Literature DB >> 29802977

MetaHMM: A webserver for identifying novel genes with specified functions in metagenomic samples.

Balázs Szalkai1, Vince Grolmusz2.   

Abstract

The fast and affordable sequencing of large clinical and environmental metagenomic datasets opens up new horizons in medical and biotechnological applications. It is believed that today we have described only about 1% of the microorganisms on the Earth, therefore, metagenomic analysis mostly deals with unknown species in the samples. Microbial communities in extreme environments may contain genes with high biotechnological potential, and clinical metagenomes, related to diseases, may uncover still unknown pathogens and pathological mechanisms in known diseases. While the species-level identification and description of the taxa in the samples do not seem to be possible today, we can search for novel genes with known functions in these samples, using numerous techniques, including artificial intelligence tools, like the hidden Markov models (HMMs). Here we describe a simple-to-use webserver, the MetaHMM, which is capable of homology-based automatic model-building for the genes to be searched for, and it also finds the closest matches in the metagenome. The webserver uses already highly successful building blocks: it performs multiple alignments by applying Clustal Omega, builds a hidden Markov model with HMMER components of hmmbuild and uses hmmsearch for finding similar sequences to the specified model in the metagenomes. The webserver is publicly available at https://metahmm.pitgroup.org.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Gene identification; HMM; Metagenome

Year:  2018        PMID: 29802977     DOI: 10.1016/j.ygeno.2018.05.016

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  4 in total

Review 1.  Using metagenomic data to boost protein structure prediction and discovery.

Authors:  Qingzhen Hou; Fabrizio Pucci; Fengming Pan; Fuzhong Xue; Marianne Rooman; Qiang Feng
Journal:  Comput Struct Biotechnol J       Date:  2022-01-03       Impact factor: 7.271

2.  Charting host-microbe co-metabolism in skin aging and application to metagenomics data.

Authors:  Wynand Alkema; Jos Boekhorst; Robyn T Eijlander; Steve Schnittger; Fini De Gruyter; Sabina Lukovac; Kurt Schilling; Guus A M Kortman
Journal:  PLoS One       Date:  2021-11-10       Impact factor: 3.240

Review 3.  A roadmap for metagenomic enzyme discovery.

Authors:  Serina L Robinson; Jörn Piel; Shinichi Sunagawa
Journal:  Nat Prod Rep       Date:  2021-11-17       Impact factor: 13.423

4.  The multiple alignments of very short sequences.

Authors:  Kristóf Takács; Vince Grolmusz
Journal:  FASEB Bioadv       Date:  2021-04-29
  4 in total

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