首页 | 本学科首页   官方微博 | 高级检索  
     


Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER
Authors:Markus?Wistrand,Erik?LL?Sonnhammer  author-information"  >  author-information__contact u-icon-before"  >  mailto:erik.sonnhammer@cgb.ki.se"   title="  erik.sonnhammer@cgb.ki.se"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author
Affiliation:(1) Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden
Abstract:

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.
Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号