cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100749
title: Supporting data for "TinderMIX: Time-Dose INtegrated moDelling of toxicogenomics data"

description: Omics technologies have been widely applied in toxicology studies in order to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow studying molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis is not able to identify dynamic (time-depended) events of dose responsiveness. <br>We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log-fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify if the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure (POD).<br>To showcase the TinderMIX method, we analyzed two drugs from the Open TG-GATEs dataset, namely cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action (MOA) of each drug and compared them. Our analysis highlights that different time and dose integrated POD recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent MOA.
DOI: 10.5524/100751
title: Supporting data for "CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems"

description: CRISPR-Cas genes are extraordinarily diverse and evolve rapidly when compared to other prokaryotic genes. With the rapid increase in newly sequenced archaeal and bacterial genomes, manual identification of CRISPR-Cas systems is no longer viable. Thus, an automated approach is required for advancing our understanding of the evolution and diversity of these systems, and for finding new candidates for genome engineering in eukaryotic models. We introduce CRISPRcasIdentifier, a new machine learning based tool that combines regression and classification models for the prediction of potentially missing proteins in instances of CRISPR-Cas systems and the prediction of their respective subtypes. In contrast to other available tools, CRISPRcasIdentifier can both detect cas genes and extract potential association rules that reveal functional modules for CRISPR-Cas systems. In our experimental benchmark on the most recently published and comprehensive CRISPR-Cas system dataset, CRISPRcasIdentifier was compared with recent and state-of-the-art tools. According to the experimental results, CRISPRcasIdentifier presented the best Cas protein identification and subtype classification performance. Overall, our tool greatly extends the classification of CRISPR cassettes and, for the first time, predicts missing Cas proteins and association rules between Cas proteins. Additionally, we investigated the properties of CRISPR subtypes. The proposed tool relies not only on the knowledge of manual CRISPR annotation but also on models trained by using machine learning.
DOI: 10.5524/100753
title: Supporting data for "CandiMeth: Powerful yet simple visualization and quantification of DNA methylation at candidate genes"

description: DNA methylation microarrays are widely used in clinical epigenetics and are often processed using R packages like ChAMP or RnBeads by trained bioinformaticians. However, looking at specific genes requires bespoke coding which wet-lab biologists or clinicians are not trained for. This leads to high demands on bioinformaticians, who in turn may lack insight into the specific biological problem. We therefore wished to develop a tool for mapping and quantification of methylation differences at candidate genomic features of interest, without using coding, to bridge this gap. <br> We generated the workflow CandiMeth (CANDIdate METHylation) in the web-based environment Galaxy. CandiMeth takes as input any table listing differences in methylation generated by either of the popular R-based packages above and maps these to the human genome. A simple interface then allows the user to query the data using lists of gene names. CandiMeth generates 1)Tracks in the popular UCSC genome browser with an intuitive visual indicator of where differences in methylation occur between samples, or groups of samples 2) Tables containing quantitative data on the candidate regions, allowing interpretation of significance. In addition to genes and promoters, CandiMeth can analyse methylation differences at LINEs and SINEs. Cross-comparison to other open-resource genomic data at UCSC facilitates interpretation of the biological significance of the data and the design of wet lab assays to further explore methylation changes and their consequences for the candidate genes. <br>CandiMeth (RRID:SCR_017974; Biotools:CandiMeth) allows rapid, quantitative analysis of methylation at user-specified features without the need for coding and is freely available, with extensive guidance, at CandiMeth GitHub repo.
DOI: 10.5524/100755
title: Genome data for the chromosome-level assembly of the humpback puffer, <i>Tetraodon palembangensis</i>

description: The humpback puffer, <i>Tetraodon palembangensis</i>, also known as <i>Pao palembangensis</i>, is a species of poisonous freshwater pufferfish mainly distributed in Southeast Asia (Thailand, Laos, Malaysia, and Indonesia). Despite interesting biological features, such as its very inactive nature, tetrodotoxin production, and body expansion mechanisms, molecular research on the humpback puffer is still rare because of the lack of a high-quality reference genome. Here, we reported a first chromosome-level genome assembly of an adult humpback puffer, of which the genome size is 362 Mb with ~1.78 Mb contig N50 and ~15.8 Mb scaffold N50s. Based on the genome, ~61.5Mb (18.11%) repeat sequences were also identified, and a total of 19,796 genes were predicted, 99.20% of which were annotated using protein-coding function databases. Finally, we constructed a phylogenetic tree using single-copy gene families from ten teleost fishes. The humpback puffer genome will be a valuable genomic resource to illustrate possible mechanisms of tetrodotoxin synthesis and tolerance, providing clues for future detailed studies of biological toxins.
DOI: 10.5524/100759
title: Supporting data for "VariantSpark: A Distributed Implementation of Random Forest Tailored for Ultra High Dimensional Genomic Data"

description: Many traits and diseases are thought to be driven by more than one gene (polygenic). Polygenic Risk Scores (PRS) hence expand on Genome-Wide Association Studies (GWAS) by taking multiple genes into account when building risk models. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions are found in small datasets, large datasets have not been processed yet due to the high computational complexity of the search for epistatic interactions. We have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to whole-genome of population-scale datasets with a hundred million genomic variants and hundred thousand samples. Compared to traditional monogenic GWAS, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high dimensional genomic data in a manageable time.
DOI: 10.5524/100760
title: Supporting data for "The chromosome-level draft genome of <i>Dalbergia odorifera</i>"

description: <i>Dalbergia odorifera</i> T. Chen (Fabaceae) is an IUCN red-listed tree. This tree is of high medicinal and commercial value due to its officinal, insect-proof, durable heartwood. However, there is a lack of genome reference, which has hindered the development of studies on heartwood formation. <br>We presented the first chromosome-scale genome assembly of <i>D. odorifera</i> obtained based on Illumina paired-end sequencing, Pacific Bioscience single-molecule real-time sequencing, 10x Genomics linked-reads, and Hi-C technology. We assembled 97.68% of the 653.45 Mb <i>D. odorifera</i> genome with scaffold N50 and contig sizes of 56.16 Mb and 5.92 Mb, respectively. Ten super-scaffolds corresponding to the 10 chromosomes were assembled with the longest scaffold reaching 79.61 Mb. Repetitive elements account for 54.17% of the genome, and 30,310 protein-coding genes were predicted from the genome, of which approximately 92.6% were functionally annotated. The phylogenetic tree showed that <i>D. odorifera</i> diverged from the ancestor of <i>A. thaliana</i> and <i>P. trichocarpa</i>, and then separated from <i>G. max</i> and <i>C. cajan</i>. <br>We sequence and reveal the first chromosome-level de novo genome of <i>D. odorifera</i>. These studies provide valuable genomic resources for the research of heartwood formation in <i>D. odorifera</i> and other timber trees. The high-quality assembled genome can also be used as a reference for comparative genomics analysis and future population genetic studies of <i>D. odorifera</i>.
DOI: 10.5524/100762
title: Supporting data for "Sequence Compression Benchmark (SCB) database"

description: Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for more efficient compression tools. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available.<br>We systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database) that allows building custom visualizations for selected subsets of benchmark results.<br>We found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows comparing compressors and their settings using a variety of performance measures, offering the opportunity to select the optimal compressor based on the data type and usage scenario specific to a particular application.
DOI: 10.5524/100767
title: Supporting data for "Imputing missing RNA-seq data from DNA methylation by using transfer learning based neural network"

description: Gene expression plays a key intermediate role in linking molecular features at DNA level and phenotype. However, due to various limitations in experiments, the RNA-seq data is missing in many samples while there exists highquality of DNA methylation data. As DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on single cancer dataset, and do not fully utilize information from large pan-cancer dataset.<br>Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through transfer learning-based neural network, namely TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-ofthe-art methods in imputation accuracy with 7%-11% increase under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylationdriving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purpose by the independent test on the Wilms tumor dataset from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) project. TDimpute is an effective method for RNA-seq imputation with limited training samples.
DOI: 10.5281/zenodo.3603221
title: GTN Tutorial: Visualization with Circos

description: Data required for completing the Galaxy tutorial entitled "Visualization with Circos"
DOI: 10.5524/100724
title: Supporting data for "Molecular layer interneurons in the cerebellum encode for valence in associative learning"

description: We used two-photon microscopy to study the role of ensembles of cerebellar molecular layer interneurons (MLIs) in a go-no go task where mice obtain a sugar water reward if they lick a spout in the presence of the rewarded odorant and avoid a time out when they refrain from licking for the unrewarded odorant. In naïve animals the MLI responses did not differ between the odorants. With learning, the rewarded odorant elicited a large increase in MLI calcium responses, and the identity of the odorant could be decoded from the differential response. Importantly, MLIs switched odorant responses when the valence of the stimuli was reversed. Finally, mice took a longer time to refrain from licking in the presence of the unrewarded odorant and had difficulty becoming proficient when MLIs were inhibited by chemogenetic intervention. Our findings support a role for MLIs in learning valence in the cerebellum.
DOI: 10.5524/100729
title: Supporting data for "CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes"

description: Easy-to-use and fast bioinformatics pipelines for long-read assembly that go beyond the contig-level to generate high-quality chromosome-scale genomes from raw data remain scarce.<br>Chromosome Scale Assembler (CSA) is a novel computationally highly efficient bioinformatics pipeline that fills this gap. CSA integrates information from scaffolded assemblies (e.g. Hi-C or 10X Genomics) or even from diverged reference genomes into the assembly process. As CSA performs automated assembly of chromosome-sized scaffolds, we benchmark its performance against state-of-the art reference genomes that have been built in a laborious fashion using multiple separate assembly tools and manual curation. CSA increases the contig length using scaffolding, local re-assembly and gap-closing. On certain datasets, initial contig N50 may be increased up to 4.5-fold. For smaller vertebrate genomes, chromosome-scale assemblies can be achieved within 12 h using low cost, high-end desktop computers. Mammalian genomes can be processed within 16 h on compute-servers. Using diverged reference genomes for fish, birds and mammals, we demonstrate that CSA calculates chromosome-scale assemblies from long-read data and genome comparisons alone. Even contig-level draft assemblies of diverged genomes are helpful for reconstructing chromosome-scale sequences. CSA is capable of assembling ultra-long reads.<br>CSA can speed-up and simplify chromosome-level assembly and significantly lower costs of large-scale family-level vertebrate genome projects.
DOI: 10.5524/100734
title: Supporting data for "Fcirc: A Comprehensive Pipeline for Exploration of Fusion Linear and Circular RNAs"

description: In cancer cells, fusion genes can produce linear- and chimeric fusion-circular ribonucleic acids (f-circRNAs) which are functional in gene expression regulation and implicated in malignant transformation, cancer progression and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g. EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging due to their general sparsity, low abundance in cancer cells but also due to imperfect computational methods. We developed the python-based workflow Fcirc to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to three different types of RNA-Seq data scenarios: 1) actual synthetic spike-in RNA-Seq data, 2) simulated RNA-Seq data and 3) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e. precision, recall, F-measure) and computing performance (i.e. lower runtimes). Fcirc is a powerful and comprehensive python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared to previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.
DOI: 10.5524/100735
title: Supporting data for "High-quality chromosome-scale assembly of the walnut (Juglans regia L) reference genome"

description: The release of the first reference genome of walnut (<i>Juglans regia</i> L.) enabled many achievements in the characterization of walnut genetic and functional variation. However, it is highly fragmented, preventing the integration of genetic, transcriptomic, and proteomic information to fully elucidate walnut biological processes. <br>Here, we report the new chromosome-scale assembly of the walnut reference genome (Chandler v2.0) obtained by combining Oxford Nanopore long-read sequencing with chromosome conformation capture (Hi-C) technology. Relative to the previous reference genome, the new assembly features an 84.4-fold increase in N50 size, with the 16 chromosomal pseudomolecules assembled and representing 95% of its total length. Using full-length transcripts from single-molecule real-time sequencing, we predicted 37,554 gene models, with a mean gene length higher than the previous gene annotations. Most of the new protein-coding genes (90%) presents both start and stop codons, which represents a significant improvement compared to Chandler v1.0 (only 48%). We then tested the potential impact of the new chromosome-level genome on different areas of walnut research. By studying the proteome changes occurring during male flower development, we observed that the virtual proteome obtained from Chandler v2.0 presents fewer artifacts than the previous reference genome, enabling the identification of a new potential pollen allergen in walnut. Also, the new chromosome scale genome facilitates in-depth studies of intra-species genetic diversity by revealing previously undetected autozygous regions in Chandler, likely resulting from inbreeding, and 195 genomic regions highly differentiated between Western and Eastern walnut cultivars. <br>Overall, Chandler v2.0 will serve as a valuable resource to understand and explore walnut biology better.
DOI: 10.5524/100741
title: Supporting data for "Smash++: an alignment-free and memory-efficient tool to find genomic rearrangements"

description: The development of high-throughput sequencing technologies and, as its result, the production of huge volumes of genomic data, has accelerated biological and medical research and discovery. Study on genomic rearrangements is crucial due to their role in chromosomal evolution, genetic disorders and cancer. We present Smash++, an alignment-free and memory-efficient tool to find and visualize small- and large-scale genomic rearrangements between two DNA sequences. This computational solution extracts information contents of the two sequences, exploiting a data compression technique, in order for finding rearrangements. We also present Smash++ visualizer, a tool that allows the visualization of the detected rearrangements along with their self- and relative complexity, by generating an SVG (Scalable Vector Graphics) image; Tested on several synthetic and real DNA sequences from bacteria, fungi, Aves and mammalia, the proposed tool was able to accurately find genomic rearrangements. The detected regions complied with previous studies which took alignment-based approaches or performed FISH (Fluorescence in situ hybridization) analysis. The maximum peak memory usage among all experiments was ~1 GB, which makes Smash++ feasible to run on present-day standard computers.
DOI: 10.5524/100742
title: Supporting data for "Assessment of fecal DNA extraction protocols for metagenomic studies"

description: Shotgun metagenomic sequencing has greatly improved our understanding of the human gut microbiota. Efforts have been made to evaluate the performance of various DNA extraction methods to recommend protocols that robustly and most accurately reflect the original microbial community structures. However, so far recommended standardized bacterial DNA extraction protocols still may be improved, especially considering future demands in relation to time and cost dealing with samples from very large cohorts. Additionally, fungal DNA extraction performance has so far been little investigated. <br>Here, we compared six DNA extraction protocols, MagPure Fast Stool DNA KF Kit B (MP), Macherey Nagel™ NucleoSpin™® Soil kit (MN), Zymo Research Quick-DNA™ Fecal/Soil Microbe kit (ZYMO), MOBIO DNeasy PowerSoil kit (PS), the manual protocol MetaHIT, and the recently published protocol Q using one microbial mock community (MMC) (containing eight bacterial and two fungal strains) and fecal samples. Evaluation of results using the MMC demonstrated that bead size was a determining factor for fungal and bacterial DNA yields. Evaluation of human fecal samples revealed that the bacterial extraction performance of protocol MP matched that of the standardized protocol Q, but consumed less time and was more cost-effective. Extraction using the protocol PS resulted in a significantly higher ratio of gram-negative to gram-positive bacteria than other protocols, which might potentially contribute to reported gut microbial differences between healthy US, Chinese and Danish adults, where fecal DNA samples were extracted using protocols PS and MetaHIT. <br>We reveal the importance of bead size selection for bacterial and fungal DNA extraction. More importantly, we demonstrate that the novel, time- and cost-effective protocol MP in terms of consistency and performance was equal to standardized protocol Q, and we recommend the use of MP for further large-scale metagenomic studies.