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Genome evolution under speciation is poorly understood in nonmodel and nonvascular plants, such as bryophytes—the largest group of nonvascular land plants. Their genomes are structurally different from angiosperms and likely subjected to stronger linked selection pressure, which may have profound consequences on genome evolution in diversifying lineages, even more so when their genome architecture is conserved. We use the highly diverse, rapidly radiated group of peatmosses (Sphagnum) to characterize the processes affecting genome diversification in bryophytes. Using whole‐genome sequencing data from populations of 12 species sampled at different phylogenetic and geographical scales, we describe high correlation of the genomic landscapes of differentiation, divergence, and diversity in Sphagnum. Coupled with evidence from the patterns of covariation among different measures of genetic diversity, phylogenetic discordance, and gene density, this provides strong support that peatmoss genome evolution has been shaped by the long‐term effects of linked selection, constrained by distribution of selection targets in the genome. Thus, peatmosses join the growing number of animal and plant groups where functional features of the genome, such as gene density, and linked selection drive genome evolution along predetermined and highly similar routes in different species. Our findings demonstrate the great potential of bryophytes for studying the genomics of speciation and highlight the urgent need to expand the genomic resources in this remarkable group of plants. [ABSTRACT FROM AUTHOR]

Metagenomic data generated from environmental samples is increasingly common in the analysis of modern and ancient biological communities. To obtain taxonomic profiles from this type of data, DNA sequences are aligned against large genomic reference databases and the lowest common ancestor (LCA) needs to be inferred for each sequence with multiple alignments. To date, efforts have mainly focused on improving the speed, sensitivity and specificity of alignment tools, and little effort has been applied to the LCA algorithm that generates the taxonomic profiles from alignments.We present ngsLCA, a command‐line toolkit with two separate modules: the main program (in C/C++) performing LCA inference, and an R package for generating tables and visualisations of the taxonomic profiles.ngsLCA processed large datasets in BAM/SAM alignment format 4–11 times faster and used less memory compared to other available programs. It is compatible with the NCBI taxonomy and has flexible parameter settings. Furthermore, the toolkit offers functions for filtering, contamination removal, taxonomic clustering, and multiple ways of visualising the generated taxonomic profiles.ngsLCA bridges a gap in current metagenomic analyses by supplying a computationally light, easy‐to‐use, accurate, fast and flexible LCA algorithm with R functions for processing and illustrating the taxonomic profiles [ABSTRACT FROM AUTHOR]

Genotyping‐by‐sequencing methods such as RADseq are popular for generating genomic and population‐scale data sets from a diverse range of organisms. These often lack a usable reference genome, restricting users to RADseq specific software for processing. However, these come with limitations compared to generic next generation sequencing (NGS) toolkits. Here, we describe and test a simple pipeline for reference‐free RADseq data processing that blends de novo elements from STACKS with the full suite of state‐of‐the art NGS tools. Specifically, we use the de novo RADseq assembly employed by STACKS to create a catalogue of RAD loci that serves as a reference for read mapping, variant calling and site filters. Using RADseq data from 28 zebra sequenced to ~8x depth‐of‐coverage we evaluate our approach by comparing the site frequency spectra (SFS) to those from alternative pipelines. Most pipelines yielded similar SFS at 8x depth, but only a genotype likelihood based pipeline performed similarly at low sequencing depth (2–4x). We compared the RADseq SFS with medium‐depth (~13x) shotgun sequencing of eight overlapping samples, revealing that the RADseq SFS was persistently slightly skewed towards rare and invariant alleles. Using simulations and human data we confirm that this is expected when there is allelic dropout (AD) in the RADseq data. AD in the RADseq data caused a heterozygosity deficit of ~16%, which dropped to ~5% after filtering AD. Hence, AD was the most important source of bias in our RADseq data. [ABSTRACT FROM AUTHOR]

ABSTRACT One essential initial step in the analysis of ancient DNA is to authenticate that the DNA sequencing reads are actually from ancient DNA. This is done by assessing if the reads exhibit typical characteristics of post‐mortem damage (PMD), including cytosine deamination and nicks. We present a novel statistical method implemented in a fast multithreaded programme, ngsBriggs that enables rapid quantification of PMD by estimation of the Briggs ancient damage model parameters (Briggs parameters). Using a multinomial model with maximum likelihood fit, ngsBriggs accurately estimates the parameters of the Briggs model, quantifying the PMD signal from single and double‐stranded DNA regions. We extend the original Briggs model to capture PMD signals for contemporary sequencing platforms and show that ngsBriggs accurately estimates the Briggs parameters across a variety of contamination levels. Classification of reads into ancient or modern reads, for the purpose of decontamination, is significantly more accurate using ngsBriggs than using other methods available. Furthermore, ngsBriggs is substantially faster than other state‐of‐the‐art methods. ngsBriggs offers a practical and accurate method for researchers seeking to authenticate ancient DNA and improve the quality of their data. [ABSTRACT FROM AUTHOR]