cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100645
title: Supporting data for "PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria"

description: Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement And Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino-acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation and estimates sequence divergence for each gene family. We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.
DOI: 10.5524/100650
title: Supporting data for "A High-Quality Genome Assembly from a Single, Field-collected Spotted Lanternfly (Lycorma delicatula) using the PacBio Sequel II System"

description: A high-quality reference genome is an essential tool for applied and basic research on arthropods. Long-read sequencing technologies may be used to generate more complete and contiguous genome assemblies than alternate technologies, however, long-read methods have historically had greater input DNA requirements and higher costs than next generation sequencing, which are barriers to their use on many samples. Here, we present a 2.3 Gb de novo genome assembly of a field-collected adult female Spotted Lanternfly (Lycorma delicatula) using a single PacBio SMRT Cell. The Spotted Lanternfly is an invasive species recently discovered in the northeastern United States, threatening to damage economically important crop plants in the region. The DNA from one individual was used to make one standard, size-selected library with an average DNA fragment size of ~20 kb. The library was run on one Sequel II SMRT Cell 8M, generating a total of 132 Gb of long-read sequences, of which 82 Gb were from unique library molecules, representing approximately 36x coverage of the genome. The assembly had high contiguity (contig N50 length = 1.5 Mb), completeness, and sequence level accuracy as estimated by conserved gene set analysis (96.8% of conserved genes both complete and without frame shift errors). Further, it was possible to segregate more than half of the diploid genome into the two separate haplotypes. The assembly also recovered two microbial symbiont genomes known to be associated with L. delicatula, each microbial genome being assembled into a single contig. We demonstrate that field-collected arthropods can be used for the rapid generation of high-quality genome assemblies, an attractive approach for projects on emerging invasive species, disease vectors, or conservation efforts of endangered species.
DOI: 10.5524/100651
title: Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures"

description: We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and rst and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to di erent plant species via transfer learning, o ering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a nal time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.
DOI: 10.5524/100652
title: Supporting data for "IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis"

description: In single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are two separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these two steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant. <br>To accelerate this process, we have developed IKAP an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, NK cells, and monocytes in two peripheral blood mononuclear cell (PBMC) datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types thereby delineating cell identities in a multi-layered ontology. <br>By tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-seq analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-seq data.
DOI: 10.5524/100657
title: Supporting data for "Trochodendron aralioides, the first chromosome-level draft genome in Trochodendrales and a valuable resource for basal eudicot research"

description: The wheel tree (<i>Trochodendron aralioides</i> ) is one of only two species in the basal eudicot order Trochodendrales. Together with <i>Tetracentron sinense</i> , the family is unique in having secondary xylem without vessel elements, long considered to be a primitive character also found in Amborella and Winteraceae. Recent studies however have shown that Trochodendraceae belong to basal eudicots and demonstrate this represents an evolutionary reversal for the group. <i>Trochodendron aralioides</i> is widespread in cultivation and popular for use in gardens and parks. We assembled the <i>T. aralioides</i> genome using a total of 679.56 Gb of clean reads that were generated using both PacBio and Illumina short-reads in combination with 10XGenomics and Hi-C data. Nineteen scaffolds corresponding to 19 chromosomes were assembled to a final size of 1.614 Gb with a scaffold N50 of 73.37 Mb in addition to 1,534 contigs. Repeat sequences accounted for 64.226% of the genome, and 35,328 protein-coding genes with an average of 5.09 exons per gene were annotated using <i>de novo</i> , RNA-seq, and homology-based approaches. According to a phylogenetic analysis of protein-coding genes, T. aralioides diverged in a basal position relatively to core eudicots, approximately 121.8-125.8 million years ago. <i>Trochodendron aralioides</i> is the first chromosome-scale genome assembled in the order Trochodendrales. It represents the largest genome assembled to date in the basal eudicot grade, as well as the closest order relative to the core-eudicots, as the position of Buxales remains unresolved. This genome will support further studies of wood morphology and floral evolution, and will be an essential resource for understanding rapid changes that took place at the base of the Eudicot tree. Finally, it can serve as a valuable source to aid both the acceleration of genome-assisted improvement for cultivation and conservation efforts of the wheel tree.
DOI: 10.5524/100658
title: Supporting data for "Systematic processing of rRNA gene amplicon sequencing data"

description: With the advent of high throughput sequencing, microbiology is increasingly becoming a data intensive field of science. Because of its low cost, robust databases and established bioinformatic workflows, sequencing of 16S/18S/ITS rRNA gene amplicons, which provides a marker of choice for phylogenetic studies, has become ubiquitous and has grown into the backbone of modern microbial ecology. Many established end-to-end bioinformatic pipelines are available to perform short amplicon sequence data analysis and have proven to be central for advancing the field of microbial ecology. These pipelines have been partly written for a general audience, which is arguably a main reason for their widespread adoption. However, few options exist for more specialized users that are experienced in code scripting, Linux-based systems and high performance computing (HPC) environments. For such an audience, existing pipelines can be limiting to fully leverage modern HPC capabilities and perform tweaking and optimization operations. Moreover, a wealth of stand-alone software packages that perform specific targeted bioinformatic tasks are increasingly accessible through code repositories and scientific publications and finding a way to easily integrate these applications in a pipeline is critical in fast-paced evolution of bioinformatic methodologies. Here we describe AmpliconTagger, a short rRNA marker gene amplicon pipeline coded in a python framework that enables fine tuning and integration of virtually any potential rRNA gene amplicon bioinformatic procedure. It is designed to work within an HPC environment, supporting a complex network of job-dependencies with a smart-restart mechanism in case of job failure or parameter modifications. As proof of concept, we present end results obtained with AmpliconTagger using 16S, 18S, ITS rRNA short gene amplicons and PacBio long read amplicon data types as input. Using a selection of published algorithms for generating Operational Taxonomic Units (OTUs) and Amplicon Sequence Variants (ASVs) and for computing downstream taxonomic summaries and diversity metrics, we demonstrate the performance and versatility of our pipeline for systematic analyses of amplicon sequence data.
DOI: 10.5524/100659
title: Supporting data for "Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations"

description: Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret "-omic" measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular; whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from five studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in "-omic" measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of
individual genes, transcripts, and proteins.
DOI: 10.5524/100662
title: Supporting data for "Deep learning for clustering of multivariate clinical patient trajectories with missing values"

description: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a perpatient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series, because (1) these diseases are multifactorial and not well described by single clinical outcome variables, and (2) disease progression needs to be monitored over time. Clinical datasets often additionally suffer from the presence of many missing
values, further complicating any clustering attempts.<br> The problem of clustering multivariate short time series with many missing values has generally not been well addressed in the literature so far. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended by (1) incorporating long short term memory units and (2) defining an appropriate approach to directly deal with missing values via implicit imputation and loss re-weighting. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify Alzheimer's disease (AD) patients and Parkinson's disease (PD) patients into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of AD and PD.<br>We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate short time series clustering in general.
DOI: 10.5524/100664
title: Supporting data for "DAISY: a Data Information System for accountability under the General Data Protection Regulation"

description: The new European legislation on data protection, namely the GDPR, has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPRs accountability principle requires institutions, projects and data hubs to document their data processings and emonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data mapping tools, and institutions creating GDPR data registers with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research. <br>We have developed a software tool to allow research institutions to comply with the accountability requirement and map the sometimes very complex data ows in biomedical research. By analysing the transparency and record keeping obligations of each GDPR principle we observe that our tool e ectively meets the accountability requirement.<br>The GDPR is bringing data protection to the center stage in research data management; necessitating dedicated tools, personnel and processes. Our tool, DAISY, is tailored speci cally for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open-source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub.
DOI: 10.5524/100666
title: Supporting data for "Arteria: An automation system for a sequencing core facility"

description: In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an explosive growth in sequencing data produced worldwide. As the amount of data increases, so does the need for automated solutions for data processing and analysis. The concept of workflows has gained favour in the bioinformatics community, but there is little in the scientific literature describing end-to-end automation systems. Arteria is an automation system which aims at providing a solution to the data-related operational challenges which face sequencing core facilities.

Arteria is built on existing open-source technologies, with a modular design allowing for a community-driven effort to create plug-and-play micro-services. In this article we describe the system, elaborate on the underlying conceptual framework, and present an example implementation. Arteria can be reduced to three conceptual levels: orchestration (using an event-based model of automation), process (the steps involved in processing sequencing data, modelled as workflows), and execution (using a series of RESTful micro-services). This creates a system which is both flexible and scalable. Arteria-based systems have been successfully deployed at three sequencing core facilities. 
The Arteria Project code, written largely in Python, is available as open source software, and more information can be found at: https://arteria-project.github.io/
DOI: 10.5524/100667
title: Supporting data for "GraphClust2: annotation and discovery of structured RNAs with scalable and accessible integrative clustering"

description: RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing (HTS) techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. Hundreds of thousands of non-coding RNAs (ncRNAs) have been detected, however, the annotation of these ncRNAs is lacking behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between HTS and structural RNA analysis, and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparsity of phylogenetically conserved local structures for a collection of long non-coding RNAs. By clustering data from two CLIP experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR's 3'UTR that contains multiple binding stem-loops which are evolutionary conserved.
DOI: 10.5524/100855
title: Supporting data for "The GenTree Platform: growth traits and tree-level environmental data in twelve European forest tree species"

description: Progress in the field of evolutionary forest ecology has been hampered by the huge challenge of phenotyping trees across their ranges in their natural environments, and the limitation in high-resolution environmental information.<br> The GenTree Platform contains phenotypic and environmental data from 4959 trees from twelve ecologically and economically important European forest tree species: <i>Abies alba</i> Mill. (Silver fir), <i> Betula pendula</i> Roth. (silver birch), <i>Fagus sylvatica</i> L. (European beech), <i>Picea abies</i> (L.) H. Karst (Norway spruce), <i>Pinus cembra</i> L. (Swiss stone pine), <i>Pinus halepensis</i> Mill. (Aleppo pine), <i>Pinus nigra</i> Arnold (European black pine), <i>Pinus pinaster</i> Aiton (maritime pine), <i>Pinus sylvestris</i> L. (Scots pine), <i>Populus nigra</i> L. (European black poplar), <i>Taxus baccata</i> L. (English yew), and <i>Quercus petraea</i> (Matt.) Liebl. (sessile oak). Phenotypic (height, DBH, crown-size, bark-thickness, biomass, straightness, forking, branch angle, fructification), regeneration, environmental <i>in-situ</i> measurements (soil depth, vegetation cover, competition indices), and environmental modeling data (precipitation, temperature, insolation, drought indices) were obtained from trees in 194 sites covering the species geographic ranges and reflecting local environmental gradients.<br>The GenTree Platform is a new resource for investigating ecological and evolutionary processes in forest trees. The coherent phenotyping and environmental characterization across 12 species in their European ranges allows for a wide range of analyses from forest ecologists, conservationists and macroecologists. In addition, the data here presented can be linked to the GenTree Dendroecological collection, the GenTree Leaf Trait collection, and the GenTree Genomic collection presented elsewhere, that together build the largest evolutionary forest ecology data collection available.
DOI: 10.5524/100617
title: Supporting data for "SeQuiLa-cov: A fast and scalable library for depth of coverage calculations"

description: Depth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next generation sequencing pipelines, including the analyses of RNA-seq data, detection of copy number variants, or quality control procedures. Building upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching more than 100x speedup over the state-of-the-art tools. Performance and scalability of our solution allows for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface. SeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.