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Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed., Competing Interests: RD, SM, JL, HL No competing interests declared, (© 2024, Deek et al.)

Objectives: Facing the emerging diseases, rapid identification of the pathogen and multi-dimensional characterization of the genomic features at both isolate-level and population-level through high-throughput sequencing data can provide invaluable information to guide the development of antiviral agents and strategies. However, a user-friendly program is in urgent need for clinical laboratories without bioinformatics background to decode the complex big genomics data., Methods: In this study, we developed an interactive online platform named PHDtools with a total of 15 functions to analyze metagenomics data to identify the potential pathogen and decode multi-dimensional genetic signatures including intra-/inter-host variations and lineage-level variations. The platform was applied to analyze the meta-genomic data of the samples collected from the 172 imported COVID-19 cases., Results: According to the analytical results of mNGS, 27 patients were found to have the co-infections of SARS-CoV-2 with either influenza virus (n = 9) or human picobirnavirus (n = 19). Enough coverages of all the assembled SARS-CoV-2 genomes provided the sub-lineages of Omicron variant, and the number of mutations in the non-structural genes and M gene was increased, as well as the intra-host variations occurred in E and M gene were under positive selection (Ka/Ks > 1). These findings of increased or changed mutations in the SARS-CoV-2 genome characterized the current adaptive evolution patterns of Omicron sub-lineages, and revealed the evolution speed of these sub-lineages might increase., Conclusions: Consequently, the application of PHDtools has proved that this platform is accurate, user-friendly and convenient for clinical users who are deficient in bioinformatics, and the clinical monitor of SARS-CoV-2 genomes by PHDtools also highlighted the potential evolution features of current SARS-CoV-2 and indicated that the development of anti-SARS-CoV-2 agents and new-designed vaccines should incorporate the gene variations other than S gene., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The world has seen unprecedented gains in the global genomic surveillance capacities for pathogens with pandemic and epidemic potential within the last 4 years. To strengthen and sustain the gains made, WHO is working with countries and partners to implement the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022-2032. A key technical product developed through these multi-agency collaborative efforts is a genomics costing tool (GCT), as sought by many countries. This tool was developed by five institutions - Association of Public Health Laboratories, FIND, The Global Fund to Fight AIDS, Tuberculosis and Malaria, UK Health Security Agency, and the World Health Organization. These institutions developed the GCT to support financial planning and budgeting for SARS-CoV-2 next-generation sequencing activities, including bioinformatic analysis. The tool costs infrastructure, consumables and reagents, human resources, facility and quality management. It is being used by countries to (1) obtain costs of routine sequencing and bioinformatics activities, (2) optimize available resources, and (3) build an investment case for the scale-up or establishment of sequencing and bioinformatics activities. The tool has been validated and is available in English and Russian at https://www.who.int/publications/i/item/9789240090866. This paper aims to highlight the rationale for developing the tool, describe the process of the collaborative effort in developing the tool, and describe the utility of the tool to countries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Akande, Afrough, Amante, Carter, Cunningham, Hull, Inamdar, Jaguparov, Marklewitz, Musul, Norberg, Pereyaslov, Poates, Samaan, Suresh, Uplekar, Wilhem, Salvi Le Garrec Zwetyenga and Whistler.)

Competing Interests: We declare no competing interests.

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias., (© 2024. The Author(s).)

Competing Interests: Disclosures None.

In response to the pressing need for continuous monitoring of emergence and circulation of pathogens through genomics, it is imperative to keep developing bioinformatics tools that can help in their rapid characterization and classification. Here, we introduce ReporType, a versatile bioinformatics pipeline designed for targeted loci screening and typing of infectious agents. Developed using the snakemake workflow manager, ReporType integrates multiple software for read quality control and de novo assembly, and then applies ABRicate for locus screening, culminating in the production of easily interpretable reports for the identification of pathogen genotypes and/or screening of specific genomic loci. The pipeline accommodates a range of input formats, from Illumina or Oxford Nanopore Technology (ONT) reads (FASTQ) to Sanger sequencing files (AB1), or FASTA files, making it flexible for application in multiple pathogens and with different purposes. ReporType is released with pre-prepared databases for some viruses and bacteria, yet it remains easily configurable to handle custom databases. ReporType performance and functionality were validated through proof-of-concept exercises, encompassing diverse pathogenic species, including viruses such as measles, Newcastle disease virus (NDV), Dengue virus (DENV), influenza, hepatitis C virus (HCV) and Human T-Cell Lymphotropic virus type 1 (HTLV-1), as well as bacteria like Chlamydia trachomatis and Legionella pneumophila . In summary, ReporType emerges as a simple, dynamic and pan-pathogen tool, poised to evolve in tandem with the ever-changing needs of the fields of pathogen genomics, infectious disease epidemiology, and one health bioinformatics. ReporType is freely available at GitHub.

Motivation: Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments., Results: Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features., Availability and Implementation: Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe., (© The Author(s) 2024. Published by Oxford University Press.)

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations., (© 2024. The Author(s).)

Importance: The Flankophile pipeline enables the analysis and visualization of flanking regions of prokaryotic sequences of interest on large data sets in one step and in a consistent manner. A specific tool for flanking region analysis with automated visualization has not been developed before, and Flankophile will make flanking region analysis easier and accessible to more people. Flankophile will be especially useful in the field of genomic epidemiology of acquired antimicrobial resistance genes. Here, information from flanking region sequences can be instrumental in rejecting or supporting the possibility of a recent common source of the same resistance gene found in different samples., Competing Interests: The authors declare no conflict of interest.

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena) is maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). The ENA is one of the three members of the International Nucleotide Sequence Database Collaboration (INSDC). It serves the bioinformatics community worldwide via the submission, processing, archiving and dissemination of sequence data. The ENA supports data types ranging from raw reads, through alignments and assemblies to functional annotation. The data is enriched with contextual information relating to samples and experimental configurations. In this article, we describe recent progress and improvements to ENA services. In particular, we focus upon three areas of work in 2023: FAIRness of ENA data, pandemic preparedness and foundational technology. For FAIRness, we have introduced minimal requirements for spatiotemporal annotation, created a metadata-based classification system, incorporated third party metadata curations with archived records, and developed a new rapid visualisation platform, the ENA Notebooks. For foundational enhancements, we have improved the INSDC data exchange and synchronisation pipelines, and invested in site reliability engineering for ENA infrastructure. In order to support genomic surveillance efforts, we have continued to provide ENA services in support of SARS-CoV-2 data mobilisation and have adapted these for broader pathogen surveillance efforts., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Bacteria cooperate by working collaboratively to defend their colonies, share nutrients, and resist antibiotics. Nevertheless, our understanding of these remarkable behaviours primarily comes from studying a few well-characterized species. Consequently, there is a significant gap in our understanding of microbial social traits, particularly in natural environments. To address this gap, we can use bioinformatic tools to identify genes that control cooperative or otherwise social traits. Existing tools address this challenge through two approaches. One approach is to identify genes that encode extracellular proteins, which can provide benefits to neighbouring cells. An alternative approach is to predict gene function using annotation tools. However, these tools have several limitations. Not all extracellular proteins are cooperative, and not all cooperative behaviours are controlled by extracellular proteins. Furthermore, existing functional annotation methods frequently miss known cooperative genes. We introduce SOCfinder as a new tool to find bacterial genes that control cooperative or otherwise social traits. SOCfinder combines information from several methods, considering if a gene is likely to [1] code for an extracellular protein [2], have a cooperative functional annotation, or [3] be part of the biosynthesis of a cooperative secondary metabolite. We use data on two extensively-studied species ( P. aeruginosa and B. subtilis ) to show that SOCfinder is better at finding known cooperative genes than existing tools. We also use theory from population genetics to identify a signature of kin selection in SOCfinder cooperative genes, which is lacking in genes identified by existing tools. SOCfinder opens up a number of exciting directions for future research, and is available to download from https://github.com/lauriebelch/SOCfinder.

Motivation: Technical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results., Results: We propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model's predictions., Availability and Implementation: The software implementation can be found at https://github.com/ideateknoloji/FPDetect., (© The Author(s) 2023. Published by Oxford University Press.)

Several bioinformatics genotyping algorithms are now commonly used to characterize antimicrobial resistance (AMR) gene profiles in whole-genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli , is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data ( n =1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance and SRST2, run with the ResFinder database) and their outputs compared. For simulation tests where 3079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3076 (99.9 %) simulations, ABRicate for 3054 (99.2 %), ARIBA for 2783 (90.4 %) and SRST2 for 2108 (68.5 %). For simulation tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance identified the correct alleles in 35 338/46 318 (76.3 %) simulations, ABRicate identified them in 11 842/46 318 (25.6 %) simulations, ARIBA identified them in 1679/46 318 (3.6 %) simulations and SRST2 identified them in 2000/46 318 (4.3 %) simulations. In real data, across all methods, 1392/1818 (76 %) isolates had discrepant allele calls for at least 1 gene. In addition to highlighting areas for improvement in challenging scenarios, (e.g. identification of AMR genes at <10× coverage, identifying multiple closely related AMR genes present in the same sample), our evaluations identified some more systematic errors that could be readily soluble, such as repeated misclassification (i.e. naming) of genes as shorter variants of the same gene present within the reference resistance gene database. Such naming errors accounted for at least 2530/4321 (59 %) of the discrepancies seen in real data. Moreover, many of the remaining discrepancies were likely 'artefactual', with reporting of cut-off differences accounting for at least 1430/4321 (33 %) discrepants. Whilst we found that comparing outputs generated by running multiple algorithms on the same dataset could identify and resolve these algorithmic artefacts, the results of our evaluations emphasize the need for developing new and more robust genotyping algorithms to further improve accuracy and performance.

K-mer-based analysis plays an important role in many bioinformatics applications, such as de novo assembly, sequencing error correction, and genotyping. To take full advantage of such methods, the k-mer content of a read set must be captured as accurately as possible. Often the use of long k-mers is preferred because they can be uniquely associated with a specific genomic region. Unfortunately, it is not possible to reliably extract long k-mers in high error rate reads with standard exact k-mer counting methods. We propose SAKE, a method to extract long k-mers from high error rate reads by utilizing strobemers and consensus k-mer generation through partial order alignment. Our experiments show that on simulated data with up to 6% error rate, SAKE can extract 97-mers with over 90% recall. Conversely, the recall of DSK, an exact k-mer counter, drops to less than 20%. Furthermore, the precision of SAKE remains similar to DSK. On real bacterial data, SAKE retrieves 97-mers with a recall of over 90% and slightly lower precision than DSK, while the recall of DSK already drops to 50%. We show that SAKE can extract more k-mers from uncorrected high error rate reads compared to exact k-mer counting. However, exact k-mer counters run on corrected reads can extract slightly more k-mers than SAKE run on uncorrected reads., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Leinonen, Salmela. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

Background: Processing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data., Results: The Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster., Conclusion: Iliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows., (© 2023. The Author(s).)

Background: In cancer, genomic rearrangements can create fusion genes that either combine protein-coding sequences from two different partner genes or place one gene under the control of the promoter of another gene. These fusion genes can act as oncogenic drivers in tumor development and several fusions involving kinases have been successfully exploited as drug targets. Expressed fusions can be identified in RNA sequencing (RNA-Seq) data, but fusion prediction software often has a high fraction of false positive fusion transcript predictions. This is problematic for both research and clinical applications., Results: We describe a method for validation of fusion transcripts detected by RNA-Seq in matched whole-genome sequencing (WGS) data. Our pipeline uses discordant read pairs to identify supported fusion events and analyzes soft-clipped read alignments to determine genomic breakpoints. We have tested it on matched RNA-Seq and WGS data for both tumors and cancer cell lines and show that it can be used to validate both new predicted gene fusions and experimentally validated fusion events. It was considerably faster and more sensitive than using BreakDancer and Manta, software that is instead designed to detect many different types of structural variants on a genome-wide scale., Conclusions: We have developed a fast and very sensitive pipeline for validation of gene fusions detected by RNA-Seq in matched WGS data. It can be used to identify high-quality gene fusions for further bioinformatic and experimental studies, including validation of genomic breakpoints and studies of the mechanisms that generate fusions. In a clinical setting, it could help find expressed gene fusions for personalized therapy., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Extreme acidophiles thrive in harsh environments characterized by acidic pH, high concentrations of dissolved metals and high osmolarity. Most of these microorganisms are chemolithoautotrophs that obtain energy from low redox potential sources, such as the oxidation of ferrous ions. Under these conditions, the mechanisms that maintain homeostasis of proteins (proteostasis), as the main organic components of the cells, are of utmost importance. Thus, the analysis of protein chaperones is critical for understanding how these organisms deal with proteostasis under such environmental conditions. In this work, using a bioinformatics approach, we performed a comparative genomic analysis of the genes encoding classical, periplasmic and stress chaperones, and the protease systems. The analysis included 35 genomes from iron- or sulfur-oxidizing autotrophic, heterotrophic, and mixotrophic acidophilic bacteria. The results showed that classical ATP-dependent chaperones, mostly folding chaperones, are widely distributed, although they are sub-represented in some groups. Acidophilic bacteria showed redundancy of genes coding for the ATP-independent holdase chaperones RidA and Hsp20. In addition, a systematically high redundancy of genes encoding periplasmic chaperones like HtrA and YidC was also detected. In the same way, the proteolytic ATPase complexes ClpPX and Lon presented redundancy and broad distribution. The presence of genes that encoded protein variants was noticeable. In addition, genes for chaperones and protease systems were clustered within the genomes, suggesting common regulation of these activities. Finally, some genes were differentially distributed between bacteria as a function of the autotrophic or heterotrophic character of their metabolism. These results suggest that acidophiles possess an abundant and flexible proteostasis network that protects proteins in organisms living in energy-limiting and extreme environmental conditions. Therefore, our results provide a means for understanding the diversity and significance of proteostasis mechanisms in extreme acidophilic bacteria., Competing Interests: The authors declare that there are no competing interests., (Copyright: © 2023 Izquierdo-Fiallo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motivation: The Jaccard similarity on k-mer sets has shown to be a convenient proxy for sequence identity. By avoiding expensive base-level alignments and comparing reduced sequence representations, tools such as MashMap can scale to massive numbers of pairwise comparisons while still providing useful similarity estimates. However, due to their reliance on minimizer winnowing, previous versions of MashMap were shown to be biased and inconsistent estimators of Jaccard similarity. This directly impacts downstream tools that rely on the accuracy of these estimates., Results: To address this, we propose the minmer winnowing scheme, which generalizes the minimizer scheme by use of a rolling minhash with multiple sampled k-mers per window. We show both theoretically and empirically that minmers yield an unbiased estimator of local Jaccard similarity, and we implement this scheme in an updated version of MashMap. The minmer-based implementation is over 10 times faster than the minimizer-based version under the default ANI threshold, making it well-suited for large-scale comparative genomics applications., Availability and Implementation: MashMap3 is available at https://github.com/marbl/MashMap., (© The Author(s) 2023. Published by Oxford University Press.)

Escherichia coli are highly diverse bacteria with different pathogenic types, serotypes and phylogenetic types/phylotypes. In recent years, infections with E. coli have increased worldwide and so has the emergence of antibiotic resistant strains. In the present study we have assembled, annotated and analysed genome sequences of three strains of the phylogroup D of E. coli. These strains were isolated from the river Yamuna, a prominent anthropogenic urban river of northern India. These strains showed varied antibiotic susceptibilities, one was susceptible to all the antibiotics tested except ampicillin while of the other two, one was multi-β-lactam resistant and the other was multi-drug resistant (resistant to multiple β-lactams, fluoroquinolones and kanamycin). The short-sequence reads were assembled into contigs using the de-novo approach and further, scaffolding of contigs was performed by using the best reference genome for a particular isolate which resulted in a significant increase in the N 50 value of each assembly. The bioinformatics assembly approach used in this study could be easily applied to study other bacterial genomes., (© 2023. Springer Nature Limited.)

We evaluated the diagnostic yield using genome-slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI-funded GREGoR Consortium site. Genome-wide reanalysis was performed using Moon™, a machine-learning-based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome-wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting., (© 2023 The Authors. Clinical Genetics published by John Wiley & Sons Ltd.)

Summary: Many existing software libraries for genomics require researchers to pick between competing considerations: the performance of compiled languages and the accessibility of interpreted languages. Go, a modern compiled language, provides an opportunity to address this conflict. We introduce Gonomics, an open-source collection of command line programs and bioinformatic libraries implemented in Go that unites readability and performance for genomic analyses. Gonomics contains packages to read, write, and manipulate a wide array of file formats (e.g. FASTA, FASTQ, BED, BEDPE, SAM, BAM, and VCF), and can convert and interface between these formats. Furthermore, our modular library structure provides a flexible platform for researchers developing their own software tools to address specific questions. These commands can be combined and incorporated into complex pipelines to meet the growing need for high-performance bioinformatic resources., Availability and Implementation: Gonomics is implemented in the Go programming language. Source code, installation instructions, and documentation are freely available at https://github.com/vertgenlab/gonomics., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Identifying variants associated with diseases is a challenging task in medical genetics research. Current studies that prioritize variants within individual genomes generally rely on known variants, evidence from literature and genomes, and patient symptoms and clinical signs. The functionalities of the existing tools, which rank variants based on given patient symptoms and clinical signs, are restricted to the coverage of ontologies such as the Human Phenotype Ontology (HPO). However, most clinicians do not limit themselves to HPO while describing patient symptoms/signs and their associated variants/genes. There is thus a need for an automated tool that can prioritize variants based on freely expressed patient symptoms and clinical signs., Results: STARVar is a Symptom-based Tool for Automatic Ranking of Variants using evidence from literature and genomes. STARVar uses patient symptoms and clinical signs, either linked to HPO or expressed in free text format. It returns a ranked list of variants based on a combined score from two classifiers utilizing evidence from genomics and literature. STARVar improves over related tools on a set of synthetic patients. In addition, we demonstrated its distinct contribution to the domain on another synthetic dataset covering publicly available clinical genotype-phenotype associations by using symptoms and clinical signs expressed in free text format., Conclusions: STARVar stands as a unique and efficient tool that has the advantage of ranking variants with flexibly expressed patient symptoms in free-form text. Therefore, STARVar can be easily integrated into bioinformatics workflows designed to analyze disease-associated genomes., Availability: STARVar is freely available from https://github.com/bio-ontology-research-group/STARVar ., (© 2023. The Author(s).)

Structural variant (SV) calling belongs to the standard tools of modern bioinformatics for identifying and describing alterations in genomes. Initially, this work presents several complex genomic rearrangements that reveal conceptual ambiguities inherent to the representation via basic SV. We contextualize these ambiguities theoretically as well as practically and propose a graph-based approach for resolving them. For various yeast genomes, we practically compute adjacency matrices of our graph model and demonstrate that they provide highly accurate descriptions of one genome in terms of another. An open-source prototype implementation of our approach is available under the MIT license at https://github.com/ITBE-Lab/MA ., (© 2023. The Author(s).)

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts., Competing Interests: The authors declare they have no conflict of interest., (Copyright: © 2023 Chicco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Marine prokaryotes are a rich source of novel bioactive secondary metabolites for drug discovery. Recent genome mining studies have revealed their great potential to bio-synthesize novel secondary metabolites. However, the exact biosynthetic chemical space encoded by the marine prokaryotes has yet to be systematically evaluated., Results: We first investigated the secondary metabolic potential of marine prokaryotes by analyzing the diversity and novelty of the biosynthetic gene clusters (BGCs) in 7541 prokaryotic genomes from cultivated and single cells, along with 26,363 newly assembled medium-to-high-quality genomes from marine environmental samples. To quantitatively evaluate the unexplored biosynthetic chemical space of marine prokaryotes, the clustering thresholds for constructing the biosynthetic gene cluster and molecular networks were optimized to reach a similar level of the chemical similarity between the gene cluster family (GCF)-encoded metabolites and molecular family (MF) scaffolds using the MIBiG database. The global genome mining analysis demonstrated that the predicted 70,011 BGCs were organized into 24,536 mostly new (99.5%) GCFs, while the reported marine prokaryotic natural products were only classified into 778 MFs at the optimized clustering thresholds. The number of MF scaffolds is only 3.2% of the number of GCF-encoded scaffolds, suggesting that at least 96.8% of the secondary metabolic potential in marine prokaryotes is untapped. The unexplored biosynthetic chemical space of marine prokaryotes was illustrated by the 88 potential novel antimicrobial peptides encoded by ribosomally synthesized and post-translationally modified peptide BGCs. Furthermore, a sea-water-derived Aquimarina strain was selected to illustrate the diverse biosynthetic chemical space through untargeted metabolomics and genomics approaches, which identified the potential biosynthetic pathways of a group of novel polyketides and two known compounds (didemnilactone B and macrolactin A 15-ketone)., Conclusions: The present bioinformatics and cheminformatics analyses highlight the promising potential to explore the biosynthetic chemical diversity of marine prokaryotes and provide valuable knowledge for the targeted discovery and biosynthesis of novel marine prokaryotic natural products. Video Abstract., (© 2023. The Author(s).)

Summary: We present pygenomics, a Python package for working with genomic intervals and bioinformatic data files. The package implements interval operations, provides both API and CLI, and supports reading and writing data in widely used bioinformatic formats, including BAM, BED, GFF3, and VCF. The source code of pygenomics is provided with in-source documentation and type annotations and adheres to the functional programming paradigm. These features facilitate seamless integration of pygenomics routines into scripts and pipelines. The package is implemented in pure Python using its standard library only and contains the property-based testing framework. Comparison of pygenomics with other Python bioinformatic packages with relation to features and performance is presented. The performance comparison covers operations with genomic intervals, read alignments, and genomic variants and demonstrates that pygenomics is suitable for computationally effective analysis., Availability and Implementation: The source code is available at https://gitlab.com/gtamazian/pygenomics., (© The Author(s) 2023. Published by Oxford University Press.)

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community., Competing Interests: Conflicts of interest None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.)

The aim of this study was to determine the global genetic diversity and transmission dynamics of coxsackievirus B4 (CVB4) and to propose future directions for disease surveillance. Next-generation sequencing was performed to obtain the complete genome sequence of CVB4, and the genetic diversity and transmission dynamics of CVB4 worldwide were analyzed using bioinformatics methods such as phylogenetic analysis, evolutionary dynamics, and phylogeographic analysis. Forty complete genomes of CVB4 were identified from asymptomatic infected individuals and hand, foot, and mouth disease (HFMD) patients. Frequent recombination between CVB4 and EV-B multiple serotypes in the 3D pol region was found and formed 12 recombinant patterns (A-L). Among these, the CVB4 isolated from asymptomatic infected persons and HFMD patients belonged to lineages H and I, respectively. Transmission dynamics analysis based on the VP1 region revealed that CVB4 epidemics in countries outside China were dominated by the D genotype, whereas the E genotype was dominant in China, and both genotypes evolved at a rate of > 6.50 × 10 -3 substitutions/site/year. CVB4 spreads through the population unseen, with the risk of disease outbreaks persisting as susceptible individuals accumulate. Our findings add to publicly available CVB4 genomic sequence data and deepen our understanding of CVB4 molecular epidemiology.

Whole genome sequencing (WGS) of clinical bacterial isolates has the potential to transform the fields of diagnostics and public health. To realize this potential, bioinformatic software that reports identification results needs to be developed that meets the quality standards of a diagnostic test. We developed GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on WGS reads. GAMBIT incorporates this algorithm with a highly curated searchable database of 48,224 genomes. Herein, we describe validation of the scoring methodology, parameter robustness, establishment of confidence thresholds and the curation of the reference database. We assessed GAMBIT by way of validation studies when it was deployed as a laboratory-developed test in two public health laboratories. This method greatly reduces or eliminates false identifications which are often detrimental in a clinical setting., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Lumpe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motivation: Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in O(ns) time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA's O(s2) memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement., Results: In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in O(s) memory while retaining WFA's time complexity of O(ns). As a result, this work improves the lowest known memory bound O(n) to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times., Availability and Implementation: All code is publicly available at https://github.com/smarco/BiWFA-paper., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

The beginning of the twenty-first century witnessed novel breakthrough research directions in the life sciences, such as genomics, transcriptomics, translatomics, proteomics, metabolomics, and bioinformatics. A newly developed single-molecule approach addresses the physical and chemical properties and the functional activity of single (individual) biomacromolecules and viral particles. Within the alternative approach, the combination of "single-molecule approaches" is opposed to "omics approaches". This new approach is fundamentally unique in terms of its research object (a single biomacromolecule). Most studies are currently performed using postgenomic technologies that allow the properties of several hundreds of millions or even billions of biomacromolecules to be analyzed. This paper discusses the relevance and theoretical, methodological, and practical issues related to the development potential of a single-molecule approach using methods based on molecular detectors.

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) maintains database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), our primary mission is to collect and distribute nucleotide sequence data, as well as their study and sample information, in collaboration with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute. In addition to INSDC resources, the Center operates databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank), and human genetic and phenotypic data (JGA: Japanese Genotype-Phenotype Archive). These databases are built on the supercomputer of the National Institute of Genetics, whose remaining computational capacity is actively utilized by domestic researchers for large-scale biological data analyses. Here, we report our recent updates and the activities of our services., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities., (Published by Oxford University Press on behalf of Nucleic Acids Research 2022.)

Motivation: JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization., Results: We have developed a package that provides a Python interface to JBrowse 2's suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google's Colab notebooks, providing reproducible interactive views., Availability and Implementation: JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner., Findings: Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case., Conclusions: metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Background: The sequencing of whole genomes is becoming increasingly affordable. In this context, large-scale sequencing projects are generating ever larger datasets of species-specific genomic diversity. As a consequence, more and more genomic data need to be made easily accessible and analyzable to the scientific community., Findings: We present DivBrowse, a web application for interactive visualization and exploratory analysis of genomic diversity data stored in Variant Call Format (VCF) files of any size. By seamlessly combining BLAST as an entry point together with interactive data analysis features such as principal component analysis in one graphical user interface, DivBrowse provides a novel and unique set of exploratory data analysis capabilities for genomic biodiversity datasets. The capability to integrate DivBrowse into existing web applications supports interoperability between different web applications. Built-in interactive computation of principal component analysis allows users to perform ad hoc analysis of the population structure based on specific genetic elements such as genes and exons. Data interoperability is supported by the ability to export genomic diversity data in VCF and General Feature Format 3 files., Conclusion: DivBrowse offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data by including features like interactive calculation of variant frequencies and principal component analysis. The use of established standard file formats for data input supports interoperability and seamless deployment of application instances based on the data output of established bioinformatics pipelines., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Background: As the amount of genomic data continues to grow, there is an increasing need for systematic ways to organize, explore, compare, analyze and share this data. Despite this, there is a lack of suitable platforms to meet this need., Results: OpenGenomeBrowser is a self-hostable, open-source platform to manage access to genomic data and drastically simplifying comparative genomics analyses. It enables users to interactively generate phylogenetic trees, compare gene loci, browse biochemical pathways, perform gene trait matching, create dot plots, execute BLAST searches, and access the data. It features a flexible user management system, and its modular folder structure enables the organization of genomic data and metadata, and to automate analyses. We tested OpenGenomeBrowser with bacterial, archaeal and yeast genomes. We provide a docker container to make installation and hosting simple. The source code, documentation, tutorials for OpenGenomeBrowser are available at opengenomebrowser.github.io and a demo server is freely accessible at opengenomebrowser.bioinformatics.unibe.ch ., Conclusions: To our knowledge, OpenGenomeBrowser is the first self-hostable, database-independent comparative genome browser. It drastically simplifies commonly used bioinformatics workflows and enables convenient as well as fast data exploration., (© 2022. The Author(s).)

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1 , HBA2, and HBB . By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework., Competing Interests: ST, MX, AM, CS, CH, CB, JT, IF, NY, FA, EE, HH, BZ, AK, MK, PK No competing interests declared, (© 2022, Tamana, Xenophontos et al.)