cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102432
title: Supporting data for "Trumpet plots: Visualizing The Relationship Between Allele Frequency And Effect Size In Genetic Association Studies"

description: Recent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results. <br>Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having a low frequency and small effects, while a small number of variants have a higher frequency and larger effects. These plots, which we call 'trumpet plots, can help to provide new and valuable insights into the genetic basis of traits and diseases, and can help prioritize efforts to discover new risk variants. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex diseases and traits, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we have developed an R package TrumpetPlots and R Shiny application, available at https://juditgg.shinyapps.io/shinytrumpets/ , that allows users to explore these results and submit their own data.
DOI: 10.5524/102433
title: Supporting data for "aws-s3-integrity-check: an open-source bash tool to verify the integrity of a dataset stored on Amazon S3"

description: Amazon Simple Storage Service (Amazon S3) has become a widely used and reliable platform for storing large biomedical datasets. However, unintended changes to the original data can occur during the data writing and transmission, ultimately altering the original contents of the object transferred and producing unexpected results when later accessed. Despite the interest in verifying end-to-end data integrity, there are no existing open-source and easy-to-use tools to accomplish this mission.<br>To bridge this gap, here we present <em>aws-s3-integrity-check</em>, a user-friendly, lightweight and reliable bash tool to verify the integrity of a dataset stored within an Amazon S3 bucket. By using this tool, we completed the integrity verification of 1,045 records ranging between 5 Bytes and 10 Gigabytes (GB) in size and occupying a total of ~935 GB of Amazon S3 cloud storage space in ~114 minutes. The <em>aws-s3-integrity-check</em> tool also provides file-by-file on-screen and log-file-based information about the status of each individual integrity check.<br>To the best of our knowledge, the <em>aws-s3-integrity-check</em> bash tool is the only open-source tool that allows verifying the integrity of a dataset uploaded to the Amazon S3 Storage system in a quick, reliable and efficient manner. The <em>aws-s3-integrity-check</em> tool can be used to test any file type and file size and it is freely available for use and download at https://github.com/SoniaRuiz/aws-s3-integrity-check and https://hub.docker.com/r/soniaruiz/aws-s3-integrity-check.
DOI: 10.5524/102435
title: Supporting data for "ensemblQueryR: fast, flexible and high-throughput querying of Ensembl API LD endpoints in R"

description: We present ensemblQueryR, a package providing an R interface to the Ensembl REST API that facilitates flexible, fast, user-friendly and R workflow integrable querying of Ensembl REST API linkage disequilibrium (LD) endpoints with a focus on supporting high-throughput querying. ensemblQueryR achieves this through functions that are intuitive and amenable to custom code integration and utilise familiar R object types as inputs and outputs. For each LD endpoint, ensemblQueryR provides two functions, permitting both single-query and multi-query modes of operation. The multi-query functions are optimised for large query sizes and provide optional parallelisation to leverage available computational resources and minimise processing time. We demonstrate that ensemblQueryR has improved performance in terms of random access memory (RAM) usage and speed, delivering a 10-fold speed increase over analogous software whilst using a third of the RAM. Finally, ensemblQueryR is near-agnostic to operating system and computational architecture through availability of Docker and singularity images, making this tool widely accessible to the scientific community.
DOI: 10.13026/4mxk-na84
title: eICU Collaborative Research Database Demo

description: This project is a demo version of the eICU Collaborative Research Database, a
multi-center database comprised of deidentified health data for over 200,000
admissions to ICUs across the United States between 2014-2015. The database
includes vital sign measurements, care plan documentation, severity of illness
measures, diagnosis information, and treatment information. Data is collected
through the Philips eICU program, a critical care telehealth program that
delivers information to caregivers at the bedside. This open access demo
allows researchers to ascertain whether the eICU Collaborative Research
Database is suitable for their work. It includes over 2,500 unit stays
selected from 20 of the larger hospitals in the eICU Collaborative Research
Database.
DOI: 10.13026/c2wm1r
title: The eICU Collaborative Research Database

description: The eICU Collaborative Research Database is a multi-center database comprising deidentified health data associated with over 200,000 admissions to ICUs across the United States between 2014-2015. The database includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, and treatment information. Data is collected through the Philips eICU program, a critical care telehealth program that delivers information to caregivers at the bedside.
DOI: 10.13026/c2xw26
title: MIMIC-III Clinical Database

description: MIMIC-III is a large, freely-available database comprising deidentified
health-related data associated with over forty thousand patients who stayed in
critical care units of the Beth Israel Deaconess Medical Center between 2001
and 2012. The database includes information such as demographics, vital sign
measurements made at the bedside (~1 data point per hour), laboratory test
results, procedures, medications, caregiver notes, imaging reports, and
mortality (including post-hospital discharge).

MIMIC supports a diverse range of analytic studies spanning epidemiology,
clinical decision-rule improvement, and electronic tool development. It is
notable for three factors: it is freely available to researchers worldwide; it
encompasses a diverse and very large population of ICU patients; and it
contains highly granular data, including vital signs, laboratory results, and
medications.
DOI: 10.13026/nkwc-js72
title: HiRID, a high time-resolution ICU dataset

description: HiRID is a freely accessible critical care dataset containing data relating to
almost 34 thousand patient admissions to the Department of Intensive Care
Medicine of the Bern University Hospital, Switzerland (ICU), an
interdisciplinary 60-bed unit admitting >6,500 patients per year. The ICU
offers the full range of modern interdisciplinary intensive care medicine for
adult patients. The dataset was developed in cooperation between the Swiss
Federal Institute of Technology (ETH) Zurich, Switzerland and the ICU.

The dataset contains de-identified demographic information and a total of 681
routinely collected physiological variables, diagnostic test results and
treatment parameters from almost 34 thousand admissions during the period from
January 2008 to June 2016. Data is stored with a uniquely high time resolution
of one entry every two minutes.
DOI: 10.13026/rrgf-xw32
title: MIMIC-IV

description: Retrospectively collected medical data has the opportunity to improve patient
care through knowledge discovery and algorithm development. Broad reuse of
medical data is desirable for the greatest public good, but data sharing must
be done in a manner which protects patient privacy. The Medical Information
Mart for Intensive Care (MIMIC)-III database provided critical care data for
over 40,000 patients admitted to intensive care units at the Beth Israel
Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and
patient identifiers were removed according to the Health Insurance Portability
and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been
integral in driving large amounts of research in clinical informatics,
epidemiology, and machine learning. Here we present MIMIC-IV, an update to
MIMIC-III, which incorporates contemporary data and improves on numerous
aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization,
highlighting data provenance and facilitating both individual and combined use
of disparate data sources. MIMIC-IV is intended to carry on the success of
MIMIC-III and support a broad set of applications within healthcare.
DOI: 10.5281/zenodo.7314838
title: Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices

description: This dataset contains the curated nanopore raw signal data in BLOW5 format used to benchmark Hardware Accelerated Read Until (HARU). This dataset was created by using the publicly available datasets: SARS-CoV-2 SP1 (1.382M reads) and NA12878 PromethION subset (500,000 reads). The tarball when extracted will have the following directory structure: haru-data<br> ├── na12878-rfc1<br> │ ├── blow5-rawsignal<br> │ │ ├── na12878_dna_0.blow5<br> │ │ ├── na12878_dna_100.blow5<br> │ │ ├── na12878_dna_101.blow5<br> │ │ ├── na12878_dna_102.blow5<br> │ │ ├── ...<br> │ └── reference<br> │ └── rfc1.fa<br> └── SARS-CoV-2-sp1<br> ├── blow5-rawsignal<br> │ ├── readgroup0<br> │ │ ├── reads_0_0.blow5<br> │ │ ├── reads_0_10.blow5<br> │ │ ├── reads_0_11.blow5<br> │ │ ├── reads_0_12.blow5<br> │ │ ├── ...<br> │ └── readgroup1<br> │ ├── reads_1_0.blow5<br> │ ├── reads_1_10.blow5<br> │ ├── reads_1_11.blow5<br> │ ├── ....<br> └── reference<br> └── nCoV-2019.reference.fasta nCoV-2019.reference.fasta is the SARS-CoV-2 MN908947.3 reference genome. rfc1.fa is the genomic region hr4:39262456-39391375 extracted from hg38 human genome.
DOI: 10.5281/zenodo.7582233
title: Relevant Datasets and Software Used for Paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description"

description: This repository contains relevant datasets and software used in a paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description". They are used to run the code of <em>KGML-xDTD </em>stored on Github and support the results of this paper. <strong>About the datasets</strong> 1. <em>bkg_rtxkg2c_v2.7.3.tar.gz</em> This tar.gz file contains three sub-folders: tsv_files, scripts, and relevant_dbs. The "tsv_files" sub-folder has the input files that the neo4j software uses. The "scripts" sub-folder contains a shell script with a relevant python script to construct the biomedical knowledge graph. The "relevant_dbs" sub-folder stores two auxiliary databases that <em>KGML-xDTD</em> needs to use. 2. <em>indication_paths.yaml</em> This file contains the DrugMechDB MOA paths that we used to evaluate the predicted MOA paths by <em>KGML-xDTD. </em>It is downloaded from the official GitHub repository of DrugMechDB. 3. <em>training_data.tar.gz</em> This tar.gz file contains the processed training data of four data sources (e.g., MyChem, SemMedDB, NDF-RT, RepoDB) mentioned in the paper. These processed drug-disease pairs have been matched to the identifiers of biological entities used in our biomedical knowledge graph and respectively split into true positive (tp) sets and true negative (tn) sets. We also provide the names of these drug identifiers and disease identifiers under a sub-folder "translated _to_name". <strong>About the software</strong> <em>neo4j-community-3.5.26.tar.gz</em> This tar.gz is the Neo4j community version 3.5.26 downloaded from Neo4j Download Center. Although the newer versions are available, due to their big changes in the Neo4j setting that are not compatible with our scripts on Github, we provide the version that we used in our research. If you would like to use the newer version, modifications to our script will be required to import the biomedical knowledge graph into your local Neo4j database with the new setting.
DOI: 10.5281/zenodo.7732330
title: Planet Microbe Functional and Taxonomic annotation of Illumina WGS Prokaryotic Fraction for Semantic Web Analysis

description: Functional and Taxonomic annotations computed from a subset of Illumina Whole-Genome Sequencing samples from the prokaryotic fraction of the Planet Microbe database. Data was computed using the pipeline available from https://github.com/hurwitzlab/planet-microbe-functional-annotation/, and post processing scripts from https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis. Files contain total annotation counts of Interpro, GO and NCBITaxon annotations, as well as additional sample metadata. See readme.txt file for more information.
DOI: 10.5281/zenodo.8305059
title: Quast outputs for "When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes"

description: Quast outputs for "When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes"
DOI: 10.5524/102390
title: Data management strategy for a Collaborative Research Centre

description: The importance of effective Research Data Management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multi-disciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans), and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements
DOI: 10.5524/102391
title: Supporting data for "The Crown Pearl V2: an improved genome assembly of the European freshwater pearl mussel <i>Margaritifera margaritifera</i> (Linnaeus, 1758)"

description: Contiguous assemblies are fundamental to deciphering the composition of extant genomes. In molluscs, this is considerably challenging owing to the large size of their genomes, heterozygosity, and widespread repetitive content. Consequently, long-read sequencing technologies are fundamental for high contiguity and quality. The first genome assembly of <i>Margaritifera margaritifera</i> (Linnaeus, 1758) (Mollusca: Bivalvia: Unionida), a culturally relevant, widespread, and highly threatened species of freshwater mussels, was recently generated. However, the resulting genome is highly fragmented since the assembly relied on short-read approaches. Here, an improved reference genome assembly was generated using a combination of PacBio CLR long reads and Illumina paired-end short reads. This genome assembly is 2.4 Gb long, organised into 1,700 scaffolds with a contig N50 length of 3.4Mbp. The ab initio gene prediction resulted in 48,314 protein-coding genes. Our new assembly is a substantial improvement and an essential resource for studying this species unique biological and evolutionary features, helping promote its conservation.
DOI: 10.5524/102392
title: Supporting data for "ricu: R's Interface to Intensive Care Data"

description: To develop a unified framework for analyzing data from five large, publicly available ICU datasets. <br>Using three American (MIMIC-III, MIMIC-IV, eICU), and two European (AUMCdb, HiRID) databases, we constructed a mapping for each database to a set of clinically relevant concepts, which are grounded in the OMOP Vocabulary wherever possible. Furthermore, we performed synchronization in the units of measurement, and data type representation. On top of this, we built functionality which allows the user to download, set up, and load data from all of the five databases, through a unified Application Programming Interface (API). The resulting ricu R-package represents the computational infrastructure for handling publicly available ICU datasets and its latest release allows the user to load 119 existing clinical concepts from the five data sources. <br>The ricu R-package (available on GitHub and CRAN) is the first tool that enables users to analyze publicly available ICU datasets simultaneously (datasets are available upon request from respective owners). Such an interface saves researchers time when analyzing ICU data, and helps reproducibility. We hope that ricu can become a community-wide effort, so that data harmonization is not repeated by each research group separately. One current limitation is that concepts were added on a case-to-case basis, and therefore the resulting dictionary of concepts is not comprehensive. Further work is needed to make the dictionary comprehensive.
DOI: 10.5524/102396
title: Supporting data for "Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices"

description: Third-generation nanopore sequencers offer a feature called selective sequencing or Read Until that allows genomic reads to be analyzed in real-time and abandoned halfway if not belonging to a genomic region of interest. This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence Dynamic Time Warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone-sized MinION sequencer. In this paper, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware-software co-design-based method that exploits a low-cost and portable heterogeneous Multiprocessor System-on-Chip (MPSoC) platform with on-chip Field-Programmable Gate Arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5×faster than a highly optimized multi-threaded software version (around 85×faster than the existing unoptimized multi-threaded software) running on a sophisticated server with 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is two orders of magnitudes lower than the same application executing on the 36-core server. The open-source code for HARU sDTW module is available at https://github.com/beebdev/HARU and an example application that utilizes HARU is available at https://github.com/beebdev/sigfish-haru.
DOI: 10.5524/102397
title: Supporting data for "Genome assemblies of <i>Vigna reflexo-pilosa</i> (créole bean) and its progenitors, <i>Vigna hirtella</i> and <i>Vigna trinervia</i>, revealed homoeolog expression bias and expression level dominance in the allotetraploid"

description: <i>Vigna reflexo-pilosa</i><em></em> (créole bean) is a wild legume belonging to the subgenus <i>Ceratoropis</i> and is widely distributed in Asia. Créole bean is the only tetraploid species in the genus <em>Vigna</em>, and it has been shown to derive from the hybridization of <i>Vigna hirtella</i> and <i>Vigna trinervia</i><em>. </em>In this study, we combined the long-read PacBio technology with the chromatin contact mapping (Hi-C) technique to obtain a chromosome-level assembly of <i>V. reflexo-pilosa</i>. The final assembly contained 998,724,903 bases with an N50 length of 42,545,650 bases. Our gene prediction recovered 97.5% of the highly conserved orthologs based on the BUSCO analysis. To investigate homoeolog expression bias and expression level dominance in the tetraploid, we also sequenced and assembled the genomes of its progenitors. Overall, the majority of the homoeolog pairs (72.9%) displayed no expression bias, and among those that exhibited biased expression, 16.3% showed unbalanced homoeolog expression bias toward the <i>V. trinervia</i> subgenome. Moreover, 41.2% and 36.2% of the expressed gene pairs exhibited transgressive expression and expression level dominance, respectively. Interestingly, the genome-wide expression level dominance in the tetraploid was biased toward the <i>V. trinervia</i> subgenome. The analysis of methylation patterns also revealed that the average methylation levels in coding regions were higher in the <i>V. hirtella</i> subgenome than those in the <i>V. trinervia</i> subgenome. The genomic/transcriptomic resources for these three species are useful not only for the development of elite cultivars in <em>Vigna</em> breeding programs but also to researchers studying comparative genomics and investigating genomic/epigenomic changes following polyploid events.
DOI: 10.5524/102400
title: Supporting data for "Genomic data of <i>Vigna trinervia</i>"

description: <i>Vigna trinervia</i> is a perennial wild legume belonging to the subgenus Ceratotropis in the genus <i>Vigna</i>, also one of the parent for the <i>Vigna reflexo-pilosa</i>.. In this study, the samples of leaves are collected from Thailand for DNA and RNA analysis.<br> This dataset represents the genome assembly data by 10X Genomics technology for <i>V. trinervia</i>. The N50 size for the assembly is 1,656,192 and the L50 scaffold number is 83. The total assembly size is 498.78 Mb with the BUSCO evaluation of completeness is 96.1%. The protein-coding gene in <i>V. trinervia</i> is 31,017 and the repetitive sequences observed is 38.7%.
DOI: 10.5524/102401
title: Supporting data for "SODAR: managing multi-omics study data and metadata"

description: Scientists employing omics in life science studies face challenges such as the modeling of multi assay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multi assay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license.