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

Abstract EN

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.