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bioRxiv2021

Pattern Detection in Multiple Genome Sequences with Applications: The Case of All SARS-CoV-2 Complete Variants

Pattern detection and string matching are fundamental problems in computer science and the accelerated expansion of bioinformatics and computational biology have made them a core topic for both disciplines. The SARS-CoV-2 pandemic has made such problems more demanding with hundreds or thousands of new genome variants discovered every week, because of constant mutations, and the need for fast and accurate analyses. Medicines and, mostly, vaccines must be altered to adapt and efficiently address mutations. The need of computational tools for genomic analysis, such as sequence alignment, is very important, although, in most cases the resources and computational power needed is vast. The presented data structures and algorithms, specifically built for text mining and pattern detection, can help to address efficiently several bioinformatics problems. With a single execution of advanced algorithms, with limited space and time complexity, it is possible to acquire knowledge on all repeated patterns that exist in multiple genome sequences and this information can be used for further meta analyses. The potentials of the presented solutions are demonstrated with the analysis of more than 55,000 SARS-CoV-2 genome sequences (collected on March 10, 2021) and the detection of all repeated patterns with length up to 60 nucleotides in these sequences, something practically impossible with other algorithms due to its complexity. These results can be used to help provide answers to questions such as all variants common patterns, sequence alignment, palindromes and tandem repeats detection, genome comparisons, etc.

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bioRxiv2021

T1TAdb: the database of Type I Toxin-Antitoxin systems

Type I toxin-antitoxin (T1TA) systems constitute a large class of genetic modules with antisense RNA (asRNA)-mediated regulation of gene expression. They are widespread in bacteria and consist of an mRNA coding for a toxic protein and a noncoding asRNA that acts as an antitoxin preventing the synthesis of the toxin by directly basepairing to its cognate mRNA. The co- and post-transcriptional regulation of T1TA systems is intimately linked to RNA sequence and structure, therefore it is essential to have an accurate annotation of the mRNA and asRNA molecules to understand this regulation. However, most T1TA systems have been identified by means of bioinformatic analyses solely based on the toxin protein sequences, and there is no central repository of information on their specific RNA features. Here we present the first database dedicated to type I TA systems, named T1TAdb. It is an open-access web database (https://d-lab.arna.cnrs.fr/t1tadb) with a collection of ~1,900 loci in ~500 bacterial strains in which a toxin-coding sequence has been previously identified. RNA molecules were annotated with a bioinformatic procedure based on key determinants of the mRNA structure and the genetic organization of the T1TA loci. Besides RNA and protein secondary structure predictions, T1TAdb also identifies promoter, ribosome-binding, and mRNA-asRNA interaction sites. It also includes tools for comparative analysis, such as sequence similarity search and computation of structural multiple alignments, which are annotated with covariation information. To our knowledge, T1TAdb represents the largest collection of features, sequences, and structural annotations on this class of genetic modules.

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bioRxiv2021

A proteomics database for CNS proteins of the great pond snail Lymnaea stagnalis

Applications of key technologies in biomedical research, such as qRT-PCR or LC-MS based proteomics, are generating large biological (-omics) data sets which are useful for the identification and quantification of biomarkers involved in molecular mechanisms of any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snail Lymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of learning and memory, ageing and age-related as well as amyloid-{beta} induced memory decline. In this investigation, we used a bioinformatics approach to designing and benchmarking a comprehensive CNS proteomics database (LymCNS-PDB) for the identification of proteins from the Central Nervous System (CNS) of Lymnaea stagnalis by LC-MS based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from an existing published Lymnaea stagnalis transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to sequences for molluscan proteins (including Lymnaea stagnalis and other molluscs) available from UniProtKB. LymCNS-PDB, which contains 9,628 identified matched proteins, was then benchmarked by performing LC-MS based proteomics analysis with proteins isolated from the CNS of Lymnaea stagnalis. MS/MS analysis using the LymCNS-PDB database led to the identification of 3,810 proteins while only 982 proteins were identified by using a non-specific Molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis to identify a plethora of protein interactomes involved in several CNS functions in Lymnaea stagnalis including learning and memory, aging-related memory decline and others.

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bioRxiv2021

Vcflib and tools for processing the VCF variant call format

Since its introduction in 2011 the variant call format (VCF) has been widely adopted for processing DNA and RNA variants in practically all population studies -- as well as in somatic and germline mutation studies. VCF can present single nucleotide variants, multi-nucleotide variants, insertions and deletions, and simple structural variants called against a reference genome. Here we present over 125 useful and much used free and open source software tools and libraries, part of vcflib tools and bio-vcf. We also highlight cyvcf2, hts-nim and slivar tools. Application is typically in the comparison, filtering, normalisation, smoothing, annotation, statistics, visualisation and exporting of variants. Our tools run daily and invisibly in pipelines and countless shell scripts. Our tools are part of a wider bioinformatics ecosystem and we consider it very important to make these tools available as free and open source software to all bioinformaticians so they can be deployed through software distributions, such as Debian, GNU Guix and Bioconda. vcflib, for example, was installed over 40,000 times and bio-vcf was installed over 15,000 times through Bioconda by December 2020. We shortly discuss the design of VCF, lessons learnt, and how we can address more complex variation that can not easily be represented by the VCF format. All source code is published under free and open source software licenses and can be downloaded and installed from https://github.com/vcflib. Author summaryMost bioinformatics workflows deal with DNA/RNA variations that are typically represented in the variant call format (VCF) -- a file format that describes mutations (SNP and MNP), insertions and deletions (INDEL) against a reference genome. Here we present a wide range of free and open source software tools that are used in biomedical sequencing workflows around the world today.

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bioRxiv2021

Tatajuba - Exploring the distribution of homopolymer tracts

Length variation of homopolymeric tracts, which induces phase variation, is known to regulate gene expression leading to phenotypic variation in a wide range of bacterial species. There is no specialised bioinformatics software which can, at scale, exhaustively explore and describe these features from sequencing data. Identifying these is non-trivial as sequencing and bioinformatics methods are prone to introducing artefacts when presented with homopolymeric tracts due to the decreased base diversity. We present tatajuba, which can automatically identify potential homopolymeric tracts and their putative phenotypic impact, allowing for rapid investigation. We use it to detect all tracts in two separate datasets, one of Campylobacter jejuni and one of three Bordetella species, and to highlight those tracts that are polymorphic across samples. With this we confirm homopolymer tract variation with phenotypic impact found in previous studies and additionally find many more with potential variability. The software is written in C and is available under the open source license GNU GPL version 3 from https://github.com/quadram-institute-bioscience/tatajuba.

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bioRxiv2021

Molecular characterization and functional annotation of a hypothetical protein (TDB29877.1) from probiotic bacteria Lactobacillus acidophilus: an in-silico approach

Lactobacillus acidophilus bacteria are widely used as probiotic and to produce various healthy fermented food products. The PNW3 strain of the bacteria has numerous proteins in its genome and some are considered as hypothetical proteins. The aim of the present study was to predict the structures and biological functions of the hypothetical protein (accession number: TDB29877.1) from L. acidophilus through an in-silico approach applying various bioinformatics tools. The sequence similarity was searched on the available biological databases using BLASTp program to find out the homologues proteins. Besides, determination of various properties like physicochemical characteristics, subcellular localization, phylogenetic analysis, functional annotation, protein-protein interaction, determination of secondary and tertiary structures, active site detection and further quality assessment analysis were done using appropriate computational methods of bioinformatics. In-silico analysis revealed that the hypothetical protein has contained TerB-N and TerB-C domains with the presence of YjbR-like superfamily. The Protein-protein interaction network analysis revealed that the protein highly interacted with various known and unknown proteins responsible for iron ion binding, DNA and RNA metabolisms and numerous repair mechanisms that maintain cellular integrity. It was also found that the protein has predominantly alpha-helices in its secondary structure and the three dimensional model has been found to be novel as it possessed expected quality while gone through various quality assessment tools. Thus, the present result indicated that the selected hypothetical protein which is cytoplasmic in nature with Belta-grasp fold, plays important role in responding during stress condition or phage defense mechanism.

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bioRxiv2021

HAM-ART: An optimised culture-free Hi-C metagenomics pipeline for tracking antimicrobial resistance genes in complex microbial communities.

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. Author SummaryAntimicrobial resistance (AMR) is one of the biggest global health threats humanity is facing. Understanding the emergence and spread of AMR between different bacterial species is crucial for the development of effective countermeasures. In this paper we describe a user-friendly, affordable and comprehensive (laboratory and bioinformatics) workflow that is able to identify, associate and track AMR genes in bacteria. We demonstrate the efficiency and reliability of the method by comparing 50 faecal microbiomes from pig farms with high-antibiotic use (conventional farms), and 50 faecal microbiomes from pig farms with low-antibiotic use (organic farms). Our method provides a novel approach to resistance gene tracking, that also leads to the generation of high quality metagenomic assembled genomes that includes genes on mobile genetic elements, such as plasmids, that would not otherwise be included in these assembled genomes.

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bioRxiv2021

Declining RNA integrity in control autopsy brain tissue is robustly and asymmetrically associated with selective neuronal mRNA signal loss

RNA integrity numbers (RINs) are a standardized method for semi-quantification of RNA degradation, and are used in quality control prior to transcriptional profiling analysis. Recent work has demonstrated that RINs are associated with downstream transcriptional profiling, and correction procedures are typically employed in bioinformatic analysis pipelines to attempt to control for RINs influence on gene expression. However, relatively little work has been done to determine whether RINs influence is random, or is specifically targeted to a subset of mRNAs. We tested the hypothesis that RIN would be associated with a robust transcriptional profile seen across multiple studies. To test this, we downloaded subsets of raw transcriptional data from six published studies. We only included control, non-pathological post-mortem human brain tissue (n = 383 samples) in which independent subjects RIN values were also reported. A robust set of mRNAs consistently and significantly correlated with RIN across multiple studies, appearing to be selectively degraded as RIN declines. Many of the affected gene expression pathways are related to neurons (e.g., vesicle, mRNA transport, synapse, and mitochondria), suggesting that neuronal synaptic mRNA may be particularly vulnerable to degradation. Subsequent analysis of the relationship between RIN and vulnerable mRNA expression revealed most of the decay occurred over a relatively narrow RIN range of 7.2-8.6, with RIN values > 8.6 showing a ceiling effect, and those < 7.2 showing a floor effect on gene expression. Our data suggests that the RIN effect is pathway selective and non-linear, which may be an important consideration for current bioinformatic RIN correcting procedures, particularly in datasets in which declining RIN is confounded with a pathology under study (e.g., in Alzheimers disease).

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bioRxiv2021

ONTdeCIPHER: An amplicon-based nanopore sequencing pipeline for tracking pathogen variants

MotivationAmplicon-based nanopore sequencing is increasingly used for molecular surveillance during epidemics (e.g. ZIKA, EBOLA) or pandemics (e.g. SARS-CoV-2). However, there is still a lack of versatile and easy-to-use tools that allow users with minimal bioinformatics skills to perform the main steps of downstream analysis, from quality testing to SNPs effect to phylogenetic analysis. ResultsHere, we present ONTdeCIPHER, an amplicon-based Oxford Nanopore Technology (ONT) sequencing pipeline to analyze the genetic diversity of SARS-CoV-2 and other pathogenes. Our pipeline integrates 13 bioinformatics tools. With a single command line and a simple configuration file, users can pre-process their data and obtain the sequencing statistics, reconstruct the consensus genome, identify variants and their effects for each viral isolate, infer lineage and, finally perform multi-sequence alignments and phylogenetic analyses. Availability and implementationONTdeCIPHER is available at https://github.com/emiracherif/ONTdeCIPHER Contactemira.cherif@ird.fr Supplementary informationSupplementary data are available at ...

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bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

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bioRxiv2021

Duesselpore: a full-stack local web server for rapid and simple analysis of Oxford Nanopore Sequencing data

Genome-wide analysis of transcriptomes offers extensive insights into the molecular mechanisms underlying the physiology of all known species and discover those that are still hidden. Oxford Nanopore Technologies (ONT) has recently been developed as a fast, miniaturized, portable and a cost effective alternative to Next Generation Sequencing. However, RNA-seq data analysis software that exploit ONT portability and allows scientists to easily analyze ONT data everywhere without bioinformatic expertise is not widely available. We developed Duesselpore, an easy-to-follow deep sequencing workflow that runs as a local webserver and allows the analysis of ONT data everywhere without requiring additional bioinformatic tools or internet connection. Duesselpore output includes differentially expressed genes and further downstream analyses, such as variance heatmap, disease and gene ontology plots, gene concept network plots and exports customized pathways for different cellular processes. We validated Duesselpore by analyzing the transcriptomic changes induced by PCB126, a dioxin-like PCB and a potent aryl hydrocarbon receptor (AhR) agonist in human HaCaT keratinocytes, a well characterized model system. Duesselpore was specifically developed to analyze ONT data but we also implemented NGS data analysis. Duesselpore is compatible with Microsoft and Mac operating systems, allows convenient, reliable and cost-effective analysis of ONT and NGS data.

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bioRxiv2021

HLAncPred: A method for predicting promiscuous non-classical HLA binding sites

In the last two decades, ample of methods have been developed to predict the classical HLA binders in an antigen. In contrast, limited attempts have been made to develop methods for predicting binders for non-classical HLA; due to the scarcity of sufficient experimental data and lack of community interest. Of Note, non-classical HLA plays a crucial immunomodulatory role and regulates various immune responses. Recent studies revealed that non-classical HLA (HLA-E & HLA-G) based immunotherapies have many advantages over classical HLA based-immunotherapy, particularly against COVID-19. In order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning based-models achieved more than 0.98 AUC for HLA-G alleles on validation or independent dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.88 on the validation dataset for HLA-E*01:01, HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA binders and compared with the models developed in this study. Moreover, we have also predicted the non-classical HLA binders in the spike protein of different variants of virus causing COVID-19 including omicron (B.1.1.529) to facilitate the community. One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. In order to predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred), and a standalone package. Key PointsO_LINon-classical HLAs play immunomodulatory roles in the immune system. C_LIO_LIHLA-E restricted T-cell therapy may reduce COVID-19 associated cytokine storm. C_LIO_LIIn silico models developed for predicting binders for HLA-G and HLA-E. C_LIO_LIIdentification of non-classical HLA binders in strains of coronavirus C_LIO_LIA webserver for predicting promiscuous binders for non-classical HLA alleles C_LI Authors BiographyO_LIAnjali Dhall is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

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bioRxiv2021

The BioImage Archive - home of life-sciences microscopy data

Despite the huge impact of data resources in genomics and structural biology, until now there has been no central archive for biological data for all imaging modalities. The BioImage Archive is a new data resource at the European Bioinformatics Institute (EMBL-EBI) designed to fill this gap. In its initial development BioImage Archive accepts bioimaging data associated with publications, in any format, from any imaging modality from the molecular to the organism scale, excluding medical imaging. The BioImage Archive will ensure reproducibility of published studies that derive results from image data and reduce duplication of effort. Most importantly, the BioImage Archive will help scientists to generate new insights through reuse of existing data to answer new biological questions, and provision of training, testing and benchmarking data for development of tools for image analysis. The archive is available at https://www.ebi.ac.uk/bioimage-archive/. HighlightsO_LIThe BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). C_LIO_LIThe BioImage Archive aims to accept all biological imaging data associated with peer-reviewed publications using approaches that probe biological structure, mechanism and dynamics, as well as other important datasets that can serve as reference examples for particular biological or technical domains. C_LIO_LIThe BioImage Archive aims to encourage the use of valuable imaging data, to improve reproducibility of published results that rely on image data, and to facilitate extraction of novel biological insights from existing data and development of new image analysis methods. C_LIO_LIThe BioImage Archive forms the foundation for an ecosystem of related databases, supporting those resources with storage infrastructure and indexing across databases. C_LIO_LIAcross this ecosystem, the BioImage Archive already stores and provides access to over 1.5 petabytes of image data from many different imaging modalities and biological domains. C_LIO_LIFuture development of the BioImage Archive will support the fast-emerging next generation file formats (NGFFs) for bioimaging data, providing access mechanisms tailored toward modern visualisation and data exploration tools, as well as unlocking the power of modern AI-based image-analysis approaches. C_LI

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bioRxiv2022

Proteomic network analysis of bronchoalveolar lavage fluid in ex-smokers to discover implicated protein targets and novel drug treatments for chronic obstructive pulmonary disease

RationaleBronchoalveolar lavage of the epithelial lining fluid can sample the profound changes in the airway lumen milieu prevalent in Chronic Obstructive Pulmonary Disease (COPD). Characterizing the proteins in bronchoalveolar lavage fluid in COPD with advanced proteomic methods will identify disease-related changes, provide insight into pathogenetic mechanisms and potential therapeutics that will aid in the discovery of more effective therapeutics for COPD. ObjectivesWe compared epithelial lining fluid proteome of ex-smokers with moderate COPD who are not in exacerbation status COPD, to non-smoking healthy control subjects using advanced proteomics methods and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins towards the treatment of COPD. MethodsProteomic profiles of bronchalveolar lavage fluid were obtained from 1) never-smoker control subjects with normal lung function (n=10) or 2) individuals with stable moderate (GOLD stage 2, FEV1 50% - 80% predicted) COPD who were ex-smokers for at least one year (n=10). NIHs Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuitys Ingenuity Pathway Analysis (IPA) were the two bioinformatics tools employed for network analysis on the differentially expressed proteins to identify potential crucial hub proteins. The drug-proteome interaction signature comparison and ranking approach implemented in the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery was utilized to identify potential repurposable drugs for the treatment of COPD based on the BALF proteome. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that would most likely ameloriate inflammatory processes by inhibition or activation of their functions. ResultsProteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were differentially expressed in COPD versus control, including proteins associated with inflammation, structural elements, and energy metabolism. Functional annotation of the differentially expressed proteins by their implicated biological processes, cellular localization, and transcription factor interactions was accomplished via DAVID. Canonical pathways containing the differential expressed proteins were detailed via the Ingenuity Pathway Analysis application. Topological network analysis demonstrated that four proteins act as central node proteins in the inflammatory pathways in COPD. The CANDO multiscale drug discovery platform was used to analyze the behavioral similarity between the interaction signatures of all FDA-approved drugs and the identified BALF proteins. The drugs with the signatures most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The analysis revealed 189 drugs that putatively target the proteins implicated in COPD. The putative COPD drugs that were identified using CANDO were subsequently analyzed using a knowledge based technique to identify an optimal two drug combination that had the most appropriate effect on the central node proteins. ConclusionAnalysis of the BALF proteome revealed novel differentially expressed proteins in the epithelial lining fluid that elucidate COPD pathogenesis. Network analyses identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.

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bioRxiv2022

High resolution shotgun metagenomics: the more data, the better?

In shotgun metagenomics (SM), the state of the art bioinformatic workflows are referred to as high resolution shotgun metagenomics (HRSM) and require intensive computing and disk storage resources. While the increase in data output of the latest iteration of high throughput DNA sequencing systems can allow for unprecedented sequencing depth at a minimal cost, adjustments in HRSM workflows will be needed to properly process these ever-increasing sequence datasets. One potential adaptation is to generate so-called shallow SM datasets that contain fewer sequencing data per sample as compared to the more classic high coverage sequencing. While shallow sequencing is a promising avenue for SM data analysis, detailed benchmarks using real data are lacking. In this case study, we took four public SM datasets, one massive and the others moderate in size and subsampled each dataset at various levels to mimic shallow sequencing datasets of various sequencing depths. Our results suggest that shallow SM sequencing is a viable avenue to obtain sound results regarding microbial community structures and that high depth sequencing does not bring additional elements for ecological interpretation. More specifically, results obtained by subsampling as little as 0.5M sequencing clusters per sample were similar to the results obtained with the largest subsampled dataset for the human gut and agricultural soil datasets. For the Antarctic dataset, which contained only a few samples, 4M sequencing clusters per sample was found to generate comparable results to the full dataset. One area where ultra-deep sequencing and maximizing the usage of all data was undeniably beneficial was in the generation of metagenome-assembled genomes (MAGs). Key points- Three public multi-sample shotgun metagenomic NovaSeq datasets totalling 12,389,583 and 202 Gb, respectively were analyzed at various sequencing depths to evaluate the accuracy of shallow shotgun metagenomic sequencing using a high resolution shotgun metagenomic bioinformatic workflow. A synthetic mock community of 20 bacterial genomes was also analyzed for validation purposes. - Datasets subsampled to low sequencing depths gave nearly identical ecological patterns (taxonomic and functional composition and beta-alpha-diversity) compared to high depth subsampled datasets. - Rare taxa and functions could be uncovered with high sequencing depth vs. low sequencing depth datasets, but did not affect global ecological patterns. - High sequencing depth was positively correlated with both quantity and quality of recovered metagenome-assembled genomes.

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bioRxiv2022

Detecting intragenic trans-splicing events with hybrid transcriptome sequencing in cancer cells

Trans-splicing can generate non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting trans-spliced RNAs (ts-RNAs) may be interfered by false positives from experimental artifacts, circular RNAs (circRNAs), and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same genes, are often mistaken for circRNAs through analyses of high-throughput transcriptome sequencing (RNA-seq) data. In addition, the biogenesis and function of ts-RNAs remain elusive. Here we developed a bioinformatics pipeline, NCLscan-hybrid, with the integration of long and short RNA-seq reads to minimize false positives and identify intragenic ts-RNAs. We utilized two features of long reads, out-of-circle and rolling circle, to distinguish intragenic ts-RNAs from circRNAs. We also designed multiple experimental validation steps to examine each type of false positives and successfully confirmed an intragenic ts-RNA (ts-ARFGEF1) in breast cancer cells. On the basis of ectopic expression and CRISPR-based endogenous genome modification experiments, we confirmed that ts-ARFGEF1 formation was significantly dependent on the reverse complementary sequences in the flanking introns of the NCL junction. Subsequent in vitro and in vivo experiments demonstrated that ts-ARFGEF1 silencing can significantly inhibit tumor cell growth. We further showed the regulatory role of ts-ARFGEF1 in p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of transcriptionally non-co-linear RNAs, expanding the discovery of this important but understudied class of RNAs.

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bioRxiv2022

Systematic benchmarking of all-in-one microbial SNP calling pipelines

Clinical and public health microbiology is increasingly utilising whole genome sequencing (WGS) technology and this has lead to the development of a myriad of analysis tools and bioinformatics pipelines. Single nucleotide polymorphism (SNP) analysis is an approach used for strain characterisation and determining isolate relatedness. However, in order to ensure the development of robust methodologies suitable for clinical application of this technology, accurate, reproducible, traceable and benchmarked analysis pipelines are necessary. To date, the approach to benchmarking of these has been largely ad-hoc with new pipelines benchmarked on their own datasets with limited comparisons to previously published pipelines. In this study, Snpdragon, a fast and accurate SNP calling pipeline is introduced. Written in Nextflow, Snpdragon is capable of handling small to very large and incrementally growing datasets. Snpdragon is benchmarked using previously published datasets against six other all-in-one microbial SNP calling pipelines, Lyveset, Lyveset2, Snippy, SPANDx, BactSNP and Nesoni. The effect of dataset choice on performance measures is demonstrated to highlight some of the issues associated with the current available benchmarking approaches. The establishment of an agreed upon gold-standard benchmarking process for microbial variant analysis is becoming increasingly important to aid in its robust application, improve transparency of pipeline performance under different settings and direct future improvements and development. Snpdragon is available at https://github.com/FordeGenomics/SNPdragon. Impact statementWhole-genome sequencing has become increasingly popular in infectious disease diagnostics and surveillance. The resolution provided by single nucleotide polymorphism (SNP) analyses provides the highest level of insight into strain characteristics and relatedness. Numerous approaches to SNP analysis have been developed but with no established gold-standard benchmarking approach, choice of bioinformatics pipeline tends to come down to laboratory or researcher preference. To support the clinical application of this technology, accurate, transparent, auditable, reproducible and benchmarked pipelines are necessary. Therefore, Snpdragon has been developed in Nextflow to allow transparency, auditability and reproducibility and has been benchmarked against six other all-in-one pipelines using a number of previously published benchmarking datasets. The variability of performance measures across different datasets is shown and illustrates the need for a robust, fair and uniform approach to benchmarking. Data SummaryO_LIPreviously sequenced reads for Escherichia coli O25b:H4-ST131 strain EC958 are available in BioProject PRJNA362676. BioSample accession numbers for the three benchmarking isolates are: O_LIEC958: SAMN06245884 C_LIO_LIMS6573: SAMN06245879 C_LIO_LIMS6574: SAMN06245880 C_LI C_LIO_LIAccession numbers for reference genomes against the E. coli O25b:H4-ST131 strain EC958 benchmark are detailed in table 2. C_LIO_LISimulated benchmarking data previously described by Yoshimura et al. is available at http://platanus.bio.titech.ac.jp/bactsnp (1). C_LIO_LISimulated datasets previously described by Bush et al. is available at http://dx.doi.org/10.5287/bodleian:AmNXrjYN8 (2). C_LIO_LIReal sequencing benchmarking datasets previously described by Bush et al. are available at http://dx.doi.org/10.5287/bodleian:nrmv8k5r8 (2). C_LI O_TBL View this table: org.highwire.dtl.DTLVardef@f2446corg.highwire.dtl.DTLVardef@16a299corg.highwire.dtl.DTLVardef@d1c7a5org.highwire.dtl.DTLVardef@8a3cbaorg.highwire.dtl.DTLVardef@198f455_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONReference genomes used in the EC958 benchmarking dataset and the percent identity against the three included samples. C_TABLECAPTION C_TBL

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bioRxiv2022

AtlasXbrowser enables spatial multi-omics data analysis through the precise determination of the region of interest

Recent developments in novel spatial sequencing technologies allow for the incorporation of spatial information into high-throughput sequencing assays. One such method, Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq, abbreviated herein as DBiT), utilizes perpendicular microfluidic channels to deliver DNA barcodes across the tissue in a spatially-encoded manner, allowing for sequenced reads to be mapped back onto the 2-D coordinates of the tissue to provide spatial coordinates to cells. DBiT has been the first spatial sequencing technology developed for epigenomic assays beyond transcriptome and proteome. However, despite existing of many open-source software packages for downstream bioinformatics analysis, there is no software available for processing DBiT image data with evenly spaced channels. To facilitate the integration of DBiT spatial and sequenced data, here we proposed a new method to precisely capture the spatial information and further developed AtlasXbrowser based on the new method to extract spatial data from the image data. AtlasXbrowser is a python-based tool with GUI that requires no technical expertise to operate and enables researchers to incorporate brightfield and epifluorescence images of processed tissue samples into downstream bioinformatics analysis tools. Availability and implementationFreely available at https://github.com/atlasxomics/AtlasXbrowser.

bioinformatics↗