cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100859
title: Supporting data for "The case for using Mapped Exonic Non-Duplicate (MEND) read counts in RNA-Seq experiments: examples from pediatric cancer datasets"

description: The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that Mapped, Exonic, Non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets utilized for gene expression analysis. In bulk RNA-Seq datasets from 2179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1-77% of all reads (med.) 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Mapped, Exonic, Non-duplicate (MEND) reads constitute 0-79% of total reads (med. 50%; IQR 31%). Since not all reads in a RNA-Seq dataset are informative for reproducibility of gene expression measurements, and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped or exonic reads. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all RNA-Seq gene expression experiments, sensitivity studies and depth recommendations use MEND units for sequencing depth.
DOI: 10.5524/100660
title: Supporting data for "A multi-day and multi-band dataset for steady-state visual evoked potential-based brain-computer interface"

description: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based braincomputer interface (BCI) research. However, there are few published SSVEP datasets for BCIs. In this study, we obtained a new SSVEP dataset based on measurements from 30 subjects, performed on two days; our dataset complements existing SSVEP datasets: i) multi-band SSVEP datasets are provided, and all three possible frequency bands (low, middle, and high) were used for SSVEP stimulation; ii) multi-day datasets are included; and iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, head motion (accelerator), and body temperature.<br>To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets, and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results.<br>Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the three frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the nonstationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.
DOI: 10.15482/usda.adc/1503745
title: Data from: A High-Quality Genome Assembly from a Single, Field-collected Spotted Lanternfly (Lycorma delicatula) using the PacBio Sequel II System
DOI: 10.5281/zenodo.3382296
title: Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations

description: This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet File descriptions dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites
DOI: 10.5524/100629
title: Supporting data for "rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data"

description: Possibility to generate large RNA-Seq datasets has led to development of various reference-based and de novo transcriptome assemblers with their own strengths and limitations. While reference-based tools are widely used in various transcriptomic studies, their application is limited to the organisms with finished and well-annotated genomes. De novo transcriptome reconstruction from short reads remains an open challenging problem, which is complicated by the varying expression levels across different genes, alternative splicing and paralogous genes. In this paper we describe a novel transcriptome assembler called rnaSPAdes, which is developed on top of SPAdes genome assembler and explores computational parallels between assembly of transcriptomes and single-cell genomes. We also present quality assessment reports for rnaSPAdes assemblies, compare it with modern transcriptome assembly tools using several evaluation approaches on various RNA-Seq datasets, and briefly highlight strong and weak points of different assemblers.
DOI: 10.5524/100631
title: Supporting data for "Genome Sequence of the Chinese White Wax Scale Insect: the First Draft Genome for the Coccidae Family of Scale Insects"

description: The Chinese white wax scale insect, <i>Ericerus pela</i>, is best known for producing wax, which has been widely used in candle production, casting, Chinese medicine, and wax printing products for thousands of years. The wax secretion and other unusual features of scale insects are thought to be adaptations to their change from an ancestral ground-dweller lifestyle to a sedentary lifestyle in which they live on the higher parts of plants. In addition to improving its economic value, studying <i>E. pela</i> should also help explain the adaptation of scale insects. However, no genomic data are currently available for <i>E. pela</i>.<br> To assemble the <i>E. pela</i> genome, a total of 303.92 Gb of data was generated using Illumina and PacBio sequencing, producing 277.22 Gb of clean data for assembly. The assembled genome size of <i>E. pela</i> was 0.66 Gb, with 1,979 scaffolds and a scaffold N50 of 735 kb. The G+C content was 33.80%. A total of 12,022 protein-coding genes were predicted, with an average coding sequence length of 1,370 bp. A total of 26 fatty acyl-CoA reductase genes and 35 acyltransferase genes were identified. Evolutionary analysis revealed that <i>E. pela</i> and aphids formed a sister group and split approximately 241.1 million years ago. There were 214 expanded gene families and 2,219 contracted gene families in <i>E. pela</i>. <br> We present the first genome from the Coccidae family. The results increase our understanding of the evolution of unique features in scale insects and supply important genetic information for further research.
DOI: 10.5524/100633
title: Supporting data for "SwiftOrtho: a Fast, Memory-Efficient, Multiple Genome Orthology Classifier"

description: Gene homology type classification is a requisite for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. A large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic datasets, these tools require high memory and CPU usage, typically available only in computational clusters. <br>Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an Affinity Propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho is the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4GB RAM. Using various standard orthology datasets, we also show that SwiftOrtho has a high accuracy.
DOI: 10.5524/100634
title: Supporting data for "High-quality Schistosoma haematobium genome achieved by single-molecule and long-range sequencing"

description: <i>Schistosoma haematobium</i> causes urogenital schistosomiasis, a neglected tropical disease affecting &gt; 100 million people worldwide. Chronic infection with this parasitic trematode can lead to urogenital pathology including female genital schistosomiasis (FGS) and bladder cancer. At the molecular level, little is known about the biology of this blood fluke and the pathogenesis of the disease that it causes. To support molecular studies of this carcinogenic worm, we reported a draft genome for <i>S. haematobium</i> in 2012. Although a useful resource, the utility of this draft genome has been somewhat limited by its fragmentation. <br>Here, we systematically enhanced the draft genome of <i>S. haematobium</i> using a single-molecule and long-range DNA sequencing approach. We achieved a major improvement in the accuracy and contiguity of the genome assembly, making it superior or comparable to assemblies for other schistosome species. We transferred curated gene models to this assembly and, using enhanced gene annotation pipelines, inferred a gene set with as many or more complete gene models compared with those of other well-studied schistosomes. Employing conserved, singlecopy orthologs, we assessed the phylogenetic position of <i>S. haematobium</i> in relation to other parasitic flatworms for which draft genomes were available. <br>We report a substantially enhanced genomic resource that represents a solid foundation for molecular research on <i> S. haematobium</i> and is poised to better underpin population and functional genomic investigations, and to accelerate the search for new disease interventions.
DOI: 10.5524/100635
title: Supporting data for "A chromosome-level draft genome of the grain aphid <i>Sitobion miscanthi</i>"

description: <i>Sitobion miscanthi</i> is an ideal model for studying host plant specificity, parthenogenesis-based phenotypic plasticity, and interactions between insects and other species of various trophic levels, such as viruses, bacteria, plants and natural enemies. However, the genome information for this species has not been published yet. Here, we analyzed the entire genome of a female aphid colony using long-read sequencing and Hi-C data to generate chromosome-length scaffolds and a highly contiguous genome assembly.<br> 1.The final draft genome assembly from 33.88 Gb of raw data was approximately 397.90 Mb with a 2.05 Mb contig N50. Nine chromosomes were further assembled based on Hi-C data to a 377.19 Mb final size with a 36.26 Mb scaffold N50. 2.The identified repeat sequences accounted for 26.41% of the genome, and 16,006 protein-coding genes were annotated. According to the phylogenetic analysis, <i>S. miscanthi</i> is closely related to Acyrthosiphon pisum, with S. miscanthi diverging from their common ancestor approximately 25.0-44.9 million years ago.<br>We generated a high-quality draft of the <i>Sitobion miscanthi</i> genome. This genome assembly promotes research on the lifestyle and feeding specificity of aphids and their interactions with each other and species at other trophic levels. It can serve as a resource for accelerating genome-assisted improvements in chemical drug resistant management and environmentally friendly aphid management.
DOI: 10.5524/100636
title: Supporting data for "rCASC: reproducible Classification Analysis of Single Cell sequencing data"

description: Single-cell RNA sequencing is an essential tool to investigate cellular heterogeneity, and to highlight cell sub-population specific signatures. Single-cell sequencing applications are now spreading from the most conventional RNAseq to epigenomics, e.g. ATAC-seq. Single-cell sequencing led to the development of a large variety of algorithms and associated tools. However, to the best of our knowledge, there are few computational workflows providing analysis flexibility and achieving at the same time functional (i.e. information about the data and the utilized tools are saved in terms of meta-data) and computational reproducibility (i.e. real image of the computational environment used to generate the data is stored) through a user-friendly environment. rCASC is a modular workflow providing integrated analysis environment (from counts generation to cell subpopulation identification) exploiting docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low quality cells and/or specific bias, e.g. cell cycle. Subpopulations discovery can be instead achieved using different clustering techniques based on different distance metrics. Quality of clusters is then estimated through a new metric namely Cell Stability Score (CSS), which describes the stability of a cell in a cluster as consequence of a perturbation induced by removing a random set of cells from the overall cells population. Our experiments highlight that CSS provides better cluster-robustness information than silhouette metric. Moreover, rCASC provides tools for the identification of clusters-specific gene-signature. rCASC is a modular workflow with valuable new features that could help researchers in defining cells subpopulations and in detecting subpopulation specific markers. It exploits docker framework to make easier its installation and to achieve a computation reproducible analysis. Moreover, a Java Graphical User Interface (GUI), is provided in rCASC to make friendly the use of the tool even for users without computational skills in R.
DOI: 10.5524/100637
title: Supporting data for "Evaluating stably expressed genes in single cells"

description: Single cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level are intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. <br>Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the 'Mouse Atlas' dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. <br>SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, 'scSEGIndex', is incorporated into the scMerge Bioconductor R package https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html and can be used for identifying genes with stable expression in scRNA-seq datasets.
DOI: 10.5524/100640
title: Supporting data for "Bionitio: demonstrating and facilitating best practices for bioinformatics command-line software"

description: Bioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices in all the code they develop. A consequence of this is the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability and interoperability; and erroneous or inaccurate results. In response to this problem we have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best-practices. With a single command, the user can create a new well-structured project in one of twelve programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardised building and packaging, user documentation, code documentation, a standard open-source software license, software revision control, and containerisation. Bionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users of the tools because they are more easily installed and consistent in their usage.
DOI: 10.5524/100641
title: Supporting data for "Evaluation of computational genotyping of Structural Variations for clinical diagnoses"

description: In recent years, Structural Variation (SV) has been identified as having a pivotal role in causing genetic disease. The discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is error-prone, suffering from low sensitivity and high false discovery. These shortcomings can be partially overcome with extensive orthogonal validation methods, or use of long reads, but currently the cost of either precludes their application for routine clinical diagnostics. In contrast, SV genotyping of known sites of SV occurrence is relatively robust. Structural Variant genotyping therefore offers a cost-effective clinical diagnostic tool, with potentially few false positives and low occurrence of false negatives, even when applied to short-read DNA sequence data. We assess five state- of-the- art SV genotyping software methods, applied to short read sequence data. The methods are characterized based on their ability to genotype different SV types, spanning different size ranges. Furthermore, we analyze their ability to parse different VCF file sub-formats and assess their reliance on specific metadata. We compare the SV genotyping methods across a range of simulated and real data including SVs that were not found with Illumina data alone. We assess sensitivity and the ability to filter initial false discovery calls. Our results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.
DOI: 10.5524/100642
title: Supporting data for "Draft genome sequence of Solanum aethiopicum provides insights into disease resistance, drought tolerance and the evolution of the genome".

description: The African eggplant (<i>Solanum aethiopicum</i>) is a nutritious traditional vegetable used in many African countries, including Uganda and Nigeria. It is believed to have been domesticated in Africa from its wild relative, <i>Solanum anguiv</i>i. <i>S. aethiopicum</i> has been routinely used as a source of disease resistance genes for several Solanaceae crops including <i>Solanum melongena</i>. Breeding of <i>S. aethiopicum</i> has lagged behind due to lack of genomic resources. <br>We assembled a 1.02 Gb draft genome of <i>S. aethiopicum</i>, which contained predominantly repetitive sequences (76.2%). We annotated 37,681 gene models including 34,906 protein-coding genes. We observed an expansion of disease resistance genes through two rounds of amplification of long terminal repeat retrotransposons (LTR-Rs), which may have occurred around 1.25 and 3.5 million years ago, respectively. We identified 14,995,740 SNPs by re-sequencing 65 <i>S. aethiopicum</i> and <i>S. anguiv</i>i genotypes, of which 41,046 SNPs were closely linked to disease resistance genes. The domestication and demographic history analysis revealed the active selection for genes involved in drought tolerance in both Gilo and Shum groups. A pan-genome of <i>S. aethiopicum</i> with a total of 51,351 protein-coding genes was assembled, 7,069 genes of which are missing in the reference genome. <br> The genome sequence of <i>S. aethiopicum</i> enhances our understanding of its extraordinary biotic and abiotic resistance nature. The SNPs identified will be available for immediate use by breeders. The information provided here will greatly accelerate the selection and breeding of the African eggplant as well as other crops within the Solanaceae family.