cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100793
title: Supporting data for "Adaptive venom evolution and toxicity in octopods is driven by extensive novel gene formation, expansion and loss"

description: Cephalopods represent a rich system for investigating the genetic basis underlying organismal novelties. This diverse group of specialised predators has evolved many adaptations including proteinaceous venom. Of particular interest is the blue-ringed-octopus genus (<i>Hapalochlaena</i>), which are the only octopods known to store large quantities of the potent neurotoxin, tetrodotoxin, within their tissues and venom gland. To reveal genomic correlates of organismal novelties, we conducted a comparative study of three octopod genomes, including the Southern blue-ringed octopus (<i>Hapalochlaena maculosa</i>). We present the genome of this species and reveal highly dynamic evolutionary patterns at both non-coding and coding organizational levels. Gene family expansions previously reported in <i>Octopus bimaculoides</i> (e.g., zinc finger and cadherins, both associated with neural functions), as well as formation of novel gene families, dominate the genomic landscape in all octopods. Examination of tissue-specific genes in the posterior salivary gland (PSG) revealed that expression was dominated by serine proteases in non-tetrodotoxin bearing octopods, while this family was a minor component in <i>H. maculosa</i>. Moreover, voltage-gated sodium channels in <i>H. maculosa</i> contain a resistance mutation found in pufferfish and garter snakes, which is exclusive to the genus. Analysis of the PSG microbiome revealed a diverse array of bacterial species, including genera that can produce tetrodotoxin, suggestive of a possible production source. We present the first tetrodotoxin-bearing octopod genome <i>H. maculosa</i>, which displays lineage-specific adaptations to tetrodotoxin acquisition. This genome, along with other recently published cephalopod genomes, represents a valuable resource from which future work could advance our understanding of the evolution of genomic novelty in this family.
DOI: 10.5524/100796
title: Supporting data for "Correcting for experiment-specific variability in expression compendia can remove underlying signals"

description: In the last two decades, scientists working in different labs have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, sometimes referred to as batch effects, may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to interpret and extract true underlying biological patterns. As more integrative analysis methods are developed and available data collections are increased in size, it is crucial to determine how technical variability affect our ability to detect desired patterns when many experiments are combined.<br>We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprised of data aggregated across multiple experiments.<br>We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability.<br>We found that the signal from a baseline compendium was obscured when the number of added sources of variability was small. Perhaps as expected, applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased we observed that detecting the original signal became increasingly easier even without correction. In fact, applying statistical correction methods reduced our power to detect the underlying signal.<br>When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.
DOI: 10.5524/100799
title: Supporting data for "Chromosome-level genome assembly of a benthic associated Syngnathiformes species: the common dragonet, <i>Callionymus lyra</i>"

description: The common dragonet, <i>Callionymus lyra</i>, is one of three <i>Callionymus</i> species inhabiting the North Sea. All three species show strong sexual dimorphism. The males show strong morphological differentiation, e.g., species-specific coloration and size relations, while the females of different species have few distinguishing characters. <i>Callionymus</i> belongs to the benthic associated clade of the order Syngnathiformes. The benthic associated clade so far is not represented by genome data and serves as an important outgroup to understand the morphological transformation in long-snouted Syngnatiforms such as seahorses and pipefishes. Here, we present the chromosome-level genome assembly of <i>C. lyra</i>. We applied Oxford Nanopore Technologies long-read sequencing, short-read DNBseq, and proximity-ligation-based scaffolding to generate a high-quality genome assembly. The resulting assembly has a contig N50 of 2.2 Mbp, a scaffold N50 of 26.7 Mbp. The total assembly length is 568.7 Mbp, of which over 538 Mbp were scaffolded into 19 chromosome-length scaffolds. The identification of 94.5% of complete BUSCO genes indicates high assembly completeness. Additionally, we sequenced and assembled a multi-tissue transcriptome with a total length of 255.5 Mbp that was used to aid the annotation of the genome assembly. The annotation resulted in 19,849 annotated transcripts and identified a repeat content of 27.66%. The chromosome-level assembly of <i>C. lyra</i> provides a high-quality reference genome for future population genomic, phylogenomic, and phylogeographic analyses.
DOI: 10.5524/100801
title: Supporting data for "File-based localization of numerical perturbations in data analysis pipelines"

description: Data analysis pipelines are known to be impacted by computational conditions, presumably due to the creation, propagation, and amplification of numerical errors. While this process could play a major role in the current reproducibility crisis, the precise causes of such instabilities and the path along which they propagate in pipelines are unclear. We present Spot, a tool to identify which processes in a pipeline create numerical differences when executed in different computational conditions. Spot leverages system-call interception through ReproZip to reconstruct and compare provenance graphs without pipeline instrumentation. By applying Spot to the structural pre-processing pipelines of the Human Connectome Project, we found that linear and non-linear registration are the cause of most numerical instabilities in these pipelines, which confirms previous findings.
DOI: 10.5524/100805
title: Supporting data for "Comparison of the two up-to-date sequencing technologies for genome assembly: HiFi reads of Pacbio Sequel II system and ultralong reads of Oxford Nanopore"

description: The availability of reference genomes has revolutionized the study of biology. Multiple competing technologies have been developed to improve the quality and robustness of genome assemblies during the last decade. The two widely-used long-read sequencing providers Pacbio (PB) and Oxford Nanopore Technologies (ONT) have recently updated their platforms: PB enables high throughput HiFi reads with baselevel resolution with &gt;99% and ONT generated reads as long as 2 Mb. We applied the two up-to-date platforms to one single rice individual and then compared the two assemblies to investigate the advantages and limitations of each. The results showed that ONT ultralong reads delivered higher contiguity producing a total of 18 contigs of which ten were assembled into a single chromosome compared to that of 394 contigs and three chromosome-level contigs for the PB assembly. The ONT ultralong reads also prevented assembly errors caused by long repetitive regions for which we observed a total of 44 genes of false redundancies and ten genes of false losses in the PB assembly leading to over/under-estimation of the gene families in those long repetitive regions. We also noted that the PB HiFi reads generated assemblies with considerably fewer errors at the level of single nucleotide and small InDels than that of the ONT assembly which generated an average 1.06 errors per Kb and finally engendered 1,475 incorrect gene annotations via altered or truncated protein predictions.
DOI: 10.5524/100808
title: Supporting data for "Efficient DNA sequence compression with neural networks"

description: The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution tolerant context models.<br>We benchmark GeCo3 as a reference-free DNA compressor in five datasets, including a balanced and comprehensive dataset of DNA sequences, the Y chromosome and human mitogenome, two compilations of archaeal and virus genomes, four whole genomes, and two collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of 2.4%, 7.1%, 6.1%, 5.8% and 6%, respectively. As a reference-based DNA compressor, we benchmark GeCo3 in four datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in 12.4%, 11.7%, 10.8% and 10.1% over the state-of-the-art. The cost of this compression improvement is some additional computational time (1.7× to 3× slower that GeCo2). The RAM is constant, and the tool scales efficiently, independently from the sequence size. Overall, these values outperform the state-of-the-art.<br>GeCo3 is a genomic sequence compressor with a neural network mixing approach, that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3
DOI: 10.5524/100810
title: Supporting data for "GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets"

description: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow us to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena.<br>We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study.<br>GigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example used on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.
DOI: 10.5524/100815
title: Supporting data for "D-EE: a distributed software for visualizing intrinsic structure of large-scale single-cell data"

description: Dimensionality reduction and visualization play vital roles in single-cell RNA sequencing (scRNA-seq) data analysis. While they have been extensively studied, state-of-the-art dimensionality reduction algorithms are often unable to preserve the global structures underlying data. Elastic Embedding (EE), a nonlinear dimensionality reduction method, has shown promise in revealing low-dimensional intrinsic local and global data structure. However, the current implementation of the EE algorithm lacks scalability to large-scale scRNA-seq data.<br>We present a distributed optimization implementation of the EE algorithm, termed distributed Elastic Embedding (D-EE). D-EE reveals the low-dimensional intrinsic structures of data with accuracy equal to that of Elastic Embedding, and it is scalable to large-scale scRNA-seq data. It leverages distributed storage and distributed computation, achieving memory efficiency and high-performance computing simultaneously. In addition, an extended version of D-EE, termed distributed optimization implementation of time series Elastic Embedding (D-TSEE), enables the user to visualize large-scale time series scRNA-seq data by incorporating experimental temporal information. Results with a large-scale scRNA-seq data indicate D-TSEE can uncover oscillatory gene expression patterns by employing experimentally temporal information.<br>D-EE is a distributed dimensionality reduction and visualization tool. Its distributed storage and distributed computation technique allow us to efficiently analyze large-scale single-cell data at the cost of constant time speedup. The source code for D-EE algorithm based on C and MPI tailored to a High Performance Computing cluster is available from our GitHub archive.
DOI: 10.5524/100817
title: Supporting data for "Genome sequencing of deep-sea hydrothermal vent snails reveals adaptions to extreme environments"

description: The scaly-foot snail (<i>Chrysomallon squamiferum</i>) is highly adapted to deep-sea hydrothermal vents and drew peoples interest once it was found. However, the limited information on its genome impeded related research and understanding of its adaptation to deep-sea hydrothermal vents. Here, we report the whole-genome sequencing and assembly of the scaly-foot snail and another snail (<i>Gigantopelta aegis</i>), which inhabits similar environments. Using ONT, 10X genomic, and Hi-C technologies, we obtained a chromosome-level genome of <i>C. squamiferum</i> with an N50 size of 20.71 Mb. By constructing a phylogenetic tree, we found that these two deep-sea snails were independent of other snails, and their divergence from each other occurred approximately 66.3 million years ago. Comparative genomic analysis showed that different snails have diverse genome sizes and repeat contents. Deep-sea snails have more DNA transposons and LTRs, but fewer LINEs, than other snails. Gene family analysis revealed that deep-sea snails experienced stronger selective pressures than freshwater snails, and the nervous system, immune system, metabolism, DNA stability, antioxidation and biomineralization-related gene families were significantly expanded in scaly-foot snails. We also found 251 class II histocompatibility antigen H2-Aal, which uniquely exist in the <i>Gigantopelta aegis</i> genome, which is important for investigating the evolution of MHC genes. Our study provides new insights into deep-sea snail genomes and valuable resources for further studies.
DOI: 10.5281/zenodo.3741446
title: NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy

description: The data presented in "NanoGalaxy: A Galaxy tool kit with workflows for third-generation sequence analysis" to illustrate the functionality of the tools was obtained from: Wick, Ryan R., et al. "Completing bacterial genome assemblies with multiplex MinION sequencing." <em>Microbial genomics</em> 3.10 (2017). + Li, Ruichao, et al. "Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data." <em>Gigascience</em> 7.3 (2018): gix132.
DOI: 10.5524/100776
title: Supporting data for "Long-read only assembly of Drechmeria coniospora genomes reveals widespread chromosome plasticity and illustrates the limitations of current nanopore methods."

description: Long read sequencing is increasingly being used to determine eukaryotic genomes. We used nanopore technology to generate chromosome-level assemblies for 3 different strains of <i>Drechmeria coniospora</i>, a nematophagous fungus used extensively in the study of innate immunity in <i>Caenorhabditis elegans</i>. One natural geographical isolate demonstrated high stability over decades, whereas a second isolate, not only had a profoundly altered genome structure but exhibited extensive instability. We conducted an in-depth analysis of sequence errors within the 3 genomes and established that even with state-of-the-art tools, nanopore methods alone are insufficient to generate sequence of a sufficient accuracy to merit inclusion in public databases.
DOI: 10.5524/100778
title: Supporting data for "Chromosome-level genome assembly of the female western mosquitofish (Gambusia affinis)"

description: The western mosquitofish (<i>Gambusia affinis</i>) is a sexual dimorphism poeciliid fish known for its worldwide biological invasion and is an important research model for studying invasion biology. This organism may also be used as a suitable model to explore sex chromosome evolution and reproductive development in terms of differentiation of ZW sex chromosomes, ovoviviparity, and specialization of reproductive organs. However, there is a lack of high-quality genomic data for the female <i>G. affinis</i>; hence, this study aimed to generate a chromosome-level genome assembly of the female <i>G. affinis</i>.<br>The chromosome-level genome assembly was constructed using Oxford nanopore sequencing, BioNano, and Hi-C technology. A <i>G. affinis</i> genome containing 217 contigs with an N50 length of 12.9 Mb and 125 scaffolds with an N50 length of 26.5 Mb was obtained by Oxford nanopore and BioNano, respectively, and the 113 scaffolds (90.4% of scaffolds containing 97.9% nucleotide bases) were assembled into 24 chromosomes by Hi-C. The Z and W chromosomes of <i>G. affinis</i> were identified by comparative genomic analysis of female and male <i>G. affinis</i>, and the mechanism of differentiation of the Z and W chromosomes was explored. Combined with transcriptome data from six tissues, a total of 23,997 protein-coding genes were predicted and 23,737 (98.9%) genes were functionally annotated.<br>The high-quality female <i>G. affinis</i> genome obtained in this study provides a valuable omics resource for future studies of comparative genomics and functional genomics to explore the evolution of Z and W chromosomes and the reproductive developmental biology of <i>G. affinis</i>.
DOI: 10.5524/100779
title: Supporting data for "Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis"

description: Some natural systems are big in size, complex and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities, but that are often derived from group-effects. However, the determination of important groups of entities, like genes or proteins, in complex systems is considered a computationally hard task. Here, we present Pyntacle, a high-performance framework designed to exploit parallel computing and Graph Theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.
DOI: 10.5524/100780
title: Supporting data for "An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population"

description: To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish National population. This includes 30 million different radiological examinations from a population of 5.4 million and over 2 petabytes of data collected since 2010. Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of that data, link it to other clinical or social data sets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. An extensive software platform has been developed to host, extract and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. The data available is from a range of radiological modalities, scanner types and collected under different environmental conditions. This real-world, heterogenous data is highly valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using Artificial Intelligence and Machine Learning technologies as well as enabling discovery science.<br> <br>If you would like to access the SMI dataset for a research project, please contact eDRIS in the first instance.
DOI: 10.5524/100782
title: Supporting data for "Unifying package managers, workflow engines, and containers with BioNix for computational reproducibility"

description: A challenge for computational biologists is to make our analyses reproducible, that is, easy to rerun, combine, and share, with the assurance that equivalent runs will generate identical results. The current best practice aims at this using a combination of package managers, workflow engines, and containers.<br>We present BioNix, a lightweight library built on the Nix deployment system. BioNix manages software dependencies, computational environments, and workflow stages together using a single abstraction: pure functions. This lets us specify workflows in a way that is more reproducible and modular than current best practices.<br>BioNix is implemented in the Nix expression language and is released on GitHub under the 3-clause BSD license: https://github.com/PapenfussLab/bionix.