On subset seeds for protein alignment

Biology – Quantitative Biology – Quantitative Methods

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2009)

Scientific paper

10.1109/TCBB.2009.4

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

On subset seeds for protein alignment does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On subset seeds for protein alignment, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On subset seeds for protein alignment will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-684156

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.