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/

Zendy Plus

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

Zendy Tools

Zendy Open

More and more developed and inexpensive next-generation sequencing (NGS) technologies allowus to extract vast sequence data from a sample containing multiple species. Characterizingthe taxonomic diversity for the planet-size data plays an important role in the metagenomicstudies, while a crucial step for doing the study is the binning process to group sequence readsfrom similar species or taxonomic classes. The metagenomic binning remains a challenge workbecause of not only the various read noises but also the tremendous data volume. In this work,we propose an unsupervised binning method for NGS reads based on the one-dimensional cellularautomaton (1D-CA). Our binning method facilities to reduce the memory usage because 1D-CAcosts only linear space. Experiments on synthetic dataset exhibit that our method is helpful toidentify species of lower abundance compared to the proposed tool.

A New Binning Method for Metagenomics by One-Dimensional Cellular Automata