TKGWV2: an ancient DNA relatedness pipeline for ultra-low coverage whole genome shotgun data

Estimation of genetically related individuals is playing an increasingly important role in the ancient DNA field. In recent years, the numbers of sequenced individuals from single sites have been increasing, reflecting a growing interest in understanding the familial and social organisation of ancient populations. Although a few different methods have been specifically developed for ancient DNA, namely to tackle issues such as low-coverage homozygous data, they require a 0.1–1× minimum average genomic coverage per analysed pair of individuals. Here we present an updated version of a method that enables estimates of 1st and 2nd-degrees of relatedness with as little as 0.026× average coverage, or around 18,000 SNPs from 1.3 million aligned reads per sample with average length of 62 bp—four times less data than 0.1× coverage at similar read lengths. By using simulated data to estimate false positive error rates, we further show that a threshold even as low as 0.012×, or around 4000 SNPs from 600,000 reads, will always show 1st-degree relationships as related. Lastly, by applying this method to published data, we are able to identify previously undocumented relationships using individuals that had been excluded from prior kinship analysis due to their very low coverage. This methodological improvement has the potential to enable relatedness estimation on ancient whole genome shotgun data during routine low-coverage screening, and therefore improve project management when decisions need to be made on which individuals are to be further sequenced.

TKGWV2: An ancient DNA relatedness pipeline for ultra-low coverage whole genome shotgun data

Estimation of genetically related individuals is playing an increasingly important role in the ancient DNA field. In recent years, the numbers of sequenced individuals from single sites have been increasing, reflecting a growing interest in understanding the familial and social organisation of ancient populations. Although a few different methods have been specifically developed for ancient DNA, namely to tackle issues such as low-coverage homozygous data, they require a 0.1 - 1x minimum average genomic coverage per analysed pair of individuals between. Here we present an updated version of a method that enables estimates of 1st and 2nd-degrees of relatedness with as little as 0.026x average coverage, or around 1.3 million aligned reads per sample - 4 times less data than 0.1x. By using simulated data to estimate false positive error rates, we further show that a threshold even as low as 0.012x, or around 600,000 reads, will always show 1st-degree relationships as related. Lastly, by applying this method to published data, we are able to identify previously undocumented relationships using individuals previously excluded from kinship analysis due to their very low coverage. This methodological improvement has the potential to enable relatedness estimation on ancient whole genome shotgun data during routine low-coverage screening, and therefore improve project management when decisions need to be made on which individuals are to be further sequenced.

HolistIC: Leveraging Hi-C and Whole Genome Shotgun Sequencing for Double Minute Chromosome Discovery

Double minute chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of double minutes (DM) can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor double minutes. This creates double minutes with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates. In this study, we describe an algorithm called “ HolistIC” that can predict double minutes in tumor genomes by integrating whole genome shotgun sequencing (WGS) and Hi-C sequencing data. The consolidation of these sources of information resolves ambiguity in double minute amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi-C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC’s ability to predict DMs from Hi-C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish double minutes that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation.AvailabilityOur software is available at http://www.github.com/mhayes20/HolistIC.

SHIMS 3.0: Highly efficient single-haplotype iterative mapping and sequencing using ultra-long nanopore reads

The reference sequence of structurally complex regions can only be obtained through a highly accurate clone-based approach that we call Single-Haplotype Iterative Mapping and Sequencing (SHIMS). In recent years, improvements to SHIMS have reduced the cost and time required by two orders of magnitude, but internally repetitive clones still require extensive manual effort to transform draft assemblies into reference-quality finished sequences. Here we introduce SHIMS 3.0, using ultra-long nanopore reads to resolve internally repetitive structures and minimize the need for manual finishing of Illumina-based draft assemblies. This protocol proceeds from clone-picking to finished assemblies in 2 weeks for about 80 dollars per clone. We have used SHIMS 3.0 to finish the structurally complex TSPY array on the human Y chromosome, which could not be resolved by previous sequencing methods. Our protocol provides access to structurally complex regions that would otherwise be inaccessible from whole-genome shotgun data or require an impractical amount of manual effort to generate an accurate assembly.

Strain Structure and Dynamics Revealed by Targeted Deep Sequencing of the Honey Bee Gut Microbiome

ABSTRACT Host-associated microbiomes can be critical for the health and proper development of animals and plants. The answers to many fundamental questions regarding the modes of acquisition and microevolution of microbiome communities remain to be established. Deciphering strain-level dynamics is essential to fully understand how microbial communities evolve, but the forces shaping the strain-level dynamics of microbial communities remain largely unexplored, mostly because of methodological issues and cost. Here, we used targeted strain-level deep sequencing to uncover the strain dynamics within a host-associated microbial community using the honey bee gut microbiome as a model system. Our results revealed that amplicon sequencing of conserved protein-coding gene regions using species-specific primers is a cost-effective and accurate method for exploring strain-level diversity. In fact, using this method we were able to confirm strain-level results that have been obtained from whole-genome shotgun sequencing of the honey bee gut microbiome but with a much higher resolution. Importantly, our deep sequencing approach allowed us to explore the impact of low-frequency strains (i.e., cryptic strains) on microbiome dynamics. Results show that cryptic strain diversity is not responsible for the observed variations in microbiome composition across bees. Altogether, the findings revealed new fundamental insights regarding strain dynamics of host-associated microbiomes. IMPORTANCE The factors driving fine-scale composition and dynamics of gut microbial communities are poorly understood. In this study, we used metagenomic amplicon deep sequencing to decipher the strain dynamics of two key members of the honey bee gut microbiome. Using this high-throughput and cost-effective approach, we were able to confirm results from previous large-scale whole-genome shotgun (WGS) metagenomic sequencing studies while also gaining additional insights into the community dynamics of two core members of the honey bee gut microbiome. Moreover, we were able to show that cryptic strains are not responsible for the observed variations in microbiome composition across bees.