Mapache: a flexible pipeline to map ancient DNA

PopGLen-a Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods

SUMMARY

PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms.

AVAILABILITY AND IMPLEMENTATION

PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the Supplementary Material.

Towards predicting the geographical origin of ancient samples with metagenomic data

Reconstructing the history-such as the place of birth and death-of an individual sample is a fundamental goal in ancient DNA (aDNA) studies. However, knowing the place of death can be particularly challenging when samples come from museum collections with incomplete or erroneous archives. While analyses of human DNA and isotope data can inform us about the ancestry of an individual and provide clues about where the person lived, they cannot specifically trace the place of death. Moreover, while ancient human DNA can be retrieved, a large fraction of the sequenced molecules in ancient DNA studies derive from exogenous DNA. This DNA-which is usually discarded in aDNA analyses-is constituted mostly by microbial DNA from soil-dwelling microorganisms that have colonized the buried remains post-mortem. In this study, we hypothesize that remains of individuals buried in the same or close geographic areas, exposed to similar microbial communities, could harbor more similar metagenomes. We propose to use metagenomic data from ancient samples' shotgun sequencing to locate the place of death of a given individual which can also help to solve cases of sample mislabeling. We used a k-mer-based approach to compute similarity scores between metagenomic samples from different locations and propose a method based on dimensionality reduction and logistic regression to assign a geographical origin to target samples. We apply our method to several public datasets and observe that individual samples from closer geographic locations tend to show higher similarities in their metagenomes compared to those of different origin, allowing good geographical predictions of test samples. Moreover, we observe that the genus Streptomyces commonly infiltrates ancient remains and represents a valuable biomarker to trace the samples' geographic origin. Our results provide a proof of concept and show how metagenomic data can also be used to shed light on the place of origin of ancient samples.

Targeted enrichment of whole-genome SNPs from highly burned skeletal remains

Genetic assessment of highly incinerated and/or degraded human skeletal material is a persistent challenge in forensic DNA analysis, including identifying victims of mass disasters. Few studies have investigated the impact of thermal degradation on whole-genome single-nucleotide polymorphism (SNP) quality and quantity using next-generation sequencing (NGS). We present whole-genome SNP data obtained from the bones and teeth of 27 fire victims using two DNA extraction techniques. Extracts were converted to double-stranded DNA libraries then enriched for whole-genome SNPs using unpublished biotinylated RNA baits and sequenced on an Illumina NextSeq 550 platform. Raw reads were processed using the EAGER (Efficient Ancient Genome Reconstruction) pipeline, and the SNPs filtered and called using FreeBayes and GATK (v. 3.8). Mixed-effects modeling of the data suggest that SNP variability and preservation is predominantly determined by skeletal element and burn category, and not by extraction type. Whole-genome SNP data suggest that selecting long bones, hand and foot bones, and teeth subjected to temperatures <350°C are the most likely sources for higher genomic DNA yields. Furthermore, we observed an inverse correlation between the number of captured SNPs and the extent to which samples were burned, as well as a significant decrease in the total number of SNPs measured for samples subjected to temperatures >350°C. Our data complement previous analyses of burned human remains that compare extraction methods for downstream forensic applications and support the idea of adopting a modified Dabney extraction technique when traditional forensic methods fail to produce DNA yields sufficient for genetic identification.

Identification of the 18 World War II executed citizens of Adele, Rethymnon, Crete using an ancient DNA approach and low coverage genomes

In the Battle of Crete during the World War II occupation of Greece, the German forces faced substantial civilian resistance. To retribute the numerous German losses, a series of mass executions took place in numerous places in Crete; a common practice reported from Greece and elsewhere. In Adele, a village in the regional unit of Rethymnon, 18 male civilians were executed and buried in a burial pit at the Sarakina site. In this study, the first one conducted for a conflict that occurred in Greece, we identified for humanitarian purposes the 18 skulls of the Sarakina victims, following a request from the local community of Adele. The molecular identification of historical human remains via ancient DNA approaches and low coverage whole genome sequencing has only recently been introduced. Here, we performed genome skimming on the living relatives of the victims, as well as high throughput historical DNA analysis on the skulls to infer the kinship degrees among the victims via genetic relatedness analyses. We also conducted targeted anthropological analysis to successfully complete the identification of all Sarakina victims. We demonstrate that our methodological approach constitutes a potentially highly informative forensic tool to identify war victims. It can hence be applied to analogous studies on degraded DNA, thus, paving the path for systematic war victim identification in Greece and beyond.

Review: Computational analysis of human skeletal remains in ancient DNA and forensic genetics

Degraded DNA is used to answer questions in the fields of ancient DNA (aDNA) and forensic genetics. While aDNA studies typically center around human evolution and past history, and forensic genetics is often more concerned with identifying a specific individual, scientists in both fields face similar challenges. The overlap in source material has prompted periodic discussions and studies on the advantages of collaboration between fields toward mutually beneficial methodological advancements. However, most have been centered around wet laboratory methods (sampling, DNA extraction, library preparation, etc.). In this review, we focus on the computational side of the analytical workflow. We discuss limitations and considerations to consider when working with degraded DNA. We hope this review provides a framework to researchers new to computational workflows for how to think about analyzing highly degraded DNA and prompts an increase of collaboration between the forensic genetics and aDNA fields.

ILIAD: a suite of automated Snakemake workflows for processing genomic data for downstream applications

BACKGROUND

Processing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data.

RESULTS

The Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster.

CONCLUSION

Iliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows.