cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57907/mirri/rmscfu
title: MIRRI0076035

description: Organism type: Filamentous Fungi ; Taxon name: Trichophyton violaceum ; Collection: CBS 120320 ; Country: Switzerland
DOI: 10.57907/mirri/skaew2
title: MIRRI0076207

description: Organism type: Filamentous Fungi ; Taxon name: Hypocrea aeruginea ; Collection: CBS 120541 ; Country: Germany
DOI: 10.57907/mirri/tsi9mh
title: MIRRI0076205

description: Organism type: Filamentous Fungi ; Taxon name: Hypocrea moravica ; Collection: CBS 120539 ; Country: Austria
DOI: 10.57907/mirri/ttnfnf
title: MIRRI0076331

description: Organism type: Filamentous Fungi ; Taxon name: Bisifusarium delphinoides ; Collection: CBS 120718 ; Country: South Africa
DOI: 10.57907/mirri/u25ocp
title: MIRRI0076274

description: Organism type: Filamentous Fungi ; Taxon name: Penicillium coffeae ; Collection: CBS 120615 ; Country: Papua New Guinea
DOI: 10.57907/mirri/ulker9/zcdrk5
title: MIRRI0075512.json

description: Strain json file
DOI: 10.57907/mirri/y5knas/1n41fu
title: MIRRI0076183.json

description: Strain json file
DOI: 10.57907/mirri/zzyxej/snsmuh
title: MIRRI0076068.json

description: Strain json file
DOI: 10.3535/gge-164-lsn
title: Chamaecytisus hirsutus (L.) Link

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/yv8-w5y-knj
title: Astrantia minor L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.57907/mirri/0kufnk
title: MIRRI0076075

description: Organism type: Filamentous Fungi ; Taxon name: Ilyonectria destructans ; Collection: CBS 120369 ; Country: Canada
DOI: 10.57907/mirri/3r16px/qcjky0
title: MIRRI0075563.json

description: Strain json file
DOI: 10.57907/mirri/4iubjt/ri77sw
title: MIRRI0076330.json

description: Strain json file
DOI: 10.57907/mirri/a2doiy
title: MIRRI0076130

description: Organism type: Filamentous Fungi ; Taxon name: Exophiala phaeomuriformis ; Collection: CBS 120445 ; Country: United States of America
DOI: 10.57907/mirri/bnvgwt
title: MIRRI0076112

description: Organism type: Filamentous Fungi ; Taxon name: Phyllosticta citriasiana ; Collection: CBS 120427 ; Country: China
DOI: 10.57907/mirri/dakwv2/bzokq5
title: MIRRI0076333.json

description: Strain json file
DOI: 10.5524/100756
title: Supporting data for "Galactic Circos: User friendly Circos Plots within the Galaxy platform"

description: Circos is a popular software package for the circular visualization of complex datasets. Circos is a highly flexible tool. While especially popular in the field of genomic analysis, Circos enables interactive graphing of any analytical data, including alternative scientific domain data and non-scientific data. This high degree of flexibility also comes with a high degree of complexity, which may present an obstacle for researchers not trained in programming or the UNIX command line. The Galaxy platform provides a user-friendly graphical interface incorporating a broad range of "wrapped" command-line tools to facilitate accessibility. We have developed a Galaxy wrapper for Circos, thus combining the power of Circos with the accessibility and ease-of-use of the Galaxy platform. The combination significantly simplifies the specification and configuration of Circos plots for end-users while retaining the power to produce publication-quality visualizations of complex multidimensional datasets. Galactic Circos enables the creation of publication-ready Circos plots within the Galaxy platform. Users may download the full set of Circos configuration files of their plots for further manual development. This version of Circos is available as an open-source installable application from the Galaxy ToolShed, with its usage clarified in a training manual hosted by the Galaxy Training Network.
DOI: 10.5524/100758
title: Supporting data for "Watchdog 2.0: New developments for reusability, reproducibility and workflow execution"

description: Advances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows. Here, we present Watchdog 2.0, which implements new developments for module creation, reusability and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may e.g. serve as a draft of a manuscript methods section. Finally, two new execution modes were implemented. One allows resuming workflow execution after interruption or modification without re-running successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters. Watchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and a comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog.
DOI: 10.5524/100763
title: Supporting data for "A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma"

description: Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas (TCGA) to generate sample-specific models of cancer.<br>By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. <br>The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
DOI: 10.5524/100764
title: Supporting data for "Gene-Set Enrichment with Mathematical Biology (GEMB)"

description: Gene-set analyses measure the association between a disease of interest and a set of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further, defining gene contributions based on biophysical properties, by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.<br> We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate by simulation, how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular Ca2+ concentration, that is significantly related to bipolar disorder in a small dataset (P=0.04; n=544). We reproduce this finding using publicly-available summary data from the Psychiatric Genetics Consortium (P=1.7×104; n=41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P=0.08). The weighted gene-set approach based on intracellular Ca2+ concentration did not detect a significant relationship with schizophrenia (P=0.09; n=65,967) or major depression disorder (P=0.30; n=500,199).<br>Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.
DOI: 10.5281/zenodo.3473960
title: ResistomeDB

description: <strong>The rise of antibiotic resistance (AR) in clinical settings is one of the biggest modern global public health concerns, therefore, the understanding of its mechanisms, evolution and global distribution is a priority due to its impact on the treatment course and patient survivability. Besides all efforts in the elucidation of AR mechanisms in clinical strains, little is known about its prevalence and evolution in environmental uncultivable microorganisms. In this study, 293 metagenomic and 10 metatranscriptomic samples from the TARA oceans project were used to detect and quantify environmental Antibiotic Resistance Genes (ARGs) using modern machine learning tools. We show here their global distribution, abundance, taxonomy and phylogeny, their potential to be horizontally transferred by plasmids or viruses and their correlation with environmental and geographical parameters. The abundance of ARGs in 293 samples showed different patterns of distribution, being some classes significantly more abundant in Coastal Biomes. ARGs conferring resistance to some of the most relevant clinical antibiotics were also identified, revealing the presence of 15 ARGs from the recently discovered MCR-1 family with high abundance on Polar Biomes. A total of 5 MCR-1 ORFs are present in the genus Psychrobacter, an opportunistic bacteria that can cause fatal infections in humans.</strong> <strong>The present dataset is a full MySQL dump of the processed data showing main discoveries described above.</strong>
description: This dataset is the second version of the data descibed in the paper "Global oceanic resistome revealed: exploring Antibiotic Resistance Genes (ARGs) abundance and distribution on TARA ocean samples through machine learning tools" submitted to the biorXiv the 11th of September 2019. This dataset is not definitive and therefore needs to be used with caution, as it does not contain the manual curation that the next versions of it do.
DOI: 10.5524/100719
title: Supporting data for "Binning unassembled short reads based on k-mer covariance using sparse coding"

description: Sequence binning techniques enable the recovery of a growing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e.g. biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets.<br>We present here a scalable pre-assembly binning scheme (i.e. operating on unassembled short reads) enabling latent genomes recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines-Deep population cohort (n=1135, &gt; 10^10 reads).<br>We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale, and that despite lower genome reconstruction yields compared to assembly-based approaches, bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles. Read enrichment levels across six orders of magnitude in relative abundance were observed, indicating that the method is able to recover genomes consistently segregating at low levels.
DOI: 10.5524/100720
title: Chromosome-level reference genome of the jellyfish <i>Rhopilema esculentum</i>

description: Jellyfish belong to the phylum Cnidaria, which occupies an important phylogenetic location in the early-branching Metazoa lineages. The jellyfish <i>Rhopilema esculentum</i> is an important fishery resource in China. However, the genome resource of <i>R. esculentum</i> has not been reported to date.<br>In this study, we constructed a chromosome-level genome assembly of <i>R. esculentum</i> using Pacific Biosciences, Illumina and Hi-C sequencing technologies. The final genome assembly was approximately 275.42 Mb, with a contig N50 length of 1.13 Mb. Using Hi-C technology to identify the contacts among contigs, 260.17 Mb (94.46%) of the assembled genome were anchored onto 21 pseudochromosomes with a scaffold N50 of 12.97 Mb. We identified 17,219 protein-coding genes, with an average CDS length of 1,575 bp. The genome-wide phylogenetic analysis indicated that <i>R. esculentum</i> might have evolved slower than the other scyphozoan species used in this study. In addition, 127 toxin-like genes were identified, and one toxin-related hub was found by a genomic survey.<br>We have generated a chromosome-level genome assembly of<i>R. esculentum</i> that could provide a valuable genomic background for studying the biology and pharmacology of jellyfish, as well as the evolutionary history of Cnidaria.
DOI: 10.5524/100721
title: Supporting data for "Chromosome-level de novo assembly of the pig-tailed macaque genome using linked-read sequencing and HiC proximity scaffolding"

description: Macaque species share over 93% genome homology with humans and develop many disease phenotypes similar to those of humans, making them valuable animal models for the study of human diseases (e.g.,HIV and neurodegenerative diseases). However, the quality of genome assembly and annotation for old world monkeys including macaque species lags behind the human genome effort. To close this gap and enhance functional genomics approaches, we employed a combination of de novo linked-read assembly and scaffolding using proximity ligation assay (HiC) to assemble the pig-tailed macaque (Macaca nemestrina) genome. This combinatorial method yielded large scaffolds at chromosome-level with a scaffold N50 of 127.5 Mb; the 23 largest scaffolds covered 90% of the entire genome. This assembly revealed large-scale rearrangements between pig-tailed macaque chromosomes 7, 12, and 13 and human chromosomes 2, 14, and 15. We subsequently annotated the genome using transcriptome and proteomics data from personalized induced pluripotent stem cells (iPSCs) derived from the same animal. Reconstruction of the evolutionary tree using whole genome annotation and orthologous comparisons among three macaque species, human and mouse genomes revealed extensive homology between human and pig-tailed macaques with regards to both pluripotent stem cell genes and innate immune gene pathways. Our results confirm that rhesus and cynomolgus macaques exhibit a closer evolutionary distance to each other than either species exhibits to humans or pig-tailed macaques. These findings demonstrate that pig-tailed macaques can serve as an excellent animal model for the study of many human diseases particularly with regards to pluripotency and innate immune pathways.
DOI: 10.5524/100726
title: Supporting data for "The Gene-Rich Genome of the Scallop Pecten maximus"

description: The King Scallop, <i>Pecten maximus</i>, is distributed in shallow waters along the Atlantic coast of Europe. It forms the basis of a valuable commercial fishery and its ubiquity means that it plays a key role in coastal ecosystems and food webs. Like other filter feeding bivalves it can accumulate potent phytotoxins, to which it has evolved some immunity. The molecular origins of this immunity are of interest to evolutionary biologists, pharmaceutical companies and fisheries management. <br>Here we report the genome sequencing of this species, conducted as part of the Wellcome Sanger 25 Genomes Project. This genome was assembled from PacBio reads and scaffolded with 10x Chromium and Hi-C data, and its 3,983 scaffolds have an N50 of 44.8 Mb (longest scaffold 60.1 Mb), with 92% of the assembly sequence contained in 19 scaffolds, corresponding to the 19 chromosomes found in this species. The total assembly spans 918.3 Mb, and is the best-scaffolded marine bivalve genome published to date, exhibiting 95.5% recovery of the metazoan BUSCO set. Gene annotation resulted in 67,741 gene models. Analysis of gene content revealed large numbers of gene duplicates, as previously seen in bivalves, with little gene loss, in comparison with the sequenced genomes of other marine bivalve species. <br>The genome assembly of <i>Pecten maximus</i> and its annotated gene set provide a high-quality platform for a wide range of investigations, including studies on such disparate topics as shell biomineralization, pigmentation, vision and resistance to algal toxins. As a result of our findings we highlight the sodium channel gene Nav1, known as a gene conferring resistance to saxitoxin and tetrodotoxin, as a candidate for further studies investigating immunity to domoic acid.
DOI: 10.5524/100727
title: Supporting data for "Species-Level Evaluation of the Human Respiratory Microbiome"

description: Changes to human respiratory tract microbiome may contribute significantly to the progression of respiratory diseases. However, there are few studies examining the relative abundance of microbial communities at the species level along the human respiratory tract. Bronchoalveolar lavage (BAL), throat swab, mouth rinse, and nasal swab samples were collected from 5 subjects. Bacterial ribosomal operons were sequenced using the Oxford Nanopore MinION to determine the relative abundance of bacterial species in 4 compartments along the respiratory tract. Over 1.8 million raw operon reads were obtained from the subjects with ~600K rRNA reads passing QA/QC (70-95% identify; &gt;1200 bp alignment) by Discontiguous MegaBlast against the EZ BioCloud 16S rRNA gene database. Nearly 3600 bacterial species were detected overall (&gt; 750 bacterial species within the 5 dominant phyla: Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Fusobacteria). The relative abundance of bacterial species along the respiratory tract indicated most microbes (95%) were being passively transported from outside into the lung. However, a small percentage (&lt;5%) of bacterial species were at higher abundance within the lavage samples. The most abundant lung-enriched bacterial species were <em>Veillonella dispar</em> and <em>Veillonella atypica</em> while the most abundant mouth-associated bacterial species were <em>Streptococcus infantis</em> and <em>Streptococcus mitis</em>. Most bacteria detected in lower respiratory samples do not seem to colonize the lung. However, over 100 bacterial species were found to be enriched in bronchial lavage samples (compared to mouth/nose) and may play a significant role in lung health.
DOI: 10.5524/100730
title: Supporting data for "Antibiotic resistomes discovered in the gut microbiomes of Korean swine and cattle"

description: Antibiotics administered to farm animals have led to increasing prevalence of resistance genes in different microbiomes and environments. While antibiotic treatments help cure infectious diseases in farm animals, the possibility of spreading antibiotic resistance genes into the environment and human microbiomes raises significant concerns. Through long-term evolution, antibiotic resistance genes have mutated, thereby complicating the resistance problems. In this study, we performed deep sequencing of the gut microbiomes of 36 swine and 41 cattle in Korean farms, and metagenomic analysis to understand the diversity and prevalence of antibiotic resistance genes. We found that aminoglycoside, beta-lactam, lincosamide, streptogramin, and tetracycline were the prevalent resistance determinants in both swine and cattle. Tetracycline resistance was abundant and prevalent in cattle and swine. Specifically, tetQ , tetW , tetO , tet32 , and tet44 were the five most abundant and prevalent tetracycline resistance genes. Their prevalence was almost 100% in swine and cattle. While tetQ was similarly abundant in both swine and cattle, tetW was more abundant in swine than in cattle. Aminoglycoside was the second highest abundant resistance determinant in swine, but not in cattle. In particular, ANT(6) and APH(3) were the dominant resistance gene families in swine. Beta-lactam was also an abundant resistance determinant in both swine and cattle. Cfx was the major contributing gene family conferring resistance against beta-lactams. Antibiotic resistome was more pervasive in swine than in cattle. Specifically, prevalent ARGs (prevalence &gt; 50%) were found more in swine than in cattle, Genomic investigation of specific resistance genes from the gut microbiomes of swine and cattle in this study should provide opportunities to better understand the exchange of antibiotic resistance genes in farm animals.
DOI: 10.5524/100738
title: Supporting data for "Community Standards for Open Cell Migration Data"

description: Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited due to the diversity of experimental protocols and non-standardised output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardised data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardisation efforts by the Cell Migration Standardisation Organization, CMSO, an open community-driven organisation to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools, and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.