cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7273767
title: RD-Connect GPAP synthetic data spiked-in variant data

description: This data is a subset of Rare Disease Synthetic Dataset (EGAD00001008392) dataset. The subset only contains the chromosomal regions with the spiked-in causative variants. The associated study is the Human genomic and phenotypic synthetic data for the study of rare diseases (EGAS00001005702) study. For more info go to https://ega-archive.org/. All this data was created with the support of the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).
DOI: 10.5281/zenodo.7340795
title: Data from: Application of Spatial-ID to large field mouse brain hemisphere dataset measured by Stereo-seq

description: The application of Spatial-ID on the large field mouse brain hemisphere dataset measured by Stereo-seq technology. Briefly, three adjacent coronal sections along the anterior-posterior axis were cut from the brain 5-week-old male mouse. It fully proves the innovation and applicability of Spatial-ID.
DOI: 10.5281/zenodo.7555467
title: Supporting data for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"

description: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan. Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×
DOI: 10.5281/zenodo.7906815
title: EAGS: efficient and adaptive gaussian smoothing applied to high-resolved spatial transcriptomics

description: This dataset is used to preserve the mouse brain and mouse olfactory bulb data (in h5ad format) involved in the EAGS study. You can get details of the different datasets from <strong>readme.txt</strong>. Abstract of the EAGS study: The emergence of high-resolved spatial transcriptomics (ST) technology has facilitated the research of novel methods to investigate biological development, growth and other complex biological processes. High-resolution and whole transcriptomics ST datasets require customized imputation methods to improve signal-to-noise ratio and the data quality. We propose an efficient and adaptive gaussian smoothing (EAGS) method for imputation on high-resolved ST. Its adaptive two-factor smoothing creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, then utilizes the weights to restore the gene expression profiles. The performance and efficiency of EAGS are verified on high-resolved ST data of mouse brain and olfactory bulb. Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretation and a significant advantage in computational consumption.
DOI: 10.5524/102440
title: Supporting data for "SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics"

description: The basic analysis steps of spatial transcriptomics involves obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generates results files in a universal format for subsequent personalized analysis. The execution time for the entire analysis process is ~148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.
DOI: 10.5524/102449
title: Supporting data for "Genome assembly of the bearded iris <i>Iris pallida</i> Lam."

description: Irises are perennial plants, representing a large genus with hundreds of species. While cultivated extensively for their ornamental value, commercial interest in irises lies in the secondary metabolites present in their rhizomes. The Dalmatian Iris (<i>Iris pallida</i> Lam.) is an ornamental plant that also produces secondary metabolites with potential value to the fragrance and pharmaceutical industries. In addition to providing base notes for the fragrance industry, iris tissues and extracts possess anti-oxidant, anti-inflammatory, and immunomodulatory effects. However, study of these secondary metabolites has been hampered by a lack of genomic information, instead requiring difficult extraction and analysis techniques. Here, we report the genome sequence of <i>Iris pallida</i> Lam., generated with Pacific Bioscience long-read sequencing, resulting in a 10.04 Gbp assembly with a scaffold N50 of 14.34 Mbp and 91.8% complete BUSCOs. This reference genome will allow researchers to study the biosynthesis of these secondary metabolites in much greater detail, opening new avenues of investigation for drug discovery and fragrance formulations.
DOI: 10.5524/102450
title: Supporting data for "Machine learning based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease"

description: Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of features, Recursive Feature Elimination (RFE) has been widely used in the bioinformatics field. However, machine learning based RFE has factors that decrease the stability of feature selection. In this paper, we suggested methods to improve stability while sustaining performance. <br>We exploited the abundance matrices of the gut microbiome (283 taxa at species level and 220 at genus level) to classify between patients with inflammatory bowel disease (IBD) and healthy control (1569 samples). We found that applying an already published data transformation before RFE improves feature stability significantly. Moreover, we performed an in-depth evaluation of different variants of the data transformation and identify those that demonstrate better improvement in stability while not sacrificing classification performance. To ensure a robust comparison, we evaluated stability using various similarity metrics, distances, the common number of features, and the ability to filter out noise features. We were able to confirm that the mapping by the Bray-Curtis similarity matrix before RFE consistently improves the stability while maintaining good performance. Multi-Layer Perceptron (MLP) algorithm exhibited the highest performance among eight different machine learning algorithms when a large number of features (a few hundred) were considered based on the best performance across 100 bootstrapped internal test sets. Conversely, when utilizing only a limited number of biomarkers as a tradeoff between optimal performance and method generalizability, the random forest algorithm demonstrated the best performance. Using the optimal pipeline we developed, we identified fourteen biomarkers for IBD at the species level and analyzed their roles using SHapley Additive exPlanations (SHAP). <br>Taken together our work showed not only how to improve biomarker discovery in the metataxonomic field without sacrificing classification performance, but also provided useful insights for future comparative studies.
DOI: 10.5524/102453
title: Supporting data for "cellsnake: a user-friendly tool for single-cell RNA sequencing analysis"

description: Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, non-expert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.
DOI: 10.5524/102454
title: Supporting data for "Imputation Methods for Single-Cell RNA-seq Data Using Neural Topic Models"

description: Single-cell RNA sequencing (scRNA-seq) technology studies transcriptome and cell-to-cell differences from higher single-cell resolution and different perspectives. Despite the advantage of high capture efficiency, downstream functional analysis of scRNA-seq data is made difficult by the excess of zero values, i.e., the dropout phenomenon. To effectively address this problem, we introduced scNTImpute, an imputation framework based on a neural topic model. A neural network encoder is used to extract underlying topic features of single-cell transcriptome data to infer high-quality cell similarity. At the same time, we determine which transcriptome data are affected by the dropout phenomenon according to the learning of the mixture model by the neural network. On the basis of stable cell similarity, the same gene information in other similar cells is borrowed to impute only the missing expression values. By evaluating the performance of real data, scNTImpute can accurately and efficiently identify the dropout values and imputes them accurately. In the meantime, the clustering of cell subsets is improved and the original biological information in cell clustering is solved which is covered by technical noise.
DOI: 10.5524/102457
title: Supporting data for "EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics"

description: The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality. We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive two-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb. Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
DOI: 10.5524/102459
title: Supporting data for "Euler Characteristic Curves and Profiles: a stable shape invariant for big data problems"

description: Tools of Topological Data Analysis provide stable summaries encapsulating the shape of the considered data. Persistent homology, the most standard and well studied data summary, suffers a number of limitations; its computations are hard to distribute, it is hard to generalize to multifiltrations and is computationally prohibitive for big datasets. In this paper we study the concept of Euler Characteristics Curves, for one parameter filtrations and Euler Characteristic Profiles, for multi-parameter filtrations. While being a weaker invariant in one dimension, we show that Euler Characteristic based approaches do not possess some handicaps of persistent homology; we show efficient algorithms to compute them in a distributed way, their generalization to multifiltrations and practical applicability for big data problems. In addition we show that the Euler Curves and Profiles enjoys certain type of stability which makes them robust tools for data analysis. Lastly, to show their practical applicability, multiple use-cases are considered.
DOI: 10.5524/102461
title: Supporting data for "Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data"

description: In recent years, convolutional neural network (CNN)-based methods have shown remarkable performance in the denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the main focus of existing studies. Recently, however, an alternative and highly competitive deep learning architecture, Swin Transformer, has been proposed for image restoration tasks. In this work, we present SwinT-fairSIM, a novel method for restoring SR-SIM images with low signal-to-noise ratio (SNR) based on Swin Transformer. The experimental results show that SwinT-fairSIM outperforms previous CNN-based denoising methods. Furthermore, the generalization capabilities of deep learning methods for image restoration tasks on real fluorescence microscopy data have not been fully explored yet, i.e., the extent to which trained artificial neural networks are limited to specific types of cell structures and noise. Therefore, as a second contribution, we benchmark two types of transfer learning, i.e., direct transfer and fine-tuning, in combination with SwinT-fairSIM and two CNN-based methods for denoising SR-SIM data. Direct transfer does not prove to be a viable strategy, but fine-tuning achieves results comparable to conventional training from scratch, while saving computational time and potentially reducing the amount of required training data. As a third contribution, we publish four datasets of raw SIM images and already reconstructed SR-SIM images. These datasets cover two different types of cell structures, tubulin filaments and vesicle structures. Different noise levels are available for the tubulin filaments. These datasets are structured in such a way that they can be easily used by the research community for research on denoising, super-resolution, and transfer learning strategies.
DOI: 10.5524/102462
title: Supporting data for "Evolutionary genomics of three agricultural pest moths reveals rapid evolution of host adaptation and immune-related genes"

description: Understanding the genotype of pest species provides an important baseline for designing integrated pest management (IPM) strategies. Recently developed long-read sequence technologies make it possible to compare genomic features of non-model pest species to disclose the evolutionary path underlying the pest species profiles. While hundreds of published genomes of lepidopteran species (moths and butterflies) are now available, only a few genome assemblies are publicly available from this diverse and notorious agricultural pest family, Gelechiidae. <br>We sequenced and assembled genomes for three gelechiids: <i>Phthorimaea absoluta</i> (tomato leafminer), <i>Keiferia lycopersicella</i> (tomato pinworm), and <i>Scrobipalpa atriplicella</i> (goosefoot groundling moth). We compared these genomes with published genomes of <i>Phthorimaea operculella</i> and <i>Pectinophora gossypiella</i>, and found that the three solanaceous feeding species, <i>Ph. absoluta, K. lycopersicella</i>, and <i>Ph. operculella</i> are clustered together. Gene family evolution analyses with the five species show clear gene family expansions on hostplant associated genes for the three solanaceous feeding species. These genes are involved in host compound sensing (e.g., gustatory receptors), detoxification (e.g., Cytochrome P450, Glucose-methanol-choline oxidoreductase, Glutathione S-transferase, Insect cuticle proteins, and UDP-glucuronosyl), and digestion (e.g., serine proteases and Aminopeptidase N-type). A gene ontology enrichment analysis of rapidly evolving genes also suggests enriched functions in host sensing and immunity. Our results indicate that host plant adaptation and pathogen defense could be important drivers in species diversification among gelechiid moths.
DOI: 10.5524/102463
title: Supporting data for "BatchEval Pipeline: Batch Effects Evaluation Workflow for Multi-batch Dataset Joint Analysis"

description: As genomic sequencing technology continues to advance, it becomes increasingly important to perform multiple dataset joint analysis of transcriptomics to understand complex biological systems. However, batch effect presents challenges for dataset integration, such as sequencing measured by different platforms and datasets collected at different times. Here, we develop a BatchEval Pipeline, which is used to evaluate batch effect of dataset integration and output a comprehensive report. This report consists of a series of HTML pages for the assessment findings, including a main page, a raw dataset evaluation page and several built-in methods evaluation pages. The main page exhibits basic information of integrated datasets, comprehensive score of batch effect and the most recommended method for batch effect removal to current datasets. The residual pages exhibit the evaluation details of raw dataset and evaluation results of many built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, BatchEval Pipeline represents a significant advancement in batch effect evaluation and is a valuable tool to improve the accuracy and reliability of the experimental results.
DOI: 10.5524/102465
title: Supporting data for "AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data"

description: Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the models ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances, namely local, medium, or distant associations. <br>This paper addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference- and alignment-free, providing additional methodologies for testing, including a highly-flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar. <br>The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multi-threading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at GitHub.
DOI: 10.5524/102467
title: Genome data of <i>Phthorimaea absoluta</i>

description: <i>Phthorimaea absoluta</i> (previously known as <i>Tuta absoluta</i>) is a small moth in the family Gelechiidae. It was first discovered in South America, and has since spread to Asia, Africa, and Europe. Larvae mainly feed on tomato plants, which is the source of the species common name: tomato leafminer. However, they can also feed on (and cause damage to) potatoes, eggplant, and other solanaceous crops. For this reason, <i>P. absoluta</i> is a species of major economic importance, and is closely monitored by many government agencies in order to mitigate its harmful impact on agriculture. <br>Understanding the genotype of pest species provides an important baseline for designing integrated pest management (IPM) strategies. We sequenced 20x coverage of the estimated 514Mb genome PacBio HiFi reads and assembled the genome of one larva of <i>P. absoluta</i>, resulting in a 652.69 Mb genome in 688 contigs with contig N50 of 1.6Mb, and a BUSCO completeness score of 96.2%.