The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA 'reads') against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30-80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software-BWA-aln, BWA-mem, NovoAlign and Bowtie2-and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

We present Genozip, a universal and fully featured compression software for genomic data. Genozip is designed to be a general-purpose software and a development framework for genomic compression by providing five core capabilities-universality (support for all common genomic file formats), high compression ratios, speed, feature-richness and extensibility. Genozip delivers high-performance compression for widelyused genomic data formats in genomics research, namely FASTQ, SAM/BAM/CRAM, VCF, GVF, FASTA, PHYLIP and 23andMe formats. Our test results show that Genozip is fast and achieves greatly improved compression ratios, even when the files are already compressed. Further, Genozip is architected with a separation of the Genozip Framework from file-format-specific Segmenters and data-type-specific Codecs. With this, we intend for Genozip to be a general-purpose compression platform where researchers can implement compression for additional file formats, as well as new codecs for data types or fields within files, in the future. We anticipate that this will ultimately increase the visibility and adoption of these algorithms by the user community, thereby accelerating further innovation in this space. AVAILABILITY AND IMPLEMENTATION: Genozip is written in C. The code is open-source and available on http://www.genozip.com. The package is free for non-commercial use. It is distributed through the Conda package manager, github, and as a Docker container on DockerHub. Genozip is tested on Linux, Mac and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: genozip is a new lossless compression tool for Variant Call Format (VCF) files. By applying field-specific algorithms and fully utilizing the available computational hardware, genozip achieves the highest compression ratios amongst existing lossless compression tools known to the authors, at speeds comparable with the fastest multi-threaded compressors. AVAILABILITY AND IMPLEMENTATION: genozip is freely available to non-commercial users. It can be installed via conda-forge, Docker Hub, or downloaded from github.com/divonlan/genozip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.