cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102469
title: Supporting data for "Chromosome-level genome assembly of the Pacific geoduck Panopea generosa"

description: The Pacific geoduck <i>Panopea generosa</i> (class Bivalvia, order Adapedonta, family Hiatellidae, genus <i>Panopea</i>) is the largest known burrowing bivalve with considerable commercial value. Pacific geoduck and other geoduck clams play important roles in maintaining ecosystem health for their filter feeding habit and coupling pelagic and benthic processes. Here, we report a high-quality chromosome-level genome assembly of <i>P. generosa</i> to characterize its phylogeny and molecular mechanisms of its life strategies. The assembled <i>P. generosa</i> genome consists of 19 chromosomes with a size of 1.47 Gb, a contig N50 length of 1.6 Mb, and a scaffold N50 length of 73.8 Mb. The BUSCO test of the genome assembly showed 93.0% completeness. Constructed chromosome synteny revealed many occurrences of inter- and intra-chromosomal rearrangements between <i>P. generosa</i> and <i>Sinonovacula constricta</i>. Of the 35,034 predicted protein-coding genes (PCGs), 30,700 (87.6%) could be functionally annotated in public databases, indicating the high quality of genome annotation. Comparison of gene copy numbers of gene families among <i>P. generosa</i> and 11 selected species identified 507 rapidly expanded <i>P. generosa</i> gene families that are functionally enriched in immune and gonad development and may be involved in its complex survival strategies. In particular, genes carrying the copine domains underwent additional duplications in <i>P. generosa</i>, which might be important for neuronal development and immune response. The availability of a fully annotated chromosome-level genome provides a valuable data set for genetic breeding of <i>P. generosa</i>.
DOI: 10.5524/102470
title: Supporting data for "The genome assembly and annotation of the Brown-Spotted Pit viper <i>Protobothrops mucrosquamatus</i>"

description: Brown-Spotted Pit viper (<i>Protobothrops mucrosquamatus</i>), also known as the Chinese habu, is a widespread and highly venomous snake distributed from from northeastern India to eastern China. In recent years, <i>P. mucrosquamatus</i> has generated a number of poisonous snake bite cases in southeastern China. Genomics research can provide much insight in understanding toxin-production mechanisms and natural selection in vipers. Here, we collected a male <i>P. mucrosquamatus</i> individual from China, and sequenced and assembled its genome, producing a highly continuous reference genome 1.53 Gb in length and consisting of 41.18% repeat element content. From this 24,799 genes were identified, and 97.97% genes could be annotated based on function. Nuclear genome single-copy genes phylogenetic tree including 6 species verified the validity of our genome assembly and annotation process. This research will contribute to further study on <i>Protobothrops</i> biology and the genetic basis of the snake venom.
DOI: 10.5524/102471
title: Supporting data for "MLcps: Machine Learning Cumulative Performance Score for Classification Problems"

description: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. <br>We propose Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several pre-computed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on four publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. <br>By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a python package at https://pypi.org/project/MLcps/ and examples of its use can be found at https://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main.
DOI: 10.5524/102472
title: Supporting data for "FAIR Data Retrieval for Sensitive Clinical Research Data in Galaxy"

description: In clinical research, data has to be accessible and reproducible, but the generated data is becoming larger and analysis complex. Here we propose a platform for FAIR data access and creating reproducible findings. Standardised access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalised omics platform for FAIR data analysis. <br>To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub and an associated tutorial was created in the Galaxy Training Network which helps researchers unfamiliar with Galaxy to run the workflow. <br>We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow byre-discovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy
DOI: 10.5524/102477
title: Supporting data for "MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation"

description: Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, and image generation, etc... Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than ten different biomedical problems, showcasing its general potential and applicability. <br>For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspiration on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im in our GitHub repository under an MIT license.
DOI: 10.5524/102478
title: Supporting data for "Computational reproducibility of Jupyter notebooks from biomedical publications"

description: Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. We address computational reproducibility at two levels: First, using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks related to publications indexed in PubMed Central. We identified such notebooks by mining the articles full text, locating them on GitHub and re-running them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. Second, this study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over two years. Out of 27271 notebooks from 2660 GitHub repositories associated with 3467 articles, 22578 notebooks were written in Python, including 15817 that had their dependencies declared in standard requirement files and that we attempted to re-run automatically. For 10388 of these, all declared dependencies could be installed successfully, and we re-ran them to assess reproducibility. Of these, 1203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. We zoom in on common problems, highlight trends and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.
DOI: 10.5524/102480
title: Supporting data for "Genome assembly and annotation of the king ratsnake, <i>Elaphe carinata</i>."

description: The king ratsnake (<i>Elaphe carinata</i>) of the genus <i>Elaphe</i> is a common large, non-venomous snake widely distributed in Southeast and East Asia. It is an economically important farmed species. As a non-venomous snake, the king ratsnake predates venomous snakes, such as cobras and pit vipers. However, the immune and digestive mechanisms of the king ratsnake remain unclear. Despite their economic and research importance, we lack genomic resources that would benefit toxicology, phylogeography, and immunogenetics studies.<br>Here, we present data from single-tube long fragment read (stLFR) sequencing used to generate the first contiguous genome of a king ratsnake from Huangshan City, Anhui province, China. The resulting genome size is 1.56 GB with a scaffold N50 of 6.53M, from which we predicted 22,339 protein-coding genes, including 22,065 with functional annotations that are available here. This genome data is potentially a useful addition to those available for snakes.
DOI: 10.5524/102487
title: Supporting data for "ToxCodAn-Genome: an automated pipeline for toxin-gene annotation in genome assembly of venomous lineages"

description: The rapid development of sequencing technologies resulted in a wide expansion of genomics studies using venomous lineages. This facilitated research focusing on understanding the evolution of adaptive traits and the search for novel compounds that can be applied in agriculture and medicine. However, the toxin annotation of genomes is a laborious and time-consuming task, and no consensus pipeline is currently available. No computational tool currently exists to address the challenges specific to toxin annotation and to ensure the reproducibility of the process. Here, we present ToxCodAn-Genome, the first software designed to perform automated toxin annotation in genomes of venomous lineages. This pipeline was designed to retrieve the full-length coding sequences of toxins and to allow the detection of novel truncated paralogs and pseudogenes. We tested ToxCodAn-Genome using 12 genomes of venomous lineages and achieved high performance on recovering their current toxin annotations. This tool can be easily customized to allow improvements in the final toxin annotation set and can be expanded to virtually any venomous lineage. ToxCodAn-Genome is fast, allowing it to run on any personal computer, but it can also be executed in multi-core mode, taking advantage of large high-performance servers. In addition, we provide a guide to direct future research in the venomics field to ensure a confident toxin annotation in the genome being studied. As a case study, we sequenced and annotated the toxin repertoire of <i>Bothrops alternatus</i>, which may facilitate future evolutionary and biomedical studies using vipers as models.ToxCodAn-Genome and the guide are freely available at https://github.com/pedronachtigall/ToxCodAn-Genome.
DOI: 10.5524/102488
title: Supporting data for "A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object"

description: Applying good data management and FAIR data principles (Findable, Accessible, Interoperable, and Reusable) in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. br /We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Next flow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. br /Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.
DOI: 10.6084/m9.figshare.22227565
title: Bothrops_alternatus_genome_assembly

description: Genome assembly of Bothrops alternatus using PacBio HiFi data. This data is linked to the BioSample SAMN16930333 in NCBI.
DOI: 10.6084/m9.figshare.22227580
title: Bothrops_alternatus_genome_annotation

description: Genome annotation of Bothrops alternatus using funannotate. This data is linked to the BioSample SAMN16930333 in NCBI.
DOI: 10.6084/m9.figshare.22227583
title: Bothrops_alternatus_toxin_annotation

description: Toxin annotation of Bothrops alternatus genome using ToxCodAn-Genome. This data is linked to the BioSample SAMN16930333 in NCBI.
DOI: 10.6084/m9.figshare.23631492
title: Bothrops_alternatus_BUSCO_tetrapoda_odb10

description: BUSCO output using the tetrapoda_odb10 dataset to analyze the genome assembly of the urutu lancehead (Bothrops alternatus). <br> This data is linked to the BioSample SAMN16930333 in NCBI. <br> <br>
DOI: 10.24432/c5ww2p
title: Glass Identification
DOI: 10.5281/zenodo.8073635
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. To address these challenges, we have developed a novel tool called <em>Machine Learning Made Easy </em>(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 fulfills 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+ naïve (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. 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: 10.5524/102441
title: Supporting data for "Deciphering the distinct transcriptomic and gene regulatory map in adult macaque basal ganglia cells"

description: The basal ganglia are a complex of interconnected subcortical structures located beneath the mammalian cerebral cortex. The degeneration of dopaminergic neurons in the basal ganglia is the primary pathological feature of Parkinson's disease. Due to a lack of integrated analysis of multi-omics datasets across multiple basal ganglia brain regions, very little is known about the regulatory mechanisms of this area. <br>We utilized high-throughput transcriptomic and epigenomic analysis to profile over 270,000 single-nucleus cells to create a cellular atlas of the basal ganglia, characterizing the cellular composition of four regions of basal ganglia in adult macaque brain, including the striatum (STR), substantia nigra (SN), globus pallidum (GP), and amygdala (AMY). We found a distinct epigenetic regulation on gene expression of neuronal and non-neuronal cells across regions in basal ganglia. We identified a cluster of SN-specific astrocyte associated with neurodegenerative diseases, and further explored the conserved and primate-specific transcriptomics in SN cell types across human, macaque and mouse. Finally, we integrated our epigenetic landscape of basal ganglia cells with human disease heritability, and identified a regulatory module consisting of candidate <em>cis</em>-regulatory elements that are specific to medium spiny neurons and associated with schizophrenia (SCZ). <br>In general, our macaque basal ganglia atlas provides valuable insights into the comprehensive transcriptome and epigenome of the most important and populous cell populations in the macaque basal ganglia. We have defined over 50 cell types with transcriptomic and epigenomic profiles, some of which exhibit region-specificity, and characterized the molecular relationships underlying these brain regions.
DOI: 10.5524/102442
title: Supporting data for "The genome assembly and annotation of the Oriental ratsnake Ptyas mucosa"

description: The Oriental ratsnake <i>Ptyas mucosa<em></em></i> is a common non-venomous snake of the colubrid family, with a wide geographic range spanning much of South and Southeast Asia. <i>P. mucosa</i> is widely cultivated due to its use in traditional medicine, scientific research, and handicrafts Therefore, genome resources could play an important role in the efficacy of traditional medicine and the analysis of the living environment of the species. We collected a snake sample in Hezhou, Guangxi, China, which was identified as <i>P. mucosa</i> by morphological identification. Here we present a highly continuous <i>P. mucosa</i> genome with a genome size of 1.74Gb. The scaffold N50 length is 9.57Mb and the maximal length of scaffold is 78.3Mb, the <i>P. mucosa</i> genome has a CG content of 37.9% and the integrity of the gene reached 86.6%. Assembled using long-reads, the total length of the repeat sequence in the genome reached 735 Mb, and its repeat content was as high as 42.19%. A total of 24,869 functional genes were annotated. This study will assist in the understanding of the <i>P. mucosa</i>, and also provide a basis for medicinal research.
DOI: 10.5524/102444
title: Supporting data for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"

description: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics <i>in vivo</i> conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. <br>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. <br>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.
DOI: 10.5524/102446
title: Supporting data for "A reference assembly for the legume cover crop, hairy vetch (<i>Vicia villosa</i>)"

description: <i>Vicia villosa</i> is an incompletely domesticated annual legume of the Fabaceae family native to Europe and Western Asia. <i>V. villosa</i> is widely used as a cover crop and as a forage due to its ability to withstand harsh winters. A reference-quality genome assembly (Vvill1.0) was prepared from low error rate long sequence reads to improve genetic-based trait selection of this species. The Vvill1.0 assembly includes seven scaffolds corresponding to the seven estimated linkage groups and comprising approximately 68% of the total genome size of 2.03 gigabase pairs (Gbp). This assembly is expected to be a useful resource for genetic improvement of this emerging cover crop species as well as to provide useful insights into plant genome evolution.