cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100909
title: Supporting data for "M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data"

description: Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Due to increasing capabilities of modern MSI devices, the use of three-dimensional (3D) and multi-modal MSI becomes feasible in routine applications - resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions.<br>We introduce M²aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M²aia extends the Medical Imaging and Interaction Toolkit (MITK), a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M²aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction and multimodal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M²aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available.<br>To our knowledge, M²aia is the first extensible opensource application that enables a fast, user-friendly and interactive exploration of large datasets. M²aia is applicable to a wide range of MSI analysis tasks.
DOI: 10.5524/100910
title: Updated data for "A Phylogenomic View of Evolutionary Complexity in Green Plants"

description: The 1000 Plants (1KP) initiative explored the genetic diversity of green plants (Viridiplantae) by sequencing RNA from 1,342 samples representing 1,173 species. All of the analyses done for the 1KP capstone, and previous studies on subsets of these data, are based on a series of de novo transcriptome assemblies and related outputs that will be described in this publication. We also describe assessments of the data quality and an analysis to remove cross-contamination between the samples. These data will be useful to researchers with interests in specific gene families, either across the green plant tree of life or in more focused lineages.<br> Some samples were not analyzed for the 1KP analysis publication because they were identified as mislabeled and/or contaminated. We released the data nonetheless as some find even these data useful. More information is given at in the original GigaScience datanote, including estimates of which particular sequences might be problematic. <b>In this updated dataset we have removed the samples and associated files for the 30 specimens where we found significant evidence of mislabeling and/or contamination.</b> If you require the data from those samples please visit the original dataset 100627.
DOI: 10.5524/100914
title: Supporting data for "Scalable Analysis of Multi-Modal Biomedical Data"

description: Targeted diagnosis and treatment options are dependent on insights drawn from multimodal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look at the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex data types. To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on "flattening'' complex data structures, and runs efficiently when alternative approaches are unable to perform at all.
DOI: 10.5524/100916
title: Supporting data for "RNAProt: An efficient and feature-rich RNA binding protein binding site predictor"

description: CLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features.<br>Here we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks (RNNs). We compare RNAProt with one traditional machine learning approach and two deep learning methods, demonstrating its state-of-the-art predictive performance, while at the same time offering better runtime efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined ones which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of a hairpin loop binding RBP.<br>RNAProt provides a complete framework for RBP binding site predictions, from dataset generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance as well as superior runtime efficiency, while at the same supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.
DOI: 10.5524/100919
title: Supporting data for "Preventing dataset shift from breaking machine-learning biomarkers"

description: Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g. because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts breaks machine-learning extracted biomarkers, as well as detection and correction strategies.
DOI: 10.5524/100920
title: Supporting data for "Bias invariant RNA-seq metadata annotation"

description: Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs. <br>Here, we investigate RNA-seq metadata prediction based on gene expression values. We present a deep-learning based domain adaptation algorithm for the automatic annotation of RNA-seq metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared to existing linear regression-based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm is able to learn biases from datasets with few samples. <br>Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of more than 10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable. The source code as well as an example are available at: https://github.com/imsb-uke/rna_augment
DOI: 10.5524/100923
title: Supporting data for "Population modeling with machine learning can enhance measures of mental health"

description: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. Instead, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgment and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? <br>Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. <br>Population modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations.
DOI: 10.6084/m9.figshare.13363502
title: Scalable Analysis of Multi-Modal Biomedical Data

description: Raw runtimes for the program performance for burden based analyses, driver gene analysis, and clinical exploratory programs. Also includes runtimes for skew and scalability experiments.
DOI: 10.17632/khnhxk38jt.1
title: Crassostrea gigas mitochondrion, complete genome in FASTA format

description: Complete mitochondrial genome sequence associated with a chromosome-level genome assembly of the Pacific oyster (Crassostrea gigas) (RefSeq assembly accession: GCF_902806645.1). The genome was obtained from a single female sampled from a hatchery in Guernsey (UK).
DOI: 10.5524/100875
title: Supporting data for "A chromosome-level genome assembly for the Pacific oyster (<i>Crassostrea gigas</i>)"

description: The Pacific oyster (<i>Crassostrea gigas</i>) is a bivalve mollusc species with vital roles in coastal ecosystems and aquaculture globally. While extensive genomic tools are available for <i>C. gigas</i>, highly contiguous reference genomes are required to support both fundamental and applied research. Herein we report the creation and annotation of a chromosome-level assembly for the Pacific oyster.<br>In the current study, high coverage long and short read sequence data generated on Pacific Biosciences and Illumina platforms from a single female individual specimen was used to generate an initial assembly, which was then scaffolded into 10 pseudo chromosomes using both Hi-C sequencing and a high density SNP linkage map. The final assembly has a scaffold N50 of 58.4 Mb and a contig N50 of 1.8 Mb, representing a step advance on the previously published <i>C. gigas</i> assembly. The new assembly was annotated using Pacific Biosciences Iso-Seq and Illumina RNA-Seq data, identifying ~30K putative protein coding genes, with an average of 3.1 transcripts per gene. Annotation of putative repeat elements highlighted an inverse relationship with gene density and identified putative centromeres of the metacentric chromosomes. An enrichment of Helitron rolling circle transposable elements was observed, suggesting their potential role in shaping the evolution of the <i>C. gigas</i> genome. This new chromosome-level assembly will be an enabling resource for genetics and genomics studies to support fundamental insight into bivalve biology, as well as for genetic improvement of <i>C. gigas</i> in aquaculture breeding programmes.
DOI: 10.5524/100877
title: Supporting data for "A chromosome-level reference genome of the hazelnut, <i>Corylus heterophylla</i> Fisch."

description: <i>Corylus heterophylla</i> Fisch. is a species of the Betulaceae family native to China. As an economically and ecologically important nut tree, <i>C. heterophylla</i> can survive in extremely low temperatures (-30 to -40 C). To deepen our knowledge of the Betulaceae species and facilitate the use of <i>C. heterophylla</i> for breeding and its genetic improvement, we have sequenced the whole genome of <i>C. heterophylla</i>.<br>Based on over 64.99 Gb (~175.31 x) of Nanopore long reads, we assembled a 370.75 Mb <i>C. heterophylla</i> genome with contig N50 and scaffold N50 sizes of 2.01 Mb and 31.33Mb, respectively, accounting for 99.23% of the estimated genome size. Furthermore, 361.9 Mb contigs were anchored to 11 chromosomes using Hi-C links data, representing 97.61% of the assembled genome sequences. Transcriptomes representing four different tissues were sequenced to assist protein-coding gene prediction. A total of 27,591 protein-coding genes were identified, of which 92.02% (25,389) were functionally annotated. The phylogenetic analysis showed that <i>C. heterophylla</i> is close to <i>Ostrya japonica</i>, and they diverged from their common ancestor approximately 52.79 million years ago. <br>We generated a high-quality chromosome-level genome of <i>C. heterophylla</i>. This genome resource will promote research on the molecular mechanisms of how the hazelnut responds to environmental stresses and serves as an important resource for genome-assisted improvement in cold and drought resistance of the <i>Corylus</i> genus.
DOI: 10.5524/100880
title: Supporting data for "An Analysis of Security Vulnerabilities in Container Images for Scientific Data Analysis"

description: Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images and widens the attack surface in the container host, and creates substantial security risks for computing infrastructures at large. This paper presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with four vulnerability scanners, focusing on the use case of neuroscience data analysis, and quantifying the effect of image update and minification on the number of vulnerabilities. We find that container images used for neuroscience data analysis contain hundreds of vulnerabilities, that software updates remove about two thirds of these vulnerabilities, and that removing unused packages is also effective. We conclude with recommendations on how to build container images with a reduced amount of vulnerabilities.
DOI: 10.5524/100884
title: Supporting data for "Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots"

description: Three-dimensional (3D) point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the 3D plant reconstruction reliability. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking.<br>In this paper, a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots is proposed. On this basis, a point cloud annotation toolkit, Label3DMaize, for maize shoot is developed. Further, the toolkit was applied to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes about 4 to 10 minutes to segment a maize shoot, and consumes 10%-20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% of the fine segmentation.<br>Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation researches based on deep learning and is expected to promote automatic point cloud processing of various plants.
DOI: 10.5524/100886
title: Genomic data for the North American water vole (Microtus richardsoni)

description: Voles of the genus <i>Microtus</i> are important research organisms, yet genomic resources in the genus are lacking. Providing such resources would benefit future studies of immunology, phylogeography, cryptic diversity, and more. We sequenced and assembled nuclear genomes from two subspecies of water vole (<i>Microtus richardsoni</i>) and from the montane vole (<i>Microtus montanus</i>). <br>The North American water vole (<i>M. richardsoni</i>) is adapted to a semi-aquatic lifestyle, relying on alpine and sub-alpine streams for creating burrows and escaping predators. The present study provides two nuclear and one mitochondrial genome assembly for <i>M. richardsoni</i>. The nuclear genomes were sequenced with Illumina and 10X Chromium plus Illumina sequencing. Sequencing of <i>M. r. arvicoloides</i> produced over 800 million reads and 47x coverage. The final genome assembly consisted of ~32,000 scaffolds with an N50 of 2.3 Mb, 1.3% missing data (N's), and a BUSCO score of 85.8%. Sequencing of <i>M. r. macropus</i> produced over 600 million reads and 35x coverage. The final genome assembly consisted of ~1,600,000 scaffolds with an N50 of 16Kb, 0.06% missing data, and a BUSCO score of 54.5%. In addition to the nuclear assemblies, a mitochondrial genome assembly was also performed for <i>M. r. arvicoloides</i>.
DOI: 10.5524/100889
title: Supporting data for "A streamlined workflow for conversion, peer review and publication of genomics metadata as Omics Data Papers"

description: Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peerreview and increases the impact of datasets by enhancing their visibility, accessibility, and re-usability.<br> We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivise researchers to publish well-described data sets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript.<br> An omics data paper template was designed by defining key article sections which encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress and BioSamples databases.<br> The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing.<br> The omics data paper structure and workflow for import of genomics metadata help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata re-use for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
DOI: 10.5524/100891
title: Supporting data for "MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations"

description: Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, which requires complicated integration of a plethora of computational and statistical approaches. Here, we present MesKit, an R/Bioconductor package, that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for two reported MRS datasets of hepatocellular carcinoma (HCC) and colorectal cancer (CRC) identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution.