Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies
By Anastasis Oulas, Christina Pavloudi, P. Polymenakou, Georgios A. Pavlopoulos, Nikolas Papanikolaou, G. Kotoulas, Christos Arvanitidis, Ioannis Iliopoulos. Bioinformatics and Biology Insights, 2015
351 citations recorded by the source corpus.
Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b2b8d0af1657d504a310653f86af2f18356310a9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Florian P. Breitwieser
- Jennifer Lu
- Steven L. Salzberg
- G. Uritskiy
- James Taylor
- J. DiRuggiero
- Derrick E. Wood
- Ben Langmead
In Molecular Biology
- Jennifer Lu
- Florian P. Breitwieser
- Steven L. Salzberg
- James Taylor
- G. Uritskiy
- J. DiRuggiero
- Ben Langmead
- Derrick E. Wood
Citations in the corpus
- A review of methods and databases for metagenomic classification and assembly (2019, background): “Researchers can quantify the structure of microbial communities using ecological and biogeographic measures such as species diversity, richness and uniformity of the communities [22, 43].”
- MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis (2018, background): “As the field of WMG expands, comprehensive and accessible software for unified analysis of metagenomic data is needed [7, 11].”
- MEGARes: an antimicrobial resistance database for high throughput sequencing (2016, background): “This dearth has led to the suboptimal use of bioinformatics tools that were designed for whole-genome sequencing data as well as a lack of consensus around a standardized metagenomics workflow (21,22).”