MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets

Genomics
Rachamalla Maheedhar ReddySharmila S Mande

Abstract

A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA - a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA.

References

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Citations

Dec 4, 2015·International Journal of Molecular Sciences·Rafael R C CuadratAlberto M R Dávila
Jun 24, 2015·Research in Microbiology·Cyrielle GascPierre Peyret
Oct 28, 2015·Nucleic Acids Research·Julia H WildschutteJeffrey M Kidd
Jan 8, 2015·Frontiers in Microbiology·Saskia L SmitsAnita C Schürch
Aug 10, 2017·Nature Reviews. Gastroenterology & Hepatology·Marcus J ClaessonPaul W O'Toole
Mar 24, 2021·Briefings in Bioinformatics·Masood Ur Rehman KayaniLei Chen

Related Concepts

Computer Programs and Programming
Sequence Determinations, DNA
Metagenome
Metagenomics
Datasets as Topic
Environment
Gene Clusters
Genome
Computer Software
Simulation

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