cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102395
title: Supporting data for "iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes"

description: DNA 5-hydroxymethylcytosine (5hmC), N6-methyladenine (6mA) and N4-methylcytosine (4mC) are three popular DNA modifications and involve in various of biological processes. Accurate genome-wide identification of 5hmC, 6mA and 4mC sites is invaluable for better understanding their biological functions. In this work, we initially proposed using K-tuple nucleotide frequency component, nucleotide chemical property and nucleotide frequency, and mono-nucleotide binary encoding scheme to formulate positive and negative samples. Subsequently, the Random Forest was utilized to perform the identification of 5hmC, 6mA and 4mC sites. Results of five-fold cross-validation test and independent dataset test showed that the proposed method could produce the good generalization ability, suggesting that our proposed method have good predictive performance to predict 5hmC, 6mA and 4mC sites. We anticipate that the iDNA-MS will become a powerful tool for large scale identification of DNA modification sites.<br> Here we provide the 3 benchmark datasets utilised in our web-service along with the analysis scripts.
DOI: 10.5524/102398
title: Supporting data for "Genomic data of créole bean, <i>Vigna reflexopilosa</i>"

description: <i>Vigna reflexo-pilosa</i><em></em> (créole bean) is a wild legume belonging to the subgenus <i>Ceratoropis</i> and is widely distributed in Asia. Créole bean is the only tetraploid species in the genus <em>Vigna</em>, and it has been shown to derive from the hybridization of <i>Vigna hirtella</i> and <i>Vigna trinervia</i><em>.<br> </em>This dataset represents a chromosome-level assembly by using the long-read PacBio technology with the chromatin contact mapping (Hi-C) technique for <i>V. reflexo-pilosa</i>. The final assembly contained 998,724,903 bases with an N50 length of 42,545,650 bases.
DOI: 10.5524/102366
title: Genomic data of the Amazonian blue butterfly, <i>Morpho achilles</i>

description: <i>Morpho achilles</i> is a Neotropical butterfly. The caterpillars feed on various species of climbing plants, while the adults mainly feed on rotting fruits or fermenting juice and tree sap. This species can be found over a huge range of countries, particularly in Argentina, Suriname, Bolivia, Colombia, Peru, Ecuador, Venezuela, Paraguay, and Brazil.<br> This dataset represents a species isolated from a tropical rainforest in French Guiana. The DNA was isolated in July 2021 from the thorax muscle of an adult male. The Hifiasm assembled genome has an estimated genome size of 478.514 Mb, an N50 of 12Mb and 12,978 protein coding genes were recovered.
DOI: 10.5524/102368
title: Genomic data of the Amazonian blue butterfly, <i>Morpho deidamia</i>

description: <i>Morpho deidamia</i> is a Neotropical butterfly. It is found in Panama, Nicaragua, Costa Rica, Suriname, Bolivia, Venezuela, Colombia, Peru, Ecuador, and Brazil.<br>This dataset represents a species isolated from a tropical rainforest in French Guiana. The DNA was isolated in July 2021 from the thorax muscle of an adult male. The Hifiasm assembled genome has an estimated genome size of 489.914 Mb, an N50 of 13Mb and 12,093 protein coding genes were recovered.
DOI: 10.5524/102378
title: Supporting data for "Genome assembly of an Australian native grass species reveals a recent whole genome duplication and biased gene retention of genes involved in stress response"

description: The adaptive significance of polyploidy has been extensively debated and chromosome-level genome assemblies of polyploids can provide insight into this topic. The Australian grass, <i>Bothriochloa decipiens</i><em>, </em>belongs to the BCD clade, a group with a complex history of hybridization and polyploidy. This is the first genome assembly and annotation of a species that belongs to this fascinating yet complex group. <br>Using a combination of Illumina short reads, 10X Genomics linked reads and Hi-C sequencing data we assembled a highly contiguous genome of <i>Bothriochloa decipiens</i>, with a total length of 1,218.22 Mb and scaffold N50 of 42.637 Mb. Comparative analysis revealed that the species is a diploidized allotetraploid. We clustered the 20 major scaffolds, representing the 20 chromosomes, into the two subgenomes of the parental species using unique repeat signatures. We found evidence of biased fractionation and differences in the activity of transposable elements between the subgenomes prior to hybridization. Duplicates were enriched for genes involved in transcription and response to external stimuli like drought, supporting a biased retention of duplicated genes following whole genome duplication. <br>Our results support hypotheses explaining the biased retention of duplicated genes following polyploidy and point to differences in repeat activity associated with subgenome dominance. <i>Bothriochloa decipiens</i> is a widespread species with the ability to establish across many soil types, making it useful for ecological restoration of Australian grasslands. This reference genome is a valuable resource for future population genomic research involving Australian grasses which may be helpful in ecological restoration projects.
DOI: 10.5524/102386
title: Supporting data for "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning"

description: Literature about SARS-CoV-2 widely discusses the effects of variations that spread in the last three years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract for each variant/mutation its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the non-mutated virus. <br>The proposed framework comprises: i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19), and ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in two distinct scenarios: (a) the batch annotation of the most relevant CORD-19 abstracts, and (b) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect Web application, which assists expert users with semi-automated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. <br>The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.
DOI: 10.5281/zenodo.5712441
title: Meta-NanoSim simulated saliva sample

description: This dataset is described in Meta-NanoSim manuscript. Reference metagenome list and abundance levels are provided at the same time. The training model used is trained from Zymo Mock microbial community Log dataset.
DOI: 10.5281/zenodo.7070890
title: On the benefits of self-taught learning for brain decoding - Data

description: Here are stored the data necessary to reproduce the full analysis of the paper "On the benefits of self-taught learning for brain decoding". We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. We show that such a self-taught learning process always improves the performance of the classifiers but the magnitude of the benefits strongly depends on the number of data available both for pre-training and finetuning the models and on the complexity of the targeted downstream task.
DOI: 10.5524/102351
title: Supporting data for "The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis."

description: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets, we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. <br>We use complex network statistics tools and simulations of randomly-inserted previously unaccounted interactions in drug-drug and drug-target interaction networks built with data from Drug Bank versions released over the last decade to investigate whether the abundance of new research data (included in the latest dataset versions) mitigate the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. <br>Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.
DOI: 10.5524/102355
title: Supporting data for "Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim"

description: Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, non-uniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical algorithms. The use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to assess the performance of bioinformatics tools with the ground truth in a controlled environment. <br>Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task.<br>The Meta-NanoSim characterization module investigates read features including chimeric information and abundance levels, while the simulation module simulates large and complex multi-sample microbial communities with different abundance profiles. All trained models and the software are freely accessible from our Github repository.
DOI: 10.5524/102357
title: Supporting data for "FAIR Data Station for Lightweight Metadata Management & Validation of Omics Studies"

description: The Life sciences are one of the biggest suppliers of scientific data. Reusing and connecting this data can uncover hidden insights and lead to new concepts. Efficient reuse of these data sets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR guiding principles have been accepted by all stakeholders, in practice there are only a limited number of easy to adopt implementations available that fulfil the needs of data producers. <br>We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of three modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process the format of the recorded values can be checked using the "validation module". Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive compatible XML metadata file. <br>Turning FAIR into reality requires the availability of easy to adopt data FAIRification workflows that are also of direct use for data producers. As such the FAIR Data Station provides in addition to the means to correctly FAIRify (Omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.
DOI: 10.5524/102359
title: Supporting data for "Contamination detection and microbiome exploration with GRIMER"

description: Contamination detection is an important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants are challenging tasks, especially in low-biomass samples or in studies lacking proper controls by design. Interactive visualizations and analysis platforms are crucial to better guide this step, helping to identify and detect noisy patterns that can potentially be a source of contamination. Additionally, external evidences, aggregation of methods and data and common contaminants reported in the literature can be used to discover and mitigate contamination. <br>We propose GRIMER, a tool that perform automated analysis and generates a portable and interactive dashboard integrating annotation, taxonomy and metadata. It unifies several sources of evidence towards contamination detection. GRIMER is independent of quantification methods and directly analyses contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for non-specialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genus and 627 species reported in 22 published articles. <br>GRIMER enables visual data exploration, analysis and contamination detection in microbiome studies. The tool and data presented are open-source and available at: https://gitlab.com/dacs-hpi/grimer.
DOI: 10.5524/102365
title: Genomic data of bloodsucking leech, <i>Hirudinaria manillensis</i>

description: The Asian buffalo leech (<i>Hirudinaria manillensis</i>), also known as the asian medicinal leech, is commonly found in natural water bodies of the South East Asian region. Adults reach a maximum width of 13mm and length of 130 - 140 mm, and can stretch up to 200mm when fully extended. They can be found in the Indo-West Pacific region within slow moving or stagnant waters, normally in rice paddies, swamps, and sluggish streams.<br>In cattle rearing countries within the region, they are very frequently found attached onto the bellies of livestock, feeding on their blood. They are known to feed on amphibians, reptiles, mammals, and - much less commonly - Telostei fishes in the wild.<br> There are currently no known predators of the Asian buffalo leech. <i>Hirudinaria manillensisis</i> is currently exploited as a medicinal leech, and has in recent times replaced the European medicinal leech, <i>Hirudo medicinalis</i>, due to similar physiology and the conservation status of the latter. They are known hermaphrodites, and mating individuals are capable of reciprocal fertilisation<br> Here we present the genome, which has an estimated genome size of 157.5Mb, scaffold N50 of 11.9Mb, contig N50 of 2.5Mb and 18,106 genes. This dataset includes the following data: repetitive sequences, gene and functional annotations, annotation of ncRNAs, prediction of secreted proteins, divergence time & transcriptome analysis.
DOI: 10.5524/102367
title: Genomic data of the Amazonian blue butterfly, <i>Morpho helenor</i>

description: <i>Morpho helenor</i> is a Neotropical butterfly found throughout Central and South America from Mexico to Argentina. The larvae feed on the leaves of Mucuna, Clover, Alfalfa, Peanut and many other plants. Adult butterflies feed on rotting fruit such as bananas.<br>This dataset represents a species isolated from a tropical rainforest in French Guiana. The DNA was isolated in July 2021 from the thorax muscle of an adult male. The Hifiasm assembled genome has an estimated genome size of 470.254 Mb, an N50 of 12Mb and 12,651 protein coding genes were recovered.
DOI: 10.5524/102370
title: Supporting data for "PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects"

description: <i>In silico</i> models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, exploration of parameter space is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance to model parameters. One widely used computational platform in the mathematical biology community is <i>PhysiCell</i> which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. One limitation of <i>PhysiCell</i>, however, is that there has not been a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Taking this into account, we present <i>PhysiCOOL,</i> an open-source Python library tailored to create standardized calibration and optimization routines of <i>PhysiCell</i> models.
DOI: 10.5524/102371
title: Supporting data for "clevRvis: Visualization Techniques for Clonal Evolution"

description: A thorough analysis of clonal evolution commonly requires integration of diverse sources of data, e.g. karyotyping, next-generation sequencing and clinical information. Subsequent to actual reconstruction of clonal evolution, detailed analysis and interpretation of the results is essential. Often, however, only few tumor samples per patient are available. Thus, information on clonal development and therapy effect may be incomplete. Furthermore, analysis of bi-allelic events - considered of high relevance with respect to disease course - can commonly only be realized by time-consuming analysis of the raw results and even raw sequencing data.<br>We developed clevRvis, a R/Bioconductor package providing an extensive set of visualization techniques for clonal evolution. In addition to common approaches for visualization, clevRvis offers a unique option for allele-aware representation: plaice plots. Bi-allelic events may be visualized and inspected at a glance. Analyzing four public data sets, we show that plaice plots help to gain new insights into tumor development and investigate hypotheses on disease progression and therapy resistance. In addition to a graphical user interface, automatic phylogeny-aware color coding of the plots, and an approach to explore alternative trees, clevRvis provides two algorithms for fully automatic time point interpolation and therapy effect estimation. Analyzing two public data sets, we show that both approaches allow for valid approximation of a tumor's development in between measured time points.<br>clevRvis represents a novel option for user-friendly analysis of clonal evolution, contributing to gaining new insights into tumor development.
DOI: 10.5524/102374
title: Supporting data for "Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation"

description: Mass spectrometry imaging, which localizes molecules in a tag-free, spatially-resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate Regions-of-Interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A Deep Learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire H&E image. Clustering is then similarly performed to produce Histology-segmentation. Since ROIs originating from instrumental noise or artifacts wouldnt be reproduced cross-modally, the consistency between histology- and MSI-segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximizes the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.