Index hopping on the Illumina HiseqX platform and its consequences for ancient DNA studies

The high‐throughput capacities of the Illumina sequencing platforms and the possibility to label samples individually have encouraged wide use of sample multiplexing. However, this practice results in read misassignment (usually <1%) across samples sequenced on the same lane. Alarmingly high rates of read misassignment of up to 10% were reported for lllumina sequencing machines with exclusion amplification chemistry. This may make use of these platforms prohibitive, particularly in studies that rely on low‐quantity and low‐quality samples, such as historical and archaeological specimens. Here, we use barcodes, short sequences that are ligated to both ends of the DNA insert, to directly quantify the rate of index hopping in 100‐year old museum‐preserved gorilla (Gorilla beringei) samples. Correcting for multiple sources of noise, we identify on average 0.470% of reads containing a hopped index. We show that sample‐specific quantity of misassigned reads depends on the number of reads that any given sample contributes to the total sequencing pool, so that samples with few sequenced reads receive the greatest proportion of misassigned reads. This particularly affects ancient DNA samples, as these frequently differ in their DNA quantity and endogenous content. Through simulations we show that even low rates of index hopping, as reported here, can lead to biases in ancient DNA studies when multiplexing samples with vastly different quantities of endogenous material.     

Genome‐based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles

Ribosomal DNA (rDNA) copy number variation (CNV) has major physiological implications for all organisms, but how it varies for fungi, an ecologically ubiquitous and important group of microorganisms, has yet to be systemically investigated. Here, we examine rDNA CNV using an in silico read depth approach for 91 fungal taxa with sequenced genomes and assess copy number conservation across phylogenetic scales and ecological lifestyles. rDNA copy number varied considerably across fungi, ranging from an estimated 14 to 1,442 copies (mean = 113, median = 82), and copy number similarity was inversely correlated with phylogenetic distance. No correlations were found between rDNA CNV and fungal trophic mode, ecological guild or genome size. Taken together, these results show that like other microorganisms, fungi exhibit substantial variation in rDNA copy number, which is linked to their phylogeny in a scale‐dependent manner.     

Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project

The Alzheimer's Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed “consensus calling,” to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.     

Prevention,diagnosis and treatment of high‐throughput sequencing data pathologies

High‐throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost‐effectively, enabling single investigator laboratories to address a variety of ‘omics’ questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS‐generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard ‘health check‐up’ QC procedures recommended for HTS data sets and establishing what ‘healthy’ HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of ‘pathologies’, discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful ‘treatment’ protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies – and consequently their pathologies – change, we argue that careful QC of HTS data is an important – yet often neglected – aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC ‘best practices’ guide.     

Chromosomal inversions associated with environmental adaptation in honeybees

Chromosomal inversions can facilitate local adaptation in the presence of gene flow by suppressing recombination between well‐adapted native haplotypes and poorly adapted migrant haplotypes. East African mountain populations of the honeybee Apis mellifera are highly divergent from neighbouring lowland populations at two extended regions in the genome, despite high similarity in the rest of the genome, suggesting that these genomic regions harbour inversions governing local adaptation. Here, we utilize a new highly contiguous assembly of the honeybee genome to characterize these regions. Using whole‐genome sequencing data from 55 highland and lowland bees, we find that the highland haplotypes at both regions are present at high frequencies in three independent highland populations but extremely rare elsewhere. The boundaries of both divergent regions are characterized by regions of high homology with each other positioned in opposite orientations and contain highly repetitive, long inverted repeats with homology to transposable elements. These regions are likely to represent inversion breakpoints that participate in nonallelic homologous recombination. Using long‐read data, we confirm that the lowland samples are contiguous across breakpoint regions. We do not find evidence for disruption of functional sequence by these breakpoints, which suggests that the inversions are likely maintained due to their allelic content conferring local adaptation in highland environments. Finally, we identify a third divergent genomic region, which contains highly divergent segregating haplotypes that also may contain inversion variants under selection. The results add to a growing body of evidence indicating the importance of chromosomal inversions in local adaptation.