cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102381
title: Supporting data for "An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy"

description: Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing.<br>An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs and several petabytes (PB) of storage to rapidly prototype and develop end-to-end AI projects. Using a JupyterLab notebook, long-running AI model training programs can also be executed remotely to create trained models, represented in open neural network exchange (ONNX) format, and other output datasets in Galaxy. Other features include Git integration for version control, the option of creating and executing pipelines of notebooks, and multiple dashboards and packages for monitoring compute resources and visualisation, respectively.<br>These features make JupyterLab in Galaxy Europe highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions in COVID-19 CT scan images is reproduced using various features of JupyterLab on Galaxy Europe. In addition, ColabFold, a faster implementation of AlphaFold2, is accessed in JupyterLab to predict the 3D structure of protein sequences. JupyterLab is accessible in two ways - one as an interactive Galaxy tool and the other by running the underlying Docker container. In both ways, long-running training can be executed on Galaxys compute infrastructure. Scripts to create the Docker container are available under MIT license at https://github.com/usegalaxy-eu/gpu-jupyterlab-docker.
DOI: 10.5524/102382
title: Supporting data for "Multi-view child motor development dataset for AI-driven assessment of child development"

description: Childrens motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities.<br>The 339 participating children were divided into three groups by age. We collected videos of four behaviors by age group from three different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DSTs gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the three age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance.<br>Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.
DOI: 10.5524/102385
title: Supporting data for "FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis"

description: Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratorys use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.<br>For more precise alignment, we add a landmark-based atlas registration to ClearMaps functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMaps threshold-based approach: Ilastiks Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3D visualization of the annotated cells.<br>As a proof-of-principle, we use FriendlyClearMap to quantify the distribution of the three main GABAergic interneuron subclasses (Parvalbumin+, Somatostatin+, and VIP+) in the mouse fore- and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
DOI: 10.5524/102336
title: Supporting data for "Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation"

description: Since the beginning of the COVID-19 pandemic there has been an explosion of sequencing of the SARS-CoV-2 virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus, most notably the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. <br>In this paper, we leverage the Frequency Chaos Game Representation (FCGR) and Convolutional Neural Networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieves a 96.29% overall accuracy, while a similar tool, Covidex, obtained a 77,12% overall accuracy. As far as we know, our method is the first using Deep Learning and FCGR for intra-species classification. Furthermore, by using some feature importance methods CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. <br>By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on Random Forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. <br>The trained models can be tested online by providing a FASTA file (with one or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL-3.0 license.
DOI: 10.5524/102342
title: Supporting data for "A molecular phenotypic map of Malignant Pleural Mesothelioma"

description: Malignant Pleural Mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients. <br>We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multi-omic MPM datasets (<i>n=374</i> in total), we provide an extensive molecular phenotype map of MPM based on the multi-task theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal. <br>This new high quality MPM multi-omics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.
DOI: 10.5524/102344
title: Supporting data for "Cell type-specific interpretation of noncoding variants using deep learning-based methods"

description: Interpretation of non-coding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcriptional and epigenetic effects caused by non-coding mutations. However, these approaches require specific experimental data for training and can not generalize across cell types where required features were not experimentally measured. We show here that available epigenetic characteristics of human cell types are extremely sparse, limiting those approaches that rely on specific epigenetic input. We propose a new neural network architecture, DeepCT, which can learn complex interconnections of epigenetic features and infer unmeasured data from any available input. Furthermore, we show that DeepCT can learn cell type-specific properties, build biologically meaningful vector representations of cell types and utilize these representations to generate cell type-specific predictions of the effects of non-coding variations in the human genome.
DOI: 10.5524/102347
title: Supporting data for "Genome assembly of <i>Musa beccarii</i> shows extensive chromosomal rearrangements and genome expansion during evolution of Musaceae genomes"

description: <i>Musa beccarii</i> is a precious ornamental banana species. It is the only species with a basic chromosome number of x=9 (2n=2x=18) in <i>Musa</i> genus, a number shared with the sister genera <i>Ensete</i> and <i>Musella</i>, while other <i>Musa</i> species are x=7, 10 or 11. Moreover, there are two sections in <i>Musa. M. beccarii</i> is in <i>Callimusa</i> section, while current assembled <i>Musa</i> genomes are in <i>Musa</i> section. To better understand genome evolution in Musaceae, we generated a high-quality chromosome-scale genome assembly of <i>M. beccarii</i>. <br><i>M. beccarii</i>genome was assembled by long read and Hi-C sequencing, with gene annotations using both long Iso-Seq and short RNA-seq reads. <i>M. beccarii</i> displayed the largest genome size, ~570 Mbp, among known Musaceae genomes, which was attributed to transposable element expansion and amplification of 45S rDNA sites. Regardless of chromosome numbers, we detected extensive genome-wide chromosome fusions and fissions between <i>M. beccarii</i> and the other <i>Musa</i> and <i>Ensete</i> species by synteny analysis. Within Musaceae, <i>M. beccarii</i> showed a reduced number of terpenoid synthase genes which are related to chemical defense and enrichment in lipid metabolism genes linked to the physical defense of the cell wall. Furthermore, type III polyketide synthase (T3PKS) was the most abundant biosynthetic gene cluster in <i>M. beccarii</i> with no conservation in the other Musaceae genomes. <br><i>M. beccarii</i>, as the first chromosome scale genome sequenced in the <i>Callimusa</i> section in <i>Musa</i> , provides an important resource for further understanding of evolutionary mechanisms in the Musaceae and adds to knowledge of the pangenome.
DOI: 10.5524/102350
title: Supporting data for "TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles"

description: Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, have motivated community driven efforts to create standardized benchmarking datasets and platforms, standardized taxonomic profile formats, as well as a benchmarking platform to assess tool performance. While this standardization is essential, there is currently a lack of tools to visualize the standardized output of the many existing taxonomic profilers. Thus, benchmarking studies rely on a single value metrics to compare performance of tools and compare to benchmarking datasets. This is one of the major problems in analyzing metagenomic profiling data, since single metrics, such as the F1 score, fail to capture the biological differences between the datasets. <br>Here we report the development of TAMPA (Taxonomic metagenome profiling evaluation) , a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community. We demonstrate the unique ability of TAMPA to generate novel biological hypothesis by highlighting the taxonomic differences between samples otherwise missed by commonly utilized metrics. <br> In this study, we show that TAMPA can help visualize the output of taxonomic profilers, enabling biologists to effectively choose the most appropriate profiling method to use on their metagenomics data. TAMPA is available on GitHub, Bioconda and Galaxy Toolshed at https://github.com/dkoslicki/TAMPA, and is released under the MIT license.
DOI: 10.5524/102358
title: Supporting data for "DivBrowse – interactive visualization and exploratory data analysis of variant call matrices"

description: The sequencing of whole genomes is becoming increasingly affordable. In this context large-scale sequencing projects are generating ever larger datasets of species-specific genomic diversity. As a consequence, more and more genomic data needs to be made easily accessible and analyzable to the scientific community. <br>We present DivBrowse, a web application for interactive visualization and exploratory analysis of genomic diversity data stored in Variant Call Format (VCF) files of any size. By seamlessly combining BLAST as an entry point together with interactive data analysis features such as principal component analysis in one graphical user interface, DivBrowse provides a novel and unique set of exploratory data analysis capabilities for genomic biodiversity datasets. The capability to integrate DivBrowse into existing web applications supports interoperability between different web applications. Built-in interactive computation of principal component analysis allows users to perform ad-hoc analysis of the population structure based on specific genetic elements such as genes and exons. Data interoperability is supported by the ability to export genomic diversity data in VCF and General Feature Format (GFF3) files. <br>DivBrowse offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data by including features like interactive calculation of variant frequencies and principal component analysis. The use of established standard file formats for data input supports interoperability and seamless deployment of application instances based on the data output of established bioinformatics pipelines.
DOI: 10.5524/102364
title: Genomic data of bloodsucking leech, <i>Hirudo nipponia</i>

description: <i>Hirudo nipponia</i> is a species of east Asian leech. Their hosts include many amphibians, reptiles, and mammals, including humans. Typically they're found in rice paddies, irrigation and drainage ditches, open sewers, and ponds. Until recently they were endemic to Japan, Korea, China, Taiwan, the Ryukyu Islands, and eastern Russian. However <i>H. nipponia</i> has disappeared from the rice paddies and drainage ditches in Taiwan, and hardly been found in the other habitats, possibly due to the use of pesticides and fertilizers.<br>They measure 30-60mm, are green to olive coloured with five yellow stripes outlined in black, and also have five pairs of eyes spread out longitudinally near the front of the body.<br> Here we present the genome, which has an estimated genome size of 203.7Mb, scaffold N50 of 18.5Mb, contig N50 of 1.1Mb and 20,430 genes. This dataset includes the following data: repetitive sequences, gene and functional annotations, annotation of ncRNAs, prediction of secreted proteins, divergence time & transcriptome analysis.
DOI: 10.5524/102369
title: Supporting data for "The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics"

description: Microbial culture collections play a key role in taxonomy by studying the diversity of their strains and providing well characterized biological material to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms (BCCM) were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (e.g. yeasts). <br>This public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenomic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. <br>Technical details Nextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository. All the workflows are based on Singularity containers to increase reproducibility. <br>The toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early-branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the <i>Gloeobacterales</i> order, the first group to diverge in the evolutionary tree of Cyanobacteria. <br>The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be used by all researchers interested in microbial taxonomy, as exemplified by our case study on <i>Gloeobacterales</i>.
DOI: 10.5524/102373
title: Supporting data for "The first genome assembly of the amphibian nematode parasite (<i>Aplectana chamaeleonis</i>)"

description: Cosmocercoid nematodes are common parasites in the digestive tract of amphibians. Genomic resources are important for understanding the evolution of a species and the molecular mechanisms of parasite adaptation. So far, no genome resource of Cosmocercoid has been reported. In 2020, a massive infection of the parasite was found in the small intestine of an amphibian toad, causing a severe intestinal blockage. We identified this parasite as <i>A. chamaeleonis</i> morphologically. Here, we report for the first time a highly contiguous <i>A. chamaeleonis</i> genome with a genome size of 1.04 Gb. The repeat content in the Ascaris genome is 72.45%, and the total length is more than 751 Mb. This study is of great significance for understanding the evolution of Cosmocercoid, and also provides a molecular basis for Cosmocercoid infection and control.
DOI: 10.5524/102375
title: Supporting data for "Genome assembly of the deep-sea coral <i>Lophelia pertusa</i>"

description: Like their shallow-water counterparts, cold-water corals create reefs that support highly diverse communities, and these structures are subject to numerous anthropogenic threats. Here, we present the genome assembly of <i>Lophelia pertusa</i> from the southeastern coast of the USA, the first one for a deep-sea scleractinian coral species. We generated PacBio CLR data for an initial assembly and proximity ligation data for scaffolding. The assembly was annotated using evidence from transcripts, proteins, and <i>ab initio</i> gene model predictions. This assembly is comparable to high-quality reference genomes from shallow-water scleractinian corals. The assembly comprises 2,858 scaffolds (N50 1.6 Mbp) and has a size of 556.9 Mbp. Approximately 57% of the genome comprises repetitive elements and 34% of coding DNA. We predicted 41,089 genes, including 91.1% of complete metazoan orthologs. This assembly will facilitate investigations into the ecology of this species and the evolution of deep-sea corals.
DOI: 10.5524/102383
title: Supporting data for "Mycobacterial Metabolic Model Development for Drug Target Identification"

description: Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are the source of widespread infections in humans. According to the World Health Organization, <i>Mycobacterium leprae</i>, which causes leprosy, is still endemic in tropical countries; <i>Mycobacterium tuberculosis</i> is the second-largest infectious disease killer worldwide after COVID-19; <i>Mycobacteroides abscessus</i>, a group of non-tuberculous mycobacteria, causes lung infections and other health-care-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical to develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseased conditions. <br>To this end, we have developed metabolic models of two bacterial pathogens, <i>M. leprae</i>, and <em></em><i>M. abscessus</i>, and used a new computational tool to identify potential drug targets, which we refer to as bottleneck reactions. We highlight the genes, reactions, and pathways in each of these organisms and potential drug targets which can be further explored as broad-spectrum antibacterials. The unique drug targets to each pathogen are significant for precision medicine initiatives. <br>The models and associating datasets are available in GigaDB and the following repositories: <i>M. abscessus</i>: Biomodels & PatMeDB, <i>M. leprae</i>: Biomodels & PatMeDB
DOI: 10.6084/m9.figshare.20400729
title: Leech genomes

description: genome sequences and gene annotation of three leeches
DOI: 10.5524/102338
title: Supporting data for "PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell"

description: Pharmacokinetics and pharmacodynamics are key considerations in any study of molecular therapies. It is thus imperative to factor their effects in to any <i>in silico</i> model of biological tissue involving such therapies. Furthermore, creation of a standardized and flexible framework will benefit the community by increasing access to such modules and enhancing their communicability. PhysiCell is an open source physics-based cell simulator, i.e. a platform for modeling biological tissue, that is quickly being adopted and utilized by the mathematical biology community. We present here PhysiPKPD, an open source PhysiCell-based package that allows users to include PKPD in PhysiCell models.