Abstract
Mapping of short metagenomic (or metatranscriptomic) read data to reference isolate or single-cell genomes or metagenome-assembled genomes (MAGs) to assess microbial population relative abundance and/or structure represents an essential task of many studies across environmental and clinical settings. The filtering for the quality of the read match and assessment of read mapping results are frequently performed without visual aids or with the assistance of visualizations produced through ad-hoc, in-house approaches. Here, we introduce RecruitPlotEasy, a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample. Hence, RecruitPlotEasy should also greatly facilitate population genetics studies.RecruitPlotEasy is implemented in Python and R languages and is freely available open source software under the Artistic License 2.0 from https://github.com/KGerhardt/RecruitPlotEasy.
Highlights
Metagenomics studies of natural microbial populations have recently revealed that bacteria and archaea predominantly form sequence-discrete populations with intra-population genomic sequence relatedness typically ranging from ∼95 to 100% genome-aggregate average nucleotide identity depending on the population considered
We have developed bioinformatic scripts that can be applied to the read mapping output of a read recruitment plot to provide information based on read mapping that is not available by previous tools such as what is the average coverage of the genome by reads, whether or not co-occurring populations exist in the dataset (Rodriguez and Konstantinidis, 2016), and which genes of the reference genome in the plot are shared or not by the metagenomic population (Meziti, et al, 2019)
The tabs organize the workflow of RecruitPlotEasy into smaller, manageable tasks where the options available on each page are directly relevant to the task that page supports
Summary
Metagenomics studies of natural microbial populations have recently revealed that bacteria and archaea predominantly form sequence-discrete populations with intra-population genomic sequence relatedness typically ranging from ∼95 to 100% genome-aggregate average nucleotide identity (or ANI) depending on the population considered (e.g., younger populations since the last population diversity sweep event show lower levels of intra-population diversity and higher ANI). Several tools that can plot read mapping patterns have been developed for this purpose e.g., (Robinson et al, 2011; Zhu et al, 2013; Jaenicke et al, 2018) These tools typically provide no additional information or capabilities such as they do not include appropriate statistics to characterize the genome, gene allelic, and gene content diversity in spatial or time-series metagenomes and do not allow targeted analyses of specific gene-based traits and exploration of selection pressure and population bottlenecks (Meziti et al, 2019). Users may optionally supply gene functional annotation in GFF for gene-level analysis
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