cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100784
title: Supporting data for "3D imaging and metabolomic profiling reveal higher neuroactive kavalactone contents in lateral roots and crown root peels of Piper methysticum (Kava)"

description: Kava is an important neuro-active medicinal plant. While kava has a large global consumer footprint for its clinical and recreational utilization, factors related to its use lack standardization and the tissue-specific metabolite profile of its neuroactive constituents is not well understood. Here we characterized the metabolomic profile and spatio-temporal characteristics of tissues from the roots and stems using cross-platform metabolomics and 3D imaging approach. GC-MS and LC-MS revealed the highest content of kavalactones in crown root peels and lateral roots. IR-MALDESI imaging revealed a unique tissue-specific presence of each target kavalactone. CT analysis demonstrated that lateral roots have morphological characteristics suitable for synthesis of the highest content of kavalactones. These results provide mechanistic insights into the social and clinical practice of the use of only peeled roots by linking specific tissue characteristics to concentrations of neuroactive compounds.
DOI: 10.5524/100791
title: Supporting data for "Construction of a chromosome-scale long-read reference genome assembly for potato"

description: Worldwide, the cultivated potato, <i>Solanum tuberosum</i> L., is the number one vegetable crop and a critical food security crop. The genome sequence of DM1-3 516 R44, a doubled monoploid clone of S. tuberosum Group Phureja, was published in 2011 using a whole-genome shotgun sequencing approach with short read sequence data. Current advanced sequencing technologies now permit generation of near-complete, high-quality chromosome-scale genome assemblies at a minimal cost. Here, we present an updated version of the DM1-3 516 R44 genome sequence (v6.1) using Oxford Nanopore Technologies long reads coupled with proximity-by-ligation scaffolding (Hi-C) yielding a chromosome-scale assembly. The new (v6.1) assembly represents 741.6 Mb of sequence (87.8 %) of the estimated 844 Mb genome, of which, 741.5 Mb is non-gapped with 731.2 Mb anchored to the 12 chromosomes. Use of Oxford Nanopore Technologies full-length cDNA sequencing enabled annotation of 32,917 high-confidence protein-coding genes encoding 44,851 gene models that had a significantly improved representation of conserved orthologs compared to the previous annotation. The new assembly has improved contiguity with a 595-fold increase in N50 contig size, 99% reduction in the number of contigs, a 44-fold increase in N50 scaffold size, and an LTR Assembly Index score of 13.56, placing it in the category of reference genome quality. The improved assembly also permitted annotation of the centromeres via alignment to sequencing reads derived from CENH3 nucleosomes. Access to advanced sequencing technologies and improved software permitted generation of a high-quality, long-read, chromosome-scale assembly and improved annotation dataset for the reference genotype of potato that will facilitate research aimed at improving agronomic traits and understanding genome evolution.
DOI: 10.5524/100795
title: Supporting data for "NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy"

description: Long-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies-based long-read sequencing nanopore platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the specialised data generated by long-read sequencing platforms means that accompanying specialised bioinformatics platforms and tools are required in order to correctly process the long-read data generated. Importantly, such tools should additionally help democratise bioinformatics analysis by enabling easy access and ease-of-use solutions for researchers. The Galaxy platform provides a user-friendly interface to computational command-line-based tools, including their software dependencies and refined workflows. The interface enables researchers, who do not necessarily possess programming experience or extended computer skills, to perform powerful bioinformatics analysis, including the assembly and analysis of short- or long-read sequence data. The newly developed NanoGalaxy is a Galaxy-based toolkit for analysing long-read sequencing data, which is suitable for diverse applications, including <i>de novo</i> genome assembly from genomic, metagenomic and plasmid sequence reads. A range of best practice tools and workflows for long-read sequence genome assembly have been integrated into a NanoGalaxy platform in order to facilitate easy access and use of bioinformatics tools for researchers. Nanogalaxy is freely available at the European Galaxy server, which includes self-learning training material.
DOI: 10.5524/100800
title: Supporting data for "CopyDetective: Detection Threshold Aware CNV Calling in WES Data"

description: Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, the detection of CNVs with whole-exome sequencing experiments is challenging. Usually, additional experiments have to be performed.<br>We developed a novel algorithm for somatic CNV calling in matched WES data called CopyDetective. Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. <br>Analyzing four WES data sets (n=100) we observe superior performance of CopyDetective compared to ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq. Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds (as it is realized by CopyDetective), can and should be performed prior to actual variant calling.
DOI: 10.5524/100803
title: Supporting data for "IDseq- An Open Source Cloud-based Pipeline and Analysis Service for Metagenomic Pathogen Detection and Monitoring"

description: Metagenomic next generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource limited environments. We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring. The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics which are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences, and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.
DOI: 10.5524/100809
title: Supporting data for "DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data"

description: Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic data sets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Here, we present DrivAER, a machine learning approach for the identification of Driving transcriptional programs using AutoEncoder based Relevance scores. DrivAER scores annotated gene sets based on their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data. By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms.
DOI: 10.5524/100816
title: Supporting data for "Fractional ridge regression: a fast, interpretable reparameterization of ridge regression"

description: Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter () that controls the amount of regularization. Cross-validation is typically used to select the best from a set of candidates. However, efficient and appropriate selection of can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable.<br>The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and MATLAB (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets.<br>Fractional ridge regression has several benefits: the solutions obtained for different are guaranteed to vary, guarding against wasted calculations, and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.
DOI: 10.5524/100769
title: Supporting data for "Genomic data imputation with variational autoencoders"

description: As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete datasets. State-of-the-art imputation techniques including Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) based methods can be computationally expensive for large datasets and it is difficult to modify these algorithms to handle certain missing-not-at-random cases. In this work, we use a deep learning framework based on the variational autoencoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most widely used imputation standards, while having a computational advantage at evaluation time. When dealing with missing-not-at-random, e.g. low values are missing, we develop simple yet effective methodologies to leverage the prior knowledge about missing data. Furthermore, we investigate the effect of varying latent space regularization strength in VAE on the imputation performances, and in this context show why VAE has a better imputation capacity compared to a regular deterministic autoencoder (AE).
DOI: 10.5524/100772
title: Supporting data for "Scientometric Trends for Coronaviruses and Other Emerging Viral Infections"

description: COVID-19 is the most rapidly expanding coronavirus outbreak in the past two decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential. Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing over 35 million papers from the last 20 years. Our results demonstrate that previous coronavirus outbreaks have been understudied compared to other viruses. We also show that the research volume of emerging infectious diseases is very high after an outbreak and drops drastically upon the containment of the disease. This can yield inadequate research and limited investment in gaining a full understanding of novel coronavirus management and prevention. Independent of the outcome of the current COVID-19 outbreak, we believe that measures should be taken to encourage sustained research in the field. <br>The code used to generate these findings is available from GigaDB (below) as well as in the GitHub repository. There are complete instructions on installation and use included in the Readme.md file within the GitHub repository, as well as additional dataset descriptions in the Dataset.md file.
DOI: 10.5524/100775
title: Supporting data for "ScanITD: detecting internal tandem duplication with robust variant allele frequency estimation"

description: Internal tandem duplications (ITDs) are tandem duplications within coding exons and are important prognostic markers and drug targets for acute myeloid leukemia (AML). Next generation sequencing has enabled the discovery of ITD at single nucleotide resolution. ITD allele frequency is used in the risk stratification of AML patients, higher ITD allele frequency has been proved for its association with poorer clinical outcomes. However, the ITD allele frequency data is often unavailable to treating physicians and the detection of ITDs with accurate variant allele frequency (VAF) estimation remains challenging for short read sequencing.<br>Here we present ScanITD approach that performs a stepwise seed-and-realignment procedure for ITD detection with accurate VAF prediction. The evaluations on simulated and real data demonstrate that ScanITD outperforms three state-of-the-art ITD detectors, especially for VAF estimation. Importantly, ScanITD yields better accuracy than general-purpose structural variations callers for predicting ITD size range duplications. <br>ScanITD enables the accurate identification of ITDs with robust VAF estimation. ScanITD is written in Python, and is open-source software and freely accessible at GitHub.
DOI: 10.5524/100783
title: Supporting data for "Interpreting k-mer based signatures for antibiotic resistance prediction"

description: Recent years witnessed the development of several k-mer-based approaches aiming to predict phenotypic traits of bacteria based on their whole-genome sequences. While often convincing in terms of predictive performance, the underlying models are in general not straightforward to interpret, the interplay between the actual genetic determinant and its translation as k-mers being generally hard to decipher.<br>We propose a simple and computationally efficient strategy allowing one to cope with the high correlation inherent to k-mer-based representations in supervised machine learning models, leading to concise and easily interpretable signatures. We demonstrate the benefit of this approach on the task of predicting the antibiotic resistance profile of a <i>Klebsiella pneumoniae</i> strain from its genome, where our method leads to signatures defined as weighted linear combinations of genetic elements that can easily be identified as genuine antibiotic resistance determinants, with state of the art predictive performance.<br>By enhancing the interpretability of genomic k-mer-based antibiotic resistance prediction models, our approach improves their clinical utility, hence will facilitate their adoption in routine diagnostics by clinicians and microbiologists. While antibiotic resistance was the motivating application, the method is generic and can be transposed to any other bacterial trait. An R package implementing our method is available on GitLab.
DOI: 10.5524/100792
title: Supporting data for "Prediction of single cell gene expression for transcription factor analysis"

description: Single cell (sc) RNA-sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from scRNA data. Here, we propose a novel approach for predicting gene expression at the single cell level using cis-regulatory motifs as well as epigenetic features. We designed a treeguided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by literature. Our proposed method allows us to identify distinct TFs that show cell-type specific regulation. This approach is not limited to TFs, but can use any type of data that can potentially be used in explaining gene expression at the single cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via GitHub.
DOI: 10.5524/100770
title: Supporting data for "Hi-C chromosome conformation capture sequencing of avian genomes using the BGISEQ-500 platform"

description: Hi-C experiments couple DNA-DNA proximity with next-generation sequencing to yield an unbiased description of genome-wide interactions. Previous methods describing Hi-C experiments have focused on the industry-standard, Illumina sequencing. With new next-generation sequencing platforms such as BGISEQ-500 becoming more widely available, protocol adaptations to fit platform-specific requirements are useful to give increased choice to researchers who routinely generate sequencing data. We describe an <em>in-situ </em> Hi-C protocol adapted to be compatible with the the BGISEQ-500 high-throughput sequencing platform. Using zebra finch (<em>Taeniopygia guttata</em>) as a biological sample, we demonstrate how Hi-C libraries can be constructed to generate informative data using the BGISEQ-500 platform, following circularization and DNA nanoball generation. Our protocol is a modification of an Illumina-compatible method, based around blunt-end ligations in library construction, using un-barcoded, distally overhanging double-stranded adapters, followed by amplification using indexed primers. The resulting libraries are ready for circularization and subsequent sequencing on the BGISEQ series of platforms, and yield data similar to what can be expected using Illumina-compatible approaches. Our straightforward modification to an Illumina-compatible <em>in-situ</em> Hi-C protocol enables data generation on the BGISEQ series of platforms, thus expanding the options available for researchers who wish to utilize the powerful Hi-C techniques in their research.
DOI: 10.5524/100774
title: Supporting data for "A haplotype-resolved, de novo genome assembly for the wood tiger moth (Arctia plantaginis) through trio binning"

description: Diploid genome assembly is typically impeded by heterozygosity, as it introduces errors when haplotypes are collapsed into a consensus sequence. Trio binning offers an innovative solution which exploits heterozygosity for assembly. Short, parental reads are used to assign parental origin to long reads from their F1 offspring before assembly, enabling complete haplotype resolution. Trio binning could, therefore, provide an effective strategy for assembling highly heterozygous genomes which are traditionally problematic, such as insect genomes. This includes the wood tiger moth (<i>Arctia plantaginis</i>), which is an evolutionary study system for warning color polymorphism.<br>We produced a high quality, haplotype-resolved assembly for <i>Arctia plantaginis</i> through trio binning. We sequenced a same-species family (F1 heterozygosity ~1.9%) and used parental Illumina reads to bin 99.98% of offspring Pacific Biosciences reads by parental origin, before assembling each haplotype separately and scaffolding with 10X linked-reads. Both assemblies are highly contiguous (mean scaffold N50: 8.2Mb) and complete (mean BUSCO completeness: 97.3%), with complete annotations and 31 chromosomes identified through karyotyping. We employed the assembly to analyze genome-wide population structure and relationships between 40 wild resequenced individuals from five populations across Europe, revealing the Georgian population as the most genetically differentiated with the lowest genetic diversity.<br>We present the first invertebrate genome to be assembled via trio binning. This assembly is one of the highest quality genomes available for Lepidoptera, supporting trio binning as a potent strategy for assembling highly heterozygous genomes. Using this assembly, we provide genomic insights into geographic population structure of <i>Arctia plantaginis</i>
DOI: 10.5524/100781
title: Supporting data for "A molecular map of lung neuroendocrine neoplasms."

description: Lung neuroendocrine neoplasms (NENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas (LCNEC) led us to the discovery of clinically relevant molecular groups as well as a new entity of pulmonary carcinoids (supra-carcinoids).<br>In order to promote the integration of lung NENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850k methylation arrays for a total of 84 lung NENs patients.<br>We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of lung NENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as a reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal. The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available respectively in a Nextjournal interactive notebook, and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. <br>We provide data and all resources needed to integrate it with future lung NENs transcriptomic studies, allowing to draw meaningful conclusions that will eventually lead to a better understanding of this rare understudied disease.