cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102452
title: Supporting data for "MetGENE: Gene-centric Metabolomics Information Retrieval Tool"

description: Biomedical research often involves contextual integration of multi-modal and multi-omic data in search of mechanisms for improved diagnosis, treatment and monitoring. Researchers need to access information from diverse sources comprising data in various and sometimes incomplete formats. The downstream processing of the data, to decipher mechanisms by reconstructing networks and developing quantitative models, warrants considerable effort. <br>MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. Further, the information can be contextualized by filtering along species, anatomy (tissue) and condition (disease or phenotype). <br>MetGENE is an open source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats, e.g., JSON, for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.
DOI: 10.5524/102458
title: Supporting data for "Finding Haplotypic Signatures in Proteins"

description: The non-random distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples, and detectable by mass spectrometry, but are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches, and the discoverability of peptides specific to haplotypes remain unknown. Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42 % of the discoverable amino acid substitutions encoded by common haplotypes, two or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multi-variant peptides, and out of the 4,582 amino acid substitutions identified, 6.37 % were covered by multi-variant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches. As these become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.
DOI: 10.5524/102460
title: Supporting data for "A high-quality pseudo-phased genome for <i>Melaleuca quinquenervia</i> shows allelic diversity of NLR-type resistance genes"

description: The coastal wetland tree species <i>Melaleuca quinquenervia</i> (broad-leaved paperbark), is a foundation species in eastern Australia, Indonesia, Papua New Guinea, and New Caledonia. The species has been widely grown as an ornamental, becoming invasive in areas such as Florida in the United States. Long-lived trees must respond to a wide range pests and pathogens, and immune receptors encoded by the nucleotide-binding leucine-rich repeat (NLR) gene family play a key role in plant stress responses. Expansion of this family is driven largely by tandem duplication, resulting in a clustering arrangement on chromosomes. Due to this clustering and their highly repetitive domain structure, comprehensive annotation of NLR encoding genes within genomes has been difficult. Additionally, as many genomes are presented in their haploid, collapsed state, the allelic diversity of the NLR gene family has not been widely published for outcrossing tree species. <br>We assembled a chromosome-level pseudo-phased genome for <i>M. quinquenervia</i> and describe the allelic diversity of plant NLRs using the novel FindPlantNLRs pipeline. Analysis reveals variation in the number of NLR genes on each haplotype, differences in clusters and in the types and numbers of novel integrated domains. <br>We anticipate that the high quality of the <i>M. quinquenervia</i> genome will provide a new framework for functional and evolutionary studies into this important tree species. Our results indicate a likely role for maintenance of NLR allelic diversity to enable response to environmental stress, and we suggest that this allelic diversity may be even more important for long-lived plants.
DOI: 10.5524/102474
title: Supporting data for "The chromosome-scale genome of Magnolia sinica (Magnoliaceae) provides insights into the conservation of plant species with extremely small populations (PSESP)"

description: <i>Magnolia sinica</i> (Magnoliaceae) is one of the most highly threatened trees endemic to Southeast Yunnan, China. In this study, we generated for the first time a high-quality chromosome-scale genome sequence from <i>M. sinica</i>, by combining Illumina and PacBio data with Hi-C mapping methods. The final assembled genome size of <i>M. sinica</i> was 1.84 Gb, with a contig N50 of ca. 45 Mb and scaffold N50 of 92 Mb. Identified repeats constituted approximately 57% of the genome, and 43,473 protein-coding genes were predicted with high support. Phylogenetic analysis showed that the magnolias form a sister clade with the eudicots and the order Ceratophyllales, while the monocots are sister to the other core angiosperms. A total of 21 individuals from the five remnant populations of <i>M. sinica</i>, as well as 22 specimens belonging to eight related Magnoliaceae species, were resequenced. The results showed that <i>M. sinica</i> had higher genetic diversity (w = 0.01126 and = 0.01158) than other related species in the Magnoliaceae. However, population structure analysis suggested that the genetic differentiation among the five <i>M. sinica</i> populations was very low. Analyses of the demographic history of the species using different models consistently revealed that two bottleneck events occurred. The contemporary effective population size of <i>M. sinica</i> was estimated to be 10.9. Additionally, different patterns of genetic loads(inbreeding and numbers of deleterious mutations) suggested constructive conservation strategies for these five different populations of <i>M. sinica</i>. Overall, this high-quality genome could be a valuable genomic resource for conservation of <i>M. sinica</i>.
DOI: 10.5524/102475
title: Supporting data for "A graph clustering algorithm for detection and genotyping of structural variants from long reads"

description: Structural variants (SV) are genomic polymorphisms defined by their length (&gt;50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long-read sequencing data have been recently developed. We present an accurate and efficient algorithm to predict germline SVs from long-read sequencing data. The algorithm starts collecting evidence (Signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algorithm, which provides the advantage of delimiting clusters with high resolution. Clusters are transformed into SVs and a Bayesian model allows to precisely genotype SVs based on their supporting evidence. This algorithm is integrated into the single sample variants detector of the Next Generation Sequencing Experience Platform (NGSEP), which facilitates the integration with other functionalities for genomics analysis. We performed multiple benchmark experiments including simulation, and real data, representing different genome profiles, sequencing technologies (PacBio HiFi, ONT), and read-depths. The results show that our approach outperformed state-of-the-art tools on germline SV calling and genotyping especially at low depths, and in error-prone repetitive regions. We believe this work significantly contributes to the development of bioinformatic strategies to maximize the use of long-read sequencing technologies.
DOI: 10.5524/102482
title: Supporting data for "DrugSim2DR: Systematic prediction of drug functional similarities in the context of specific disease for drug repurposing"

description: Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years. Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anti-cancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some FDA-approved anti-cancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by ROC analysis and multi-approach comparisons in various cancer datasets. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).
DOI: 10.5524/102483
title: Supporting data for "DriverMP enables improved identification of cancer driver genes"

description: Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amidst an extensive array of passenger genes. <br>We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply non-silent somatic mutation data, proteinprotein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in ten TCGA cancer datasets demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis. <br>The new method, DriverMP, is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, which is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database (available at http://www.liulab.top/DriverMP/table) for ten cancer types and an online service that can be freely accessed without registration for users (available at http://liulab.top/DriverMP/server). The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.
DOI: 10.5524/102484
title: Supporting data for "Early microbial intervention reshapes phenotypes of newborn Bos taurus through metabolic regulations"

description: The rumen of neonatal calves has limited functionality and establishing intestinal microbiota may play a crucial role in their health and performance. We aim to explore the temporal colonization of the gut microbiome and the benefits of early microbial intervention in newborn calves. We evaluated the impact of microbiota transplantation (MT) on metabolism and later milk production performance. <br>We followed 36 newborn calves for two months and found that the composition and ecological interactions of their gut microbiomes likely reached maturity one month after birth. Temporal changes in the gut microbiome of newborn calves are widely associated with changes in their physiological statuses, such as growth and fiber digestion. Importantly, we observed that MT reshapes the gut microbiome of newborns by altering the abundance and interaction of <i>Bacteroides</i> species, as well as amino acid pathways, such as arginine biosynthesis. Two-year follow-up of those calves further showed that MT improves their later milk production. Notably, MT improves fiber digestion, antioxidant capacity of newborns while reducing diarrhea. MT also contributes to significant changes in the metabolomic landscape, and with putative causal mediation analysis, we suggest that altered gut microbial composition in newborns may influence physiological status through microbial-derived metabolites. <br>Our study provides metagenomic and metabolomic atlas of the temporal development of the gut microbiome in newborn calves. MT can alter the gut microbiome of newborns, leading to improved physiological status and later milk production. The data may help develop strategies to manipulate the gut microbiota during early life, which may be relevant to the health and production of newborn calves.
DOI: 10.5524/102485
title: Supporting data for "Nanopore Adaptive Sampling Enriches for Antimicrobial Resistance Genes in Microbial Communities"

description: Antimicrobial resistance (AMR) is a global public health threat. Environmental microbial communities act as reservoirs for AMR, containing genes associated with resistance, their precursors, and the selective pressures to encourage their persistence. Genomic surveillance could provide insight into how these reservoirs are changing and their impact on public health. The ability to enrich for AMR genomic signatures in complex microbial communities would strengthen surveillance efforts and reduce time-to-answer. Here, we test the ability of nanopore sequencing and adaptive sampling to enrich for AMR genes in a mock community of environmental origin. Our setup implemented the MinION mk1B, an NVIDIA Jetson Xavier GPU, and flongle flow cells. We observed consistent enrichment by composition when using adaptive sampling. On average, adaptive sampling resulted in a target composition that was 4x higher than a treatment without adaptive sampling. Despite a decrease in total sequencing output, the use of adaptive sampling increased target yield in most replicates. We used an environmental sample to demonstrate enrichment in a diverse community.
DOI: 10.5524/102486
title: Supporting data for "Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis"

description: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. <br>To address these challenges, we have developed a novel tool called <i>Machine Learning Made Easy</i> (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfils the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. <br>MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.
DOI: 10.6084/m9.figshare.21408117.v3
title: Supplementary Data: Finding Haplotypic Signatures in Proteins

description: Supplementary data related to the paper "Identifying Protein Haplotypes by Mass Spectrometry" <br> SD1: FASTA file including all target protein sequences (Ensembl reference proteome, protein haplotype sequences, contaminant sequences), excluding decoys SD2: FASTA file including all target and decoy sequences SD3: List of all peptide-spectrum matches (PSMs) with all related metadata and quality control measures SD4: List of substitutions identified, along with IDs of corresponding PSMs SD5: List of variant PSMs and peptide candidates suggested by PepQuery, along with confidence scores for each peptide candidate <br> To reproduce the post-processing steps, you can use the pipeline published at https://github.com/ProGenNo/IdentifyingHaplotypesByMS The repository also contains additional explanations of supplementary files contents.
DOI: 10.17632/kg3dfkyjv5.2
title: Single-cell_transcriptome_analysis_illuminating_the_characteristics_of_species-specific_innate_immune_responses_against_viral_infections_Aso_et_al

description: This is a repository of data used in the paper ([Aso et al., GigaScience, 2023](https://doi.org/10.1101/2022.12.06.519403)). The details of analytical pipelines are described elsewhere in the paper. The codes used in this study are deposited on GitHub (https://github.com/TheSatoLab/scRNA-seq_PBMC_Animals_Aso_et_al). Supporting data for this study are deposited on GigaDB (http://dx.doi.org/10.5524/102448).

As many of the viruses that have emerged in human societies originate from bat species, it is critical to understand the unique characteristics of bat immunity. To describe the differences in innate immune responses against viral infections among animal species, we analysed single-cell RNA sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) obtained from humans (Homo sapiens, Hs), chimpanzees (Pan troglodytes, Pt), rhesus macaques (Macaca mulatta), and Egyptian fruit bats (Rousettus aegyptiacus, Ra) stimulated with pathogenic stimuli, including herpes simplex virus type I (HSV-1), Sendai virus (SeV), and lipopolysaccharide (LPS).
DOI: 10.5281/zenodo.6810477
title: Datasets used for the performance evaluation of kataegis detection tools

description: These two datasets (.csv files) were used for the performance evaluation of katdetectr and other publicly available kataegis detection tools (link to paper, not yet published). The synthetic data was generated using the generateSyntheticData function from the katdetectr package (https://github.com/ErasmusMC-CCBC/katdetectr). Additionally we provide all files and RData objects needed for running the scripts for the performance evaluation which can be found on GitHub at: https://github.com/ErasmusMC-CCBC/evaluation_katdetectr. We present this dataset under the Public Domain Dedication (CC0) waiver.
description: {"references": ["Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, B\u00f8rresen-Dale AL, Boyault S. Signatures of mutational processes in human cancer. Nature. 2013 Aug;500(7463):415-21."]}
DOI: 10.5524/102438
title: Supporting data for "GERONIMO: A Tool for Systematic Retrieval of Structural RNAs in Broad Evolutionary Context"

description: While web-based tools such as BLAST have made identifying conserved gene homologs appear easy, genes with variable sequences pose significant challenges. Functionally important non-coding RNAs often show low sequence conservation due to genetic variations, including insertions and deletions. Rather than conserved sequences, these RNAs possess highly conserved structural features across a broad phylogenetic range. Such features can be identified using the covariance models approach, which combines sequence alignment with a secondary RNA structure consensus. However, running standard implementation of that approach (Infernal) requires advanced bioinformatics knowledge compared to user-friendly web services like BLAST. The issue is partially addressed by RNAcentral, which can be used to search for homologs across a broad range of ncRNA sequence collections from diverse organisms but not across the genome assemblies. <br> Here, we present GERONIMO, which conducts evolutionary searches across hundreds of genomes in a fully-automated way. It provides results extended with taxonomy context, as summary tables and visualisations to facilitate analysis for user convenience. Additionally, GERONIMO supplements homologous sequences with genomic regions to analyse promoter motifs or gene collinearity, enhancing the validation of results. <br>GERONIMO, built using Snakemake, has undergone extensive testing on hundreds of genomes, establishing itself as a valuable tool in the identification of non-coding RNA homologs across diverse taxonomic groups. Consequently, GERONIMO facilitates the investigation of the evolutionary patterns of functionally significant ncRNA players, whose understanding has previously been limited to individual organisms and close relatives.
DOI: 10.3535/6r2-97s-tz6
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jsx-yr2-23k
title: Styliola subula (Quoy & Gaimard, 1827)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lgc-1vc-saq
title: Cephalopoda Cuvier, 1795

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sdw-ndt-00y
title: Neorotalia calcar (d'Orbigny in Deshayes, 1830)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.r99hju
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eucalyptus camaldulensis",
 "Country is Angola",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018241-250811113504898/datasets/export for details.
DOI: 10.3535/7kx-zsz-8ye
title: †Cladiscites gorgiae Gemmellaro, 1904

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xbj-bs0-x6d
title: Griesbachites

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6q5-jes-rwa
title: Echinodermata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a8y-m28-hmt
title: †Javanechinus Jeannet in Lambert & Jeannet, 1935

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5z2-g5e-mq7
title: †Simplorbites papyraceus (Boubée, 1832)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.