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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Expression Based Species Deconvolution and Realignment Removes Misalignent Error in Multi-species Single Cell Data

Although single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals for a species presumed to originate from ambient RNA of the other species and applied a computational method to remove them. Misaligned reads account for an average of 0.5% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpsons Index scores than those derived from Cell Ranger. We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured. We present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address major consideration points when analyzing multispecies single-cell data for other applications.

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bioRxiv2021

Ultrafast, alignment-free detection of repeat expansions in NGS and RNAseq data

Short tandem repeat expansions are an established cause of diseases such as Huntingtons disease. Bioinformatic methods for detecting repeat expansions in short-read sequencing have revealed new repeat expansions in humans. Current bioinformatic methods to detect repeat expansions require alignment information to identify repetitive motif enrichment at genomic locations. We present superSTR, an ultrafast method that does not require alignment. We demonstrate superSTRs ability to efficiently process both whole-genome and whole-exome sequencing data. Using superSTR we perform the first analysis of the UK Biobank to efficiently screen the exomes of 49,953 biobank participants for repeat expansions. We identify known mutations, as well as diseases not previously associated with REs. We further demonstrate the first bioinformatic screening of RNA sequencing data to detect repeat expansions in patients with spinocerebellar ataxia and Fuchs endothelial corneal dystrophy, and mouse models of myotonic dystrophy. superSTR is a highly computationally-efficient repeat expansion tool screening and detection tool for genomewide novel repeat expansion analysis, significantly outperforming existing methods. superSTR is available from https://github.com/bahlolab/superSTR.

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bioRxiv2021

M&Ms: A software for building realistic Microbial Mock communities

MotivationAdvances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze these data. As these tools need to be tested, it is important to simulate datasets that resemble realistic conditions. Although there is a large amount of software dedicated to produce reads from in silico microbial communities, often the simulated data diverge widely from real situations. ResultsHere, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce realistic amplicon datasets from reference sequences, based on pragmatic ecological parameters. This tool creates sequence libraries for in silico microbial communities with user-controlled richness, evenness, microdiversity, and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. M&Ms also provides additional figures and files with extensive details on how each synthetic community is composed, so that users can make informed choices when designing their benchmarking pipelines. AvailabilityThe source code of M&Ms is freely available from https://github.com/ggnatalia/MMs Contactngarcia@cnb.csic.es

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bioRxiv2021

Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

BackgroundEyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. ObjectiveThis study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and methodThe Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. ResultsA total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. ConclusionThe result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non-Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC.

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bioRxiv2021

Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica

Emerging mutations and genotypes of the SARS-CoV-2 virus, responsible for the COVID-19 pandemic, have been reported globally. In Costa Rica during the year 2020, a predominant genotype carrying the mutation T1117I in the spike (S:T1117I) was previously identified. To investigate the possible effects of this mutation on the function of the spike, i.e. the biology of the virus, different bioinformatic pipelines based on phylogeny, natural selection and co-evolutionary models, molecular docking and epitopes prediction were implemented. Results of the phylogeny of sequences carrying the S:T1117I worldwide showed a polyphyletic group, with the emergency of local lineages. In Costa Rica, the mutation is found in the lineage B.1.1.389 and it is suggested to be a product of positive/adaptive selection. Different changes in the function of the spike protein and more stable interaction with a ligand (nelfinavir drug) were found. Only one epitope out 742 in the spike was affected by the mutation, with some different properties, but suggesting scarce changes in the immune response and no influence on the vaccine effectiveness. Jointly, these results suggest a partial benefit of the mutation for the spread of the virus with this genotype during the year 2020 in Costa Rica, although possibly not strong enough with the introduction of new lineages during early 2021 which became predominant later. In addition, the bioinformatics pipeline offers an integrative and exhaustive in silico strategy to eventually study other mutations of interest for the SARS-CoV-2 virus and other pathogens. HighlightsO_LIIn Costa Rica during the year 2020, a predominant SARS-CoV-2 genotype carrying the mutation T1117I in the spike (S:T1117I) was identified. C_LIO_LIThe S:T1117I was assessed for possible effects of this mutation on the function of the spike with a in silico approach. C_LIO_LIPhylogeny revealed that sequences carrying the S:T1117I worldwide define a polyphyletic group, with the emergency of local lineages, including the lineage B.1.1.389 in Costa Rica. C_LIO_LIA positive/adaptive selection was identified for S:T1117I, with different changes in the function of the spike protein, more stable interaction with ligands and scarce changes in the immune response. C_LIO_LIThe bioinformatics pipeline can be eventually used to study other mutations of the SARS-CoV-2 virus and other pathogens. C_LI

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bioRxiv2021

Open access in silico tools to predict the ADMET profiling and PASS (Prediction of Activity Spectra for Substances of Bioactive compounds of Garlic (Allium sativum L.)

BackgroundGarlic (Allium sativum L.) is a common spice with many health benefits, mainly due to its diverse bioactive compounds, (see below) such as organic sulphides, saponins, phenolic compounds, and polysaccharides. Several studies have demonstrated its functions such as anti-inflammatory, antibacterial, and antiviral, antioxidant, cardiovascular protective and anticancer property. In this work we have investigated the main bioactive components of garlic through a bioinformatics approach. Indeed, we are in an era of bioinformatics where we can predict data in the fields of medicine. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. MethodsThis paper encompasses the fundamental functions of open access in silico prediction tools, as PASS database (Prediction of Activity Spectra for Substances) that it estimates the probable biological activity profiles for compounds. This paper also aims to help support new researchers in the field of drug design and to investigate best bioactive compounds in garlic. Resultsscreening through each of pharmacokinetic criteria resulted in identification of Garlic compounds that adhere to all the ADMET properties. ConclusionsIt was established an open-access database (PASS database, available bioinformatics tool SwissADME, PreADMET pkCSM database) servers were employed to determine the ADMET (metabolism, distribution, excretion, absorption, and toxicity) attributes of garlic molecules and to enable identification of promising molecules that follow ADMET properties. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/452815v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1344e43org.highwire.dtl.DTLVardef@fe1674org.highwire.dtl.DTLVardef@1742577org.highwire.dtl.DTLVardef@47a966_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2021

SubcellulaRVis: an app to visualize subcellular compartment enrichment

High-throughput omics methods result in lists of differentially regulated or expressed genes or proteins, whose function is generally studied through statistical methods such as enrichment analyses. One aspect of protein regulation is subcellular localization, which is crucial for their correct processing and function and can change in response to various cellular stimuli. Enrichment of proteins for subcellular compartments is often based on Gene Ontology Cellular Compartment annotations. Results of enrichment are typically visualized using bar-charts, however enrichment analyses can result in a long list of significant annotations which are highly specific, preventing researchers from gaining a broad understanding of the subcellular compartments their proteins of interest may be located in. Schematic visualization of known subcellular locations has become increasingly available for single proteins via the UniProt and COMPARTMENTS platforms. However, it is not currently available for a list of proteins (e.g. from the same experiment) or for visualizing the results of enrichment analyses. To generate an easy-to-interpret visualization of protein subcellular localization after enrichment we developed the SubcellulaRVis web app, which visualizes the enrichment of subcellular locations of gene lists in an easy and impactful manner. SubcellulaRVis projects the results of enrichment analysis on a graphical representation of a eukaryotic cell. Implemented as a web app and an R package, this tool is user-friendly, provides exportable results in different formats, and can be used for gene lists derived from multiple organisms. Here, we show the power of SubcellulaRVis to assign proteins to the correct subcellular compartment using gene list enriched in previously published spatial proteomics datasets. We envision SubcellulaRVis will be useful for cell biologists with limited bioinformatics expertise wanting to perform precise and quick enrichment analysis and immediate visualization of gene lists. Author SummaryCells contain intracellular compartments, such as membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins can be found in different cellular compartments; depending on the subcellular compartment they are localized to, they can be differentially regulated or perform location-specific functions. High-throughput omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through their subcellular localization, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatic tool, named SubcellulaRVis, that compellingly visualizes the results of protein localization after enrichment and simplifies the results for a quick interpretation. We demonstrate that SubcellulaRVis precisely describes the subcellular localization of gene lists whose locations have been previously ascertained in publications. SubcellulaRVis can be accessed via the web or as a stand-alone app. SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise analyzing data related to cellular signalling, protein (re)localization and regulation, and location-specific functional modules within the cell.

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bioRxiv2021

fingeRNAt - a novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt - a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt) - a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction-based similarity based on fingerprints as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of molecular fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. AUTHOR SUMMARYWe present a novel bioinformatic tool, fingeRNAt, aiming to support scientists in the analysis of complexes of nucleic acids with various types of ligands. The software automatically detects non-covalent interactions and presents them in a form that is understandable to both humans and computers. Such data can help decipher the nature of interactions between nucleic acids and ligands and determine the main factors responsible for forming such complexes in nature. fingeRNAt finds application in multiple studies, both structure- and drug discovery-oriented. Here, we analyzed the experimentally solved structures of RNA complexes with small molecules to determine which binding features are most prevalent, i.e., most common interactions or their hot spots. The results of this analysis may help elucidate the mechanisms of binding and design new active molecules. Moreover, we propose to use the data generated by our software as a new metric for the quantitative comparison of two molecule complexes. We have shown that it is more reliable than the currently used methods in certain "difficult" cases. We have shown that the results of our program can be used for high-throughput analysis of molecular complexes and the search for active molecules. We are confident that fingeRNAt will be a valuable tool for exploring the complex world of interactions of nucleic acids with ligands.

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bioRxiv2022

Assessing and assuring interoperability of a genomics file format

BackgroundBioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, and only rarely do the creators of these tools robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results. MethodsWe sought (1) to assess the interoperability of a wide range of bioinformatics software using a shared genomics file format and (2) to provide a simple, reproducible method for enhancing inter-operability. As a focus, we selected the popular Browser Extensible Data (BED) file format for genomic interval data. Based on the file formats original documentation, we created a formal specification. We developed a new verification system, Acidbio (https://github.com/hoffmangroup/acidbio), which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases--potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the BED format. We also used a fuzzing approach to automatically perform additional testing. ResultsOf 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the softwares performance on the test suite. DiscussionAcidbio makes it easy to assess interoperability of software using the BED format, and therefore to identify areas for improvement in individual software packages. Applying our approach to other file formats would increase the reliability of bioinformatics software and data.

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bioRxiv2022

Repositioning linifanib as a potent anti-necroptosis agent for sepsis

BackgroundSepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection with high mortality rate. The role of tumour necrosis factor (TNF-)-induced necroptosis in promoting the pathophysiology of sepsis has been identified. Effective prevention of necroptosis is expected to improve the prognosis of sepsis patients. MethodsWe conducted bioinformatics prediction of candidate drugs by analyzing differentially expressed genes of sepsis patients extracted from GEO database, combining library of integrated network-based cellular signatures (LINCS) L1000 perturbation database. Biological experiments based on TNF--induced necroptosis in cellular and mouse model were performed to verify the protection of candidate drugs from SIRS. Cell viability was measured by CellTiter-Glo luminescent ATP assay. Effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and in vitro RIPK1 kinase assay. Survival curve analysis of SIRS mice treated by linifanib was performed. ResultsA total of 16 candidate drugs was screened out through bioinformatics analysis. Our experiments demonstrated that linifanib effectively protected cells from necroptosis and rescued the death of SIRS mice from shock induced by TNF-. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced the overexpressed level of IL-6, a good marker of severity during severe sepsis, in the lung of SIRS mice. ConclusionWe provide preclinical evidence for the potential clinical utility of linifanib in sepsis. Study of drug repositioning using bioinformatical predictions combined with experimental validations provides novel strategies for the development of sepsis drug.

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bioRxiv2022

Variable RNA sampling biases mediate concordance of single-cell and nucleus sequencing across cell types

A mechanistic understanding of the biological and technical factors that impact cell and nuclear transcript measurements is essential to designing, analyzing, and interpreting single-cell and single-nucleus RNA sequencing experiments. RNA sampling in nuclei and cells is fundamentally different as nuclei contain the same pre-mRNA population as cells, yet contain a small subset of the largely-cytoplasmic mRNAs. Nonetheless, early studies argued that including pre-mRNA in single-nucleus analysis led to results comparable to cellular samples. However, typical bioinformatic workflows do not distinguish between pre-mRNA and mRNA when analyzing gene expression, and variation in the relative abundance of pre-mRNA and mRNA across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA in routine gene expression analysis is now commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public datasets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling in single-cell and nucleus RNA-seq. We disentangle the roles of bioinformatic processing, assay choice, and biological variability on measured gene expression and marker gene selection. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias within and between assays and limits the generalizability of a recently-published normalization method intended to correct for this bias. As an alternative solution, we demonstrate the applicability of an existing post hoc gene length-based correction method developed for conventional RNA-seq gene set enrichment analysis. Finally, we show that the inclusion of pre-mRNA in bioinformatic processing can impart a larger effect on gene expression estimates than the choice of cell versus nuclear assay, which is pivotal to the effective reuse of existing data. Broadly, these analyses advance our understanding of the biological and technical factors underlying variation in single-cell and single-nucleus RNA-seq experiments to promote more informed choices in experimental design, data analysis, and data sharing and reuse.

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bioRxiv2022

On the synergies between ribosomal assembly and machine learning tools for microbial identification

Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start out by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools has the potential to improve identification performance relative to using the tools individually. We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content). Author SummaryMetagenomic studies focus on the challenging problem of identifying the presence and abundance of different species in a sample. This process typically involves the creation of digital reads from the sample which correspond to small parts of the genome sequence, and then have to be assembled together by a genome assembly tool. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification, and how they might complement traditional bioinformatics approaches. We conduct experiments with two representative state-of-the-art machine learning approaches and six metagenomic assembly tools in the context of two synthetic datasets. We find that for organisms with certain characteristics (levels of repetitiveness, GC content, and genome size), ensembling metagenome assembly tools with machine learning tools has the potential to improve species identification performance relative to using the tools individually.

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bioRxiv2022

A workflow reproducibility scale for automatic validation of biological interpretation results.

BackgroundReproducibility of data analysis workflow is a key issue in the field of bioinformatics. Recent computing technologies, such as virtualization, have made it possible to reproduce workflow execution with ease. However, the reproducibility of results is not well discussed; that is, there is no standard way to verify whether the biological interpretation of reproduced results are the same. Therefore, it still remains a challenge to automatically evaluate the reproducibility of results. ResultsWe propose a new metric, a reproducibility scale of workflow execution results, to evaluate the reproducibility of results. This metric is based on the idea of evaluating the reproducibility of results using biological feature values (e.g., number of reads, mapping rate, and variant frequency) representing their biological interpretation. We also implemented a prototype system that automatically evaluates the reproducibility of results using the proposed metric. To demonstrate our approach, we conducted an experiment using workflows used by researchers in real research projects and the use cases that are frequently encountered in the field of bioinformatics. ConclusionsOur approach enables automatic evaluation of the reproducibility of results using a fine-grained scale. By introducing our approach, it is possible to evolve from a binary view of whether the results are superficially identical or not to a more graduated view. We believe that our approach will contribute to more informed discussion on reproducibility in bioinformatics.

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bioRxiv2022

The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics .

BackgroundMicrobial culture collections play a key role in taxonomy by studying the diversity of their accessions and providing well characterized strains to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms (BCCM) were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (i.e. yeasts). FindingsThis public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenetic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. Technical detailsNextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository (https://github.com/Lcornet/GENERA). All the workflows are based on Singularity containers to increase reproducibility. TestingThe toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early-branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the Gloeobacterales order, the first group to diverge in the evolutionary tree of Cyanobacteria. ConclusionThe GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be useful for other applications, as shown by our case study on Gloeobacterales.

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bioRxiv2022

HISS: Snakemake-based workflows for performing SMRT-RenSeq assembly, AgRenSeq and dRenSeq for the discovery of novel plant disease resistance genes.

BackgroundIn the ten years since the initial publication of the RenSeq protocol, the method has proved to be a powerful tool for studying disease resistance in plants and providing target genes for breeding programmes. Since the initial publication of the methodology, it has continued to be developed as new technologies have become available and the increased availability of computing power has made new bioinformatic approaches possible. Most recently, this has included the development of a k-mer based association genetics approach, the use of PacBio HiFi data, and graphical genotyping with diagnostic RenSeq. However, there is not yet a unified workflow available and researchers must instead configure approaches from various sources themselves. This makes reproducibility and version control a challenge and limits the ability to perform these analyses to those with bioinformatics expertise. ResultsHere we present HISS, consisting of three workflows which take a user from raw RenSeq reads to the identification of candidates for disease resistance genes. These workflows conduct the assembly of enriched HiFi reads from an accession with the resistance phenotype of interest. A panel of accessions both possessing and lacking the resistance are then used in an association genetics approach (AgRenSeq) to identify contigs positively associated with the resistance phenotype. Candidate genes are then identified on these contigs and assessed for their presence or absence in the panel with a graphical genotyping approach that uses dRenSeq. These workflows are implemented via Snakemake, a python-based workflow manager. Software dependencies are either shipped with the release or handled with conda. All code is freely available and is distributed under the GNU GPL-3.0 license. ConclusionsHISS provides a user-friendly, portable, and easily customised approach for identifying novel disease resistance genes in plants. It is easily installed with all dependencies handled internally or shipped with the release and represents a significant improvement in the ease of use of these bioinformatics analyses.

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bioRxiv2022

Towards increased accuracy and reproducibility in SARS-CoV-2 next generation sequence analysis for public health surveillance

During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

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bioRxiv2022

CircPrime: a web-based platform for design of specific circular RNA primers

BackgroundCircular RNAs (circRNAs) are covalently closed-loop RNAs with critical regulatory roles in cells. The tenth of thousands of circRNAs have been unveiled due to the recent advances in high throughput RNA sequencing technologies and bioinformatic tools development. At the same time, polymerase chain reaction (PCR) cross-validation for circRNAs predicted by bioinformatic tools remains an essential part of any circRNA study before publication. ResultsHere, we present the CircPrime web-based platform, providing a user-friendly solution for DNA primer design and thermocycling conditions for circRNA identification with routine PCR methods. ConclusionsUser-friendly CircPrime web platform (http://circprime.elgene.net/) works with outputs of the most popular bioinformatic predictors of circRNAs to design specific circular RNA primers. CircPrime works with circRNA coordinates and any reference genome from the National Center for Biotechnology Information database (NCBI).

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bioRxiv2022

BioNumPy: Fast and easy analysis of biological data with Python

Python is a popular and widespread programming language for scientific computing, in large part due to the powerful array programming library NumPy, which makes it easy to write clean, vectorized and efficient code for handling large datasets. A challenge with using array programming for biological data is that the data is often non-numeric and variable-length (such as DNA sequences), inhibiting out-of-the-box use of standard array programming techniques. Thus, a tradition in bioinformatics has been to use low-level languages like C and C++ to write efficient code. This makes the tools less transparent to the average computational biologist - making them harder to understand, modify and contribute to. We here present a new Python package BioNumPy, which adds a layer on top of NumPy in order to enable intuitive array programming on biological datasets. BioNumPy is able to efficiently load biological datasets (e.g. FASTQ-files, BED-files and BAM-files) into NumPy-like data structures, so that NumPy operations like indexing, vectorized functions and reductions can be applied to the data. We show that BioNumPy is considerably faster than vanilla Python and other Python packages for common bioinformatics tasks, and in many cases as fast as tools written in C/C++. BioNumPy thus bridges a long-lasting gap in bioinformatics, allowing the same programming language (Python) to be used across the full spectrum from quick and simple scripts to computationally efficient processing of large-scale data.

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