- Despoina Roumpeka
- R. John Wallace
- Frank Escalettes
- Ian Fotheringham
- Michael Watson
- Download as Adobe PDF
Rights statement: Copyright © 2017 Roumpeka, Wallace, Escalettes, Fotheringham and Watson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Final published version, 951 KB, PDF document
Licence: Creative Commons: Attribution (CC-BY)
| Original language | English |
|---|
| Journal | Frontiers in genetics |
|---|
| Volume | 8 |
|---|
| Issue number | 23 |
|---|
| Early online date | 16 Feb 2017 |
|---|
| DOIs | |
|---|
| Publication status | E-pub ahead of print - 16 Feb 2017 |
|---|
The microbiome can be defined as the community of microorganisms that live in a particular environment. Metagenomics is the practice of sequencing DNA from the genomes of all organisms present in a particular sample, and has become a common method for the study of microbiome population structure and function. Increasingly, researchers are finding novel genes encoded within metagenomes, many of which may be of interest to the biotechnology and pharmaceutical industries. However, such "bioprospecting" requires a suite of sophisticated bioinformatics tools to make sense of the data. This review summarizes the most commonly used bioinformatics tools for the assembly and annotation of metagenomic sequence data with the aim of discovering novel genes.
- metagenomics, bioinformatics, next generation sequencing, assembly, gene prediction, bioprospecting
ID: 31234325
