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Background The PhageDx™ Listeria Assay is a simple, specific, and sensitive assay based on the infection of Listeria spp. by selected bacteriophages and the resultant expression of a luciferase reporter gene. Results are generated in as little as 24.5 h for stainless steel and ceramic environmental surfaces. Objective An AOAC Performance Tested MethodsSM (PTM) study was conducted to validate the PhageDx Listeria Assay for the detection of Listeria on stainless steel and ceramic surfaces. Method The performance of the PhageDx method was compared to that of the U.S. Food and Drug Administration (FDA) Bacterial Analytical Manual (BAM) Ch. 10. Inclusivity/exclusivity, product consistency and stability, and robustness testing also were conducted. Results Inclusivity testing demonstrated that the reporter bacteriophages were specific for Listeria ssp. and detected 58/61 Listeria strains tested, including all 34 L. monocytogenes strains. The reporter bacteriophage also was shown to not detect 46/47 non-Listeria bacteria in exclusivity testing. Robustness testing showed that the method performed well with specific deviations from the standard protocol. Consistency and stability testing demonstrated that the recombinant phage gave consistent results across three production lots and was stable when stored under appropriate conditions for at least 6 months. Matrix studies on stainless steel and ceramic surfaces showed that there was no significant difference between the PhageDx Listeria Assay and the FDA/BAM Chapter 10 reference method. Conclusions and Highlights The validation study demonstrates that the PhageDx Listeria Assay is an effective method for the detection of Listeria spp. on stainless steel and ceramic environmental surfaces and meets the qualifications for AOAC PTM status.

High throughput and high-resolution lipid analyses are important for many biological model systems and research questions. This comprises both monitoring at the individual lipid species level and broad lipid classes. Here, we present a nontarget semiquantitative lipidomics workflow based on ultrahigh performance supercritical fluid chromatography (UHPSFC)-mass spectrometry (MS). The optimized chromatographic conditions enable the base-line separation of both nonpolar and polar classes in a single 7-minute run. Ionization efficiencies of lipid classes vary 10folds in magnitude and great care must be taken in a direct interpretation of raw data. Therefore, the inclusion of internal standards or experimentally determined Response factors (RF) are highly recommended for the conversion of raw abundances into (semi) quantitative data. We have deliberately developed an algorithm for automatic semiquantification of lipid classes by RF. The workflow was tested and validated using a bovine liver extract with satisfactory results. The RF corrected data provide a more representative relative lipid class determination, but also the interpretation of individual lipid species should be performed on RF corrected data. In addition, semiquantification can be improved by using internal or also external standards when more accurate quantitative data are of interest but this requires validation for all new sample types. The workflow established greatly extends the potential of nontarget UHPSFC–MS/MS based analysis.

Amplifex Diene reagent was employed to derivatize estradiol (E2) to enhance the analyte signal at low picogram concentrations. This derivatization enabled measurement of E2 (and other estrogens) in ESI+ mode, earlier retention times for analytes than other methods, avoidance of MS harmful ammonium fluoride in mobile phases, and an LLOQ below 1 pg/mL. The sample preparation workflow involved liquid–liquid extraction followed by Amplifex Diene derivatization for 10 min at ambient temperature. Samples were chromatographed using a standard C18 column and analyzed using a SCIEX 6500+ mass spectrometer. The assay calibrators were prepared in-house, traceable to certified reference materials, and ranged from 1.29 to 624 pg/mL. A method comparison to samples from the CDC HoSt program yielded a correlation coefficient of 0.9858 and bias of −1.37%. The LLOQ using certified reference material was 0.66 pg/mL. The intra-run precision was

Background BioSystems has developed a histamine kit for automated procedure, through the use of a Y15 analyzer, for quantification of histamine in fish and fishery products. Objective Validate the method under the specific guidelines of the AOAC Research Institute Performance Tested MethodSM (PTM) program. Method Samples are extracted with boiling water. The enzymatic method is based on histamine dehydrogenase that catalyzes the oxidation of histamine in the presence of an electron mediator that reduces a dye that is measured at 420 nm. The increase of absorbance is proportional to the histamine concentration. Dispensing of reagents and sample, absorbance readings, calibration, and calculation of results are performed automatically in the analyzer BioSystems Y15. Results The linearity ranges from 0 to 200 mg/kg (r2 > 0.99). The LOQ is 10 mg/kg in all the matrixes. Recoveries range from 75 to 107% at concentrations from 5 to 200 mg/kg, with repeatability precision values between 0.8 and 5.5%. Comparisons with the HPLC reference method shows a good correlation. Cross-reactivity of the assay is negligible for all biogenic amines tested except for agmatine (6.3%). Product consistency was verified by validating lot-to-lot variations and variations within the same lot. Shelf life was verified by real-time stability testing during 40 months at 2–8°C. No differences in histamine detection were observed in robustness testing, in which minor changes are introduced to the assay protocol. Conclusions The automated, simple, and rapid BioSystems Y15 Histamine Dehydrogenase Kit has been successfully validated. Highlights The method is qualified for PTM certification No. 072001.

Background Flavanols and procyanidins are complex bioactives found in many foods such as cocoa. As their consumption is associated with health benefits, cocoa flavanols and procyanidins are receiving increasing attention from consumers, industry, researchers, and regulators. Objective The objective of this study is to validate a method using hydrophilic interaction chromatography (HILIC) with fluorescence detection (FLD) and a commercially available reference material for the determination of flavanols and procyanidins (CF) in cocoa-based products. Methods Method performances were evaluated for cocoa matrices with CF content that ranged from 0.8 to 500 mg/g, which included low CF matrices (milk and dark chocolate, cocoa powder, and liquor) and high CF matrices (cocoa extract and dietary supplement products). The method was validated in a single-laboratory by determining sensitivity, selectivity, linearity, stability, robustness, accuracy, and precision for each of the matrices. Results The method was validated for cocoa matrices with CF content that ranged from 0.8 to 500 mg/g. Accuracy ranged from 86 to 99% and repeatability (RSDr) from 1.5 to 8.6% for CF. Conclusions Analytical performances acquired through this single-laboratory validation study for a wide range of cocoa-based matrices demonstrate that this method is fit-for-purpose for the determination of flavanols and procyanidins in cocoa-based products. Highlights Hydrophilic interaction chromatography (HILIC) with fluorescence detection was successfully used to determine total CF content in multiple product types. Single-laboratory method validation results demonstrate that the method is fit for purpose for cocoa-based matrices containing

The use of designer benzodiazepines appears to be increasing in many countries, but data concerning blood concentrations are scarce, making interpretation of concentrations difficult. The aim of this study was to report blood concentrations of clonazolam, diclazepam, etizolam, flualprazolam, flubromazepam, flubromazolam and phenazepam and to investigate the relationship between blood concentrations and impairment. The concentration data are from blood samples collected from living cases (apprehended drivers and other drug offences) and medico-legal autopsies. The blood samples were analysed for the seven designer benzodiazepines mentioned above by ultra high performance liquid chromatography–tandem mass spectrometry. Positive cases from between 1 June 2016 and 30 September 2019 were included. Blood concentrations and the conclusion from a clinical test of impairment (when available) are reported. The presented seven benzodiazepines were detected in a total of 575 cases, where 554 of these cases concerned apprehended drivers or other criminal offenders. The number of findings and the median (range) concentrations were as follows: clonazolam, n = 22, 0.0041 mg/L (0.0017–0.053 mg/L); diclazepam, n = 334, 0.0096 mg/L (0.0016–0.25 mg/L); etizolam, n = 40, 0.054 mg/L (0.015–0.30 mg/L); flualprazolam, n = 10, 0.0080 mg/L (0.0033–0.056 mg/L); flubromazepam, n = 5, 0.037 mg/L (0.0070–0.70 mg/L); flubromazolam, n = 20, 0.0056 mg/L (0.0004–0.036 mg/L); and phenazepam, n = 138, 0.022 mg/L (0.0018–0.85 mg/L). A designer benzodiazepine was the only drug detected with relevance for impairment in 25 of the 554 living cases. The physician concluded with impairment in 19 of the 25 cases. Most of the concentrations in these cases were relatively similar to or higher than the median reported concentrations. The most frequent other drugs detected were amphetamine, tetrahydrocannabinol, clonazepam and methamphetamine. The presented blood concentrations can be helpful with the interpretation of cases involving one or more of these seven benzodiazepines. The results indicate that concentrations commonly observed in forensic cases are associated with impairment.

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

The epigenetic state of chromatin is altered by regulators which influence gene expression in response to environmental stimuli. While several post-translational modifications contribute to chromatin accessibility and transcriptional programs, our understanding of the role that specific phosphorylation sites play is limited. In cancer, kinases and phosphatases are commonly deregulated resulting in increased oncogenic signaling and loss of epigenetic regulation. Aberrant epigenetic states are known to promote cellular plasticity and the development of therapeutic resistance in many cancer types, highlighting the importance of these mechanisms to cancer cell phenotypes. Protein Phosphatase 2A (PP2A) is a heterotrimeric holoenzyme that targets a diverse array of cellular proteins. The composition of the PP2A complex influences its cellular targets and activity. For this reason, PP2A can be tumor suppressive or oncogenic depending on cellular context. Understanding the nuances of PP2A regulation and its effect on epigenetic alterations can lead to new therapeutic avenues that afford more specificity and contribute to the growth of personalized medicine in the oncology field. In this review, we summarize the known PP2A-regulated substrates and potential phosphorylation sites that contribute to cancer cell epigenetics and possible strategies to therapeutically leverage this phosphatase to suppress tumor growth., Graphical Abstract Graphical AbstractThe diverse family of PP2A holoenzymes dynamically regulates the cancer epigenome.

Changes in DNA methylation have been found to be strongly correlated with age, enabling the creation of ‘epigenetic clocks’. Previously, studies on the relationship between ageing and DNA methylation have assumed a linear relationship. Here, we show that several markers show a non-linear behaviour. In particular, we observe a tendency for saturation with age, especially in the cerebellum. Further, we show that the relationships between significant methylation changes and ageing are different in different tissues. We suggest a straightforward method of assessing all methylation-age relationships and cluster them according to their relative fold change. Our fold change selection outperforms the most common epigenetic clocks in predicting age for the cerebellum, but not for Blood or the Frontal Cortex. Further, we find that the saturation of methylation observed at older ages for the cerebellum explains why epigenetic clocks consistently underestimate the age there. The findings imply that assuming linear correlations might cause biologically important markers to be missed.

The identity of cancer cells is defined by the interplay between genetic, epigenetic transcriptional and post-transcriptional variation. A lot of this variation is present in RNA-seq data and can be captured at once using reference-free, k-mer analysis. An important issue with k-mer analysis, however, is the difficulty of distinguishing signal from noise. Here, we use two independent lung adenocarcinoma datasets to identify all reproducible events at the k-mer level, in a tumor versus normal setting. We find reproducible events in many different locations (introns, intergenic, repeats) and forms (spliced, polyadenylated, chimeric etc.). We systematically analyze events that are ignored in conventional transcriptomics and assess their value as biomarkers and for tumor classification, survival prediction, neoantigen prediction and correlation with the immune microenvironment. We find that unannotated lincRNAs, novel splice variants, endogenous HERV, Line1 and Alu repeats and bacterial RNAs each contribute to different, important aspects of tumor identity. We argue that differential RNA-seq analysis of tumor/normal sample collections would benefit from this type k-mer analysis to cast a wider net on important cancer-related events. The code is available at https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort., Graphical Abstract Graphical AbstractDiscovery and analysis of shared differential contigs from two cancer RNA-seq datasets.

Although microRNAs (miRNAs) contribute to all hallmarks of cancer, miRNA dysregulation in metastasis remains poorly understood. The aim of this work was to reliably identify miRNAs associated with metastatic progression of colorectal cancer (CRC) using novel and previously published next-generation sequencing (NGS) datasets generated from 268 samples of primary (pCRC) and metastatic CRC (mCRC; liver, lung and peritoneal metastases) and tumor adjacent tissues. Differential expression analysis was performed using a meticulous bioinformatics pipeline, including only bona fide miRNAs, and utilizing miRNA-tailored quality control and processing. Five miRNAs were identified as up-regulated at multiple metastatic sites Mir-210_3p, Mir-191_5p, Mir-8-P1b_3p [mir-141–3p], Mir-1307_5p and Mir-155_5p. Several have previously been implicated in metastasis through involvement in epithelial-to-mesenchymal transition and hypoxia, while other identified miRNAs represent novel findings. The use of a publicly available pipeline facilitates reproducibility and allows new datasets to be added as they become available. The set of miRNAs identified here provides a reliable starting-point for further research into the role of miRNAs in metastatic progression.

Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond., NAR Genomics and Bioinformatics, 3 (4), ISSN:2631-9268

In mammals, DNA methyltransferases DNMT1 and DNMT3’s (A, B and L) deposit and maintain DNA methylation in dividing and nondividing cells. Although these enzymes have an unremarkable DNA sequence specificity (CpG), their regional specificity is regulated by interactions with various protein factors, chromatin modifiers, and post-translational modifications of histones. Changes in the DNMT expression or interacting partners affect DNA methylation patterns. Consequently, the acquired gene expression may increase the proliferative potential of cells, often concomitant with loss of cell identity as found in cancer. Aberrant DNA methylation, including hypermethylation and hypomethylation at various genomic regions, therefore, is a hallmark of most cancers. Additionally, somatic mutations in DNMTs that affect catalytic activity were mapped in Acute Myeloid Leukemia cancer cells. Despite being very effective in some cancers, the clinically approved DNMT inhibitors lack specificity, which could result in a wide range of deleterious effects. Elucidating distinct molecular mechanisms of DNMTs will facilitate the discovery of alternative cancer therapeutic targets. This review is focused on: (i) the structure and characteristics of DNMTs, (ii) the prevalence of mutations and abnormal expression of DNMTs in cancer, (iii) factors that mediate their abnormal expression and (iv) the effect of anomalous DNMT-complexes in cancer., Graphical Abstract Graphical AbstractModulation of DNMT activity in cancer.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies., Graphical Abstract Graphical AbstractCLEARER is a machine learning approach for predicting essential genes across eukaryotes. The classifier is trained on multiple species, allowing identification of essential genes in a new species with high accuracy.

Cancer genomics has been evolving rapidly, fueled by the emergence of numerous studies and public databases through next-generation sequencing technologies. However, the downstream programs used to preprocess and analyze data on somatic mutations are scattered in different tools, most of which require specific input formats. Here, we developed a user-friendly Python toolkit, MutScape, which provides a comprehensive pipeline of filtering, combination, transformation, analysis and visualization for researchers, to easily explore the cohort-based mutational characterization for studying cancer genomics when obtaining somatic mutation data. MutScape not only can preprocess millions of mutation records in a few minutes, but also offers various analyses simultaneously, including driver gene detection, mutational signature, large-scale alteration identification and actionable biomarker annotation. Furthermore, MutScape supports somatic variant data in both variant call format and mutation annotation format, and leverages caller combination strategies to quickly eliminate false positives. With only two simple commands, robust results and publication-quality images are generated automatically. Herein, we demonstrate the ability of MutScape to correctly reproduce known results using breast cancer samples from The Cancer Genome Atlas. More significantly, discovery of novel results in cancer genomic studies is enabled through the advanced features in MutScape. MutScape is freely available on GitHub, at https://github.com/anitalu724/MutScape.

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals., Graphical Abstract Graphical AbstractThe accumulation of alterations in the epigenome and chromatin organization via additive effects of risk factors participates in risk stratification in populations.

MotivationAs chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects.ResultsPEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project.AvailabilityBSD2-licensed code and documentation at https://pepatac.databio.org.

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.

Publishing, discussing, envisioning, modeling, designing and experimentally determining RNA three-dimensional (3D) structures involve preparation of two-dimensional (2D) drawings that depict critical functional features of the subject molecules, such as noncanonical base pairs and protein contacts. Here, we describe RiboDraw, new software for crafting these drawings. We illustrate the features of RiboDraw by applying it to several RNAs, including the Escherichia coli tRNA-Phe, the P4–P6 domain of Tetrahymena ribozyme, a −1 ribosomal frameshift stimulation element from beet western yellows virus and the 5′ untranslated region of SARS-CoV-2. We show secondary structure diagrams of the 23S and 16S subunits of the E. coli ribosome that reflect noncanonical base pairs, ribosomal proteins and structural motifs, and that convey the relative positions of these critical features in 3D space. This software is a MATLAB package freely available at https://github.com/DasLab/RiboDraw.

Recent efforts to measure epigenetic marks across a wide variety of different cell types and tissues provide insights into the cell type-specific regulatory landscape. We use these data to study whether there exists a correlate of epigenetic signals in the DNA sequence of enhancers and explore with computational methods to what degree such sequence patterns can be used to predict cell type-specific regulatory activity. By constructing classifiers that predict in which tissues enhancers are active, we are able to identify sequence features that might be recognized by the cell in order to regulate gene expression. While classification performances vary greatly between tissues, we show examples where our classifiers correctly predict tissue-specific regulation from sequence alone. We also show that many of the informative patterns indeed harbor transcription factor footprints.

Yarrowia lipolytica is an oleaginous yeast that is particularly suitable for the sustainable production of secondary metabolites. The genome of this yeast is characterized by its relatively large size and its high number of different rDNA clusters located in its telomeric regions. However, due to the presence of long repetitive elements in the sub-telomeric regions, rDNA clusters and telomeres are missing in current genome assemblies of Y. lipolytica. Here, we present the near-contiguous genome sequence of the biotechnologically relevant strain DSM 3286. We employed a hybrid assembly strategy combining Illumina and nanopore sequencing reads to integrate all six rDNA clusters as well as telomeric repeats into the genome sequence. By fine-tuning of DNA isolation and library preparation protocols, we were able to create ultra-long reads that not only contained multiples of mitochondrial genomes but also shed light on the inter- and intra-chromosomal diversity of rDNA cluster types. We show that there are ten different rDNA units present in this strain that additionally appear in a predefined order in a cluster. Based on single reads, we also demonstrate that the number of rDNA repeats in a specific cluster varies from cell to cell within a population.

Tn5 transposase, which can efficiently tagment the genome, has been widely adopted as a molecular tool in next-generation sequencing, from short-read sequencing to more complex methods such as assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we systematically map Tn5 insertion characteristics across several model organisms, finding critical parameters that affect its insertion. On naked genomic DNA, we found that Tn5 insertion is not uniformly distributed or random. To uncover drivers of these biases, we used a machine learning framework, which revealed that DNA shape cooperatively works with DNA motif to affect Tn5 insertion preference. These intrinsic insertion preferences can be modeled using nucleotide dependence information from DNA sequences, and we developed a computational pipeline to correct for these biases in ATAC-seq data. Using our pipeline, we show that bias correction improves the overall performance of ATAC-seq peak detection, recovering many potential false-negative peaks. Furthermore, we found that these peaks are bound by transcription factors, underscoring the biological relevance of capturing this additional information. These findings highlight the benefits of an improved understanding and precise correction of Tn5 insertion preference.

The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA’s utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes., Graphical Abstract Graphical AbstractSCISSOR™ preprocessed and hosted The Cancer Genome Atlas (TCGA) bulk transcriptomic data and tissue-specific single-cell transcriptomic data. Using these data, SCISSOR™ infers the proportion of tissue-specific cell types in tumor microenvironment. With the omics data and survival data from TCGA, the relationship and interaction can be tested and visualized in five modules, including Overview, Survival, Gene–TME cell correlation with two submodules for gene expression and genetic aberration, Genome-wide TME–omics association and Deconvolution. The Deconvolution module will allow users to upload their bulk transcriptome data and perform deconvolution with tissue-specific single-cell transcriptome profile references and deconvolution methods specified by users. The result will be automatically sent by email.

ConsHMM is a method recently introduced to annotate genomes into conservation states, which are defined based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multi-species DNA sequence alignment. Previously, ConsHMM was only applied to a single genome for one multi-species sequence alignment. Here, we apply ConsHMM to produce 22 additional genome annotations covering human and seven other organisms for a variety of multi-species alignments. Additionally, we extend ConsHMM to generate allele-specific annotations, which we use to produce conservation state annotations for every possible single-nucleotide mutation in the human genome. Finally, we provide a web interface to interactively visualize parameters and annotation enrichments for ConsHMM models. These annotations and visualizations comprise the ConsHMM Atlas, which we expect will be a valuable resource for analyzing a variety of major genomes and genetic variation.

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g., transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens, and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

Ewing sarcoma breakpoint region 1 (EWSR1) encodes a multifunctional protein that can cooperate with the transcription factor ERG to promote prostate cancer. The EWSR1 gene is also commonly involved in oncogenic gene rearrangements in Ewing sarcoma. Despite the cancer relevance of EWSR1, its regulation is poorly understood. Here we find that in prostate cancer, androgen signaling upregulates a 5′ EWSR1 isoform by promoting usage of an intronic polyadenylation site. This isoform encodes a cytoplasmic protein that can strongly promote cell migration and clonogenic growth. Deletion of an Androgen Receptor (AR) binding site near the 5′ EWSR1 polyadenylation site abolished androgen-dependent upregulation. This polyadenylation site is also near the Ewing sarcoma breakpoint hotspot, and androgen signaling promoted R-loop and breakpoint formation. RNase H overexpression reduced breakage and 5′ EWSR1 isoform expression suggesting an R-loop dependent mechanism. These data suggest that androgen signaling can promote R-loops internal to the EWSR1 gene leading to either early transcription termination, or breakpoint formation.

A positive non-linear relation between the dose of ethanol ingested and the area under the curve (AUC) for ethyl glucuronide (EtG) in urine is previously observed. The relation between both doses and AUC of ethanol and the AUC for EtG in blood is not previously published, and this study aimed to investigate this relationship. After an overnight fast, 10 healthy volunteers ingested 0.5-g ethanol per kilo body weight (low dose) in one occasion and 1.0-g ethanol per kilo body weight (high dose) in the next occasion. Results showed that there was a significant higher median ratio between blood AUC for EtG and dose of ethanol in the high-dose (8.99; range 7.37–10.94) group compared to the low-dose (5.02; range 4.25–6.15) group (P = 0.005). The median ratio between the AUC for EtG and AUC for ethanol was actually significantly higher in the low-dose (1.77; range 1.51–2.24) group compared to the high-dose (1.67; range 1.30–2.02) group (P = 0.005), although values are quite similar. This study therefore showed that the ratio between the AUC for EtG in blood and dose of ethanol is higher after intake of 1.0 g/kg than 0.5 g/kg. This pattern is however not seen when AUC for EtG is compared to AUC for ethanol. Results therefore support that the percentage of ethanol converted to EtG is not increasing when the doses increase. An explanation for the positive non-linear relation previously observed between the dose of ethanol ingested and amount of EtG formed may be a relative higher first-pass metabolism of ethanol at lower doses.

Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Acquisition of mutations can confer heritable resistance to cancer cells, enabling their clonal selection to establish a drug-resistant population. Recent studies have demonstrated that cells can tolerate drug treatment without any genetic alterations too; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial-mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for the underlying regulatory network reveals that these two axes can drive one another, thus conferring bidirectional plasticity. This network can also enable non-genetic heterogeneity in a population of cells by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell-cell cooperation. Finally, we propose the potential therapeutic use of MET (mesenchymal-epithelial transition) inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.

In order to evaluate cell- and disease-specific changes in the interacting strength of chromatin targets, ChIP-seq signal across multiple conditions must undergo robust normalization. However, this is not possible using the standard ChIP-seq scheme, which lacks a reference for the control of biological and experimental variabilities. While several studies have recently proposed different solutions to circumvent this problem, substantial analytical differences among methodologies could hamper the experimental reproducibility and quantitative accuracy. Here, we propose a computational method to accurately compare ChIP-seq experiments, with exogenous spike-in chromatin, across samples in a genome-wide manner by using a local regression strategy (spikChIP). In contrast to the previous methodologies, spikChIP reduces the influence of sequencing noise of spike-in material during ChIP-seq normalization, while minimizes the overcorrection of non-occupied genomic regions in the experimental ChIP-seq. We demonstrate the utility of spikChIP with both histone and non-histone chromatin protein, allowing us to monitor for experimental reproducibility and the accurate ChIP-seq comparison of distinct experimental schemes. spikChIP software is available on GitHub (https://github.com/eblancoga/spikChIP). Spanish of Economy, Industry and Competitiveness (MEIC) (BFU2016-75008-P, and PID2019-108322GB-100), “Fundación Vencer El Cancer” (VEC), the European Regional Development Fund (FEDER), and from AGAUR to L.D.C. The Ramon y Cajal program of the Ministerio de Ciencia, Innovación y Universidades and the European Social Fund under the reference number RYC-2018-025002-I, and the Instituto de Salud Carlos III-FEDER (PI19/01814), to S.A. We acknowledge the funding support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme / Generalitat de Catalunya.

Deep learning algorithms have revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence (XAI) has emerged as a novel area of research that goes beyond pure prediction improvement. Knowledge embodied in deep learning methodologies is extracted by interpreting their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers’ decisions by applying layerwise relevance propagation as one example from the pool of XAI techniques. The resulting importance scores are eventually used to determine a subset of most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 WTCCC study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw p-value thresholding as well as other baseline methods. Moreover, two novel disease associations (rs10889923 for hypertension and rs4769283 for type 1 diabetes) were identified.

Eye diseases are remarkably common and encompass a large and diverse range of morbidities that affect different components of the visual system and visual function. With advances in omics technology of eye disorders, genome-scale datasets have been rapidly accumulated in genetics and epigenetics field. However, the efficient collection and comprehensive analysis of different kinds of omics data are lacking. Herein, we developed EyeDiseases (https://eyediseases.bio-data.cn/), the first database for multi-omics data integration and interpretation of human eyes diseases. It contains 1344 disease-associated genes with genetic variation, 1774 transcription files of bulk cell expression and single-cell RNA-seq, 105 epigenomics data across 185 kinds of human eye diseases. Using EyeDiseases, we investigated SARS-CoV-2 potential tropism in eye infection and found that the SARS-CoV-2 entry factors, ACE2 and TMPRSS2 are highly correlated with cornea and keratoconus, suggest that ocular surface cells are susceptible to infection by SARS-CoV-2. Additionally, integrating analysis of Age-related macular degeneration (AMD) GWAS loci and co-expression data revealed 9 associated genes involved in HIF-1 signaling pathway and voltage-gate potassium channel complex. The EyeDiseases provides a valuable resource for accelerating the discovery and validation of candidate loci and genes contributed to the molecular diagnosis and therapeutic vulnerabilities with various eyes diseases.

High-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters, such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.

Chemotherapy is used as a standard-of-care against cancers that display high levels of inherent genome instability. Chemotherapy induces DNA damage and intensifies pressure on the DNA repair pathways that can lead to deregulation. There is an urgent clinical need to be able to track the emergence of DNA repair driven chemotherapy resistance and tailor patient staging appropriately. There have been numerous studies into chemoresistance but to date no study has elucidated in detail the roles of the key DNA repair components in resistance associated with the frontline clinical combination of anthracyclines and taxanes together. In this study, we hypothesized that the emergence of chemotherapy resistance in triple negative breast cancer was driven by changes in functional signaling in the DNA repair pathways. We identified that consistent pressure on the non-homologous end joining pathway in the presence of genome instability causes failure of the key kinase DNA-PK, loss of p53 and compensation by p73. In-turn a switch to reliance on the homologous recombination pathway and RAD51 recombinase occurred to repair residual double strand DNA breaks. Further we demonstrate that RAD51 is an actionable target for resensitization to chemotherapy in resistant cells with a matched gene expression profile of resistance highlighted by homologous recombination in clinical samples.

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance., Graphical Abstract Graphical AbstractCancer LncRNA Census 3 integrates manual and automated curation to identify high confidence functional cancer lncRNAs, whose disease roles are supported by a range of clinical features.

Topoisomerase inhibitors are potent DNA damaging agents which are widely used in oncology, and they demonstrate robust synergistic tumor cell killing in combination with DNA repair inhibitors, including poly(ADP)-ribose polymerase (PARP) inhibitors. However, their use has been severely limited by the inability to achieve a favorable therapeutic index due to severe systemic toxicities. Antibody-drug conjugates address this issue via antigen-dependent targeting and delivery of their payloads, but this approach requires specific antigens and yet still suffers from off-target toxicities. There is a high unmet need for a more universal tumor targeting technology to broaden the application of cytotoxic payloads. Acidification of the extracellular milieu arises from metabolic adaptions associated with the Warburg effect in cancer. Here we report the development of a pH-sensitive peptide-drug conjugate to deliver the topoisomerase inhibitor, exatecan, selectively to tumors in an antigen-independent manner. Using this approach, we demonstrate potent in vivo cytotoxicity, complete suppression of tumor growth across multiple human tumor models, and synergistic interactions with a PARP inhibitor. These data highlight the identification of a peptide-topoisomerase inhibitor conjugate for cancer therapy that provides a high therapeutic index, and is applicable to all types of human solid tumors in an antigen-independent manner., Graphical Abstract Graphical AbstractCBX-12 is a pH-sensitive peptide-drug conjugate that selectively delivers the TOP1 inhibitor exatecan into tumor cells in an antigen agnostic manner. The peptide forms an alpha helix only in the low pH tumor microenvironment, allowing directional insertion of the peptide across the tumor membrane, cleavage of the linker within the cytosol, and release of free exatecan within the tumor cell. This pH-based delivery approach results in a high therapeutic index universally across solid tumors.

Genetic instability is a hallmark of cancer cells. Homologous recombination (HR) plays key roles in genome stability and variability due to its roles in DNA double-strand break and interstrand crosslink repair, and in the protection and resumption of arrested replication forks. HR deficiency leads to genetic instability, and, as expected, many HR genes are downregulated in cancer cells. The link between HR deficiency and cancer predisposition is exemplified by familial breast and ovarian cancers and by some subgroups of Fanconi anaemia syndromes. Surprisingly, although RAD51 plays a pivotal role in HR, i.e., homology search and in strand exchange with a homologous DNA partner, almost no inactivating mutations of RAD51 have been associated with cancer predisposition; on the contrary, overexpression of RAD51 is associated with a poor prognosis in different types of tumours. Taken together, these data highlight the fact that RAD51 differs from its HR partners with regard to cancer susceptibility and expose what we call the ‘RAD51 paradox’. Here, we catalogue the dysregulations of HR genes in human pathologies, including cancer and Fanconi anaemia or congenital mirror movement syndromes, and we discuss the RAD51 paradox., Graphical Abstract Graphical abstractThe RAD51 paradox.

The development of new synthetic biology circuits for biotechnology and medicine requires deeper mechanistic insight into allosteric transcription factors (aTFs). Here we studied the aTF UxuR, a homodimer of two domains connected by a highly flexible linker region. To explore how ligand binding to UxuR affects protein dynamics we performed molecular dynamics simulations in the free protein, the aTF bound to the inducer D-fructuronate or the structural isomer D-glucuronate. We then validated our results by constructing a sensor plasmid for D-fructuronate in Escherichia coli and performed site-directed mutagenesis. Our results show that zinc coordination is necessary for UxuR function since mutation to alanines prevents expression de-repression by D-fructuronate. Analyzing the different complexes, we found that the disordered linker regions allow the N-terminal domains to display fast and large movements. When the inducer is bound, UxuR can sample an open conformation with a more pronounced negative charge at the surface of the N-terminal DNA binding domains. In opposition, in the free and D-glucuronate bond forms the protein samples closed conformations, with a more positive character at the surface of the DNA binding regions. These molecular insights provide a new basis to harness these systems for biological systems engineering.

Genome instability is a characteristic enabling factor for carcinogenesis. HelQ helicase is a component of human DNA maintenance systems that prevent or reverse genome instability arising during DNA replication. Here, we provide details of the molecular mechanisms that underpin HelQ function—its recruitment onto ssDNA through interaction with replication protein A (RPA), and subsequent translocation of HelQ along ssDNA. We describe for the first time a functional role for the non-catalytic N-terminal region of HelQ, by identifying and characterizing its PWI-like domain. We present evidence that this domain of HelQ mediates interaction with RPA that orchestrates loading of the helicase domains onto ssDNA. Once HelQ is loaded onto the ssDNA, ATP-Mg2+ binding in the catalytic site activates the helicase core and triggers translocation along ssDNA as a dimer. Furthermore, we identify HelQ-ssDNA interactions that are critical for the translocation mechanism. Our data are novel and detailed insights into the mechanisms of HelQ function relevant for understanding how human cells avoid genome instability provoking cancers, and also how cells can gain resistance to treatments that rely on DNA crosslinking agents.

Background The Thermo Scientific™ SARS-CoV-2 reverse transcription–polymerase chain reaction (RT-PCR) Detection Workflow, packaged with Applied Biosystems™ TaqMan™ 2019-nCoV Assay Kit v1 targets three different SARS-CoV-2 genomic regions in a single RT-PCR reaction. Objective To validate the Thermo Scientific SARS-CoV-2 RT-PCR Workflow, for the detection of SARS-CoV-2 virus on stainless-steel surfaces as part of the AOAC Performance Tested MethodSM Emergency Response Validation program. Method The Applied Biosystems TaqMan 2019-nCoV Assay Kit v1, as part of the Thermo Scientific SARS-CoV-2 RT-PCR Workflow, was evaluated for specificity using in silico analysis of 15 764 SARS-CoV-2 sequences and 65 exclusivity organisms. The Thermo Scientific SARS-CoV-2 RT-PCR Workflow was evaluated in an unpaired study for one environmental surface (stainless steel) and compared to the U.S. Centers for Disease Control and Prevention 2019-Novel Coronavirus RT-PCR Diagnostic Panel, Instructions for Use (Revision 4, Effective 6/12/2020). Results In silico analysis showed that, of the 15 756 target SARS-CoV-2 genomes analyzed, 99% of the strains/isolates are perfectly matched to at least two of the three assays, and more than 90% have 100% homology to all three assays (ORF1ab, N-gene, S-gene) in the SARS-CoV-2 Kit. None of the 65 non-target strain genomes analyzed showed matching sequences. In the matrix study, the Thermo Scientific SARS-CoV-2 workflow showed comparable detection to the centers of disease control and prevention (CDC) method. Conclusions The Thermo Scientific SARS-CoV-2 RT-PCR Workflow is an effective procedure for detection of RNA from SARS-CoV-2 virus from stainless steel. Highlights The workflow provides equivalent performance results with the two tested RNA extraction platforms and the two tested RT-PCR instruments.

Identifying active transcriptional regulators (TRs) associating with cis-regulatory elements in the genome to regulate gene expression is a key task in gene regulation research. TR binding profiles from numerous public ChIP-seq data can be utilized for association analysis with query data for TR identification, as an alternative to DNA sequence motif analysis. However, integration of the massive ChIP-seq datasets has been a major challenge in such approaches. Here we present BARTweb, an interactive web server for identifying TRs whose genomic binding patterns associate with input genomic features, by leveraging over 13 000 public ChIP-seq datasets for human and mouse. Using an updated binding analysis for regulation of transcription (BART) algorithm, BARTweb can identify functional TRs that regulate a gene set, have a binding profile correlated with a ChIP-seq profile or are enriched in a genomic region set, without a priori information of the cell type. BARTweb can be a useful web server for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org and the source code is available at https://github.com/zanglab/bart2.

Background The VERIPRO® SARS-CoV-2 Env assay uses reverse transcriptase (RT) PCR to detect SARS-CoV-2, the causative agent of COVID-19, from stainless-steel environmental sample swabs. Objective To validate the VERIPRO SARS-CoV-2 Env assay as part of the AOAC Research Institute’s Emergency Response Validation Performance Tested Method(s)SM program. Method The VERIPRO SARS-CoV-2 Env assay was evaluated for specificity using in silico analysis of 15 764 SARS-CoV-2 sequences and 65 exclusivity organisms (both near neighbors and background organisms). The candidate method was evaluated in an unpaired study design for one environmental surface (stainless steel) and compared to the U.S. Centers for Disease Control and Prevention 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, Instructions for Use (Revision 4, Effective 6/12/2020). Results Results of the in silico analysis demonstrate the specificity of the method in being able to detect target sequences and discriminate them from near neighbors. In the matrix study, the candidate method demonstrated statistically significant better recovery of the target analyte than the reference method. Conclusions The VERIPRO SARS-CoV-2 Env assay is a rapid and accurate method that can be utilized by food producers to detect the causative agent of COVID-19 on food contact surfaces. Highlights The VERIPRO SARS-CoV-2 Env assay can be performed without the need for an optional RNA purification step to detect SARS-CoV-2 from environmental surfaces.

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

The RNA methyltransferase TRDMT1 has recently emerged as a key regulator of homologous recombination (HR) in the transcribed regions of the genome, but how it is regulated and its relevance in cancer remain unknown. Here, we identified that TRDMT1 is poly-ubiquitinated at K251 by the E3 ligase TRIM28, removing TRDMT1 from DNA damage sites and allowing completion of HR. Interestingly, K251 is adjacent to G155 in the 3D structure, and the G155V mutation leads to hyper ubiquitination of TRDMT1, reduced TRDMT1 levels and impaired HR. Accordingly, a TRDMT1 G155V mutation in an ovarian cancer super responder to platinum treatment. Cells expressing TRDMT1-G155V are sensitive to cisplatin in vitro and in vivo. In contrast, high expression of TRDMT1 in patients with ovarian cancer correlates with platinum resistance. A potent TRDMT1 inhibitor resensitizes TRDMT1-high tumor cells to cisplatin. These results suggest that TRDMT1 is a promising therapeutic target to sensitize ovarian tumors to platinum therapy.

Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Recent cancer sequencing efforts have uncovered asymmetry in DNA damage induced mutagenesis between the transcribed and non-transcribed strands of genes. Here, we investigate the major type of damage induced by ultraviolet (UV) radiation, the cyclobutane pyrimidine dimers (CPDs), which are formed primarily in TT dinucleotides. We reveal that a transcriptional asymmetry already exists at the level of TT dinucleotide frequency and therefore also in CPD damage formation. This asymmetry is conserved in vertebrates and invertebrates and is completely reversed between introns and exons. We show the asymmetry in introns is linked to the transcription process itself, and is also found in enhancer elements. In contrast, the asymmetry in exons is not correlated to transcription, and is associated with codon usage preferences. Reanalysis of nucleotide excision repair, normalizing repair to the underlying TT frequencies, we show repair of CPDs is more efficient in exons compared to introns, contributing to the maintenance and integrity of coding regions. Our results highlight the importance of considering the primary sequence of the DNA in determining DNA damage sensitivity and mutagenic potential.

Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic—slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment—including gaps—of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the best compromise between sensitivity and precision while being faster than BLAST, Mash, MUMmer4 and USEARCH by 2–80 times. Identity was the best performing tool when searching for low-identity matches. While constructing phylogenetic trees from about 6000 transcripts, the tree due to the scores reported by Identity was the closest to the reference tree (in contrast to andi, FSWM and Mash). Identity is capable of producing pairwise identity scores of millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any global-alignment-based tool. Availability: https://github.com/BioinformaticsToolsmith/Identity.

Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.