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Tag hmmer [6 articles]

Recent papers classified by the tag hmmer.

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	The Pfam protein families database Nucl. Acids Res., Vol. 38, No. suppl_1. (1 January 2010), pp. D211-222. by Robert D. Finn, Jaina Mistry, John Tate, et al.Penny Coggill, Andreas Heger, Joanne E. Pollington, O. Luke Gavin, Prasad Gunasekaran, Goran Ceric, Kristoffer Forslund, Liisa Holm, Erik L. L. Sonnhammer, Sean R. Eddy, Alex Bateman posted to update protein pfam hmmer family database by neils on 2009-11-18 23:00:24 along with 5 people engelhardt deborabt druvus flbarroso sobolevnrm Abstract Copy My Attachments My Copy Abstract Pfam is a widely used database of protein families and domains. This article describes a set of major updates that we have implemented in the latest release (version 24.0). The most important change is that we now use HMMER3, the latest version of the popular profile hidden Markov model package. This software is [~]100 times faster than HMMER2 and is more sensitive due to the routine use of the forward algorithm. The move to HMMER3 has necessitated numerous changes to Pfam ...
	HSP-HMMER: a tool for protein domain identification on a large scale In SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing (2009), pp. 766-770. by Bhanu Rekapalli, Christian Halloy, Igor B. Zhulin posted to hmmer hmm domain detection 13-nov-09 by robertrentzsch to the group Orengo Group Journal Picks on 2009-11-13 11:17:38 Abstract Copy My Attachments My Copy Abstract HMMER is arguably the best tool for protein domain identification, which is essential for biological function prediction. There are many software and hardware enhancements of HMMER; however, most of them are not scalable to a large number of processors. The exponential growth of the number of protein sequences in public databases, which is currently set at more than 13 million, demands the use of HMMER on a very large scale. We have developed a highly scalable parallel (HSP) HMMER approach that ...
	HMMEditor: a visual editing tool for profile hidden Markov model. BMC genomics, Vol. 9 Suppl 1 (2008) by Jianyong Dai, Jianlin Cheng posted to bioinformatics hmm hmmer sequence by pansapiens on 2008-10-21 02:18:25 along with 3 people BioNica barriot justintalbot Abstract Copy My Attachments My Copy Abstract BACKGROUND: Profile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. RESULTS: We develop a visual editor for profile Hidden Markov Models (HMMEditor). HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission ...
	Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER. BMC Bioinformatics, Vol. 6, No. 1. (15 April 2005) by M. Wistrand, E. L. Sonnhammer posted to hmm hmmer sam sensitivity by msierk on 2006-06-28 18:16:40 along with 4 people albert_swart mdimmic rabio yoanjacquemin Abstract Copy My Attachments My Copy 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. RESULTS: Using default settings, SAM was ...
	A sequence sub-sampling algorithm increases the power to detect distant homologues. Nucleic Acids Res, Vol. 33, No. 12. (2005), pp. 3772-3778. by C. R. Johnston, D. C. Shields posted to hmm hmmer sensitivity by msierk on 2006-06-28 18:15:34 Abstract Copy My Attachments My Copy Abstract Searching databases for distant homologues using alignments instead of individual sequences increases the power of detection. However, most methods assume that protein evolution proceeds in a regular fashion, with the inferred tree of sequences providing a good estimation of the evolutionary process. We investigated the combined HMMER search results from random alignment subsets (with three sequences each) drawn from the parent alignment (Rand-shuffle algorithm), using the SCOP structural classification to determine true similarities. At false-positive rates of 5%, the Rand-shuffle algorithm ...
	Profile hidden Markov models Bioinformatics, Vol. 14, No. 9. (1 October 1998), pp. 755-763. by S. R. Eddy posted to hmmer method sequence similarity by miguel on 2006-02-12 22:19:55 along with 14 people ldiambra farhat BioNica druvus albert_swart chad_davis eweaver dgront rabio cjeans suizan nklee yoanjacquemin ca3295 Abstract Copy My Attachments My Copy Abstract 10.1093/bioinformatics/14.9.755 ...

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