cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100894
title: Supporting data for "Accelerated Deciphering of the Genetic Architecture of Agricultural Economic Traits in Pigs Using the Low Coverage Whole-genome Sequencing Strategy"

description: Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. <br>We described a Tn5-based highly accurate, cost- and time-efficient, low coverage sequencing (LCS) method to obtain 11.3 M whole-genome SNPs in 2,869 Duroc boars at an average depth of 0.73x. Based on these SNPs, a genome-wide association study (GWAS) detected 14 quantitative trait loci (QTLs) for seven of 21 important agricultural traits in pigs, such as ABCD4 for total teat number and HMGA1 for backfat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. <br>GWAS results for 21 important agricultural traits identified 14 QTLs/genes and showed their various genetic architectures, providing promising guidance for genetic improvement harnessing genomic features. The Tn5-based LCS method can be applied to large-scale genome studies for any species without a good reference panel and can be widely used for agricultural breeding.
DOI: 10.15468/dl.2k9gcg
title: Occurrence Download

description: A dataset containing 284 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prionailurus viverrinus (Bennett, 1833)"
 ]
}

 The dataset includes 284 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002013-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x7wehv
title: Occurrence Download

description: A dataset containing 9923 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is one of (South America, North America)",
 "Country is one of (Ecuador, Mexico)",
 "IucnRedListCategory is CR",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 9923 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002008-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15479249
title: MD Data set for the paper "UMD H2ase" - Part 1

description: This is the Part 1 of the UMD simulation Data of (un)binding pathways of H2 in complex with Df and Mdg H2ase. This part, Part 1, contains Trajectory files of Replica1 to Replica37 of Df H2ase.
DOI: 10.5281/zenodo.15479248
title: MD Data set for the paper "UMD H2ase" - Part 1

description: This is the Part 1 of the UMD simulation Data of (un)binding pathways of H2 in complex with Df and Mdg H2ase. This part, Part 1, contains Trajectory files of Replica1 to Replica37 of Df H2ase.
DOI: 10.5281/zenodo.15520928
title: MD Data set for the paper "UMD H2ase" - Part 1

description: This is the Part 1 of the UMD simulation Data of (un)binding pathways of H2 in complex with Df and Mdg H2ase. This part, Part 1, contains Trajectory files of Replica1 to Replica37 of Df H2ase.
DOI: 10.15468/dl.68yqpn
title: Occurrence Download

description: A dataset listing the 67 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Arcidae"
 ]
}

 The dataset's 67 records were derived from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002015-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6h82kd
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Florida Herbarium - Bryophytes",
 "Country is Croatia"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002014-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5524/100833
title: Supporting data for "PM4NGS, a project management framework for Next-Generation Sequencing data analysis"

description: FAIR (Findability, Accessibility, Interoperability, and Reusability) Next-Generation Sequencing (NGS) data analysis rely 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.<br>Here, we present a project management framework for NGS data analysis called PM4NGS. This framework is comprised 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 common workflow language (CWL) format. Pre-configured Jupyter notebooks with minimum Python code are included in PM4NGS to produce a project report and publication-ready figures. Three pipelines are presented in this manuscript for demonstration purposes including the analysis of RNA-Seq, ChIP-Seq and ChIP-exo datasets.<br>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 docs
DOI: 10.5524/100840
title: Supporting data for "biotoolsSchema : a formalised schema for bioinformatics software description"

description: 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 utilisation. Although several standardisation efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description - and cataloguing - of bioinformatics resources. Here we describe biotoolsSchema, a formalised information model which balances the needs of conciseness for rapid adoption, while still providing 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 more than 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. biotoolsSchema supports the formalised, 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.
DOI: 10.5524/100841
title: Supporting data for "Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data"

description: 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, 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-PH model and the deep-learning based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression. In summary, Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.
DOI: 10.5524/100843
title: Supporting data for "A chromosome-level genome assembly of the Oriental river prawn, Macrobrachium nipponense"

description: The Oriental river prawn, iMacrobrachium nipponense/i , 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 critical to reveal sex-differentiation and development mechanisms of the Oriental river prawn for genetic improvements.br /We sequenced 293.3 Gb of raw Illumina short reads and 405.7 Gb of Pacbio long reads. The final whole genome assembly of the Oriental river prawn was about 4.5 Gb, 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 predicted to have happened 109.8 million years ago. By integration of genome and transcriptome data, 21 genes were predicted as strong candidate sex-related genes. In particular, signal transduction and endocrine system signaling pathways were suggested to play essential roles in sex- differentiation and development in this prawn species.br /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.
DOI: 10.5524/100847
title: Supporting data for "Transcriptome annotation in the cloud: complexity, best practices and cost."

description: The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to industry-leading commercial cloud providers, such as Amazon Web Services (AWS; Seattle, WA, USA) and Google Cloud Platform (GCP; Mountain View, CA, USA). 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 integration of multiple biological databases and 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.<br>We present a comparative study of multiple BLAST sequence alignments using two public cloud providers: AWS and GCP. We have 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 (SSD), the time to execute the Common Workflow Language (CWL) script and the time for the creation, setup 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.<br>We demonstrate that the public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low ~500,000 transcripts can be processed inv less than 2 hours with a compute cost of about 200-250 USD. 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 provthe very large genetic sequence databases, such as nr, RefSeq and SRA, on both GCP and AWS). 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 application programming interfaces (APIs) to deploy the workflow.ider (e.g. NCBI maintains updated copies of the very large genetic sequence databases, such as nr, RefSeq and SRA, on both GCP and AWS). 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 application programming interfaces (APIs) to deploy the workflow.
DOI: 10.5524/100848
title: Supporting data for "<i>De novo</i> construction of a transcriptome for the stink bug crop pest <i>Chinavia impicticornis</i> during late development"

description: <i>Chinavia impicticornis</i> is a Neotropical stink-bug (Hemiptera: Pentatomidae) with economic importance for different crops. Little is known about the development of the species, as well as the genetic mechanisms that may favor the establishment of populations in cultivated plants. Here we conduct the first large-scale molecular study with <i>C. impicticornis</i>. We generated RNA-seq data for males and females, at two immature stages, for the genitalia separately and for the rest of the body. We assembled the transcriptome and conduct a functional annotation. <i>De novo</i> assembled transcriptome based on whole bodies and genitalia of males and females contained around 400,000 contigs with an average length of 688 bp. After pruning duplicated sequences and conducting a functional annotation, the final annotated transcriptome comprised 39,478 transcripts of which 12,665 had GO terms assigned. These novel datasets will provide invaluable data for the discovery of molecular processes related to morphogenesis and immature biology. We hope to contribute to the growing research on stink bug <i>evo-devo</i> as well as the development of bio-rational solutions for pest management.
DOI: 10.5524/100850
title: Supporting data for "FASTQuick: Rapid and comprehensive quality assessment of raw sequence reads"

description: Rapid and thorough quality assessment of sequenced genomes in an ultra-high-throughput scale is crucial for successful large-scale genomic studies. Comprehensive quality assessment typically requires full genome alignment, which costs a significant amount of computational resources and turnaround time. Existing tools are either computational expensive due to full alignment or lacking essential quality metrics by skipping read alignment.<br>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. <br>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.
DOI: 10.5524/100851
title: Supporting data for "MB-GAN: Microbiome Simulation via Generative Adversarial Network"

description: Trillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies (MWAS) and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. However, simulating the real microbiome data is challenging since its correlation structure is difficult to be modeled by explicit statistical models. To address the challenge of simulating realistic microbiome data, we designed a novel simulation framework termed MB-GAN, by using a generative adversarial network (GAN) and utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given microbial abundances and compute simulated abundances that are indistinguishable from it. In practice, MB-GAN showed the following advantages. First, MB-GAN avoids explicit statistical modeling assumptions, and it only requires real datasets as inputs. Second, unlike the traditional GANs, MB-GAN is easily applicable and can converge efficiently. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.
DOI: 10.5524/100854
title: Supporting data for "AXIOME3: Automation, eXtension, and Integration Of Microbial Ecology"

description: Advances in high-throughput sequencing accessibility have democratized small subunit rRNA gene sequence data collection, coincident with the increasing availability of computational tools for sequence data processing, multivariate statistics, and data visualization. However, existing tools often require programming ability and frequent user intervention that may not be suitable for fast-paced and large-scale data analysis by end user microbiologists who are unfamiliar with the Linux command line environment or prefer interactions with a graphical user interface. Here we present AXIOME3, which is a completely redeveloped AXIOME pipeline that streamlines SSU rRNA data analysis by managing QIIME2, R, and Python-associated analyses through an interactive web interface.<br>AXIOME3 comes with web graphical user interface to improve usability by simplifying configuration processes and task status tracking. Internally, it uses an automated pipeline that is wrapped around QIIME2 to generate a range of outputs including amplicon sequence variant tables, taxonomic classifications, phylogenetic trees, biodiversity metrics, and ordinations. The extension module for AXIOME3 provides advanced data visualization tools such as principle coordinate analysis, bubble plots, and triplot ordinations that can be used to visualize interactions between a distance matrix, ASV taxonomy, and sample metadata.<br>Because repeat analysis of SSU rRNA amplicon sequence data is challenging for those who have limited experience in command line environments, AXIOME3 now offers rapid and user-friendly options within an automated pipeline, with advanced data visualization tools and the ability for users to incorporate additional analyses easily through extension. AXIOME3 is completely open source ( https://github.com/neufeld/AXIOME3 , https://github.com/neufeld/AXIOME3-GUI ) and researchers are encouraged to modify and redistribute the package.
DOI: 10.5524/100856
title: Supporting data for "<i>De-novo</i> genome assemblies of butterflies"

description: The current genomic age has led to the availability of thousands of genomes and enabled new advancements in biology. However, as the number of genomes increases, considerable attention should be given to their quality. Here we examine these trends in a taxonomically diverse and well-known group, butterflies, and provide draft, <i>de-novo</i> assemblies for all available butterfly genomes. Due to massive genome sequencing investment and taxonomic curation, this is an excellent group to explore genome quality. We provide <i>de-novo</i> assemblies for all 822 available butterfly genomes and interpret their quality in terms of completeness and continuity. Further, we discuss reuse cases and provide general guidelines in these cases. These assemblies will serve as a key resource for papilionoid genomics, especially for researchers without computational resources. We identify the 50 highest quality genomes across butterflies, and conclude that the ringlet, <i>Aphantopus hyperantus</i> has the highest quality papilionoid genome. Our post-processing of these draft genome assemblies identified 118 butterfly genomes that should not be reused due to contamination or extremely low quality. However, many draft genomes are of high utility, especially because permissibility of low-quality genomes is dependent on the objective of the study. Quality metrics and assemblies are typically presented with annotated genome accessions, but rarely with <i>de-novo</i> genomes. We recommend that studies presenting genome sequences provide the assembly and some metrics of quality, as quality will significantly impact downstream results. Transparency in quality metrics are needed to improve the field of genome science and encourage data reuse.
DOI: 10.5524/100860
title: Supporting data for the chromosome-level genome assembly of the malaria vector Anopheles arabiensis

description: <i>Anopheles arabiensis</i> belongs to the <i>An. gambiae</i> complex and is among the major malaria vectors in Sub-Saharan Africa. However, a chromosome-level reference genome assembly for this medically important species is not yet available. <br>In this study, we produced <i>de novo</i> chromosome-level genome assemblies for both <i>An. coluzzii</i> and <i>An. arabiensis</i> using the long-read Oxford Nanopore sequencing technology and the Hi-C scaffolding approach. <br>We obtained a 256.8 Mbp assembly for <i>An. arabiensis</i>, consisting of three chromosome-scale scaffolds (X, 2, 3), complete mitochondrion, and unordered contigs identified as autosomal pericentromeric DNA, X pericentromeric DNA, and Y sequences. A comparison of our assembly with the existing assemblies for these species demonstrated that we obtained improved reference-quality genomes. Together, the new assemblies of <i>An. coluzzii</i> and <i>An. arabiensis</i> allowed us to identify genomic coordinates for the breakpoint regions of fixed and polymorphic chromosomal inversions. <br>This new chromosome-level assembly will facilitate functional and population genomic studies and the presented assembly pipeline will accelerate progress toward creating high-quality genome references for other disease vectors.