From genomics to metagenomics.
Next-generation sequencing has changed metagenomics. However, sequencing DNA is no longer the bottleneck, rather, the bottleneck is computational analysis and also interpretation. Computational cost is the obvious issue, as is tool limitations, considering most of the tools we routinely use have been built for clonal genomics or are being adapted to microbial communities. The current trend in metagenomics analysis is toward reducing computational costs through improved algorithms and through analysis strategies. Data sharing and interoperability between tools are critical, since computation for metagenomic datasets is very high.
Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.
Narayan
N
Desai
Dion
D
Antonopoulos
Jack A
JA
Gilbert
Elizabeth M
EM
Glass
Folker
F
Meyer
D000465:Algorithms; D019295:Computational Biology / Q000191:economics / Q000379:methods*; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D033181:Information Dissemination; D056186:Metagenomics / Q000379:methods*; D017422:Sequence Analysis, DNA