HMMER: Fast and sensitive sequence similarity searches

 

A cornerstone of modern molecular biology is the electronic transfer of annotations from a few experimentally characterised sequences to the vast number being determined from DNA modern sequencing technologies. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software and associated website housed at EMBL-EBI, which in the latest version, uses a combination of sophisticated acceleration heuristics and mathematical and computational optimisations to enable the use of profile hidden Markov models (HMMs) for sequence analysis. In this presentation, Rob Finn covers some of the basics about the HMMER algorithm and the use of the HMMER via the website. 

You can find more information on using HMMER website and on using HMMER with the command line on the HMMER website help pages.  You can also read papers on HMMER including HMMER web server: 2018 update and The HMMER Web Server for Protein Sequence Similarity Search.

HMMER is also on GitHub where you will find more information about the project in general and the translated searches that Rob mentions in the webinar.

This webinar was recorded on 14 November 2018. It is best viewed in full screen mode using Google Chrome. The slides from this webinar can be downloaded below.

See the EMBL-EBI training pages for a list of upcoming webinars.

This webinar is aimed at individuals who wish to learn more about the HMMER. No prior knowledge of bioinformatics is required, but an undergraduate level understanding of biology would be useful.

About this course

Author(s): 
Rob Finn
Learning objectives: 
  • Describe reasons for using similarity searches
  • Outline algorithm used in HMMER
  • Describe tools available in HMMER
  • Identify sources of help and more information
Attachments: 
PDF icon 20181114Webinar.pdf
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