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Abstract Conifers are long-lived and slow-evolving, thus requiring effective defences against their fast-evolving insect natural enemies. The copy number variation (CNV) of two key acetophenone biosynthesis genes Ugt5/Ugt5b and βglu-1 may provide a plausible mechanism underlying the constitutively variable defence in white spruce (Picea glauca) against its primary defoliator, spruce budworm. This study develops a long-insert sequence capture probe set (Picea_hung_p1.0) for quantifying copy number of βglu-1-like, Ugt5-like genes and single-copy genes on 38 Norway spruce (Picea abies) and 40 P. glauca individuals from eight and nine provenances across Europe and North America respectively. We developed local assemblies (Piabi_c1.0 and Pigla_c.1.0), full-length transcriptomes (PIAB_v1 and PIGL_v1), and gene models to characterise the diversity of βglu-1 and Ugt5 genes. We observed very large copy numbers of βglu-1, with up to 381 copies in a single P. glauca individual. We observed among-provenance CNV of βglu-1 in P. glauca but not P. abies. Ugt5b was predominantly single-copy in both species. This study generates critical hypotheses for testing the emergence and mechanism of extreme CNV, the dosage effect on phenotype, and the varying copy number of genes with the same pathway. We demonstrate new approaches to overcome experimental challenges in genomic research in conifer defences.

The rocky reefs of British Columbia’s (BC) coast are a productive ecosystem, home to 38 rockfish species (Genus: Sebastes) that are culturally and economically important. Quantitatively assessing rockfish populations is vital to support conservation and stock assessment needs. Self-contained underwater breathing apparatus (SCUBA) diving surveys are a commonly used monitoring method in BC. However, this resource-intensive approach is challenging, particularly for cryptic or deeper species. Herein, we compared environmental DNA (eDNA) detection methods with SCUBA diving surveys to capture overall rockfish biodiversity. We employed two eDNA methods: 1) a targeted quantitative real-time polymerase chain reaction (qPCR) approach to monitor species of particular importance to First Nations collaborators and decision makers, and 2) a metabarcoding approach for assessing community composition using the previously published MiSebastes assay. Both approaches are confounded by the little DNA sequence divergence among species and high sequence variation within species. Overcoming these challenges using a whole mitochondrial approach with the mtGrasp and unikseq pipelines, we generated highly useful eDNA tools. We found that eDNA methods were highly comparable to dive surveys, as both methods indicated a similar ecological reality, including species detections and distributions. Though there are certain species that cannot be distinguished by the MiSebastes assay, eDNA metabarcoding still detected more rockfish species overall. Both eDNA methods show potential for use alongside conventional methods for scalable incorporation into community-based monitoring programs.

Abstract Environmental DNA (eDNA) is revolutionizing species monitoring in nature. At the heart of any eDNA approach is the reliance upon sufficient DNA sequence information to satisfy the demands of eDNA assay specificity and sensitivity. The most common source of this information has been restricted to short barcoding regions of the mitochondrial genome (mitogenome) and marker genes. The use of these limited regions for assay design has often resulted in substantial trade‐offs in assay performance. With increased accessibility of full mitogenome assemblies, the potential for designing more robust eDNA assays is considerably enhanced. However, this also poses a new challenge to effectively identify suitable regions for assay design using considerably larger sequences. We present unikseq, a utility that uses words of length k (k‐mers) to identify unique regions in a reference sequence relative to tolerated (ingroup) and not‐tolerated (outgroup or non‐target) sequence sets, quickly and with low memory that can yield highly specific assays. We illustrate its application within an assay development workflow through use‐case examples for the design and validation of four quantitative real‐time polymerase chain reaction (qPCR)‐based assays selective for American bullfrog (Rana [Lithobates] catesbeiana), Burbot (Lota lota), Lake trout (Salvelinus namaycush), and Quillback rockfish (Sebastes maliger). The chosen target species vary in range, habitat, and degree of relatedness to their sympatric species that, consequently, impact eDNA assay design difficulty. We demonstrate the effectiveness of unikseq through assay validation and characterization using DNA from voucher specimens, synthetic DNA, and, where possible, field samples, to verify the specificity and sensitivity of the newly designed assays. By facilitating whole mitogenome sequence comparison, the creation of high‐performing eDNA assays is substantially enhanced. Having several adjustable parameters for specifying user requirements within unikseq, this approach can facilitate the identification of suitable regions for a broad range of applications requiring nucleotide sequence comparisons.

Abstract Background The mountain pine beetle, Dendroctonus ponderosae, is an irruptive bark beetle that causes extensive mortality to many pine species within the forests of western North America. Driven by climate change and wildfire suppression, a recent mountain pine beetle (MPB) outbreak has spread across more than 18 million hectares, including areas to the east of the Rocky Mountains that comprise populations and species of pines not previously affected. Despite its impacts, there are few tactics available to control MPB populations. Beauveria bassiana is an entomopathogenic fungus used as a biological agent in agriculture and forestry and has potential as a management tactic for the mountain pine beetle population. This work investigates the phenotypic and genomic variation between B. bassiana strains to identify optimal strains against a specific insect. Results Using comparative genome and transcriptome analyses of eight B. bassiana isolates, we have identified the genetic basis of virulence, which includes oosporein production. Genes unique to the more virulent strains included functions in biosynthesis of mycotoxins, membrane transporters, and transcription factors. Significant differential expression of genes related to virulence, transmembrane transport, and stress response was identified between the different strains, as well as up to nine-fold upregulation of genes involved in the biosynthesis of oosporein. Differential correlation analysis revealed transcription factors that may be involved in regulating oosporein production. Conclusion This study provides a foundation for the selection and/or engineering of the most effective strain of B. bassiana for the biological control of mountain pine beetle and other insect pests populations.

Abstract Long-read sequencing technologies have improved significantly since their emergence. Their read lengths, potentially spanning entire transcripts, is advantageous for reconstructing transcriptomes. Existing long-read transcriptome assembly methods are primarily reference-based and to date, there is little focus on reference-free transcriptome assembly. We introduce “RNA-Bloom2 [ https://github.com/bcgsc/RNA-Bloom ]”, a reference-free assembly method for long-read transcriptome sequencing data. Using simulated datasets and spike-in control data, we show that the transcriptome assembly quality of RNA-Bloom2 is competitive to those of reference-based methods. Furthermore, we find that RNA-Bloom2 requires 27.0 to 80.6% of the peak memory and 3.6 to 10.8% of the total wall-clock runtime of a competing reference-free method. Finally, we showcase RNA-Bloom2 in assembling a transcriptome sample of Picea sitchensis (Sitka spruce). Since our method does not rely on a reference, it further sets the groundwork for large-scale comparative transcriptomics where high-quality draft genome assemblies are not readily available.

Abstract Objectives Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs in databases of peptide sequences. In our tests, AMPlify outperformed the state-of-the-art. We have illustrated its use on data describing the American bullfrog (Rana [Lithobates] catesbeiana) genome. Here we present the model files and training/test data sets we used in that study. The original model (the balanced model) was trained on a balanced set of AMP and non-AMP sequences curated from public databases. In this data note, we additionally provide a model trained on an imbalanced set, in which non-AMP sequences far outnumber AMP sequences. We note that the balanced and imbalanced models would serve different use cases, and both would serve the research community, facilitating the discovery and development of novel AMPs. Data description This data note provides two sets of models, as well as two AMP and four non-AMP sequence sets for training and testing the balanced and imbalanced models. Each model set includes five single sub-models that form an ensemble model. The first model set corresponds to the original model trained on a balanced training set that has been described in the original AMPlify manuscript, while the second model set was trained on an imbalanced training set.

Abstract Emu (Dromaius novaehollandiae) farming has been gaining wide interest for fat production. Oil rendered from this large flightless bird’s fat is valued for its anti-inflammatory and antioxidant properties for uses in therapeutics and cosmetics. We analyzed the seasonal and sex-dependent differentially expressed (DE) genes involved in fat metabolism in emus. Samples were taken from back and abdominal fat tissues of a single set of four male and four female emus in April, June, and November for RNA-sequencing. We found 100 DE genes (47 seasonally in males; 34 seasonally in females; 19 between sexes). Seasonally DE genes with significant difference between the sexes in gene ontology terms suggested integrin beta chain-2 (ITGB2) influences fat changes, in concordance with earlier studies. Six seasonally DE genes functioned in more than two enriched pathways (two female: angiopoietin-like 4 (ANGPTL4) and lipoprotein lipase (LPL); four male: lumican (LUM), osteoglycin (OGN), aldolase B (ALDOB), and solute carrier family 37 member 2 (SLC37A2)). Two sexually DE genes, follicle stimulating hormone receptor (FSHR) and perilipin 2 (PLIN2), had functional investigations supporting their influence on fat gain and loss. The results suggested these nine genes influence fat metabolism and deposition in emus.

Abstract Detection of short tandem repeat (STR) expansions with standard short-read sequencing is challenging due to the difficulty in mapping multicopy repeat sequences. In this study, we explored how the long-range sequence information of barcode linked-read sequencing (BLRS) can be leveraged to improve repeat-read detection. We also devised a novel algorithm using BLRS barcodes for distance estimation and evaluated its application for STR genotyping. Both approaches were designed for genotyping large expansions (> 1 kb) that cannot be sized accurately by existing methods. Using simulated and experimental data of genomes with STR expansions from multiple BLRS platforms, we validated the utility of barcode and phasing information in attaining better STR genotypes compared to standard short-read sequencing. Although the coverage bias of extremely GC-rich STRs is an important limitation of BLRS, BLRS is an effective strategy for genotyping many other STR loci.

Abstract Background Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Results Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli. Conclusions We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.

Abstract Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of Caenorhabditis elegans, Oryza sativa, and three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 1.2-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently improves upon human assemblies in under five hours using less than 23 GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch .

Abstract Background Screening for short tandem repeat (STR) expansions in next-generation sequencing data can enable diagnosis, optimal clinical management/treatment, and accurate genetic counseling of patients with repeat expansion disorders. We aimed to develop an efficient computational workflow for reliable detection of STR expansions in next-generation sequencing data and demonstrate its clinical utility. Methods We characterized the performance of eight STR analysis methods (lobSTR, HipSTR, RepeatSeq, ExpansionHunter, TREDPARSE, GangSTR, STRetch, and exSTRa) on next-generation sequencing datasets of samples with known disease-causing full-mutation STR expansions and genomes simulated to harbor repeat expansions at selected loci and optimized their sensitivity. We then used a machine learning decision tree classifier to identify an optimal combination of methods for full-mutation detection. In Burrows-Wheeler Aligner (BWA)-aligned genomes, the ensemble approach of using ExpansionHunter, STRetch, and exSTRa performed the best (precision = 82%, recall = 100%, F1-score = 90%). We applied this pipeline to screen 301 families of children with suspected genetic disorders. Results We identified 10 individuals with full-mutations in the AR, ATXN1, ATXN8, DMPK, FXN, or HTT disease STR locus in the analyzed families. Additional candidates identified in our analysis include two probands with borderline ATXN2 expansions between the established repeat size range for reduced-penetrance and full-penetrance full-mutation and seven individuals with FMR1 CGG repeats in the intermediate/premutation repeat size range. In 67 probands with a prior negative clinical PCR test for the FMR1, FXN, or DMPK disease STR locus, or the spinocerebellar ataxia disease STR panel, our pipeline did not falsely identify aberrant expansion. We performed clinical PCR tests on seven (out of 10) full-mutation samples identified by our pipeline and confirmed the expansion status in all, showing absolute concordance between our bioinformatics and molecular findings. Conclusions We have successfully demonstrated the application of a well-optimized bioinformatics pipeline that promotes the utility of genome-wide sequencing as a first-tier screening test to detect expansions of known disease STRs. Interrogating clinical next-generation sequencing data for pathogenic STR expansions using our ensemble pipeline can improve diagnostic yield and enhance clinical outcomes for patients with repeat expansion disorders.

Abstract Tandem repeat (TR) expansion is the underlying cause of over 40 neurological disorders. Long-read sequencing offers an exciting avenue over conventional technologies for detecting TR expansions. Here, we present Straglr, a robust software tool for both targeted genotyping and novel expansion detection from long-read alignments. We benchmark Straglr using various simulations, targeted genotyping data of cell lines carrying expansions of known diseases, and whole genome sequencing data with chromosome-scale assembly. Our results suggest that Straglr may be useful for investigating disease-associated TR expansions using long-read sequencing.

Background The Human Leukocyte Antigen (HLA) gene locus plays a fundamental role in human immunity, and it is established that certain HLA alleles are disease determinants. Previously, we have identified prevalent HLA class I and class II alleles, including DPA1*02:02, in two small patient cohorts at the COVID-19 pandemic onset. Methods We have since analyzed a larger public patient cohort data (n = 126 patients) with controls, associated demographic and clinical data. By combining the predictive power of multiple in silico HLA predictors, we report on HLA-I and HLA-II alleles, along with their associated risk significance. Results We observe HLA-II DPA1*02:02 at a higher frequency in the COVID-19 positive cohort (29%) when compared to the COVID-negative control group (Fisher’s exact test [FET] p = 0.0174). Having this allele, however, does not appear to put this cohort’s patients at an increased risk of hospitalization. Inspection of COVID-19 disease severity outcomes, including admission to intensive care, reveal nominally significant risk associations with A*11:01 (FET p = 0.0078) and C*04:01 (FET p = 0.0087). The association with severe disease outcome is especially evident for patients with C*04:01, where disease prognosis measured by mechanical ventilation-free days was statistically significant after multiple hypothesis correction (Bonferroni p = 0.0323). While prevalence of some of these alleles falls below statistical significance after Bonferroni correction, COVID-19 patients with HLA-I C*04:01 tend to fare worse overall. This HLA allele may hold potential clinical value.

Antimicrobial peptides (AMPs) are a diverse class of short, often cationic biological molecules that present promising opportunities in the development of new therapeutics to combat antimicrobial resistance. Newly developed in silico methods offer the ability to rapidly discover numerous novel AMPs with a variety of physiochemical properties. Herein, using the rAMPage AMP discovery pipeline, we bioinformatically identified 51 AMP candidates from amphibia and insect RNA-seq data and present their in-depth characterization. The studied AMPs demonstrate activity against a panel of bacterial pathogens and have undetected or low toxicity to red blood cells and human cultured cells. Amino acid sequence analysis revealed that 30 of these bioactive peptides belong to either the Brevinin-1, Brevinin-2, Nigrocin-2, or Apidaecin AMP families. Prediction of three-dimensional structures using ColabFold indicated an association between peptides predicted to adopt a helical structure and broad-spectrum antibacterial activity against the Gram-negative and Gram-positive species tested in our panel. These findings highlight the utility of associating the diverse sequences of novel AMPs with their estimated peptide structures in categorizing AMPs and predicting their antimicrobial activity.

Antibiotic resistance is a global health crisis increasing in prevalence every day. To combat this crisis, alternative antimicrobial therapeutics are urgently needed. Antimicrobial peptides (AMPs), a family of short defense proteins, are produced naturally by all organisms and hold great potential as effective alternatives to small molecule antibiotics. Here, we present rAMPage, a scalable bioinformatics discovery platform for identifying AMP sequences from RNA sequencing (RNA-seq) datasets. In our study, we demonstrate the utility and scalability of rAMPage, running it on 84 publicly available RNA-seq datasets from 75 amphibian and insect species—species known to have rich AMP repertoires. Across these datasets, we identified 1137 putative AMPs, 1024 of which were deemed novel by a homology search in cataloged AMPs in public databases. We selected 21 peptide sequences from this set for antimicrobial susceptibility testing against Escherichia coli and Staphylococcus aureus and observed that seven of them have high antimicrobial activity. Our study illustrates how in silico methods such as rAMPage can enable the fast and efficient discovery of novel antimicrobial peptides as an effective first step in the strenuous process of antimicrobial drug development.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

As the year 2020 came to a close, several new strains have been reported of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent responsible for the coronavirus disease 2019 (COVID-19) pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the pandemic. Availability: The tool used to perform the reported mutation analysis results, ntEdit, is available from GitHub. Genome mutation reports are available for download from BCGSC. Mutation time maps are available from https://bcgsc.github.io/SARS2/.

Abstract Background Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembled sequence. Although tools exist to identify and correct misassemblies using Illumina paired-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint to address this gap. Results To demonstrate the effectiveness of Tigmint, we applied it to assemblies of a human genome using short reads assembled with ABySS 2.0 and other assemblers. Tigmint reduced the number of misassemblies identified by QUAST in the ABySS assembly by 216 (27%). While scaffolding with ARCS alone more than doubled the scaffold NGA50 of the assembly from 3 to 8 Mbp, the combination of Tigmint and ARCS improved the scaffold NGA50 of the assembly over five-fold to 16.4 Mbp. This notable improvement in contiguity highlights the utility of assembly correction in refining assemblies. We demonstrate the utility of Tigmint in correcting the assemblies of multiple tools, as well as in using Chromium reads to correct and scaffold assemblies of long single-molecule sequencing. Conclusions Scaffolding an assembly that has been corrected with Tigmint yields a final assembly that is both more correct and substantially more contiguous than an assembly that has not been corrected. Using single-molecule sequencing in combination with linked reads enables a genome sequence assembly that achieves both a high sequence contiguity as well as high scaffold contiguity, a feat not currently achievable with either technology alone.

Abstract Background RNA-seq is a powerful and cost-effective technology for molecular diagnostics of cancer and other diseases, and it can reach its full potential when coupled with validated clinical-grade informatics tools. Despite recent advances in long-read sequencing, transcriptome assembly of short reads remains a useful and cost-effective methodology for unveiling transcript-level rearrangements and novel isoforms. One of the major concerns for adopting the proven de novo assembly approach for RNA-seq data in clinical settings has been the analysis turnaround time. To address this concern, we have developed a targeted approach to expedite assembly and analysis of RNA-seq data. Results Here we present our Targeted Assembly Pipeline (TAP), which consists of four stages: 1) alignment-free gene-level classification of RNA-seq reads using BioBloomTools, 2) de novo assembly of individual targets using Trans-ABySS, 3) alignment of assembled contigs to the reference genome and transcriptome with GMAP and BWA and 4) structural and splicing variant detection using PAVFinder. We show that PAVFinder is a robust gene fusion detection tool when compared to established methods such as Tophat-Fusion and deFuse on simulated data of 448 events. Using the Leucegene acute myeloid leukemia (AML) RNA-seq data and a set of 580 COSMIC target genes, TAP identified a wide range of hallmark molecular anomalies including gene fusions, tandem duplications, insertions and deletions in agreement with published literature results. Moreover, also in this dataset, TAP captured AML-specific splicing variants such as skipped exons and novel splice sites reported in studies elsewhere. Running time of TAP on 100–150 million read pairs and a 580-gene set is one to 2 hours on a 48-core machine. Conclusions We demonstrated that TAP is a fast and robust RNA-seq variant detection pipeline that is potentially amenable to clinical applications. TAP is available at http://www.bcgsc.ca/platform/bioinfo/software/pavfinder

Abstract Background Alternative polyadenylation (APA) results in messenger RNA molecules with different 3′ untranslated regions (3’ UTRs), affecting the molecules’ stability, localization, and translation. APA is pervasive and implicated in cancer. Earlier reports on APA focused on 3’ UTR length modifications and commonly characterized APA events as 3’ UTR shortening or lengthening. However, such characterization oversimplifies the processing of 3′ ends of transcripts and fails to adequately describe the various scenarios we observe. Results We built a cloud-based targeted de novo transcript assembly and analysis pipeline that incorporates our previously developed cleavage site prediction tool, KLEAT. We applied this pipeline to elucidate the APA profiles of 114 genes in 9939 tumor and 729 tissue normal samples from The Cancer Genome Atlas (TCGA). The full set of 10,668 RNA-Seq samples from 33 cancer types has not been utilized by previous APA studies. By comparing the frequencies of predicted cleavage sites between normal and tumor sample groups, we identified 77 events (i.e. gene-cancer type pairs) of tumor-specific APA regulation in 13 cancer types; for 15 genes, such regulation is recurrent across multiple cancers. Our results also support a previous report showing the 3’ UTR shortening of FGF2 in multiple cancers. However, over half of the events we identified display complex changes to 3’ UTR length that resist simple classification like shortening or lengthening. Conclusions Recurrent tumor-specific regulation of APA is widespread in cancer. However, the regulation pattern that we observed in TCGA RNA-seq data cannot be described as straightforward 3’ UTR shortening or lengthening. Continued investigation into this complex, nuanced regulatory landscape will provide further insight into its role in tumor formation and development.

Abstract Background The long-range sequencing information captured by linked reads, such as those available from 10× Genomics (10xG), helps resolve genome sequence repeats, and yields accurate and contiguous draft genome assemblies. We introduce ARKS, an alignment-free linked read genome scaffolding methodology that uses linked reads to organize genome assemblies further into contiguous drafts. Our approach departs from other read alignment-dependent linked read scaffolders, including our own (ARCS), and uses a kmer-based mapping approach. The kmer mapping strategy has several advantages over read alignment methods, including better usability and faster processing, as it precludes the need for input sequence formatting and draft sequence assembly indexing. The reliance on kmers instead of read alignments for pairing sequences relaxes the workflow requirements, and drastically reduces the run time. Results Here, we show how linked reads, when used in conjunction with Hi-C data for scaffolding, improve a draft human genome assembly of PacBio long-read data five-fold (baseline vs. ARKS NG50 = 4.6 vs. 23.1 Mbp, respectively). We also demonstrate how the method provides further improvements of a megabase-scale Supernova human genome assembly (NG50 = 14.74 Mbp vs. 25.94 Mbp before and after ARKS), which itself exclusively uses linked read data for assembly, with an execution speed six to nine times faster than competitive linked read scaffolders (~ 10.5 h compared to 75.7 h, on average). Following ARKS scaffolding of a human genome 10xG Supernova assembly (of cell line NA12878), fewer than 9 scaffolds cover each chromosome, except the largest (chromosome 1, n = 13). Conclusions ARKS uses a kmer mapping strategy instead of linked read alignments to record and associate the barcode information needed to order and orient draft assembly sequences. The simplified workflow, when compared to that of our initial implementation, ARCS, markedly improves run time performances on experimental human genome datasets. Furthermore, the novel distance estimator in ARKS utilizes barcoding information from linked reads to estimate gap sizes. It accomplishes this by modeling the relationship between known distances of a region within contigs and calculating associated Jaccard indices. ARKS has the potential to provide correct, chromosome-scale genome assemblies, promptly. We expect ARKS to have broad utility in helping refine draft genomes.

The globally-distributed Ranidae (true frogs) are the largest frog family. Here, Hammond et al. present a draft genome of the North American bullfrog, Rana (Lithobates) catesbeiana, as a foundation for future understanding of true frog genetics as amphibian species face difficult environmental challenges.

Plant diseases caused by fungi and Oomycetes represent worldwide threats to crops and forest ecosystems. Effective prevention and appropriate management of emerging diseases rely on rapid detection and identification of the causal pathogens. The increase in genomic resources makes it possible to generate novel genome-enhanced DNA detection assays that can exploit whole genomes to discover candidate genes for pathogen detection. A pipeline was developed to identify genome regions that discriminate taxa or groups of taxa and can be converted into PCR assays. The modular pipeline is comprised of four components: (1) selection and genome sequencing of phylogenetically related taxa, (2) identification of clusters of orthologous genes, (3) elimination of false positives by filtering, and (4) assay design. This pipeline was applied to some of the most important plant pathogens across three broad taxonomic groups: Phytophthoras (Stramenopiles, Oomycota), Dothideomycetes (Fungi, Ascomycota) and Pucciniales (Fungi, Basidiomycota). Comparison of 73 fungal and Oomycete genomes led the discovery of 5,939 gene clusters that were unique to the targeted taxa and an additional 535 that were common at higher taxonomic levels. Approximately 28% of the 299 tested were converted into qPCR assays that met our set of specificity criteria. This work demonstrates that a genome-wide approach can efficiently identify multiple taxon-specific genome regions that can be converted into highly specific PCR assays. The possibility to easily obtain multiple alternative regions to design highly specific qPCR assays should be of great help in tackling challenging cases for which higher taxon-resolution is needed.

The Steller sea lion is the largest member of the Otariidae family and is found in the coastal waters of the northern Pacific Rim. Here, we present the Steller sea lion genome, determined through DNA sequencing approaches that utilized microfluidic partitioning library construction, as well as nanopore technologies. These methods constructed a highly contiguous assembly with a scaffold N50 length of over 14 megabases, a contig N50 length of over 242 kilobases and a total length of 2.404 gigabases. As a measure of completeness, 95.1% of 4104 highly conserved mammalian genes were found to be complete within the assembly. Further annotation identified 19,668 protein coding genes. The assembled genome sequence and underlying sequence data can be found at the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA475770.

The grizzly bear (Ursus arctos ssp. horribilis) represents the largest population of brown bears in North America. Its genome was sequenced using a microfluidic partitioning library construction technique, and these data were supplemented with sequencing from a nanopore-based long read platform. The final assembly was 2.33 Gb with a scaffold N50 of 36.7 Mb, and the genome is of comparable size to that of its close relative the polar bear (2.30 Gb). An analysis using 4104 highly conserved mammalian genes indicated that 96.1% were found to be complete within the assembly. An automated annotation of the genome identified 19,848 protein coding genes. Our study shows that the combination of the two sequencing modalities that we used is sufficient for the construction of highly contiguous reference quality mammalian genomes. The assembled genome sequence and the supporting raw sequence reads are available from the NCBI (National Center for Biotechnology Information) under the bioproject identifier PRJNA493656, and the assembly described in this paper is version QXTK01000000.

The linked read sequencing library preparation platform by 10X Genomics produces barcoded sequencing libraries, which are subsequently sequenced using the Illumina short read sequencing technology. In this new approach, long fragments of DNA are partitioned into separate micro-reactions, where the same index sequence is incorporated into each of the sequencing fragment inserts derived from a given long fragment. In this study, we exploited this property by using reads from index sequences associated with a large number of reads, to assemble the chloroplast genome of the Sitka spruce tree (Picea sitchensis). Here we report on the first Sitka spruce chloroplast genome assembled exclusively from P. sitchensis genomic libraries prepared using the 10X Genomics protocol. We show that the resulting 124,049 base pair long genome shares high sequence similarity with the related white spruce and Norway spruce chloroplast genomes, but diverges substantially from a previously published P. sitchensis- P. thunbergii chimeric genome. The use of reads from high-frequency indices enabled separation of the nuclear genome reads from that of the chloroplast, which resulted in the simplification of the de Bruijn graphs used at the various stages of assembly.

The beluga whale is a cetacean that inhabits arctic and subarctic regions, and is the only living member of the genus Delphinapterus. The genome of the beluga whale was determined using DNA sequencing approaches that employed both microfluidic partitioning library and non-partitioned library construction. The former allowed for the construction of a highly contiguous assembly with a scaffold N50 length of over 19 Mbp and total reconstruction of 2.32 Gbp. To aid our understanding of the functional elements, transcriptome data was also derived from brain, duodenum, heart, lung, spleen, and liver tissue. Assembled sequence and all of the underlying sequence data are available at the National Center for Biotechnology Information (NCBI) under the Bioproject accession number PRJNA360851A.

The northern sea otter inhabits coastal waters of the northern Pacific Ocean and is the largest member of the Mustelidae family. DNA sequencing methods that utilize microfluidic partitioned and non-partitioned library construction were used to establish the sea otter genome. The final assembly provided 2.426 Gbp of highly contiguous assembled genomic sequences with a scaffold N50 length of over 38 Mbp. We generated transcriptome data derived from a lymphoma to aid in the determination of functional elements. The assembled genome sequence and underlying sequence data are available at the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA388419.

In this work we studied the liver transcriptomes of two frog species, the American bullfrog (Rana (Lithobates) catesbeiana) and the African clawed frog (Xenopus laevis). We used high throughput RNA sequencing (RNA-seq) data to assemble and annotate these transcriptomes, and compared how their baseline expression profiles change when tadpoles of the two species are exposed to thyroid hormone. We generated more than 1.5 billion RNA-seq reads in total for the two species under two conditions as treatment/control pairs. We de novo assembled these reads using Trans-ABySS to reconstruct reference transcriptomes, obtaining over 350,000 and 130,000 putative transcripts for R. catesbeiana and X. laevis, respectively. Using available genomics resources for X. laevis, we annotated over 97% of our X. laevis transcriptome contigs, demonstrating the utility and efficacy of our methodology. Leveraging this validated analysis pipeline, we also annotated the assembled R. catesbeiana transcriptome. We used the expression profiles of the annotated genes of the two species to examine the similarities and differences between the tadpole liver transcriptomes. We also compared the gene ontology terms of expressed genes to measure how the animals react to a challenge by thyroid hormone. Our study reports three main conclusions. First, de novo assembly of RNA-seq data is a powerful method for annotating and establishing transcriptomes of non-model organisms. Second, the liver transcriptomes of the two frog species, R. catesbeiana and X. laevis, show many common features, and the distribution of their gene ontology profiles are statistically indistinguishable. Third, although they broadly respond the same way to the presence of thyroid hormone in their environment, their receptor/signal transduction pathways display marked differences.

Hortaea werneckii, ascomycetous yeast from the order Capnodiales, shows an exceptional adaptability to osmotically stressful conditions. To investigate this unusual phenotype we obtained a draft genomic sequence of a H. werneckii strain isolated from hypersaline water of solar saltern. Two of its most striking characteristics that may be associated with a halotolerant lifestyle are the large genetic redundancy and the expansion of genes encoding metal cation transporters. Although no sexual state of H. werneckii has yet been described, a mating locus with characteristics of heterothallic fungi was found. The total assembly size of the genome is 51.6 Mb, larger than most phylogenetically related fungi, coding for almost twice the usual number of predicted genes (23333). The genome appears to have experienced a relatively recent whole genome duplication, and contains two highly identical gene copies of almost every protein. This is consistent with some previous studies that reported increases in genomic DNA content triggered by exposure to salt stress. In hypersaline conditions transmembrane ion transport is of utmost importance. The analysis of predicted metal cation transporters showed that most types of transporters experienced several gene duplications at various points during their evolution. Consequently they are present in much higher numbers than expected. The resulting diversity of transporters presents interesting biotechnological opportunities for improvement of halotolerance of salt-sensitive species. The involvement of plasma P-type H⁺ ATPases in adaptation to different concentrations of salt was indicated by their salt dependent transcription. This was not the case with vacuolar H⁺ ATPases, which were transcribed constitutively. The availability of this genomic sequence is expected to promote the research of H. werneckii. Studying its extreme halotolerance will not only contribute to our understanding of life in hypersaline environments, but should also identify targets for improving the salt- and osmotolerance of economically important plants and microorganisms.

As the year 2020 came to a close, several new strains have been reported of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent responsible for the coronavirus disease 2019 (COVID-19) pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the pandemic. Availability: The tool used to perform the reported mutation analysis results, ntEdit, is available from GitHub. Genome mutation reports are available for download from BCGSC. Mutation time maps are available from https://bcgsc.github.io/SARS2/.

As the year 2020 came to a close, several new strains have been reported of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent responsible for the coronavirus disease 2019 (COVID-19) pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the year. Availability: The tool used to perform the reported mutation analysis results, ntEdit, is available from GitHub. Genome mutation reports are available for download from BCGSC. Mutation time maps are available from https://bcgsc.github.io/SARS2/.