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Summary RNA sequencing (RNA-seq) can be applied to diverse tasks including quantifying gene expression, discovering quantitative trait loci and identifying gene fusion events. Although RNA-seq can detect germline variants, the complexities of variable transcript abundance, target capture and amplification introduce challenging sources of error. Here, we extend DeepVariant, a deep-learning-based variant caller, to learn and account for the unique challenges presented by RNA-seq data. Our DeepVariant RNA-seq model produces highly accurate variant calls from RNA-sequencing data, and outperforms existing approaches such as Platypus and GATK. We examine factors that influence accuracy, how our model addresses RNA editing events and how additional thresholding can be used to facilitate our models' use in a production pipeline. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

Circular consensus sequencing with Pacific Biosciences (PacBio) technology generates long (10–25 kilobases), accurate 'HiFi' reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation, pbccs, uses a hidden Markov model. We introduce DeepConsensus, which uses an alignment-based loss to train a gap-aware transformer–encoder for sequence correction. Compared to pbccs, DeepConsensus reduces read errors by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27% and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9 megabases (Mb) to 17.2 Mb), increase gene completeness (94% to 97%), reduce the false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45) and reduce variant-calling errors by 24%. DeepConsensus models could be trained to the general problem of analyzing the alignment of other types of sequences, such as unique molecular identifiers or genome assemblies. Deep learning reduces errors in sequences from PacBio HiFi reads. [ABSTRACT FROM AUTHOR]

Parasitic nematodes impose a debilitating health and economic burden across much of the world. Nematode resistance to anthelmintic drugs threatens parasite control efforts in both human and veterinary medicine. Despite this threat, the genetic landscape of potential resistance mechanisms to these critical drugs remains largely unexplored. Here, we exploit natural variation in the model nematodes Caenorhabditis elegans and Caenorhabditis briggsae to discover quantitative trait loci (QTL) that control sensitivity to benzimidazoles widely used in human and animal medicine. High-throughput phenotyping of albendazole, fenbendazole, mebendazole, and thiabendazole responses in panels of recombinant lines led to the discovery of over 15 QTL in C. elegans and four QTL in C. briggsae associated with divergent responses to these anthelmintics. Many of these QTL are conserved across benzimidazole derivatives, but others show drug and dose specificity. We used near-isogenic lines to recapitulate and narrow the C. elegans albendazole QTL of largest effect and identified candidate variants correlated with the resistance phenotype. These QTL do not overlap with known benzimidazole target resistance genes from parasitic nematodes and present specific new leads for the discovery of novel mechanisms of nematode benzimidazole resistance. Analyses of orthologous genes reveal conservation of candidate benzimidazole resistance genes in medically important parasitic nematodes. These data provide a basis for extending these approaches to other anthelmintic drug classes and a pathway towards validating new markers for anthelmintic resistance that can be deployed to improve parasite disease control. [ABSTRACT FROM AUTHOR]

Telomeres are involved in the maintenance of chromosomes and the prevention of genome instability. Despite this central importance, significant variation in telomere length has been observed in a variety of organisms. The genetic determinants of telomerelength variation and their effects on organismal fitness are largely unexplored. Here, we describe natural variation in telomere length across the Caenorhabditis elegans species. We identify a large-effect variant that contributes to differences in telomere length. The variant alters the conserved oligonucleotide/oligosaccharide-binding fold of protection of telomeres 2 (POT-2), a homolog of a human telomere-capping shelterin complex subunit. Mutations within this domain likely reduce the ability of POT-2 to bind telomeric DNA, thereby increasing telomere length. We find that telomere-length variation does not correlate with offspring production or longevity in C. elegans wild isolates, suggesting that naturally long telomeres play a limited role in modifying fitness phenotypes in C. elegans. [ABSTRACT FROM AUTHOR]

Genome-wide association studies hold the potential for discovering genetic causes for a wide range of diseases, traits, and behaviors. However, the incredible amount of data handling, advanced statistics, and visualization have made conducting these studies difficult for researchers. Here we provide a tool, manhattan, for helping investigators easily visualize genome-wide association studies data in Stata. [ABSTRACT FROM AUTHOR]

Phoresy is an interspecies interaction that facilitates spatial dispersal by attaching to a more mobile species. Hitchhiking species have evolved specific traits for physical contact and successful phoresy, but the regulatory mechanisms involved in such traits and their evolution are largely unexplored. The nematode Caenorhabditis elegans displays a hitchhiking behavior known as nictation during its stress-induced developmental stage. Dauer-specific nictation behavior has an important role in natural C. elegans populations, which experience boom-and-bust population dynamics. In this study, we investigated the nictation behavior of 137 wild C. elegans strains sampled throughout the world. We identified species-wide natural variation in nictation and performed a genome-wide association mapping. We show that the variants in the promoter of nta-1, encoding a putative steroidogenic enzyme, underlie differences in nictation. This difference is due to the changes in nta-1 expression in glial cells, which implies that glial steroid metabolism regulates phoretic behavior. Population genetic analysis and geographic distribution patterns suggest that balancing selection maintained two nta-1 haplotypes that existed in ancestral C. elegans populations. Our findings contribute to further understanding of the molecular mechanism of species interaction and the maintenance of genetic diversity within natural populations. [ABSTRACT FROM AUTHOR]

The distribution of fitness effects (DFE) for new mutations is one of the most theoretically important but difficult to estimate properties in population genetics. A crucial challenge to inferring the DFE from natural genetic variation is the sensitivity of the site frequency spectrum to factors like population size change, population substructure, genome structure, and nonrandom mating. Although inference methods aim to control for population size changes, the influence of nonrandom mating remains incompletely understood, despite being a common feature of many species. We report the DFE estimated from 326 genomes of Caenorhabditis elegans, a nematode roundworm with a high rate of self-fertilization. We evaluate the robustness of DFE inferences using simulated data that mimics the genomic structure and reproductive life history of C. elegans. Our observations demonstrate how the combined influence of self-fertilization, genome structure, and natural selection on linked sites can conspire to compromise estimates of the DFE from extant polymorphisms with existing methods. These factors together tend to bias inferences toward weakly deleterious mutations, making it challenging to have full confidence in the inferred DFE of new mutations as deduced from standing genetic variation in species like C. elegans. Improved methods for inferring the DFE are needed to appropriately handle strong linked selection and selfing. These results highlight the importance of understanding the combined effects of processes that can bias our interpretations of evolution in natural populations. [ABSTRACT FROM AUTHOR]

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it can be used with other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples. [ABSTRACT FROM AUTHOR]

Summary: The variant call format (VCF) is a popular standard for storing genetic variation data. As a result, a large collection of tools has been developed that perform diverse analyses using VCF files. However, some tasks common to statistical and population geneticists have not been created yet. To streamline these types of analyses, we created novel tools that analyze or annotate VCF files and organized these tools into a command-line based utility named VCF-kit. VCF-kit adds essential utilities to process and analyze VCF files, including primer generation for variant validation, dendrogram production, genotype imputation from sequence data in linkage studies, and additional tools. [ABSTRACT FROM AUTHOR]

The nematode Caenorhabditis elegans is among the most widely studied organisms, but relatively little is known about its natural ecology. Genetic diversity is low across much of the globe but high in the Hawaiian Islands and across the Pacific Rim. To characterize the niche and genetic diversity of C. elegans on the Hawaiian Islands and to explore how genetic diversity might be influenced by local adaptation, we repeatedly sampled nematodes over a three‐year period, measured various environmental parameters at each sampling site, and whole‐genome sequenced the C. elegans isolates that we identified. We found that the typical Hawaiian C. elegans niche comprises moderately moist native forests at high elevations (500–1,500 m) where ambient air temperatures are cool (15–20°C). Compared to other Caenorhabditis species found on the Hawaiian Islands (e.g., Caenorhabditis briggsae and Caenorhabditis tropicalis), we found that C. elegans were enriched in native habitats. We measured levels of genetic diversity and differentiation among Hawaiian C. elegans and found evidence of seven genetically distinct groups distributed across the islands. Then, we scanned these genomes for signatures of local adaptation and identified 18 distinct regions that overlap with hyper‐divergent regions, which may be maintained by balancing selection and are enriched for genes related to environmental sensing, xenobiotic detoxification, and pathogen resistance. These results provide strong evidence of local adaptation among Hawaiian C. elegans and contribute to our understanding of the forces that shape genetic diversity on the most remote volcanic archipelago in the world. [ABSTRACT FROM AUTHOR]

Objective: Measurements of neonatal metabolites are commonly used in newborn screening (NBS) programs to detect inborn errors of metabolism. Variation in these metabolites, particularly in infants born preterm (<37 weeks gestation), can result from multiple etiologies. We sought to evaluate the impact of maternal complications of pregnancy and environmental stressors on NBS metabolites. Methods: We examined 49 metabolic biomarkers obtained from routine NBS in 452 infants born preterm for association with maternal environmental stressors and complications of pregnancy. Results: Neonatal free carnitine (C0, p = 1.4 × 10−7), acetylcarnitine (C2, p = 2.7 × 10−7), octenoylcarnitine (C8:1, p = 5.2 × 10−11) and linoleoylcarnitine (C18:2, p = 9.1 × 10−7) were elevated in infants born to preeclamptic mothers. Similar elevations were observed in small for gestational age infants and in infants where labor was not initiated prior to delivery. When accounting for all three factors, associations remained strongest between acylcarnitines and preeclampsia. Conclusion: We observed that maternal conditions, particularly preeclampsia, influence NBS biomarkers. This is important for identifying maternal conditions that influence metabolites measured during routine NBS that are also markers of fetal growth and overall health. [ABSTRACT FROM AUTHOR]

To understand the genetic basis of complex traits, it is important to be able to efficiently phenotype many genetically distinct individuals. In the nematode Caenorhabditis elegans, individuals have been isolated from diverse populations around the globe and whole-genome sequenced. As a result, hundreds of wild strains with known genome sequences can be used for genome-wide association studies (GWAS). However, phenotypic analysis of these strains can be laborious, particularly for quantitative traits requiring multiple measurements per strain. Starvation resistance is likely a fitness-proximal trait for nematodes, and it is related to metabolic disease risk in humans. However, natural variation in C. elegans starvation resistance has not been systematically characterized, and precise measurement of the trait is time-intensive. Here, we developed a population-selection-and-sequencing-based approach to phenotype starvation resistance in a pool of 96 wild strains. We used restriction site-associated DNA sequencing (RAD-seq) to infer the frequency of each strain among survivors in a mixed culture over time during starvation. We used manual starvation survival assays to validate the trait data, confirming that strains that increased in frequency over time are starvation-resistant relative to strains that decreased in frequency. Further, we found that variation in starvation resistance is significantly associated with variation at a region on chromosome III. Using a near-isogenic line (NIL), we showed the importance of this genomic interval for starvation resistance. This study demonstrates the feasibility of using population selection and sequencing in an animal model for phenotypic analysis of quantitative traits, documents natural variation of starvation resistance in C. elegans, and identifies a genomic region that contributes to such variation. [ABSTRACT FROM AUTHOR]

Bleomycin is a powerful chemotherapeutic drug used to treat a variety of cancers. However, individual patients vary in their responses to bleomycin. The identification of genetic differences that underlie this response variation could improve treatment outcomes by tailoring bleomycin dosages to each patient. We used the model organism Caenorhabditis elegans to identify genetic determinants of bleomycin-response differences by performing linkage mapping on recombinants derived from a cross between the laboratory strain (N2) and a wild strain (CB4856). This approach identified a small genomic region on chromosome V that underlies bleomycin-response variation. Using near-isogenic lines, and strains with CRISPR-Cas9 mediated deletions and allele replacements, we discovered that a novel nematode-specific gene (scb-1) is required for bleomycin resistance. Although the mechanism by which this gene causes variation in bleomycin responses is unknown, we suggest that a rare variant present in the CB4856 strain might cause differences in the potential stress-response function of scb-1 between the N2 and CB4856 strains, thereby leading to differences in bleomycin resistance. [ABSTRACT FROM AUTHOR]

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a highthroughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation. [ABSTRACT FROM AUTHOR]

Many chemotherapeutic drugs are differentially effective from one patient to the next. Understanding the causes of this variability is a critical step towards the development of personalized treatments and improvements to existing medications. Here, we investigate sensitivity to a group of anti-neoplastic drugs that target topoisomerase II using the model organism Caenorhabditis elegans. We show that wild strains of C. elegans vary in their sensitivity to these drugs, and we use an unbiased genetic approach to demonstrate that this natural variation is explained by a methionine-to-glutamine substitution in topoisomerase II (TOP-2). The presence of a non-polar methionine at this residue increases hydrophobic interactions between TOP-2 and its poison etoposide, as compared to a polar glutamine. We hypothesize that this stabilizing interaction results in increased genomic instability in strains that contain a methionine residue. The residue affected by this substitution is conserved from yeast to humans and is one of the few differences between the two human topoisomerase II isoforms (methionine in hTOPIIα and glutamine in hTOPIIβ). We go on to show that this amino acid difference between the two human topoisomerase isoforms influences cytotoxicity of topoisomerase II poisons in human cell lines. These results explain why hTOPIIα and hTOPIIβ are differentially affected by various poisons and demonstrate the utility of C. elegans in understanding the genetics of drug responses. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Identifying common clinical and environmental factors that influence newborn metabolic biomarkers will improve the utilization of metabolite panels for clinical diagnostic medicine. Design and methods: Environmental effects including gender, season of birth, gestational age, birth weight, feeding method and age at time of collection were evaluated for over 50 metabolites collected by the Iowa Neonatal Metabolic Screening Program on 221,788 newborns over a six year period. Results: We replicated well known observations that low birth weight and preterm infants have higher essential amino acids and lower medium and long chain acylcarnitine levels than their term counterparts. Smaller, but still significant, differences were observed for gender and timing of sample collection, specifically the season in which the infant was born. Most intriguing were our findings of higher thyroid stimulating hormone in the winter months (P <1×10−40) which correlated with an increased false positive rate of congenital hypothyroidism in the winter (0.9%) compared to summer (0.6%). Previous studies, conducted globally, have identified an increased prevalence of suspected and confirmed cases of congenital hypothyroidism in the winter months. We found that the percentage of unresolved suspected cases were slightly higher in the winter (0.3% vs. 0.2%). Conclusions: We identified differences in metabolites by gestational age, birth weight, gender and season. Some are widely reported such as gestational age and birth weight, while others such as the effect of seasonality are not as well studied. [Copyright &y& Elsevier]