cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102434
title: Supporting data for "simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods"

description: Machine learning (ML) has seen an increase in interest in classifying immune states in adaptive immune receptor repertoires (AIRR) to aid the development of immunodiagnostics and therapeutics. Simulated data are crucial and necessary for the development and comprehensive evaluation of AIRR-ML methods e.g. through crowdsourced ML competitions. <br>We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR constructs antigen-experienced-like baseline repertoires by introducing signals that follow the empirical relationship between VDJ generation probability and population incidence of public sequences calibrated on real-world experimental datasets. By allowing users to provide a set of true immune state-associated sequences, simAIRR can be used for constructing repertoire-level benchmarks based on a range of assumptions (or experimental data source) for what constitutes receptor level immune signals. This includes the possibility of either making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. <br>This study not only sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets, but also provides a solution through a simulation strategy implemented as a Python package: https://github.com/KanduriC/simAIRR.
DOI: 10.5524/102436
title: Supporting data for "A community resource to mass explore the wheat grain proteome and its application to the Late Maturity Alpha-Amylase (LMA) problem"

description: Late maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point alpha-amylase following a temperature shock during mid-grain development or prolonged cold throughout grain development, both leading to starch degradation. Whilst the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have applied high-throughput proteomics to 4,061wheat flours displaying a range of LMA activities. Using an array of statistical analyses to select LMA-responsive biomarkers, we have mined them using a suite of tools applicable to wheat proteins. <br>We observed that LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis, TCA cycle, along with DNA-and RNA binding mechanisms, as well as protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as well as protein assembly via dimerisation and complexing. The secondary metabolism was also mobilised with the up-regulation of phytohormones, chemical and defense responses. LMA further invoked cellular structures among which ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain starch and other carbohydrates with the up-regulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose and UDP-glucose were down-regulated. <br>To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue, but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed.
DOI: 10.15468/v8fvyn
title: Anopheles collections in the health districts of Korhogo (Côte d'Ivoire) and Diébougou (Burkina Faso) (2016-2018)

description: This dataset contains the result of a set of anopheles mosquitoes (Diptera : Culicidae) collections performed in several villages of the rural districts of Korhogo (Côte d'Ivoire) and Diébougou (Burkina Faso) between 2016 and 2018. 

These mosquito collections were carried-out in the frame of the REACT project (Insecticide resistance management in Burkina Faso and Côte d'Ivoire: research on vector control strategies), involving three partners : Institut de Recherche pour le Développement (IRD, France), Institut de Recherche en Science de la Santé (IRSS, Burkina Faso) and Institut Pierre Richet (IPR, Côte d'Ivoire).

The collections were performed using the Human Landing Catch method. In the Diébougou (Burkina Faso) area, seven entomological surveys were carried-out in 27 villages. In the Korhogo (Côte d'Ivoire) area, eight entomological surveys were carried-out in 28 villages. Mosquitoes were collected from 17:00 to 09:00 both indoors and outdoors at 4 sites per village. Overall, more than 3000 human-nights of collection were performed, representing a total sampling effort of approximately 45000 hours, during which more than 60000 anopheles were captured. The dataset provides the dates and places of samplings (Event core) along with the identification of each Anopheles captured (Occurrence, 1 row by mosquito). 

Additionally, we provide three kinds of information to enrich this dataset :

- for all the mosquitoes, the place of collection - i.e. indoors or outdoors (available in the Extended Measurement or Facts extension) ;
- for a sub-sample of the mosquitoes, a set of characteristics of importance for malaria transmission risk (genetic mutations conferring resistance to insecticides, plasmodium falciparum infection, parity state) (available in the Extended Measurement or Facts extension) ;
- for all the sampling events, a set of environmental data (related to temperature, rainfall and landscape) extracted from satellite data (available in the Measurement or Facts extension).
A data paper describing the data exhaustively is available at: 
Taconet P, Soma DD, Zogo B, Mouline K, Simard F, Koffi AA, Dabiré RK, Pennetier C, Moiroux N (2023). Anopheles sampling collections in the health districts of Korhogo (Côte d’Ivoire) and Diébougou (Burkina Faso) between 2016 and 2018. Gigabyte. https://doi.org/10.46471/gigabyte.83
DOI: 10.24432/c52p4x
title: Heart Disease
DOI: 10.24432/c5dw2b
title: Breast Cancer Wisconsin (Diagnostic)
DOI: 10.24432/c5gs39
title: Blood Transfusion Service Center
DOI: 10.24432/c5j30w
title: Real Estate Valuation
DOI: 10.24432/c5k306
title: Bank Marketing
DOI: 10.24432/c5pk67
title: Concrete Compressive Strength
DOI: 10.24432/c5s896
title: Residential Building
DOI: 10.24432/c5tg7t
title: Student Performance
DOI: 10.24432/c5xw20
title: Adult
DOI: 10.5524/102388
title: Supporting data for "Identification of transcriptional regulatory variants in pig duodenum, liver and muscle tissues"

description: In humans and livestock species, genome-wide association studies (GWAS) have been applied to study the association between variants distributed across the genome and a phenotype of interest. To discover genetic polymorphisms affecting the duodenum, liver, and muscle transcriptomes of 300 pigs from three different breeds (Duroc, Landrace and Large White), we performed expression GWAS between 25,315,878 polymorphisms and the expression of 13,891 genes in duodenum, 12,748 genes in liver, and 11,617 genes in muscle. <br>More than 9.68×10<sup>11</sup> association tests were performed, yielding 14,096,080 significantly associated variants, which were grouped in 26,414 expression quantitative trait locus (eQTL) regions. Over 56% of the variants were within 1Mb of their associated gene. In addition to the 100kb region upstream of the transcription start site, we identified the importance of the 100kb region downstream of the 3UTR for gene regulation, as most of the <em>cis</em>-regulatory variants were located within these two regions. We also observed 39,874 hotspot regulatory polymorphisms associated with the expression of 10 or more genes that could modify the protein structure or the expression of a regulator gene. In addition, two motifs (5-GATCCNGYGTTGCYG-3 and a poly(A) sequence) were enriched across the three tissues within the neighbouring sequences of the most significant SNPs in each <em>cis</em>-eQTL region. <br>The 14 million significant associations obtained in this study are publicly available and have enabled the identification of expression-associated <em>cis</em>-, <em>trans</em>- and hotspot regulatory variants within and across tissues, thus shedding light on the molecular mechanisms of regulatory variations that shape end-trait phenotypes.
DOI: 10.5524/102389
title: Supporting data for "Hetnet connectivity search provides rapid insights into how two biomedical entities are related"

description: Hetnets, short for heterogeneous networks, contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open source implementation of these methods in our new Python package named hetmatpy.
DOI: 10.5524/102394
title: Supporting data for "A new haplotype-resolved turkey genome to enable turkey genetics and genomics research"

description: The domesticated turkey (<i>Meleagris gallopavo</i>) is a species of significant agricultural importance and is the second largest contributor, behind broiler chickens, to world poultry meat production. The previous genome is of draft quality and partly based on the chicken (<i>Gallus gallus</i>) genome. A high-quality reference genome of Meleagris gallopavo is essential for turkey genomics and genetics research and the breeding industry. <br>By adopting the trio-binning approach, we were able to assemble a high-quality chromosome-level F1 assembly and two parental haplotype assemblies, leveraging long-read technologies and genome-wide chromatin interaction data (Hi-C). From a total of 40 chromosomes (2n=80), we capture 35 chromosomes in a single scaffold, and show much improved genome completeness and continuity compared to the old assembly build. The three assemblies are of higher quality than the previous draft quality assembly and comparable to the chicken assemblies (GRCg7) shown by the largest contig N50 (26.6 Mb) and comparable BUSCO gene set completeness scores (96-97%). Comparative analyses confirms a previously identified large inversion of around 19 Mbp on the Z chromosome not found in other Galliformes. Structural variation between the parent haplotypes were identified which pose potential new target genes for breeding. <br>We contribute a new high-quality turkey genome at chromosome-level, benefiting turkey genetics and other avian genomics research as well as turkey breeding industry.
DOI: 10.5524/102399
title: Supporting data for "Genomic data of <i>Vigna hirtella</i>"

description: <i>Vigna hirtella</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 leaf, flower, pod, and root are collected from Thailand for DNA and RNA analysis.<br> This dataset represents the genome assembly data by 10X Genomics technology for <i>V. hirtella</i>. The N50 size for the assembly is 2,078,642 and the L50 scaffold number is 64. The total assembly size is 474.13 Mb with the BUSCO evaluation of completeness is 98.1%. The protein-coding gene in <i>V. hirtella</i> is 34,371 and the repetitive sequences observed is 41.0%.
DOI: 10.5524/102402
title: Supporting data for "MuLan-Methyl - Multiple Transformer-based Language Models for Accurate DNA Methylation Prediction"

description: Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep-learning framework for predicting DNA methylation sites, which is based on five popular transformer-based language models. The framework identifies methylation sites for three different types of DNA methylation, namely N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the pre-train and fine-tune paradigm. Pre-training is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA-methylation status of each type. The five models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we show that the model captures characteristic differences between different species that are relevant for methylation. This demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source and we provide a web server that implements the approach.
DOI: 10.5524/102406
title: Supporting data for "Training Infrastructure as a Service"

description: Hands-on training, whether in bioinformatics or other domains, often requires significant technical resources and knowledge to set up and run. Instructors must have access to powerful compute infrastructure that can support resource-intensive jobs running efficiently. Often this is achieved using a private server where there is no contention for the queue. However, this places a significant prerequisite knowledge or labour barrier for instructors, who must spend time coordinating deployment and management of compute resources. Furthermore, with the increase of virtual and hybrid teaching, where learners are located in separate physical locations, it is difficult to track student progress as efficiently as during in-person courses. <br>Originally developed by Galaxy Europe and the Gallantries project, together with the Galaxy community we have created Training Infrastructure-as-a-Service (TIaaS), aimed at providing user-friendly training infrastructure to the global training community. TIaaS provides dedicated training resources for Galaxy-based courses and events. Event organisers register their course, after which trainees are transparently placed in a private queue on the compute infrastructure, which ensures jobs complete quickly, even when the main queue is experiencing high wait times. A built-in dashboard allows instructors to monitor student progress. <br>TIaaS provides a significant improvement for instructors and learners, as well as infrastructure administrators. The instructor dashboard makes remote events not only possible but easy. Students experience continuity of learning, as all training happens on Galaxy which they can continue to use after the event. In the past 60 months, 504 training events with over 24000 learners have used this infrastructure for Galaxy training.
DOI: 10.5524/102407
title: Supporting data for "Chromosome-level genome and recombination map of the male buffalo"

description: The swamp buffalo (<i>Bubalus bubalis carabanesis</i>) is an economically important livestock supplying milk, meat, leather and draft power. Several female buffalo genomes have been available, but the lack of high-quality male genomes hinders studies on chromosome evolution, especially Y, as well as meiotic recombination. <br>Here, a chromosome-level genome with a contig N50 of 72.2Mb, and a fine-scale recombination map of male buffalo were reported. We found that transposable elements (TEs) and structure variants (SVs) may contribute to buffalo evolution by influencing adjacent gene expressions. We further found that the pseudoautosomal region (PAR) of the Y chromosome is subject to stronger purification selection. The meiotic recombination map showed that there were two obvious recombination hotspots on chromosome 8, and the genes around them were mainly related to tooth development, which helped to enhance the adaption of buffalo to inferior feed. Among several genomic features, TE has the strongest correlation with recombination rates. Moreover, the TE subfamily, SINE/tRNA, is likely to play a role in driving recombination into SVs. <br>The male genome and sperm sequencing will facilitate the understanding of the buffalo genomic evolution and functional research.
DOI: 10.5524/102416
title: Supporting data for "<i>Anopheles</i> sampling collections in the health districts of Korhogo (Côte d'Ivoire) and Diébougou (Burkina Faso) between 2016 and 2018"

description: Characterizing the entomological profile of malaria transmission at fine spatiotemporal scales is essential for developing and implementing effective vector control strategies. Here, we present a fine-grained dataset of <i>Anopheles</i> (Diptera: Culicidae) mosquitoes resulting from a series of collections in 55 villages of the rural districts of Korhogo (Northern Côte dIvoire) and Diébougou (South-West Burkina Faso) between 2016 and 2018. In the framework of a randomized controlled trial, <i>Anopheles</i> mosquitoes have been periodically collected by Human Landing Catch inside and outside households, and analyzed individually to identify genus and - for a subsample - species, insecticide resistance genetic mutations, <i>Plasmodium falciparum</i> infection using molecular tools, and parity status. Overall, more than 3000 human-nights of collection were performed, representing a total sampling effort of approximately 45000 hours. More than 60000 <i>Anopheles</i> were collected (mainly <i>An. gambiae s.s.</i>, <i>An. coluzzii</i>, and <i>An. funestus</i>). The data are published as a Darwin Core archive in the Global Biodiversity Information Facility - France, comprising 4 files (events, occurrences, mosquito characterizations, and environmental data) and available at https://doi.org/10.15468/v8fvyn.
DOI: 10.5524/102420
title: Supporting data for "Confound-leakage: Confound Removal in Machine Learning Leads to Leakage"

description: Machine learning (ML) approaches are a crucial component of modern data analysis in many fields including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ feature wise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood. br /We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention deficit hyperactivity disorder is overestimated using speech-derived features when using depression as a confound. br /Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models.
DOI: 10.5524/102430
title: Supporting data for "Construction and analysis of the chromosome-level haplotype-resolved genomes of two <i>Crassostrea</i> oyster congeners: <i>Crassostrea angulata</i> and <i>Crassostrea gigas</i>"

description: The Portuguese oyster <i>Crassostrea angulata</i> and the Pacific oyster <i>C. gigas</i> are two major <i>Crassostrea</i> species that are naturally distributed along the Northwest Pacific coast and possess great ecological and economic value. Here, we report the construction and comparative analysis of the chromosome-level haplotype-resolved genomes of two oyster congeners. <br>Based on a trio-binning strategy, the PacBio high-fidelity and Illumina Hi-C reads of the offspring of the hybrid cross <i>C. angulata</i> () × <i>C. gigas</i> () were partitioned and independently assembled to construct two chromosome-level fully phased genomes. The assembly size (contigN50 size, BUSCO completeness) of the two genomes were 582.4 M (12.8 M, 99.1%) and 606.4 M (5.46 M, 98.9%) for <i>C. angulata</i> and <i>C. gigas</i>, respectively, ranking at the top of mollusk genomes with high contiguity and integrity. The general features of the two genomes were highly similar, and 15,475 highly conserved ortholog gene pairs shared identical gene structures and similar genomic locations. Highly similar sequences can be primarily identified in the coding regions, whereas most non-coding regions and introns of genes in the same ortholog group contain substantial small genomic and/or structural variations. Based on population resequencing analysis, a total of 2,756 species-specific SNPs and 1,088 genes possibly under selection were identified. <br>This is the first report of trio-binned fully-phased chromosome-level genomes in marine invertebrates. The study provides fundamental resources for the research on mollusk genetics, comparative genomics and molecular evolution.