Mallawaarachchi, V. | Search bioRxiv

bioRxiv2021

MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs

Metagenomics binning has allowed us to study and characterize various genetic material of different species and gain insights into microbial communities. While existing binning tools bin metagenomics de novo assemblies, they do not make use of the assembly graphs that produce such assemblies. Here we propose MetaCoAG, a tool that utilizes assembly graphs with the composition and coverage information to bin metagenomic contigs. MetaCoAG uses single-copy marker genes to estimate the number of initial bins, assigns contigs into bins iteratively and adjusts the number of bins dynamically throughout the binning process. Experimental results on simulated and real datasets demonstrate that MetaCoAG significantly outperforms state-of-the-art binning tools, producing more high-quality bins than the second-best tool, with an average median F1-score of 88.40%. To the best of our knowledge, MetaCoAG is the first stand-alone binning tool to make direct use of the assembly graph information. MetaCoAG is available at https://github.com/Vini2/MetaCoAG.

bioinformatics↗

bioRxiv2021

PlasLR Enables Adaptation of Plasmid Prediction for Error-Prone Long Reads

Plasmids are extra-chromosomal genetic elements commonly found in bacterial cells that support many functional aspects including environmental adaptations. The identification of these genetic elements is vital for the further study of function and behaviour of the organisms. However it is challenging to separate these small sequences from longer chromosomes within a given species. Machine learning approaches have been successfully developed to classify assembled contigs into two classes (plasmids and chromosomes). However, such tools are not designed to directly perform classification on long and error-prone reads which have been gaining popularity in genomics studies. Assembling complete plasmids is still challenging for many long-read assemblers with a mixed input of long and error-prone reads from plasmids and chromosomes. In this paper, we present PlasLR, a tool that adapts existing plasmid detection approaches to directly classify long and error-prone reads. PlasLR makes use of both the composition and coverage information of long and error-prone reads. We evaluate PlasLR on multiple simulated and real long-read datasets with varying compositions of plasmids and chromosomes. Our experiments demonstrate that PlasLR substantially improves the accuracy of plasmid detection on top of the state-of-the-art plasmid detection tools. Moreover, we show that using PlasLR before long-read assembly helps to enhance the assembly quality in terms of plasmid recovery and near complete chromosome assembly from metagenomic datasets.

bioinformatics↗