NGS Datasets Research Articles

Discovery of a new class of cell-penetrating peptides by novel phage display platform

The primary hurdles for small interference RNA (siRNA) in clinical use are targeted and cytosolic delivery. To overcome both challenges, we have established a novel platform based on phage display, called NNJA. In this approach, a lysosomal cathepsin substrate is engineered within the flexible loops of PIII, that is displaying a unique random sequence at its N-terminus. NNJA library selection targeting cell-expressed targets should yield specific peptides localized in the cytoplasm. That is because phage internalization and subsequent localization to lysosome, upon peptide binding to the cell expressed target, will result in cleavage of PIII, rendering phage non-infective. Such phage will be eliminated from the selected pool and only peptide-phage that escapes lysosomes will advance to the next round. Proof of concept studies with the NNJA library demonstrated cytosolic localization of selected peptide-phage and peptide-siRNA, confirmed through confocal microscopy. More importantly, conjugation of siHPRT to monomeric or multimeric NNJA peptides resulted in significant reduction in HPRT mRNA in various cell types without significant cytotoxicity. Sequence similarity and clustering analysis from NGS dataset provide insights into sequence composition facilitating cell penetration. NNJA platform offers a highly efficient peptide discovery engine for targeted delivery of oligonucleotides to cytosol.

Insights into the genome of the ‘Loco’ Concholepas concholepas (Gastropoda: Muricidae) from low-coverage short-read sequencing: genome size, ploidy, transposable elements, nuclear RNA gene operon, mitochondrial genome, and phylogenetic placement in the family Muricidae

BackgroundThe Peruvian ‘chanque’ or Chilean ‘loco’ Concholepas concholepas is an economically, ecologically, and culturally important muricid gastropod heavily exploited by artisanal fisheries in the temperate southeastern Pacific Ocean. In this study, we have profited from a set of bioinformatics tools to recover important biological information of C. concholepas from low-coverage short-read NGS datasets. Specifically, we calculated the size of the nuclear genome, ploidy, and estimated transposable elements content using an in silico k-mer approach, we discovered, annotated, and quantified those transposable elements, we assembled and annotated the 45S rDNA RNA operon and mitochondrial genome, and we confirmed the phylogenetic position of C. concholepas within the muricid subfamily Rapaninae based on translated protein coding genes.ResultsUsing a k-mer approach, the haploid genome size estimated for the predicted diploid genome of C. concholepas varied between 1.83 Gbp (with kmer = 24) and 2.32 Gbp (with kmer = 36). Between half and two thirds of the nuclear genome of C. concholepas was composed of transposable elements. The most common transposable elements were classified as Long Interspersed Nuclear Elements and Short Interspersed Nuclear Elements, which were more abundant than DNA transposons, simple repeats, and Long Terminal Repeats. Less abundant repeat elements included Helitron mobile elements, 45S rRNA DNA, and Satellite DNA, among a few others.The 45S rRNA DNA operon of C. concholepas that encodes for the ssrRNA, 5.8S rRNA, and lsrRNA genes was assembled into a single contig 8,090 bp long. The assembled mitochondrial genome of C. concholepas is 15,449 bp long and encodes 13 protein coding genes, two ribosomal genes, and 22 transfer RNAs.ConclusionThe information gained by this study will inform the assembly of a high quality nuclear genome for C. concholepas and will support bioprospecting and biomonitoring using environmental DNA to advance development of conservation and management plans in this overexploited marine snail.

RFGR: Repeat Finder for Complete and Assembled Whole Genomes and NGS Reads.

Repetitive DNA sequences cause genomic instability and are important genetic markers. Identification of repeats is a critical step in genome annotation and analysis. On the other hand, repeats also pose a technical challenge for genome assembly and alignment programs using NGS data. RFGR is a comprehensive tool that can find exact repetitive sequences in complete genomes and assembled genomes, as well as NGS reads of prokaryotes. For complete genomes, RFGR uses a suffix trees to find seed repeats of repetitive sequences of fixed length with indels. For assembled genomes, RFGR uses a modified Bowtie aligner to find seed repeats of exact repetitive sequences in the contigs/ scaffolds, which are then extended to maximal repeats. The repeats are classified and for repeats near a gene, RFGR reports the gene as well. For the control dataset of E. coli UTI89 and E. coli K12, RFGR reports 35,141 and 49,352 repeats, respectively. For NGS reads, RFGR uses the frequency of the repetitive k-mers to determine FASTQ reads containing repetitive sequences and removes them from the dataset. An E. coli K12 NGS dataset pre-processed using RFGR, on comparison with the original dataset, gives an improved assembly. The N50 value improves by 22.86% with a decrease in size of the assembly graph by nearly 50%. Thus, with RFGR, we achieve a better assembly with reduced computation. RFGR can be improved in terms of the length of the minimum repeat found, extending to find approximate repeats and to be applicable to Eukaryotes as well.

5′ copyback defective viral genomes are major component in clinical and non-clinical influenza samples

Clinical samples from people with influenza disease have been analyzed to assess the presence and abundance of Defective Viral Genomes (DVGs), but these have not been assessed using the same bioinformatic pipeline. The type of DVG most described for influenza infections (deletion DVGs) differs from the most commonly described DVGs from non-segmented negative stranded viruses (5′ copyback). This could be attributed to either differences between viruses or the tools used to detect and characterize DVGs. Here we analyze several NGS datasets from people infected with different types of influenza virus using the same bioinformatic pipeline. We observe that 5′ copyback DVGs are prevalent in all human clinical samples but not in the cultured samples. To address this discrepancy between clinical and laboratory cultures, we infected cell culture and ferrets with an H5N8 influenza A virus (FLUAV) and analyzed the DVG composition. The results demonstrate that the DVG population is skewed toward 5′ copyback DVGs in the in vivo infections and deletion DVGs in the in vitro infections. This demonstrates that there are differences in vivo genome production and in vitro genome production, and this has implications for how the role of DVGs in clinical disease is studied. We also investigate the role the host cofactor ANP32B has in DVG production.

SeQual-Stream: approaching stream processing to quality control of NGS datasets

BackgroundQuality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing.ResultsIn this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7times when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features.ConclusionOur solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream.

CRISPR-GRANT: a cross-platform graphical analysis tool for high-throughput CRISPR-based genome editing evaluation

BackgroudCRISPR/Cas is an efficient genome editing system that has been widely used for functional genetic studies and exhibits high potential in biomedical translational applications. Indel analysis has thus become one of the most common practices in the lab to evaluate DNA editing events generated by CRISPR/Cas. Several indel analysis tools have been reported, however, it is often required that users have certain bioinformatics training and basic command-line processing capability.ResultsHere, we developed CRISPR-GRANT, a stand-alone graphical CRISPR indel analysis tool, which could be easily installed for multi-platforms, including Linux, Windows, and macOS. CRISPR-GRANT offered a straightforward GUI by simple click-and-run for genome editing analysis of single or pooled amplicons and one-step analysis for whole-genome sequencing without the need of data pre-processing, making it ideal for novice lab scientists. Moreover, it also exhibited shorter run-time compared with tools currently available.ConclusionTherefore, CRISPR-GRANT is a valuable addition to the current CRISPR toolkits that significantly lower the barrier for wet-lab researchers to conduct indel analysis from large NGS datasets. CRISPR-GRANT binaries are freely available for Linux (above Ubuntu 16.04), macOS (above High Sierra 10.13) and Windows (above Windows 7) at https://github.com/fuhuancheng/CRISPR-GRANT. CRISPR-GRANT source code is licensed under the GPLv3 license and free to download and use.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms.

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

AbstractNon‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data.

Anesthetic‑specific lncRNA and mRNA profile changes in blood during colorectal cancer resection: A prospective, matched‑case pilot study.

Prometastatic and antitumor effects of different anesthetics have been previously analyzed in several studies with conflicting results. Thus, the underlying perioperative molecular mechanisms mediated by anesthetics potentially affecting tumor phenotype and metastasis remain unclear. It was hypothesized that anesthetic‑specific long non‑coding RNA (lncRNA) expression changes are induced in the blood circulation and play a crucial role in tumor outcome. In the present study, high‑throughput sequencing and quantitative PCR were performed in order to identify lncRNA and mRNA expression changes affected by two therapeutic regimes, total intravenous anesthesia (TIVA) and volatile anesthetic gas (VAG) in patients undergoing colorectal cancer (CRC) resection. Total blood RNA was isolated prior to and following resection and characterized using RNA sequencing. mRNA‑lncRNA interactions and their roles in cancer‑related signaling of differentially expressed lncRNAs were identified using bioinformatics analyses. The comparison of these two time points revealed 35 differentially expressed lncRNAs in the TIVA‑group, and 25 in the VAG‑group, whereas eight were shared by both groups. Two lncRNAs in the TIVA‑group, and 23 in the VAG‑group of insilico identified target‑mRNAs were confirmed as differentially regulated in the NGS dataset of the present study. Pathway analysis was performed and cancer relevant canonical pathways for TIVA were identified. Target‑mRNA analysis of VAG revealed a markedly worsened immunological response against cancer. In this proof‑of‑concept study, anesthesic‑specific expression changes in lncRNA and mRNA profiles in blood were successfully identified. Moreover, the data of the present study provide the first evidence that anesthesia‑induced lncRNA pattern changes may contribute further in the observed differences in CRC outcome following tumor resection.

Forensic Analysis of Novel SARS2r-CoV Identified in Game Animal Datasets in China Shows Evolutionary Relationship to Pangolin GX CoV Clade and Apparent Genetic Experimentation

Pangolins are the only animals other than bats proposed to have been infected with SARS-CoV-2 related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic. Here, we examine the novel SARS2r-CoV we previously identified in game animal metatranscriptomic datasets sequenced by the Nanjing Agricultural University in 2022, and find that sections of the partial genome phylogenetically group with Guangxi pangolin CoVs (GX PCoVs), while the full RdRp sequence groups with bat-SL-CoVZC45. While the novel SARS2r-CoV is found in 6 pangolin datasets, it is also found in 10 additional NGS datasets from 5 separate mammalian species and is likely related to contamination by a laboratory researched virus. Absence of bat mitochondrial sequences from the datasets, the fragmentary nature of the virus sequence and the presence of a partial sequence of a cloning vector attached to a SARS2r-CoV read suggests that it has been cloned. We find that NGS datasets containing the novel SARS2r-CoV are contaminated with significant Homo sapiens genetic material, and numerous viruses not associated with the host animals sampled. We further identify the dominant human haplogroup of the contaminating H. sapiens genetic material to be F1c1a1, which is of East Asian provenance. The association of this novel SARS2r-CoV with both bat CoV and the GX PCoV clades is an important step towards identifying the origin of the GX PCoVs.

Metagenomic analysis of heavy metal-contaminated soils reveals distinct clades with adaptive features

Heavy metal pollution poses a serious threat to soil, water, and the atmospheric environment. Many microbes sustain and respond to the metal stress, and the mechanisms involved in these processes remain unclear. The metagenomics and metatranscriptomics approaches were applied to study the structure and function of eukaryotic microbial communities in heavy metal-contaminated soils of two geographic locations situated in different climatic regions. To achieve this, amplicons of the hypervariable V4 region of 18S rDNA and cDNA synthesized from 18S rRNA extracted from these soils were generated and sequenced through paired-end sequencing chemistry on the Illumina-MiSeq platform. The NGS dataset processed by the Mothur pipeline and analyzed by Parallel-Meta 3 pipeline illustrated the presence of all the major eukaryotic phyla. Taxa diversity and community structure of micro-eukaryotes within and between the samples from two locations were compared. Clustering, heatmap, and PCA analysis supported the variation in taxonomic diversity and community structure in the datasets of these two sites. Analysis of taxa abundance in both sites identified marker organisms for the further characterization of such types of environments. A functional metatranscriptomics study revealed the identification of various expressed eukaryotic genes, which are involved in metal tolerance. These metal-tolerant gene families were phylogenetically related to the different eukaryotic lineages reported in the metagenomic analysis. Hence, this approach could be widely applied in microbial ecology to understand the role of active microbes in such specific environmental conditions.

BreakAlign: a Perl program to align chimaeric (split) genomic NGS reads and allow visual confirmation of novel retroviral integrations

BackgroundRetroviruses replicate by integrating a DNA copy into a host chromosome. Detecting novel retroviral integrations (ones not in the reference genome sequence of the host) from genomic NGS data is bioinformatically challenging and frequently produces many false positives. One common method of confirmation is visual inspection of an alignment of the chimaeric (split) reads that span a putative novel retroviral integration site. We perceived the need for a program that would facilitate this by producing a multiple alignment containing both the viral and host regions that flank an integration.ResultsBreakAlign is a Perl program that uses blastn to produce such a multiple alignment. In addition to the NGS dataset and a reference viral sequence, the program requires either (a) the ~ 500nt host genome sequence that spans the putative integration or (b) coordinates of this putative integration in an installed copy of the reference human genome (multiple integrations can be processed automatically). BreakAlign is freely available from https://github.com/marchiem/breakalign and is accompanied by example files allowing a test run.ConclusionBreakAlign will confirm and facilitate characterisation of both (a) germline integrations of endogenous retroviruses and (b) somatic integrations of exogenous retroviruses such as HIV and HTLV. Although developed for use with genomic short-read NGS (second generation) data and retroviruses, it should also be useful for long-read (third generation) data and any mobile element with at least one conserved flanking region.

Regulatory Mechanisms of Endogenous APOBEC3B Expression in HPV(+) and (–) Oral Premalignancies and Head and Neck Cancer

<h3>Introduction</h3> HPV(+) oral and oropharyngeal squamous cell carcinoma (OOPSCC) shows strong association with high-risk HPV16 and 18. The DNA cytosine deaminase APOBEC3B constitutes a major endogenous source of muta- tions in a wide variety of cancers. APOBEC3B causes C-to-T/-G base substitutions in 5'-TCA/T trinucleotide motifs and is overexpressed in head and neck cancer (HNC). High-risk HPV infection and dysregulation of the RB/E2F axis regu- late <i>APOBEC3B in vitro</i>. Here, we investigate changes of APOBEC3B levels in HPV(+) and (–) oral epithelial dysplasia (OED) and OOPSCC, and probe whether APOBEC3B upregulation correlates with cellular proliferation. <h3>Materials and Methods</h3> APOBEC3B and Ki67 expression was assessed by immunohistochemistry in HPV(+) OED (n=10) and OOPSCC (n=28), and HPV(–) low-grade (n=42) and high-grade OED (n=38), and OSCC (n=46). Epithelial hyperplasia (OEH, n=14) and normal adjacent epithelium (n=10) served as controls. APOBEC3B H-scores and per- centage of Ki67(+) cells were calculated using the Aperio ScanScope XT platform. Public NGS datasets available through TCGA were mined to identify molecular mechanisms associated with APOBEC3B upregulation in HNC. <h3>Results</h3> High-risk HPV significantly upregulated APOBEC3B in OED (Kruskal-Wallis test, p<0.05) and OOPSCC (p<0.01) compared to OEH. HPV(+) OEDs and tumors showed strong and diffuse, nuclear APOBEC3B immunore- activity which mirrored p16 staining and coincided with disrupted p53 expression. Progressive APOBEC3B up- regulation was observed in HPV(–) lesions; high-grade premalignancies and OSCCs showed significantly elevated APOBEC3B H-scores compared to low-grade OED (p<0.01) and controls (p<0.01). High mRNA and protein APOBEC3B levels strongly associated with advanced histologic grading in HPV(–) OSCC (p<0.01). A positive linear correlation was evident between APOBEC3B and Ki67 expression (Pearson's r=0.5), while RNAseq analysis suggests cell cycle regulation of APOBEC3B in HPV(–) HNC. <h3>Conclusions</h3> High-risk HPV drives upregulation of APOBEC3B in viral-related OED and HNC. In non-viral lesions, APOBEC3B levels also increase in a mechanism associated in part with cellular proliferation.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data.

ABSTRACTNext-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens.IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

De Novo and Dominantly Inherited SPTAN1 Mutations Cause Spastic Paraplegia and Cerebellar Ataxia.

Pathogenic variants in SPTAN1 have been linked to a remarkably broad phenotypical spectrum. Clinical presentations include epileptic syndromes, intellectual disability, and hereditary motor neuropathy. We investigated the role of SPTAN1 variants in rare neurological disorders such as ataxia and spastic paraplegia. We screened 10,000 NGS datasets across two international consortia and one local database, indicative of the level of international collaboration currently required to identify genes causative for rare disease. We performed in silico modeling of the identified SPTAN1 variants. We describe 22 patients from 14 families with five novel SPTAN1 variants. Of six patients with cerebellar ataxia, four carry a de novo SPTAN1 variant and two show a sporadic inheritance. In this group, one variant (p.Lys2083del) is recurrent in four patients. Two patients have novel de novo missense mutations (p.Arg1098Cys, p.Arg1624Cys) associated with cerebellar ataxia, in one patient accompanied by intellectual disability and epilepsy. We furthermore report a recurrent missense mutation (p.Arg19Trp) in 15 patients with spastic paraplegia from seven families with a dominant inheritance pattern in four and a de novo origin in one case. One further patient carrying a de novo missense mutation (p.Gln2205Pro) has a complex spastic ataxic phenotype. Through protein modeling we show that mutated amino acids are located at crucial interlinking positions, interconnecting the three-helix bundle of a spectrin repeat. We show that SPTAN1 is a relevant candidate gene for ataxia and spastic paraplegia. We suggest that for the mutations identified in this study, disruption of the interlinking of spectrin helices could be a key feature of the pathomechanism. © 2022 International Parkinson and Movement Disorder Society.

Lacking conservation genomics in the giant Galápagos tortoise

Conservation policy in the giant Galpagos tortoise, an iconic endangered animal, has been assisted by genetic markers for 15 years: a dozen loci have been used to delineate thirteen (sub)species, between which hybridization is prevented. Here, comparative reanalysis of a previously published NGS data set reveals a conflict with traditional markers. Genetic diversity and population substructure in the giant Galpagos tortoise are found to be particularly low, questioning the genetic relevance of current conservation practices. Further examination of giant Galapagos tortoise population genomics is critically needed.

Large-scale analysis of SARS-CoV-2 spike-glycoprotein mutants demonstrates the need for continuous screening of virus isolates.

Due to the widespread of the COVID-19 pandemic, the SARS-CoV-2 genome is evolving in diverse human populations. Several studies already reported different strains and an increase in the mutation rate. Particularly, mutations in SARS-CoV-2 spike-glycoprotein are of great interest as it mediates infection in human and recently approved mRNA vaccines are designed to induce immune responses against it. We analyzed 1,036,030 SARS-CoV-2 genome assemblies and 30,806 NGS datasets from GISAID and European Nucleotide Archive (ENA) focusing on non-synonymous mutations in the spike protein. Only around 2.5% of the samples contained the wild-type spike protein with no variation from the reference. Among the spike protein mutants, we confirmed a low mutation rate exhibiting less than 10 non-synonymous mutations in 99.6% of the analyzed sequences, but the mean and median number of spike protein mutations per sample increased over time. 5,472 distinct variants were found in total. The majority of the observed variants were recurrent, but only 21 and 14 recurrent variants were found in at least 1% of the mutant genome assemblies and NGS samples, respectively. Further, we found high-confidence subclonal variants in about 2.6% of the NGS data sets with mutant spike protein, which might indicate co-infection with various SARS-CoV-2 strains and/or intra-host evolution. Lastly, some variants might have an effect on antibody binding or T-cell recognition. These findings demonstrate the continuous importance of monitoring SARS-CoV-2 sequences for an early detection of variants that require adaptations in preventive and therapeutic strategies.

Reads in a haystack: extracting complete mitogenome sequences hidden in baculovirus datasets.

Thaumatotibia leucotreta (Lepidoptera, Tortricidae) is one of many economically important insect pests for which no complete mitogenome sequence is available. The complete mitochondrial sequences for this species and other key pests could assist in the development of novel molecular techniques, such as enabling the identification of population-specific markers which could assist in improved monitoring of populations. The objective of this study was to determine whether NGS datasets generated for entomopathogenic viruses contain reads originating from host mitochondrial DNA. A total of 28 NGS datasets generated for the baculovirus Cryptophlebia leucotreta granulovirus (CrleGV) were analysed in this study. Three datasets contained sufficient reads providing adequate coverage for the assembly of complete mitogenomes. All 13 protein-coding genes, 22 tRNAs and both rRNAs present in the mitogenomes of other species within the Grapholitini tribe, were identified. Phylogenetic analysis of the mitogenomes at both an intrafamilial and interspecies level grouped the sequences within the Olethreutinae and T. leucotreta clades, respectively. Analysis of single nucleotide variations (SNVs) between each T. leucotreta sequence indicated up to 75 differences across the mitogenome. The methodology used in this study could be expanded to other baculovirus NGS datasets enabling the generation of novel lepidopteran mitogenome sequences.

Interpretable detection of novel human viruses from genome sequencing data.

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes\u2019 factors

BackgroundNext-generation sequencing (NGS) is widely used for genome-wide identification and quantification of DNA elements involved in the regulation of gene transcription. Studies that generate multiple high-throughput NGS datasets require data integration methods for two general tasks: 1) generation of genome-wide data tracks representing an aggregate of multiple replicates of the same experiment; and 2) combination of tracks from different experimental types that provide complementary information regarding the location of genomic features such as enhancers.ResultsNGS-Integrator is a Java-based command line application, facilitating efficient integration of multiple genome-wide NGS datasets. NGS-Integrator first transforms all input data tracks using the complement of the minimum Bayes’ factor so that all values are expressed in the range [0,1] representing the probability of a true signal given the background noise. Then, NGS-Integrator calculates the joint probability for every genomic position to create an integrated track. We provide examples using real NGS data generated in our laboratory and from the mouse ENCODE database.ConclusionsOur results show that NGS-Integrator is both time- and memory-efficient. Our examples show that NGS-Integrator can integrate information to facilitate downstream analyses that identify functional regulatory domains along the genome.