cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100430
title: Supporting data for "Field of Genes: Using Apache Kafka as a Bioinformatic Data Repository"

description: Bioinformatic research is increasingly dependent on large-scale data sets, accessed either from private or public repositories. An example of a public repository is NCBI's RefSeq. These repositories must decide in what form to make their data available. Unstructured data can be put to almost any use, but are limited in how access to them can be scaled. Highly structured data offer improved performance for specific algorithms but limit the wider usefulness of the data. We present an alternative: lightly structured data stored in Apache Kafka in a way that is amenable to parallel access and streamed processing, including subsequent transformations into more highly structured representations. We contend that this approach could provide a flexible and powerful nexus of bioinformatic data, bridging the gap between low-structure on one hand, and high-performance and scale on the other. To demonstrate this, we present a proof of concept version of NCBI's RefSeq database using this technology. We measure the performance and scalability characteristics of this alternative with respect to flat files.<br>The proof of concept scales almost linearly as more compute nodes are added, outperforming the standard approach using files. <br> Apache Kafka merits consideration as a fast and more scalable but general-purpose way to store and retrieve bioinformatic data, for public, centralized reference datasets like RefSeq, and private clinical and experimental data.
DOI: 10.5524/100434
title: Supporting data for "Error Correcting Optical Mapping Data"

description: Optical mapping is a unique system that is capable of producing high-resolution, high-throughput genomic map data that gives information about the structure of a genome. Recently it has been used for scaffolding contigs and assembly validation for large-scale sequencing projects, including the maize, goat, and amborella genomes. However, a major impediment in the use of this data is the variety and quantity of errors in the raw optical mapping data, which are called Rmaps. The challenges associated with using Rmap dataand thus, optical mapping datais analogous to dealing with insertions and deletions in the alignment of long reads. Moreover, they are arguably harder to tackle since the data is integral and susceptible to inaccuracy. We develop cOMet to error correct Rmap data, which to the best of our knowledge is the only optical mapping error correction method. Our experimental results demonstrate that cOMet corrects 82.49% of insertion errors and 77.38% of deletion errors in Rmap data generated from the E. coli K-12 reference genome. Out of the deletion errors corrected, 98.26% are true errors. Similarly, out of the insertion errors corrected, 82.19% are true errors. It also successfully scales to large genomes, improving the quality of 78% and 99% of the Rmaps in the plum and goat genomes, respectively. Lastly, we show the utility of error correction by demonstrating how it improves the assembly of Rmap data. Error corrected Rmap data results in an assembly that is more contiguous, and covers a larger fraction of the genome.
DOI: 10.5524/100435
title: The Monopterus genome

description: The teleost fish Monopterus albus is emerging as a new model for biological studies due to its natural sex transition and small genome, in addition to its enormous economic and potential medical value. However, no genomic information for the Monopterus is currently available. <br>Here, we sequenced and de novo assembled whole genome of the Monopterus, and report the de novo chromosome assembly by FISH walking assisted by conserved synteny (Cafs). Using Cafs, 328 scaffolds were assembled into 12 chromosomes, which cover genomic sequences of 555 Mb, accounting for 81.3% of the sequences assembled in scaffolds (~689 Mb). A total of 18,660 genes were mapped on the chromosomes and show a non-random distribution along chromosomes. <br>We report the first reference genome of the Monopterus and provided an efficient Cafs strategy for a de novo chromosome-level assembly of the Monopterus genome which provides a valuable resource, not only for further studies in genetics, evolution and development, particularly sex determination, but also for breed improvement of the species.
DOI: 10.5524/100436
title: Supporting data for "Molecular modularity and asymmetry of the molluscan mantle revealed by a gene expression atlas"

description: Conchiferan molluscs construct a biocalcified shell that likely supported much of their evolutionary success. However beyond broad proteomic and transcriptomic surveys of molluscan shells and the shell-forming mantle tissue, little is known of the spatial and ontogenetic regulation of shell fabrication. In addition, most efforts have been focused on species that deposit nacre, which is at odds with the majority of conchiferan species that fabricate shells using a crossed lamellar microstructure, <em>sensu lato</em>. By combining proteomic and transcriptomic sequencing with <em>in situ </em>hybridisation we have identified a suite of gene products associated with the production of the crossed-lamellar shell in <em>Lymnaea stagnalis</em>. With this spatial expression data we are able to generate novel hypotheses of how the adult mantle tissue coordinates the deposition of the calcified shell. These hypotheses include functional roles for unusual and otherwise difficult to study proteins such as those containing repetitive low complexity domains (RLCDs). The spatial expression readouts of the shell-forming genes we studied also reveal cryptic patterns of asymmetry and modularity in the shell-forming cells of larvae and adult mantle tissue. This molecular modularity of the shell-forming mantle tissue hints at an intimate association between structure, function and evolvability and may provide an elegant explanation for the evolutionary success of the second largest phylum among the Metazoa.
DOI: 10.5524/100437
title: Software and supporting data for "Fast-SG: An alignment-free algorithm for hybrid assembly"

description: Long read sequencing technologies are the ultimate solution for genome repeats, allowing near reference level reconstructions of large genomes. However, long read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods which combine short and long read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. Here, we propose a new method, called Fast-SG, which uses a new ultra-fast alignment-free algorithm specifically designed for constructing a scaffolding graph using light-weight data structures.Fast-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how Fast-SG outperforms the state-of-the-art short read aligners when building the scaffolding graph, and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using Fast-SG with shallow long read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878).
DOI: 10.5524/100438
title: Supporting data for "Necklace: combining reference and assembled transcriptomes for more comprehensive RNA-Seq analysis"

description: RNA-Seq analyses can benefit from performing a genome-guided and de novo assembly, in particular for species where the reference genome or the annotation is incomplete. However, tools for integrating assembled transcriptome with reference annotation are lacking. <br>Necklace is a software pipeline that runs genome-guided and de novo assembly and combines the resulting transcriptomes with reference genome annotations. Necklace constructs a compact but comprehensive superTranscriptome out of the assembled and reference data. Reads are subsequently aligned and counted in preparation for differential expression testing.<br> Necklace allows a comprehensive transcriptome to be built from a combination of assembled and annotated transcripts which results in a more comprehensive transcriptome for the majority of organisms. In addition RNAseq data is mapped back to this newly created superTranscript reference to enable differential expression testing with standard methods. Necklace is available from https://github.com/Oshlack/necklace/wiki under GPL 3.0.
DOI: 10.5524/100442
title: Supporting data for "Massive NGS Data Analysis Reveals Hundreds Of Potential Novel Gene Fusions in Human Cell Lines"

description: Gene fusions derive from chromosomal rearrangements and the resulting chimeric transcripts are often endowed with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with different prognosis and, in some cases, they can provide specific drug targets. So far, many efforts have been carried out to study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive Next Generation Sequencing dataset for all the existing human tumor cell lines has provided the opportunity to further investigate these data in order to identify novel and still uncharacterized gene fusion events. In our work, we have extensively reanalyzed 935 paired-end RNA-seq experiments downloaded from "The Cancer Cell Line Encyclopedia" repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has been performed by the execution of four different gene fusion detection algorithms. The results have been further prioritized by running a bayesian classifier which makes an in silico validation. The collection of fusion events supported by all of the predictive softwares results in a robust set of ~1,700 in-silico predicted novel candidates suitable for downstream analyses. Given the huge amount of data and information produced, computational results have been systematized in a database named LiGeA. The database can be browsed through a dynamical and interactive web portal, further integrated with validated data from other well known repositories. Taking advantage of the intuitive query forms, the users can easily access, navigate, filter and select the putative gene fusions for further validations and studies. They can also find suitable experimental models for a given fusion of interest. We believe that the LiGeA resource can represent not only the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant cell lines, but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer biomarkers and specific drug targets.
DOI: 10.5524/100444
title: Supporting data for "Consistent decreased activity in the putamen in Parkinson's disease: A meta-analysis and an independent validation of resting-state fMRI"

description: Resting-state functional magnetic resonance imaging (RS-fMRI) has frequently been used to investigate local spontaneous brain activity in Parkinsons disease (PD) in a whole-brain, voxel-wise manner. To quantitatively integrate these studies, we conducted a coordinate-based meta-analysis (CB meta-analysis), using the seed-based <em>d</em> mapping (SDM) method, on 15 studies that used amplitude of low frequency fluctuation (ALFF) and 11 studies that used regional homogeneity (ReHo). All these ALFF and ReHo studies have compared PD patients with healthy controls. We also performed a validation RS-fMRI study of ALFF and ReHo in a frequency-dependent manner for a novel dataset consisting of 49 PD and 49 healthy controls. Decreased ALFF was found in the left putamen in PD by meta-analysis. This finding was replicated in our independent validation dataset in the 0.027 - 0.073 Hz band, but not in the conventional frequency band of 0.01 - 0.08 Hz. Findings from the current study suggested that decreased ALFF in the putamen of PD is the most consistent finding. RS-fMRI is a promising technique for the precise localization of abnormal spontaneous activity in PD. However, more frequency-dependent studies using the same analytical methods are needed to replicate that results.
DOI: 10.5524/100447
title: Supporting data for "zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs"

description: Single cell RNA-seq (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplication of the cDNA. The high throughput is made possible by the early introduction of sample-specific barcodes (BCs) and the amplication bias is alleviated by unique molecular identiers (UMIs). Thus the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. zUMIs is such a pipeline, it can handle both known and random BCs and also efficiently collapses UMIs, either just for Exon mapping reads or for both Exon and Intron mapping reads. Another unique feature of zUMIs is the adaptive downsampling function, that facilitates dealing with hugely varying library sizes, but also allows to evaluate whether the library has been sequenced to saturation. zUMIs flexibility allows to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs. To illustrate the utility of zUMIs, we analysed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to Introns. We furthermore show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution.
DOI: 10.5524/100448
title: Supporting data for "Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments"

description: Taxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. QIIME and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene. <br>We show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently below 2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost two and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline. <br>Our results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision and/or computational performance required.
DOI: 10.5524/100449
title: The PE150 reference human genome dataset of the BGISEQ-500 and NovaSeq6000 sequencer

description: The BGISEQ-500 is a new desktop sequencer developed by BGI. Using DNA nanoballs (DNB) and combinational probe-anchor synthesis (cPAS) developed from Complete Genomics sequencing technology, it generates short reads at a large scale, which can help fulfill the growing demands for sequencing. Here, we present the first human whole genome sequencing dataset from the BGISEQ-500. The dataset was generated by sequencing the widely-used Genome in a Bottle Consortium cell line, HG001 (NA12878). We have previously released the paired end 50bp (PE50) sequences (DOI:10.5524/100252) and paired end (PE100) sequences (DOI:10.5524/100274). Now we present the PE150 reads from same sample and library protocol, together with the PE150 PCR library of Illumina new sequencer. We also included the base and quality distribution figures of the new datasets. Moreover, we refined the variation calling parameters based on the new and previously dataset and compared that to similar amounts of publicly available NovaSeq6000 data from Illumina BaseSpace Sequence Hub.
DOI: 10.5524/100452
title: Supporting data for "Improved <i>de novo</i> genome assembly and analysis of the Chinese cucurbit <i>Siraitia grosvenorii,</i> also known as monk fruit or luo-han-guo"

description: Luo-han-guo (Siraitia grosvenorii), also called monk fruit, is a member of the Cucurbitaceae family. Currently, monk fruit has become important for research because of the pharmacological and economic potential of its non-caloric, extremely sweet components (mogrosides). It is also commonly used in traditional Chinese medicine for the treatment of lung congestion, sore throat and constipation. Recently, a single reference genome became available for monk fruit, assembled from 36.9 x genome coverage reads via Illumina sequencing platforms. This genome assembly has a relatively short (34.2 Kb) contig N50 length and lacks integrated annotations. These drawbacks make it difficult to use as a reference in assembling transcriptomes and discovering novel functional genes. <br> Here, we offer a new high-quality draft of the S. grosvenorii genome assembled using 31 Gb (~ 73.8 x) long single molecule real time sequencing (SMRT) reads and polished with ~ 50 Gb Illumina paired-end reads. The final genome assembly is approximately 469.5 Mb, with a contig N50 length of 432,384 bp, representing a 12.6-fold improvement. We further annotated 237.3 Mb of repetitive sequence and 30,565 consensus protein coding genes with combined evidence. Phylogenetic analysis showed that S. grosvenorii The availability of this new monk fruit genome assembly, as well as the annotations, will facilitate the discovery of new functional genes and the genetic improvement of monk fruit.
DOI: 10.5524/100455
title: Supporting data for "The Research Data Management Platform (RDMP)"

description: The Health Informatics Centre (HIC) at the University of Dundee provides a service to securely host clinical datasets and extract relevant data for anonymised cohorts to researchers to enable them to answer key research questions. As is common in research using routine healthcare data, the service was historically delivered using ad-hoc processes resulting in the slow provision of data whose provenance was often hidden to the researchers using it. This paper describes the development and evaluation of the Research Data Management Platform (RDMP): an open source tool to load, manage, clean, and curate longitudinal healthcare data for research and provide reproducible and updateable datasets for defined cohorts to researchers. <br>Between 2013 and 2017, RDMP tool implementation tripled the productivity of Data Analysts producing data releases for researchers from 7.1 to 25.3 per month; and reduced the error rate from 12.7% to 3.1%. The effort on data management reduced from a mean of 24.6 to 3.0 hours per data release. The waiting time for researchers to receive data after agreeing a specification reduced from approximately 6 months to less than one week. The software is scalable and currently manages 163 datasets. 1,321 data extracts for research have been produced with the largest extract linking data from 70 different datasets. <br>The tools and processes that encompass the RDMP not only fulfil the research data management requirements of researchers but also support the seamless collaboration of data cleaning, data transformation, data summarisation and data quality assessment activities by different research groups.
DOI: 10.5524/100456
title: Supporting data for "MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data"

description: With the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform reanalyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts. <br>We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-humanmapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from &gt;17,000 samples from &gt;400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource. <br> Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.
DOI: 10.5524/100457
title: Supporting data for "Arabidopsis phenotyping through Geometric Morphometrics"

description: Recently, much technical progress was done regarding plant phenotyping. High-throughput platforms and the development of improved algorithms for the rosette image segmentation make now possible to massively extract shape and size parameters for genetic, physiological and environmental studies. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations amongst groups and measure them in shape distance units. Here, it is proposed a particular scheme of landmarks placement on Arabidopsis rosette images to study shape variation in the case of viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.