cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100852
title: Supporting data for "metaXplor: an interactive viral and microbial metagenomic data manager"

description: Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g. Qiime, Mothur, Megan, MetaVir, IMG/VR, Anvio), scientists lack comprehensive software for easily building scalable, searchable, online data repositories that they may rely on during their ongoing research.<br>metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing and exploring metagenomic data. Being based on a flexible NoSQL data model, it has very few constraints regarding dataset contents, and thus proves useful for handling outputs from both shot-gun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find very specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. <br>metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud or even on personal computers, will undoubtedly facilitate its adoption.
DOI: 10.5524/100857
title: Supporting data for "Genome sequence and genetic diversity analysis of an under-domesticated orphan crop, white fonio (Digitaria exilis)"

description: <i>Digitaria exilis</i>, white fonio, is a minor but vital crop of West Africa that is valued for its resilience in hot, dry and low fertility environments and for the exceptional quality of its grain for human nutrition. The crop is plagued, however, by a low degree of improvement.<br>We sequenced the fonio genome with long-read SMRT-cell technology, yielding a ∼761 Mb assembly in 3329 contigs (N50 1.73 Mb, L50 126). The assembly approaches a high level of completion, with a BUSCO score of greater than 99%. The fonio genome was found to be a tetraploid, with most of the genome retained as homoeologous duplications that differ overall by ∼4.3%, neglecting indels. The two genomes within fonio were found to have begun their independent divergence ∼3.1 million years ago. The repeat content (&gt;49%) is fairly standard for a grass genome of this size, but the ratio of <i>Gypsy</i> to <i>Copia</i> LTR-retrotransposons (∼6.7) was found to be exceptionally high. Several genes related to future improvement of the crop were identified including shattering, plant height and grain size. Analysis of fonio population genetics, primarily in Mali, indicated that the crop has extensive genetic diversity that is largely partitioned across a north-south gradient coinciding with the Sahel and Sudan grassland domains. <br>We provide a high-quality assembly, annotation and diversity analysis for a vital African crop. The availability of this information should empower future research into further domestication and improvement of fonio.
DOI: 10.5524/100863
title: Supporting data for "Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework.""

description: Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard SIR model into one Bayesian framework to evaluate and compare their short term forecasts. We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020. None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.
DOI: 10.5524/100867
title: Supporting data for "HTSlib - C library for reading/writing high-throughput sequencing data"

description: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health.<br>We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading.<br> Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded over a million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT / BSD license.
DOI: 10.5524/100872
title: Supporting data for "Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit"

description: This paper introduces the HASTE Toolkit, a proof-of-concept cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is performed online and based on data content rather than <i>a priori</i> metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope.
DOI: 10.5281/zenodo.4289365
title: Benchmark data used in the evaluation of ChiRA tool-suite.

description: The reads generated mimic CLASH experimental data. Each read is a fusion of a human hg38 miRBase mature miRNAs and some random TargetScan target sequences. The numbers 10, 12, 15, 18, and 20 in the file names represent the length of the chimeric arms. The are 1million reads in FASTA file. Files with "Insert" in their names contain a short 5 nt random sequence, whereas the "noInsert" files do not. TargetScanSites_merged.fa.fasta and hg38_MIR_mature.fa.fasta contain the reference sequences. The sequence identifiers in the FASTA file are in the following format: &gt;hsa-miR-193b-5p_MIMAT0004767:1-20||chr7:114656010-114656173+:82-95 where, hsa-miR-193b-5p_MIMAT0004767 is the sequence ID of the miRNA from which first chimeric arm of this read was derived from. This ID can be found in hg38_MIR_mature.fa.fasta. 1-20 represents the 1-based start and end positions on hsa-miR-193b-5p_MIMAT0004767 representing the origin of the first chimeric arm. chr7:114656010-114656173+ is the sequence ID of the TargetScan target site from which the second chimeric arm of this read was derived from. This ID can be found in TargetScanSites_merged.fa.fasta 82-95 represents the 1-based start and end positions on chr7:114656010-114656173+ representing the origin of the second chimeric arm.
DOI: 10.5524/100820
title: Supporting data for "iGenomics: Comprehensive DNA Sequence Analysis on your Smartphone"

description: Following the miniaturization of integrated circuitry and other computer hardware over the past several decades, DNA sequencing is following a similar path. Leading this trend is the Oxford Nanopore sequencing platform, which currently offers the hand-held MinION instrument and even smaller instruments on the horizon. This technology has been used in several important applications, including the analysis of genomes of major pathogens in remote stations around the world. However, despite the simplicity of the sequencer, an equally simple and portable analysis platform is not yet available. <br>iGenomics is the first comprehensive mobile genome analysis application, with capabilities to align reads, call variants, and visualize the results entirely on an iOS device. Implemented in Objective-C using the FM-index, banded dynamic programming, and other high-performance bioinformatics techniques, iGenomics is optimized to run in a mobile environment. We benchmark iGenomics using a variety of real and simulated Nanopore sequencing datasets of viral and bacterial genomes and show that iGenomics has performance comparable to the popular BWA-MEM/Samtools/IGV suite, without needing a laptop or server cluster. iGenomics is available open-source in GitHub and for free on Apples App Store.
DOI: 10.5524/100824
title: Efficient and stable metabarcoding sequencing data using DNBSEQ-G400 sequencer validated by comprehensive community analyses

description: Metabarcoding has become a widely used method for fast characterizing microbial communities in complex environmental samples. Here we demonstrate a high-throughput metabarcoding approach on microbial community assignment using DNBSEQ-G400 sequencing developed by MGI Tech. The accuracy of DNBSEQ-G400 on quantifying relative abundance was evaluated by bacterial and fungal mock samples, as well as using fungal mocks with various mixing ratios. Sequencing throughput and consistency were characterized by sequencing the fungal ITS2 region from 1144 soil samples as well as technical replicates of three randomly picked samples. DNBSEQ-G400 showed high accuracy in reproducing mock communities containing different proportions of bacteria and fungi. The taxonomic profiles of the 1144 soil samples generated by two DNBSEQ-G400 sequencing modes closely resembled each other. Sequencing across two platforms, DNBSEQ-G400 and MiSeq also showed similar patterns of beta diversity. These datasets provided to allow further study of utility, capacity and bias in short-read metabarcoding of microbial communities.
DOI: 10.5524/100830
title: Supporting data for "Parliament2: Accurate Structural Variant Calling At Scale"

description: Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the usage of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.<br>We present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates preinstalled SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in less than a day on GRCH38. Parliament2 improves the runtime performance of individual methods, is open-source, and a Docker image as well as a WDL implementation are available.<br>Parliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.
DOI: 10.5524/100834
title: GALLO: An R package for Genomic Annotation and integration of multiple data source in livestock for positional candidate LOci

description: The development of high-throughput sequencing and genotyping methodologies allowed the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits. Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as Genome-Wide Association Studies and transcriptomics using RNA-Sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chickens, etc. Consequently, GALLO is a useful package for the annotation, identification of hidden patterns across datasets, datamining previously reported associations, as well as the efficient scrutinization of the genetic architecture of complex traits in livestock.
DOI: 10.5524/100836
title: Supporting data for "SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data"

description: Droplet based single-cell RNA sequence analyses assume all acquired RNAs are endogenous to cells. However, any cell free RNAs contained within the input solution are also captured by these assays. This sequencing of cell free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. We demonstrate that contamination from this soup of cell free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating background corrected cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. We present SoupX, a tool for removing ambient RNA contamination from droplet based single cell RNA sequencing experiments. This tool has broad applicability and its application can improve the biological utility of existing and future data sets.
DOI: 10.5524/100838
title: Supporting data for "Tool recommender system in Galaxy using deep learning"

description: Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis. A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units (GRU) neural network, a variant of a recurrent neural network (RNN). In the neural network training, the weights of tools used are derived from their usage frequencies over time and the sequences of tools are uniformly sampled from training data. Hyperparameters of the neural network are optimised using Bayesian optimisation. Mean accuracy of 98% in recommending tools is achieved for the top-1 metric. The model is accessed by a Galaxy API to provide researchers with recommended tools in an interactive manner using multiple user interface (UI) integrations on the European Galaxy server. Good quality and highly-used tools are shown at the top of the recommendations. The scripts and data to create the recommendation system are available under MIT license at https://github.com/anuprulez/galaxy_tool_recommendation.
DOI: 10.5524/100845
title: Supporting data for "ChiRA: an integrated framework for Chimeric Read Analysis from RNA-RNA interactome and RNA structurome data"

description: With the advances in next-generation sequencing technologies, it is possible to determine RNA-RNA interaction and RNA structure predictions on a genome-wide level. The reads from these experiments usually are chimeric with each arm generated from one of the interaction partners. Due to short read lengths, often these sequenced arms ambiguously map to multiple locations. Thus, inferring the origin of these can be quite complicated. Here we present ChiRA, a generic framework for sensitive annotation of these chimeric reads, which in turn predict the sequenced hybrids. Grouping reference loci based on aligned common reads and quantification improved the handling of the multi-mapped reads in contrast to common strategies like the selection of the longest hit or a random choice among all hits. On benchmark data ChiRA improved the number of correct alignments to the reference up to 3-fold. It is shown that the genes that belong to the common read loci share the same protein families or similar pathways. In published data, ChiRA could detect 3 times as many new interactions compared to existing approaches. In addition, ChiRAViz can be used to visualize and filter large chimeric datasets intuitively.
DOI: 10.5524/100853
title: long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data

description: The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that address these challenges for different genomics applications are difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal, and for developers to identify areas of need and existing solutions to benchmark against.<br> We describe the implementation of long-read-tools.org, an open-source database that organises the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python and the most frequent analysis tasks include base-calling, <i>de novo</i> assembly, error-correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available. <br>Continued growth in the application of long-read sequencing in genomics research, positions the long-read-tools.org database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs.
DOI: 10.5524/100861
title: Supporting data for the chromosome-level genome assembly of the malaria vectors Anopheles coluzzii

description: <i>Anopheles coluzzii</i> belongs to the <i>An. gambiae</i> complex and is among the major malaria vectors in Sub-Saharan Africa. However, a chromosome-level reference genome assembly for this medically important species is not yet available. <br>In this study, we produced <i>de novo</i> chromosome-level genome assemblies for both <i>An. coluzzii</i> and <i>An. arabiensis</i> using the long-read Oxford Nanopore sequencing technology and the Hi-C scaffolding approach.<br>We obtained a 273.4 Mbp assembly for <i>An. coluzzii</i> consisting of three chromosome-scale scaffolds (X, 2, 3), complete mitochondrion, and unordered contigs identified as autosomal pericentromeric DNA, X pericentromeric DNA, and Y sequences. A comparison of our assembly with the existing assemblies for these species demonstrated that we obtained improved reference-quality genomes. <br>Together, the new assemblies of <i>An. coluzzii</i> and <i>An. arabiensis</i> allowed us to identify genomic coordinates for the breakpoint regions of fixed and polymorphic chromosomal inversions. <br>This new chromosome-level assembly will facilitate functional and population genomic studies and the presented assembly pipeline will accelerate progress toward creating high-quality genome references for other disease vectors.
DOI: 10.5524/100864
title: Supporting data for "The genome of Tripterygium wilfordii and characterization of the celastrol biosynthesis pathway"

description: <i>Tripterygium wilfordii</i> is a vine from the Celastraceae family that is used in traditional Chinese medicine (TCM). The active ingredient, celastrol, is a friedelane-type pentacyclic triterpenoid with putative roles as an antitumor, immunosuppressive, and anti-obesity agent. Here, we report a reference genome assembly of <i>T. wilfordii</i> with high-quality annotation using a hybrid sequencing strategy. The total genome size obtained is 340.12 Mb, with a contig N50 value of 3.09 Mb. We successfully anchored 91.02% of sequences into 23 pseudochromosomes using high-throughput chromosome conformation capture (Hi-C) technology. The super-scaffold N50 value was 13.03 Mb. We also annotated 31,593 structural genes, with a repeat percentage of 44.31%. These data demonstrate that <i>T. wilfordii</i> diverged from Malpighiales species approximately 102.4 million years ago. By integrating genome, transcriptome and metabolite analyses, as well as <i>in vivo</i> and <i>in vitro</i> enzyme assays of two cytochrome P450 (CYP450) genes, TwCYP712K1 and TwCYP712K2, it is possible to investigate the second biosynthesis step of celastrol and demonstrate that this was derived from a common ancestor. These data provide insights and resources for further investigation of pathways related to celastrol, and valuable information to aid the conservation of resources, as well as understand the evolution of Celastrales.