Mikhail A. Roytberg and Anna Gambin and Laurent Noé and Slawomir Lasota and Eugenia Furletova and Ewa Szczurek and Gregory Kucherov},
journal= {arXiv preprint arXiv:0901.3198},
year = {2011}
}
Comments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2009)