Genome sequence assemblies provide the basis for our understanding of biology. Generating error-free assemblies is therefore the ultimate, but sadly still unachieved goal of a multitude of research projects. Despite the ever-advancing improvements in data generation, assembly algorithms and pipelines, no automated approach has so far reliably generated near error-free genome assemblies for eukaryotes. Whilst working towards improved datasets and fully automated pipelines, assembly evaluation and curation is actively used to bridge this shortcoming and significantly reduce the number of assembly errors. In addition to this increase in product value, the insights gained from assembly curation are fed back into the automated assembly strategy and contribute to notable improvements in genome assembly quality. We describe our tried and tested approach for assembly curation using gEVAL, the genome evaluation browser. We outline the procedures applied to genome curation using gEVAL and also our recommendations for assembly curation in a gEVAL-independent context to facilitate the uptake of genome curation in the wider community.

BACKGROUND: The evolving antibiotic-resistant behavior of health care-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) USA100 strains are of major concern. They are resistant to a broad class of antibiotics such as macrolides, aminoglycosides, fluoroquinolones, and many more. FINDINGS: The selection of appropriate antibiotic susceptibility examination media is very important. Thus, we use bacteriological (cation-adjusted Mueller-Hinton broth) as well as physiological (R10LB) media to determine the effect of vancomycin on USA100 strains. The study includes the profiling behavior of HA-MRSA USA100 D592 and D712 strains in the presence of vancomycin through various high-throughput assays. The US100 D592 and D712 strains were characterized at sub-inhibitory concentrations through growth curves, RNA sequencing, bacterial cytological profiling, and exo-metabolomics high throughput experiments. CONCLUSIONS: The study reveals the vancomycin resistance behavior of HA-MRSA USA100 strains in dual media conditions using wide-ranging experiments.

BACKGROUND: FAIR (Findability, Accessibility, Interoperability, and Reusability) next-generation sequencing (NGS) data analysis relies on complex computational biology workflows and pipelines to guarantee reproducibility, portability, and scalability. Moreover, workflow languages, managers, and container technologies have helped address the problem of data analysis pipeline execution across multiple platforms in scalable ways. FINDINGS: Here, we present a project management framework for NGS data analysis called PM4NGS. This framework is composed of an automatic creation of a standard organizational structure of directories and files, bioinformatics tool management using Docker or Bioconda, and data analysis pipelines in CWL format. Pre-configured Jupyter notebooks with minimum Python code are included in PM4NGS to produce a project report and publication-ready figures. We present 3 pipelines for demonstration purposes including the analysis of RNA-Seq, ChIP-Seq, and ChIP-exo datasets. CONCLUSIONS: PM4NGS is an open source framework that creates a standard organizational structure for NGS data analysis projects. PM4NGS is easy to install, configure, and use by non-bioinformaticians on personal computers and laptops. It permits execution of the NGS data analysis on Windows 10 with the Windows Subsystem for Linux feature activated. The framework aims to reduce the gap between researcher in experimental laboratories producing NGS data and workflows for data analysis. PM4NGS documentation can be accessed at https://pm4ngs.readthedocs.io/.

BACKGROUND: Argiope bruennichi, the European wasp spider, has been investigated intensively as a focal species for studies on sexual selection, chemical communication, and the dynamics of rapid range expansion at a behavioral and genetic level. However, the lack of a reference genome has limited insights into the genetic basis for these phenomena. Therefore, we assembled a high-quality chromosome-level reference genome of the European wasp spider as a tool for more in-depth future studies. FINDINGS: We generated, de novo, a 1.67 Gb genome assembly of A. bruennichi using 21.8× Pacific Biosciences sequencing, polished with 19.8× Illumina paired-end sequencing data, and proximity ligation (Hi-C)-based scaffolding. This resulted in an N50 scaffold size of 124 Mb and an N50 contig size of 288 kb. We found 98.4% of the genome to be contained in 13 scaffolds, fitting the expected number of chromosomes (n = 13). Analyses showed the presence of 91.1% of complete arthropod BUSCOs, indicating a high-quality assembly. CONCLUSIONS: We present the first chromosome-level genome assembly in the order Araneae. With this genomic resource, we open the door for more precise and informative studies on evolution and adaptation not only in A. bruennichi but also in arachnids overall, shedding light on questions such as the genomic architecture of traits, whole-genome duplication, and the genomic mechanisms behind silk and venom evolution.

BACKGROUND: Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis. FINDINGS: A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units neural network, a variant of a recurrent neural network. In the neural network training, the weights of tools used are derived from their usage frequencies over time and the sequences of tools are uniformly sampled from training data. Hyperparameters of the neural network are optimized using Bayesian optimization. Mean accuracy of 98% in recommending tools is achieved for the top-1 metric. CONCLUSIONS: The model is accessed by a Galaxy API to provide researchers with recommended tools in an interactive manner using multiple user interface integrations on the European Galaxy server. High-quality and highly used tools are shown at the top of the recommendations. The scripts and data to create the recommendation system are available under MIT license at https://github.com/anuprulez/galaxy_tool_recommendation.

BACKGROUND: Ducks have a typical avian karyotype that consists of macro- and microchromosomes, but a pair of much less differentiated ZW sex chromosomes compared to chickens. To elucidate the evolution of chromosome architectures between ducks and chickens, and between birds and mammals, we produced a nearly complete chromosomal assembly of a female Pekin duck by combining long-read sequencing and multiplatform scaffolding techniques. RESULTS: A major improvement of genome assembly and annotation quality resulted from the successful resolution of lineage-specific propagated repeats that fragmented the previous Illumina-based assembly. We found that the duck topologically associated domains (TAD) are demarcated by putative binding sites of the insulator protein CTCF, housekeeping genes, or transitions of active/inactive chromatin compartments, indicating conserved mechanisms of spatial chromosome folding with mammals. There are extensive overlaps of TAD boundaries between duck and chicken, and also between the TAD boundaries and chromosome inversion breakpoints. This suggests strong natural selection pressure on maintaining regulatory domain integrity, or vulnerability of TAD boundaries to DNA double-strand breaks. The duck W chromosome retains 2.5-fold more genes relative to chicken. Similar to the independently evolved human Y chromosome, the duck W evolved massive dispersed palindromic structures, and a pattern of sequence divergence with the Z chromosome that reflects stepwise suppression of homologous recombination. CONCLUSIONS: Our results provide novel insights into the conserved and convergently evolved chromosome features of birds and mammals, and also importantly add to the genomic resources for poultry studies.

BACKGROUND: Rapid and thorough quality assessment of sequenced genomes on an ultra-high-throughput scale is crucial for successful large-scale genomic studies. Comprehensive quality assessment typically requires full genome alignment, which costs a substantial amount of computational resources and turnaround time. Existing tools are either computationally expensive owing to full alignment or lacking essential quality metrics by skipping read alignment. FINDINGS: We developed a set of rapid and accurate methods to produce comprehensive quality metrics directly from a subset of raw sequence reads (from whole-genome or whole-exome sequencing) without full alignment. Our methods offer orders of magnitude faster turnaround time than existing full alignment-based methods while providing comprehensive and sophisticated quality metrics, including estimates of genetic ancestry and cross-sample contamination. CONCLUSIONS: By rapidly and comprehensively performing the quality assessment, our tool will help investigators detect potential issues in ultra-high-throughput sequence reads in real time within a low computational cost at the early stages of the analyses, ensuring high-quality downstream results and preventing unexpected loss in time, money, and invaluable specimens.

BACKGROUND: The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems. FINDINGS: We compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment. CONCLUSIONS: We demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200-$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.

BACKGROUND: With the advances in next-generation sequencing technologies, it is possible to determine RNA-RNA interaction and RNA structure predictions on a genome-wide level. The reads from these experiments usually are chimeric, with each arm generated from one of the interaction partners. Owing to short read lengths, often these sequenced arms ambiguously map to multiple locations. Thus, inferring the origin of these can be quite complicated. Here we present ChiRA, a generic framework for sensitive annotation of these chimeric reads, which in turn can be used to predict the sequenced hybrids. RESULTS: Grouping reference loci on the basis of aligned common reads and quantification improved the handling of the multi-mapped reads in contrast to common strategies such as the selection of the longest hit or a random choice among all hits. On benchmark data ChiRA improved the number of correct alignments to the reference up to 3-fold. It is shown that the genes that belong to the common read loci share the same protein families or similar pathways. In published data, ChiRA could detect 3 times more new interactions compared to existing approaches. In addition, ChiRAViz can be used to visualize and filter large chimeric datasets intuitively. CONCLUSION: ChiRA tool suite provides a complete analysis and visualization framework along with ready-to-use Galaxy workflows and tutorials for RNA-RNA interactome and structurome datasets. Common read loci built by ChiRA can rescue multi-mapped reads on paralogous genes without requiring any information on gene relations. We showed that ChiRA is sensitive in detecting new RNA-RNA interactions from published RNA-RNA interactome datasets.

BACKGROUND: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources. FINDINGS: Here we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the bio.tools registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability. CONCLUSIONS: biotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as bio.tools that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in bio.tools promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.

BACKGROUND: Biogas production with anaerobic digestion (AD) is one of the most promising solutions for both renewable energy production and resolving the environmental problem caused by the worldwide increase in organic waste. However, the complex structure of the microbiome in AD is poorly understood. FINDINGS: In this study, we constructed a microbial gene catalog of AD (22,840,185 genes) based on 1,817 Gb metagenomic data derived from digestate samples of 56 full-scale biogas plants fed with diverse feedstocks. Among the gene catalog, 73.63% and 2.32% of genes were taxonomically annotated to Bacteria and Archaea, respectively, and 57.07% of genes were functionally annotated with KEGG orthologous groups. Our results confirmed the existence of core microbiome in AD and showed that the type of feedstock (cattle, chicken, and pig manure) has a great influence on carbohydrate hydrolysis and methanogenesis. In addition, 2,426 metagenome-assembled genomes were recovered from all digestate samples, and all genomes were estimated to be ≥80% complete with ≤10% contamination. CONCLUSIONS: This study deepens our understanding of the microbial composition and function in the AD process and also provides a huge number of reference genome and gene resources for analysis of anaerobic microbiota.

BACKGROUND: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software. RESULTS: here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. CONCULSION: Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

BACKGROUND: The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. METHODS: Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. RESULTS: Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. CONCLUSIONS: This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.

BACKGROUND: During evolutionary history, molecular mechanisms have emerged to cope with deleterious mutations. Frameshift insertions in protein-coding sequences are extremely rare because they disrupt the reading frame. There are a few known examples of their correction through translational frameshifting, a process that enables ribosomes to skip nucleotides during translation to regain proper reading frame. Corrective frameshifting has been proposed to act on the single base pair insertion at position 174 of the mitochondrial NADH dehydrogenase subunit 3 gene (ND3) that has been observed in several turtles and birds. However, the relatively sparse taxonomic representation has hampered our understanding of the evolution of this insertion in vertebrates. RESULTS: Here, we analyzed 87,707 ND3 sequences from 10,309 vertebrate taxa to reveal the evolutionary history of this insertion and its common genomic characteristics. We confirmed that the insertion only appears in turtles and birds and reconstructed that it evolved independently in both groups with complex patterns of gains and losses. The insertion was observed in almost all bird orders but was absent in all members of the diverse Passeriformes. We found strong conservation in the nucleotides surrounding the insertion in both turtles and birds, which implies that the insertion enforces structural constraints that could be involved in its correction. CONCLUSIONS: Our study demonstrates that frameshifts can be widespread and can be retained for millions of years if they are embedded in a conserved sequence theme.

BACKGROUND: The oriental river prawn, Macrobrachium nipponense, is an economically important shrimp in China. Male prawns have higher commercial value than females because the former grow faster and reach larger sizes. It is therefore important to reveal sex-differentiation and development mechanisms of the oriental river prawn to enable genetic improvement. RESULTS: We sequenced 293.3 Gb of raw Illumina short reads and 405.7 Gb of Pacific Biosciences long reads. The final whole-genome assembly of the Oriental river prawn was ∼4.5 Gb in size, with predictions of 44,086 protein-coding genes. A total of 49 chromosomes were determined, with an anchor ratio of 94.7% and a scaffold N50 of 86.8 Mb. A whole-genome duplication event was deduced to have happened 109.8 million years ago. By integration of genome and transcriptome data, 21 genes were predicted as sex-related candidate genes. CONCLUSION: The first high-quality chromosome-level genome assembly of the oriental river prawn was obtained. These genomic data, along with transcriptome sequences, are essential for understanding sex-differentiation and development mechanisms in the oriental river prawn, as well as providing genetic resources for in-depth studies on developmental and evolutionary biology in arthropods.

BACKGROUND: The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. RESULTS: The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population. CONCLUSIONS: Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.

As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field.

BACKGROUND: Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs). RESULTS: Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. CONCLUSIONS: BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.