English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce a parallel algorithmic architecture for metagenomic sequenceassembly, termed MetaPar, which allows for significant reductions in assemblytime and consequently enables the processing of large genomic datasets oncomputers with low memory usage. The gist of the approach is to iterativelyperform read (re)classification based on phylogenetic marker genes andassembler outputs generated from random subsets of metagenomic reads. Once asufficiently accurate classification within genera is performed, de novometagenomic assemblers (such as Velvet or IDBA-UD) or reference basedassemblers may be used for contig construction. We analyze the performance ofMetaPar on synthetic data consisting of 15 randomly chosen species from theNCBI database through the effective gap and effective coverage metrics.

MetaPar: Metagenomic Sequence Assembly via Iterative Reclassification