A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional, and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to a higher titer as pseudotyped virions. In infected individuals, G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, but not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus and support continuing surveillance of Spike mutations to aid with development of immunological interventions.

Summary: In this study, we followed the genomic, lipidomic and metabolomic changes associated with the selection of miltefosine (MIL) resistance in two clinically derived Leishmania donovani strains with different inherent resistance to antimonial drugs (antimony sensitive strain Sb‐S; and antimony resistant Sb‐R). MIL‐R was easily induced in both strains using the promastigote‐stage, but a significant increase in MIL‐R in the intracellular amastigote compared to the corresponding wild‐type did not occur until promastigotes had adapted to 12.2 μM MIL. A variety of common and strain‐specific genetic changes were discovered in MIL‐adapted parasites, including deletions at the LdMT transporter gene, single‐base mutations and changes in somy. The most obvious lipid changes in MIL‐R promastigotes occurred to phosphatidylcholines and lysophosphatidylcholines and results indicate that the Kennedy pathway is involved in MIL resistance. The inherent Sb resistance of the parasite had an impact on the changes that occurred in MIL‐R parasites, with more genetic changes occurring in Sb‐R compared with Sb‐S parasites. Initial interpretation of the changes identified in this study does not support synergies with Sb‐R in the mechanisms of MIL resistance, though this requires an enhanced understanding of the parasite's biochemical pathways and how they are genetically regulated to be verified fully.

Accurate detection of somatic structural variants (SVs) and somatic copy number aberrations (SCNAs) is critical to study the mutational processes underpinning cancer evolution. Here we describe SAVANA, an algorithm designed to detect somatic SVs and SCNAs at single-haplotype resolution and estimate tumor purity and ploidy using long-read sequencing data with or without a germline control sample. We also establish best practices for benchmarking SV detection algorithms across the entire genome in a data-driven manner using replication and read-backed phasing analysis. Through the analysis of matched Illumina and nanopore whole-genome sequencing data for 99 human tumor-normal pairs, we show that SAVANA has significantly higher sensitivity and 13- and 82-times-higher specificity than the second and third-best performing algorithms. Moreover, SVs reported by SAVANA are highly consistent with those detected using short-read sequencing. In summary, SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably.

Accurate massively parallel sequencing (MPS) of genetic variants is key to many areas of science and medicine, such as cataloging population genetic variation and diagnosing genetic diseases. Certain genomic positions can be prone to higher rates of systematic sequencing and alignment bias that limit accuracy, resulting in false positive variant calls. Current standard practices to differentiate between loci that can and cannot be sequenced with high confidence utilize consensus between different sequencing methods as a proxy for sequencing confidence. These practices have significant limitations, and alternative methods are required to overcome them. We have developed a novel statistical method based on summarizing sequenced reads from whole-genome clinical samples and cataloging them in “Incremental Databases” that maintain individual confidentiality. Allele statistics were cataloged for each genomic position that consistently showed systematic biases with the corresponding MPS sequencing pipeline. We found systematic biases present at ∼1%–3% of the human autosomal genome across five patient cohorts. We identified which genomic regions were more or less prone to systematic biases, including large homopolymer flanks (odds ratio = 23.29–33.69) and the NIST high confidence genomic regions (odds ratio = 0.154–0.191). We confirmed our predictions on a gold-standard reference genome and showed that these systematic biases can lead to suspect variant calls within clinical panels. Our results recommend increased caution to address systematic biases in whole-genome sequencing and alignment. This study provides the implementation of a simple statistical approach to enhance quality control of clinically sequenced samples by flagging variants at suspect loci for further analysis or exclusion.

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 × 10⁻⁴). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response.

Background Highly accurate next-generation sequencing (NGS) of genetic variants is key to many areas of science and medicine, such as cataloguing population genetic variation and diagnosing patients with genetic diseases. Certain genomic loci and regions can be prone to higher rates of systematic sequencing and alignment bias that pose a challenge to achieving high accuracy, resulting in false positive variant calls. Current standard practices to differentiate between loci that can and cannot be sequenced with high confidence utilise consensus between different sequencing methods as a proxy for sequencing confidence. This assumption is not accurate in cases where all sequencing pipelines have consensus on the same errors due to similar systematic biases in sequencing. Alternative methods are therefore required to identify systematic biases. Methods We have developed a novel statistical method based on summarising sequenced reads from whole genome clinical samples and cataloguing them in “Incremental Databases” (IncDBs) that maintain individual confidentiality. Variant statistics were analysed and catalogued for each genomic position that consistently showed systematic biases with the corresponding sequencing pipeline. Results We have demonstrated that systematic errors in NGS data are widespread, with persistent low-fraction alleles present at 1.26-2.43% of the human autosomal genome across three different Illumina-based pipelines, each consisting of at least 150 patient samples. We have identified a variety of genomic regions that are more or less prone to systematic biases, such as GC-rich regions (OR = 6.47-8.19) and the NIST high-confidence genomic regions (OR = 0.154-0.191). We have verified our predictions on a gold-standard reference genome and have shown that these systematic biases can lead to suspect variant calls at clinically important loci, including within introns and exons. Conclusions Our results recommend increased caution to minimise the effect of systematic biases in whole genome sequencing and alignment. This study supports the utility of a statistical approach to enhance quality control of clinically sequenced samples in order to flag up variant calls made at known suspect loci for further analysis or exclusion, using anonymised summary databases from which individual patients cannot be re-identified, so that results can be shared more widely.