cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100798
title: Supporting data for "The Dynamic Proteome of Oligodendrocyte Lineage Differentiation Features Planar Cell Polarity and Macroautophagy Pathways"

description: Generation of oligodendrocytes is a sophisticated multi-step process, mechanistic underpinnings of which are not fully understood and demand further investigation. To systematically profile proteome dynamics during human embryonic stem cell differentiation into oligodendrocytes, we applied in-depth quantitative proteomics at different developmental stages and monitored changes in protein abundance using a multiplexed tandem mass tag-based proteomics approach. <br>Our proteome data provided a comprehensive protein expression profile that highlighted specific expression clusters based on the protein abundances over the course of human oligodendrocyte lineage differentiation. We identified the eminence of the planar cell polarity signaling and autophagy (particularly macroautophagy) in the progression of oligodendrocyte lineage differentiation; the cooperation of which is assisted by 106 and 77 proteins, respectively, that showed significant expression changes in this differentiation process. Further, differentially expressed protein analysis of the proteome profile of oligodendrocyte lineage cells revealed 378 proteins that were specifically up-regulated only in one differentiation stage. In addition, comparative pairwise analysis of differentiation stages demonstrated that abundances of 352 proteins differentially changed between consecutive differentiation time points. <br>Our study provides a comprehensive systematic proteomics profile of oligodendrocyte lineage cells that can serve as a rich resource for identifying novel biomarkers from these cells, and for indicating numerous proteins that may contribute to the regulation of the development of myelinating oligodendrocytes and other cells of oligodendrocyte lineage. We showed the importance of planar cell polarity signaling in oligodendrocyte lineage differentiation and revealed the autophagy-related proteins that participate in oligodendrocyte lineage differentiation.
DOI: 10.5281/zenodo.2587201
title: The reference index files used for RNA-seq workflow benchmark in CWL-metrics manuscript

description: The reference files used in the RNA-Seq workflow benchmark in the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).
DOI: 10.5524/100584
title: Supporting workflows for "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection"

description: Container virtualization technologies such as Docker are popular in the bioinformatics domain as they improve portability and reproducibility of software deployment. Along with software packaged in containers, the workflow description standards Common Workflow Language also enable to perform data analysis on multiple different computing environments with ease. These technologies accelerate the use of on-demand cloud computing platform which can scale out according to the amount of data. However, to optimize the time and the budget on a cloud usage, users need to select a suitable instance type corresponding to the resource requirements of their workflows. <br> We developed CWL-metrics, a utility tool for cwltool, the reference implementation of CWL, to collect runtime metrics of Docker containers and workflow metadata to analyze resource requirement of workflows. We demonstrate the analysis by using seven transcriptome quantification workflows on six instance types. The result showed instance type options of lower financial cost and faster execution time with required amount of computational resources.<br>The summary of resource requirements of workflow executions provided by CWL-metrics can help users to optimize the selection of cloud computing instances. The runtime metrics data also help users to share workflows among different workflow management frameworks. A Jupyter notebook file reproducing all the figures in the manuscript is available here
DOI: 10.5524/100585
title: Supporting data for "Gigwa v2 – Extended and improved genotype investigator"

description: The study of genetic variations is the basis of many research domains in biology. From genome structure to population dynamics, many applications involve the use of genetic variants. The advent of NGS technologies lead to such a flood of data that the daily work of scientists is often more focused on data management than data analysis. This mass of genotyping data poses several computational challenges in terms of storage, search, sharing, analysis and visualization. While existing tools try to solve these challenges, few of them offer a comprehensive and scalable solution.<br> Gigwa v2 is an easy to use, species-agnostic web application for managing and exploring high density genotyping data. It can handle multiple databases and may be installed on a local computer or deployed as an online data portal. It supports various standard import and export formats, provides advanced filtering options, and offers means to visualize density charts or push selected data into various standalone or online tools. It implements two standard RESTful APIs, GA4GH, which is healthoriented, and BrAPI, which is breeding-oriented, thus offering wide possibilities of interaction with third party applications. The project home page
http://www.southgreen.fr/content/gigwa provides a list of live instances allowing to test the system on public data (or reasonably-sized user-provided data).<br> This new version of Gigwa provides a more intuitive and more powerful way to explore large amounts of genotyping data by offering a scalable solution to search for genotype patterns, functional annotations, or more complex filtering. Furthermore, its userfriendliness and interoperability make it widely accessible to the life science community.
DOI: 10.5524/100592
title: Supporting data for "High-Resolution Computational Modeling of Immune Responses in the Gut"

description: Helicobacter pylori causes gastric cancer in 1-2% of cases, but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori-colonized individuals do not present any detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing driven ENteric Immunity Simulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary and partial differential equations. <br>Results: The outputs were analyzed using two sequential stages: the first used a partial rank correlation coefficient regression-based and the second employed a metamodel based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In-silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. <br>Conclusion: The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
DOI: 10.5524/100596
title: Supporting data for "A haplotype-resolved draft genome of the European sardine (Sardina pilchardus)"

description: The European sardine (Sardina pilchardus Walbaum, 1792) has a high cultural and economic importance throughout its distribution. Monitoring studies of sardine populations report an alarming decrease in stocks due to overfishing and environmental change, which has resulted in historically low captures along the Iberian Atlantic coast. Consequently, there is an urgent need to better understand the causal factors of this continuing decrease in the sardine stock. Important biological and ecological features such as levels of population diversity, structure, and migratory patterns can be addressed with the development and use of genomics resources. The sardine genome of a single female individual was sequenced using Illumina HiSeq X Ten 10X Genomics linked-reads generating 113.8 Gb of data. Three draft genomes were assembled: two haploid genomes with a total size of 935 Mbp (N50 103Kb) each, and a consensus genome with a total size of 950 Mbp (N50 97Kb). The genome completeness assessment captured 84% of Actinopterygii Benchmarking Universal Single-Copy Orthologs. To obtain a more complete analysis, the transcriptomes of eleven tissues were sequenced and used to aid the functional annotation of the genome, resulting in 40 777 genes predicted. Variant calling on nearly half of the haplotype genome resulted in the identification of more than 2.3 million phased SNPs with heterozygous loci. A draft genome was obtained with the 10X Genomics linked-reads technology, despite a high level of sequence repeats and heterozygosity that are expected genome characteristics of a wild sardine. The reference sardine genome and respective variant data are a cornerstone resource of ongoing population genomics studies to be integrated into future sardine stock assessment modelling to better manage this valuable resource.
DOI: 10.5524/100598
title: Supporting data for "Screening methods for detection of ancient Mycobacterium tuberculosis complex fingerprints in NGS data derived from skeletal samples"

description: Recent advances in ancient DNA (aDNA) studies, especially in increasing isolated DNA yields and quality, opened the possibility of analysis of ancient host microbiome. However, this analysis could lead to numerous pitfalls, including spurious identification of pathogens based on fragmentary data or environmental contamination, leading to incorrect epidemiological conclusions. Within the Mycobacterium genus, MTBC (Mycobacterium tuberculosis complex) members responsible for tuberculosis share up to ~99% genomic sequence identity, while other more distantly related MOTT (Mycobacteria other than tuberculosis) can be causative agents for pulmonary diseases or soil dwellers. Therefore, reliable determination of species complex is highly relevant for interpretation of sequencing results. Here we present a novel bioinformatical approach, used for screening of ancient tuberculosis in sequencing data, derived from 28 individuals (dated 4400 - 4000 BC and 3100 - 2900 BC) from Central Poland. We demonstrate that cost effective next generation screening sequencing data (c.a 20M reads per sample) could yield enough information to provide statistically supported identification of probable ancient disease cases. Application of appropriate bioinformatic tools, including an unbiased selection of genomic alignment targets for species specificity, makes it possible to extract valid data from full-sample sequencing results (without subjective targeted enrichment procedures). This approach broadens the potential scope of paleoepidemiology both to older, suboptimally preserved samples and to pathogens with difficult intrageneric taxonomy.
DOI: 10.5524/100603
title: Supporting data for "A chromosome-scale genome assembly of cucumber (<em>Cucumis sativus</em> L.)"

description: An accurate and complete reference genome assembly is fundamental for biological research. Cucumber is an important vegetable crop and model system for sex determination and vascular biology, and its draft genomes have been assembled using low coverage of Sanger sequences and high coverage of short Illumina sequences, but the incompleteness and low quality of these genomes limit their usage in comparative genomics and genetic research. Therefore, a high-quality and complete cucumber genome assembly is of great necessity. We assembled single-molecule real-time (SMRT) long reads into a significantly improved cucumber reference genome containing 174 contigs with a total length of 226.2 Mb and an N50 of 8.9 Mb, providing an extra of 29.0 Mb sequences. Using 10X genomics, and high-throughput chromosome conformation capture (Hi-C) data, 89 contigs (~211.0 Mb) were directly linked into the seven pseudo-chromosome sequences. The newly assembled regions abundantly show higher GC or AT content, likely inaccessible to Illumina sequencing. The new assembly contains 1,374 full-length long terminal retrotransposons (LTRs) and 1,078 novel genes including 239 tandemly duplicated genes. For example, four tandemly duplicated tyrosylprotein sulfotransferases (TPSTs), which are a single copy gene in the previous assembly and most other plants, were assembled and predicted. <br>This high-quality genome represents novel features of the cucumber genome, and will serve as a valuable resource for genetic research in cucumber as well as plant comparative genomics.
DOI: 10.5524/100604
title: Supporting data for "Boxed, stalked and upside-down? Draft genomes from diverse jellyfish (Cnidaria, Acraspeda) lineages: Alatina alata (Cubozoa), Calvadosia cruxmelitensis (Staurozoa), and Cassiopea xamachana (Scyphozoa)"

description: Anthozoa, Endocnidozoa, and Medusozoa comprise the three major clades of Cnidaria. Medusozoa is further divided into four clades, Hydrozoa, Staurozoa, Cubozoa, and Scyphozoathe latter three lineages make up the clade Acraspeda. Acraspeda encompasses extraordinary diversity in terms of life history, numerous nuisance species, taxa with complex eyes rivaling other animals, and some of the most venomous organisms on the planet. Genomes have recently become available within Scyphozoa, but there are currently no published genomes within Staurozoa and Cubozoa. Here we present three new draft genomes of <i>Calvadosia cruxmelitensis</i> (Staurozoa), <i>Alatina alata</i> (Cubozoa), and <i>Cassiopea xamachana</i> (Scyphozoa) for which we provide a preliminary orthology analysis that includes an inventory of their respective venom-related genes. Additionally, we identify synteny between POU and Hox genes that had previously been reported in a hydrozoan, suggesting this linkage is highly conserved, possibly dating back to at least the last common ancestor of Medusozoa, yet likely independent of vertebrate POU-Hox linkages. These draft genomes provide a valuable resource for studying the evolutionary history and biology of these extraordinary animals, and for identifying genomic features underlying venom, vision, and life history traits in Acraspeda.
DOI: 10.5524/100609
title: Supporting data for "A collection of yeast cellular electron cryotomography data"

description: Cells are powered by a large set of macromolecular complexes, which work together in a crowded environment. The <em>in situ</em> mechanisms of these complexes are unclear because their 3-D distribution, organization, and interactions are largely unknown. Electron cryotomography (cryo-ET) is a key tool to address these knowledge gaps because it produces cryotomograms 3-D images that reveal biological structure at approximately 4-nm resolution. Cryo-ET does not involve any fixation, dehydration, staining, or plastic embedment, meaning that cellular features are visualized in a life-like, frozen-hydrated state. To study chromatin and mitotic machinery <em>in situ</em>, we have subjected yeast cells to a variety of genetic and/or chemical perturbations, cryosectioned them, and then imaged the cells by cryo-ET. Every study from our group has generated more cryo-ET data than needed. Only the small subset of data that contributed to figures in these studies have been publicly shared. Here we share more than 1,000 cryo-ET raw datasets of cryosectioned budding yeast <em>S. cerevisiae</em>. This data will be valuable to cell biologists who are interested in the nanoscale organization of yeasts and of eukaryotic cells in general. To facilitate access, all the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the cell-biology community to use freely. To improve tilt series discoverability, we have uploaded metadata and preliminary notes to publicly accessible Google Sheets, EMPIAR and GigaDB. Cellular cryo-ET data can be mined to obtain new cell-biological, structural, and 3-D statistical insights <em>in situ</em>. Because these data capture cells in a life-like state, they contain some structures that are either absent or not visible in traditional EM data. Template matching and subtomogram averaging of known macromolecular complexes can reveal their 3-D distributions and low-resolution structures. Furthermore, these data can serve as testbeds for high-throughput image-analysis pipelines, as training sets for feature-recognition software, for feasibility analysis when planning new structural cell-biology projects, and as practice data for students who are learning cellular cryo-ET.
DOI: 10.5524/100614
title: Supporting data for "A Chromosomal-Level Genome Assembly for the insect vector for Chagas disease, Triatoma rubrofasciata"

description: <i>Triatoma rubrofasciata</i> is a widespread pathogen vector for Chagas disease, an illness that affects approximately seven million people worldwide. Despite of its importance to human health, its evolutionary origin has not been conclusively determined. A reference genome for <i>T. rubrofasciata</i> is not yet available. <br> We have sequenced the genome of a female <i>T. rubrofasciata</i> individual using a single molecular DNA sequencing technology (i.e., PacBio Sequel platform) and have successfully reconstructed a whole-genome (680 Mb) assembly that covers 90% of the nuclear genome (757 Mb). Through Hi-C analysis, we have reconstructed full-length chromosomes of this female individual that has 13 unique chromosomes (2n = 24 = 22 + X1 + X2) with a contig N50 of 2.72Mb and a scaffold N50 of 50.7 Mb. This genome has achieved a high base-level accuracy of 99.99%. This platinum-grade genome assembly has 12,691 annotated protein-coding genes. More than 95.1% BUSCO genes were single-copy completed, indicating a high level of completeness of the genome.<br>The platinum-grade genome assembly and its annotation provide valuable information for future in-depth comparative genomics studies including sexual determination analysis in <i>T. rubrofasciata</i> and the pathogenesis of Chagas disease.
DOI: 10.5524/100818
title: Supporting data for "Identification of a differentiation stall in epithelial mesenchymal transition in histone H3 mutant diffuse midline glioma"

description: Diffuse midline gliomas with Histone H3 K27M (H3K27M) mutations occur in early childhood and are marked by an invasive phenotype and global decrease in H3K27me3, an epigenetic mark which regulates differentiation and development. H3K27M mutation timing and effect on early embryonic brain development are not fully characterized. We analyzed multiple publicly available RNA sequencing datasets to identify differentially expressed genes between H3K27M and nonK27M pediatric gliomas.<br>We found that genes involved in the epithelial-mesenchymal transition (EMT) were significantly overrepresented among differentially expressed genes. Overall, the expression of pre-EMT genes was increased in the H3K27M tumors as compared to nonK27M tumors, while the expression of post-EMT genes was decreased. We hypothesized that H3K27M may contribute to gliomagenesis by stalling an EMT required for early brain development, and evaluated this hypothesis by employing another publicly available dataset of single-cell and bulk RNA sequencing data from developing cerebral organoids. This analysis revealed similarities between H3K27M tumors and pre-EMT normal brain cells. Finally, a previously published single-cell RNA sequencing dataset of H3K27M and nonK27M gliomas revealed subgroups of cells at different stages of EMT. In particular, H3.1K27M tumors resemble a later EMT stage compared to H3.3K27M tumors. <br>Our data analyses indicate that this mutation may be associated with a differentiation stall evident from failure to proceed through the EMT-like developmental processes and that H3K27M cells preferentially exist in a pre-EMT cell phenotype. This study demonstrates how novel biological insights could be derived from combined analysis of several previously published datasets, highlighting the importance of making genomic data available to the community in a timely manner.
DOI: 10.5524/100566
title: Supporting data for " High throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat"

description: Precise measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this breeder-trained network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and very high accuracy against the ground-truth values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping as it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticist, pathologists and physiologists to train the networks.