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.
Spruces (Picea spp.) are coniferous trees widespread in boreal and mountainous forests of the northern hemisphere, with large economic significance and enormous contributions to global carbon sequestration. Spruces harbor very large genomes with high repetitiveness, hampering their comparative analysis. Here, we present and compare the genomes of four different North American spruces: the genome assemblies for Engelmann spruce (Picea engelmannii) and Sitka spruce (P. sitchensis) together with improved and more contiguous genome assemblies for white spruce (P. glauca) and for a naturally occurring introgress of these three species known as interior spruce (P. engelmannii × glauca × sitchensis). The genomes were structurally similar, and a large part of scaffolds could be anchored to a genetic map. The composition of the interior spruce genome indicated asymmetric contributions from the three ancestral genomes. Phylogenetic analysis of the nuclear and organelle genomes revealed a topology indicative of ancient reticulation. Different patterns of expansion of gene families among genomes were observed and related with presumed diversifying ecological adaptations. We identified rapidly evolving genes that harbored high rates of nonsynonymous polymorphisms relative to synonymous ones, indicative of positive selection and its hitchhiking effects. These gene sets were mostly distinct between the genomes of ecologically contrasted species, and signatures of convergent balancing selection were detected. Stress and stimulus response was identified as the most frequent function assigned to expanding gene families and rapidly evolving genes. These two aspects of genomic evolution were complementary in their contribution to divergent evolution of presumed adaptive nature. These more contiguous spruce giga-genome sequences should strengthen our understanding of conifer genome structure and evolution, as their comparison offers clues into the genetic basis of adaptation and ecology of conifers at the genomic level. They will also provide tools to better monitor natural genetic diversity and improve the management of conifer forests.
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.
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.
High-quality genome assemblies are crucial to many biological studies, and utilizing long sequencing reads can help achieve higher assembly contiguity. While long reads can resolve complex and repetitive regions of a genome, their relatively high associated error rates are still a major limitation. Long reads generally produce draft genome assemblies with lower base quality, which must be corrected with a genome polishing step. Hybrid genome polishing solutions can greatly improve the quality of long-read genome assemblies by utilizing more accurate short reads to validate bases and correct errors. Currently available hybrid polishing methods rely on read alignments, and are therefore memory-intensive and do not scale well to large genomes. Here we describe ntEdit+Sealer, an alignment-free, k-mer-based genome finishing protocol that employs memory-efficient Bloom filters. The protocol includes ntEdit for correcting base errors and small indels, and for marking potentially problematic regions, then Sealer for filling both assembly gaps and problematic regions flagged by ntEdit. ntEdit+Sealer produces highly accurate, error-corrected genome assemblies, and is available as a Makefile pipeline from https://github.com/bcgsc/ntedit_sealer_protocol. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Automated long-read genome finishing with short reads Support Protocol: Selecting optimal values for k-mer lengths (k) and Bloom filter size (b).
G3: Genes, Genomes, Genetics
The highly diverse insect family of true weevils, Curculionidae, includes many agricultural and forest pests. Pissodes strobi, commonly known as the spruce weevil or white pine weevil, is a major pest of spruce and pine forests in North America. P. strobi larvae feed on the apical shoots of young trees, causing stunted growth and can destroy regenerating spruce or pine forests. Here we describe the nuclear and mitochondrial P. strobi genomes and their annotations, as well as the genome of an apparent Wolbachia endosymbiont. We report a substantial expansion of the weevil nuclear genome, relative to other Curculionidae species, possibly driven by an abundance of Class II DNA transposons. The endosymbiont observed belongs to a group (supergroup A) of Wolbachia species that generally form parasitic relationships with their arthropod host.
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.
Angewandte Chemie International Edition in English
We report on the synthesis of bivalent water-soluble calixarene and calixarene hosts, Super-sCx4 and Super-sCx5 as new broad-spectrum supramolecular binders of neuromuscular blocking agents (NMBAs). Synthesis was achieved using the target bisquaternary amine NMBAs as a template to link two highly anionic p-sulfonatocalixarene building blocks in aqueous solution. Bivalent anionic hosts Super-sCx4 and Super-sCx5 bind by engaging both quaternary amines present on a variety of NMBAs. We report low μM binding to structurally diverse alkyl, steroidal, curarine and benzylisoquinoline NMBAs with high selectivity over the neurotransmitter acetylcholine and a variety of other hydrophobic amines.
NAR Genomics and Bioinformatics
Recent advances in single-cell RNA sequencing technologies have made detection of transcripts in single cells possible. The level of resolution provided by these technologies can be used to study changes in transcript usage across cell populations and help investigate new biology. Here, we introduce RNA-Scoop, an interactive cell cluster and transcriptome visualization tool to analyze transcript usage across cell categories and clusters. The tool allows users to examine differential transcript expression across clusters and investigate how usage of specific transcript expression mechanisms varies across cell groups.
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.
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.
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.
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.
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.
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.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Short-read DNA sequencing instruments can yield over 10^12 bases per run, typically composed of reads 150 bases long. Despite this high throughput, de novo assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads due to both repetitive and difficult-to-sequence regions in these genomes. Some of the short read assembly challenges are mitigated by scaffolding assembled sequences using paired-end reads. However, unresolved sequences in these scaffolds appear as "gaps". Here, we introduce GapPredict an implementation of a proof of concept that uses a character-level language model to predict unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the state-of-the-art gap-filling tool Sealer, and observed that the former can fill 65.6% of the sampled gaps that were left unfilled by the latter with high similarity to the reference genome, demonstrating the practical utility of deep learning approaches to the gap-filling problem in genome assembly.
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: 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.
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.
In this study, researchers assessed if there was a link between host immunity genes (e.g., Human Leukocyte Antigen (HLA) genes) and susceptibility or resistance to COVID-19 using transcriptome sequencing (RNA-Seq) on libraries made from the bronchoalveolar lavage (BAL) fluid and peripheral blood mononuclear cell (PMBC) samples of COVID-19 patients.
Presence or absence of gene fusions is one of the most important diagnostic markers in many cancer types. Consequently, fusion detection methods using various genomics data types, such as RNA sequencing (RNA-seq) are valuable tools for research and clinical applications. While information-rich RNA-seq data have proven to be instrumental in discovery of a number of hallmark fusion events, bioinformatics tools to detect fusions still have room for improvement. Here, we present Fusion-Bloom, a fusion detection method that leverages recent developments in de novo transcriptome assembly and assembly-based structural variant calling technologies (RNA-Bloom and PAVFinder, respectively). We benchmarked Fusion-Bloom against the performance of five other state-of-the-art fusion detection tools using multiple datasets. Overall, we observed Fusion-Bloom to display a good balance between detection sensitivity and specificity. We expect the tool to find applications in translational research and clinical genomics pipelines.
Haploid cell lines are a valuable research tool with broad applicability for genetic assays. As such the fully haploid human cell line, eHAP1, has been used in a wide array of studies. However, the absence of a corresponding reference genome sequence for this cell line has limited the potential for more widespread applications to experiments dependent on available sequence, like capture-clone methodologies. We generated ~15× coverage Nanopore long reads from ten GridION flowcells and utilized this data to assemble a de novo draft genome using minimap and miniasm and subsequently polished using Racon. This assembly was further polished using previously generated, low-coverage, Illumina short reads with Pilon and ntEdit. This resulted in a hybrid eHAP1 assembly with >90% complete BUSCO scores. We further assessed the eHAP1 long read data for structural variants using Sniffles and identify a variety of rearrangements, including a previously established Philadelphia translocation. Finally, we demonstrate how some of these variants overlap open chromatin regions, potentially impacting regulatory regions. By integrating both long and short reads, we generated a high-quality reference assembly for eHAP1 cells. The union of long and short reads demonstrates the utility in combining sequencing platforms to generate a high-quality reference genome de novo solely from low coverage data. We expect the resulting eHAP1 genome assembly to provide a useful resource to enable novel experimental applications in this important model cell line.
Bioinformatics (Oxford, England), 2019
In the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes.
Bioinformatics (Oxford, England), 2019
The ORCA bioinformatics environment is a Docker image that contains hundreds of bioinformatics tools and their dependencies. The ORCA image and accompanying server infrastructure provide a comprehensive bioinformatics environment for education and research. The ORCA environment on a server is implemented using Docker containers, but without requiring users to interact directly with Docker, suitable for novices who may not yet have familiarity with managing containers. ORCA has been used successfully to provide a private bioinformatics environment to external collaborators at a large genome institute, for teaching an undergraduate class on bioinformatics targeted at biologists, and to provide a ready-to-go bioinformatics suite for a hackathon. Using ORCA eliminates time that would be spent debugging software installation issues, so that time may be better spent on education and research.
Microbiology resource announcements, 2019
Engelmann spruce () is a conifer found primarily on the west coast of North America. Here, we present the complete chloroplast genome sequence of genotype Se404-851. This chloroplast sequence will benefit future conifer genomic research and contribute resources to further species conservation efforts.
Microbiology resource announcements, 2019
Here, we present the complete chloroplast genome sequence of white spruce (, genotype WS77111), a coniferous tree widespread in the boreal forests of North America. This sequence contributes to genomic and phylogenetic analyses of the genus that are part of ongoing research to understand their adaptation to environmental stress.
Scientific reports, 2019
Antimicrobial peptides (AMPs) exhibit broad-spectrum antimicrobial activity, and have promise as new therapeutic agents. While the adult North American bullfrog (Rana [Lithobates] catesbeiana) is a prolific source of high-potency AMPs, the aquatic tadpole represents a relatively untapped source for new AMP discovery. The recent publication of the bullfrog genome and transcriptomic resources provides an opportune bridge between known AMPs and bioinformatics-based AMP discovery. The objective of the present study was to identify novel AMPs with therapeutic potential using a combined bioinformatics and wet lab-based approach. In the present study, we identified seven novel AMP precursor-encoding transcripts expressed in the tadpole. Comparison of their amino acid sequences with known AMPs revealed evidence of mature peptide sequence conservation with variation in the prepro sequence. Two mature peptide sequences were unique and demonstrated bacteriostatic and bactericidal activity against Mycobacteria but not Gram-negative or Gram-positive bacteria. Nine known and seven novel AMP-encoding transcripts were detected in premetamorphic tadpole back skin, olfactory epithelium, liver, and/or tail fin. Treatment of tadpoles with 10 nM 3,5,3'-triiodothyronine for 48 h did not affect transcript abundance in the back skin, and had limited impact on these transcripts in the other three tissues. Gene mapping revealed considerable diversity in size (1.6-15 kbp) and exon number (one to four) of AMP-encoding genes with clear evidence of alternative splicing leading to both prepro and mature amino acid sequence diversity. These findings verify the accuracy and utility of the bullfrog genome assembly, and set a firm foundation for bioinformatics-based AMP discovery.
The grizzly bear ( ssp. ) 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.
BMC bioinformatics, 2018
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.
BMC medical genomics, 2018
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.
BMC genomics, 2018
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.