MOTIVATION: The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. RESULTS: For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AVAILABILITY AND IMPLEMENTATION: AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

SUMMARY: Visualization and inference of population structure is increasingly important for fundamental and applied research. Here, we present Struct-f4, providing automated solutions to characterize and summarize the genetic ancestry profile of individuals, assess their genetic affinities, identify admixture sources and quantify admixture levels. AVAILABILITY AND IMPLEMENTATION: Struct-f4 is written in Rcpp and relies on f4-statistics and Markov Chain Monte Carlo (MCMC) optimization. It is freely available under GNU General Public License in Bitbucket (https://bitbucket.org/plibradosanz/structf4/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Ancient DNA datasets are increasingly difficult to visualize for users lacking computational experience. Here, we describe mapDATAge, which aims to provide user-friendly automated modules for the interactive mapping of allele, haplogroup and/or ancestry distributions through space and time. mapDATAge enhances collaborative data sharing while assisting the assessment and reporting of spatiotemporal patterns of genetic changes. AVAILABILITY AND IMPLEMENTATION: mapDATAge is a Shiny R application designed for exploring spatiotemporal patterns in ancient DNA data through a graphical user interface. It is freely available under GNU Public License in Github: https://github.com/xuefenfei712/mapDATAge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico , we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets. Availability and Implementation: The package is publicly available on github: https://grenaud.github.io/gargammel/ and released under the GPL. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies. RESULTS: We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41-197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology. AVAILABILITY AND IMPLEMENTATION: http://www2.imm.dtu.dk/∼gigu/Spasiba/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Ancient DNA (aDNA) molecules in fossilized bones and teeth, coprolites, sediments, mummified specimens and museum collections represent fantastic sources of information for evolutionary biologists, revealing the agents of past epidemics and the dynamics of past populations. However, the analysis of aDNA generally faces two major issues. Firstly, sequences consist of a mixture of endogenous and various exogenous backgrounds, mostly microbial. Secondly, high nucleotide misincorporation rates can be observed as a result of severe post-mortem DNA damage. Such misincorporation patterns are instrumental to authenticate ancient sequences versus modern contaminants. We recently developed the user-friendly mapDamage package that identifies such patterns from next-generation sequencing (NGS) sequence datasets. The absence of formal statistical modeling of the DNA damage process, however, precluded rigorous quantitative comparisons across samples. RESULTS: Here, we describe mapDamage 2.0 that extends the original features of mapDamage by incorporating a statistical model of DNA damage. Assuming that damage events depend only on sequencing position and post-mortem deamination, our Bayesian statistical framework provides estimates of four key features of aDNA molecules: the average length of overhangs (λ), nick frequency (ν) and cytosine deamination rates in both double-stranded regions ( ) and overhangs ( ). Our model enables rescaling base quality scores according to their probability of being damaged. mapDamage 2.0 handles NGS datasets with ease and is compatible with a wide range of DNA library protocols. AVAILABILITY: mapDamage 2.0 is available at ginolhac.github.io/mapDamage/ as a Python package and documentation is maintained at the Centre for GeoGenetics Web site (geogenetics.ku.dk/publications/mapdamage2.0/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Ancient DNA extracts consist of a mixture of contaminant DNA molecules, most often originating from environmental microbes, and endogenous fragments exhibiting substantial levels of DNA damage. The latter introduce specific nucleotide misincorporations and DNA fragmentation signatures in sequencing reads that could be advantageously used to argue for sequence validity. mapDamage is a Perl script that computes nucleotide misincorporation and fragmentation patterns using next-generation sequencing reads mapped against a reference genome. The Perl script outputs are further automatically processed in embedded R script in order to detect typical patterns of genuine ancient DNA sequences. AVAILABILITY AND IMPLEMENTATION: The Perl script mapDamage is freely available with documentation and example files at http://geogenetics.ku.dk/all_literature/mapdamage/. The script requires prior installation of the SAMtools suite and R environment and has been validated on both GNU/Linux and MacOSX operating systems.