cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100458
title: Supporting data for "Chromosome-level genome assembly of the spotted sea bass, <i>Lateolabrax maculatus</i>"

description: The spotted sea bass (Lateolabrax maculatus) is a valuable commercial fish that is widely cultured in China. While analyses using molecular markers and population genetics have been conducted, genomic resources are lacking. Here, we report a chromosome-scale assembly of the spotted sea bass genome by high-depth genome sequencing, assembly and annotation. The genome scale was 0.62 Gb with contig and scaffold N50 length to be 31 Kb and 1,040 Kb, respectively. Hi-C scaffolding of the genome resulted in 24 pseudochromosomes containing 77.68% of the total assembled sequences. A total of 132.38Mb repeat sequences were detected, accounting for 20.73% of the assemble genome. 22,015 protein-coding genes were predicted, of which 96.52% were homologous to proteins in various databases. In addition, we constructed a phylogenetic tree using 1,586 single-copy gene families and identified 125 unique gene families in the spotted sea bass genome. We assembled a spotted sea bass genome, which will be a valuable genomic resource to understand the biology of the spotted sea bass, and will also lead to the development of molecular breeding techniques to generate spotted sea bass with better economical traits.
DOI: 10.5524/100460
title: Supporting data for "Clinker: visualising fusion genes detected in RNA-seq data"

description: Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes. While there are many methods for identifying fusion genes from RNA-seq data, visualising these transcripts and their supporting reads remains challenging. Clinker is a bioinformatics tool written in Python, R and Bpipe, that leverages the superTranscript method to visualise fusion genes (Davidson et al., 2017). We demonstrate the use of Clinker to obtain interpretable visualisations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell Acute Lymphoblastic Leukaemia (B-ALL).
DOI: 10.5524/100462
title: Supporting data for "Binning Enables Efficient Host Genome Reconstruction in Cnidarian Holobionts"

description: Many cnidarians, including stony corals, engage in complex symbiotic associations, comprising the eukaryotic host, photosynthetic algae, and highly diverse microbial communities together referred to as holobiont. This taxonomic complexity makes sequencing and assembling coral host genomes extremely challenging. Therefore, previous cnidarian genomic projects were based on symbiont-free tissue samples. However, this approach may not be applicable to the majority of cnidarian species for ecological reasons. We therefore evaluated the performance of an alternative method based on sequence binning for reconstructing the genome of the stony coral <i>Porites rus</i> from a hologenomic sample, and compared it to traditional approaches. <br> Our results demonstrate that binning performs well for hologenomic data, producing sufficient reads for assembling the draft genome of <i>P. rus</i>. An assembly evaluation based on operational criteria showed comparable results to symbiont-free approaches in terms of completeness and usefulness, despite a high degree of fragmentation in our assembly. In addition, we found that binning provides sufficient data for exploratory k-mer estimation of genomic features, such as genome size and heterozygosity. <br> Binning constitutes a powerful approach for disentangling taxonomically complex coral hologenomes. Considering the recent decline of coral reefs on the one hand and previous limitations to coral genome sequencing on the other hand, binning may facilitate rapid and reliable genome assembly. This study also provides an important milestone in advancing binning from the metagenomic to the hologenomic and from the prokaryotic to the eukaryotic level.
DOI: 10.5281/zenodo.1068229
title: Data For Yamp (Https://Github.Com/Alesssia/Yamp)

description: This dataset includes all the databases/datasets queried by YAMP (https://github.com/alesssia/YAMP). This dataset has been created to help the users to get started with YAMP, and to save them from the hassle of collecting and downloading the data from different sources.

More in details, this dataset contains:


	a FASTA file listing the adapter sequences to remove in the trimming step. This file is usually provided within the BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68). 
	two FASTA files describing synthetic contaminants (sequencing_artifacts.fa.gz and phix174_ill.ref.fa.gz). These files are usually provided within the BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68). 
	a FASTA file provided by Brian Bushnell for removing human contamination (described here: http://seqanswers.com/forums/showthread.php?t=42552).  Please note that this file should be indexed beforehand. This can be done using BBMap, using the following command: `bbmap.sh -Xmx24G ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz`. 
	the BowTie2 database file for MetaPhlAn2. This file is usually provided within the MetaPhlAn2 installation (version 2.6.0)
	the ChocoPhlAn and UniRef (Uniref50, Uniref90) databases, downloaded directly by HUMAnN2 (version 0.9.9), as explained here: https://bitbucket.org/biobakery/humann2/wiki/Home#markdown-header-5-download-the-databases


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DOI: 10.5524/100415
title: Supporting data for "G-Anchor: a novel approach for whole-genome comparative mapping utilising evolutionarily conserved DNA sequences"

description: Cross-species whole-genome sequence alignment is a critical first step for genome comparative analyses ranging from the detection of sequence variants to studies of chromosome evolution. Animal genomes are large and complex, and whole genome alignment is a computationally intense process, requiring expensive high performance computing systems due to the need to explore extensive local alignments. With hundreds of sequenced animal genomes available now from multiple projects there is an increasing demand for genome comparative analyses. <br>Here we introduce G-Anchor, a new, fast, and efficient pipeline that uses a strictly limited but highly effective set of local sequence alignments to anchor (or map) an animal genome to another species reference genome. G-Anchor makes novel use of a databank of highly conserved DNA sequence elements. We demonstrate how these elements may be aligned to a pair of genomes, creating anchors. These anchors enable the rapid mapping of scaffolds from a de novo assembled genome to chromosome assemblies of a reference species. Our results demonstrate that G-Anchor can successfully anchor a vertebrate genome onto a phylogenetically related reference species genome using a desktop or laptop computer within a few hours, and with comparable accuracy to that achieved by a highly accurate whole genome alignment tool such as LASTZ. G-Anchor thus makes whole-genome comparisons accessible to researchers with limited computational resources. <br>G-Anchor is a ready-to-use tool for anchoring a pair of vertebrate genomes. It may be used with large genomes that contain a significant fraction of evolutionarily conserved DNA sequences, and that are not highly repetitive, polyploid or excessively fragmented. G-Anchor is not a substitute for whole-genome aligning software but can be used for fast and accurate initial genome comparisons. G-Anchor is freely available via the github repo.
DOI: 10.5524/100420
title: Supporting data for "The Scientific Filesystem (SCIF)".

description: Here we present the Scientific Filesystem (SCIF), an organizational format that supports exposure of executables and metadata for discoverability of scientific applications. The format includes a known filesystem structure, a definition for a set of environment variables describing it, and functions for generation of the variables and interaction with the libraries, metadata, and executables located within. SCIF makes it easy to expose metadata, multiple environments, installation steps, files, and entrypoints to render scientific applications consistent, modular, and discoverable. We will start by reviewing the background and rationale for the Scientific Filesystem, followed by an overview of the specification, and the different levels of internal modules ("apps") that the organizational format affords. Finally, we demonstrate that SCIF is useful by implementing and discussing several use cases that improve user interaction and understanding of scientific applications. SCIF is released along with a client and integration in the Singularity 2.4 software to quickly install and interact with Scientific Filesystems. When used inside of a reproducible container, a scientific filesystem is a recipe for reproducibility and introspection of the functions and users that it serves. <br>We use SCIF to evaluate container software, provide metrics, serve scientific workflows, and execute a primary function under different contexts. To encourage collaboration and sharing of apps, we have developed tools along with an open source, version controlled, tested, and programmatically accessible web infrastructure. SCIF and associated resources are available at https://sci-f.github.io. The ease of using SCIF, especially in the context of containers, offers promise for scientists work to be self-documenting, and programatically parseable for maximum reproducibility. SCIF opens up an abstraction from underlying programming languages and packaging logic to work with scientific applications, opening up new opportunities for scientific software development.
DOI: 10.5524/100421
title: Supporting data for "Tracking the NGS revolution: managing life science research on shared high-performance computing clusters"

description: Next-Generation Sequencing (NGS) has transformed the life sciences and many research groups are newly dependent upon computer clusters to store and analyse large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing centre at Uppsala University, Sweden, where core hours usage by 800 NGS and 200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden and cluster usage of NGS projects with projects from other sciences. <br>The number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS than non-NGS projects. We develop usage and efficiency metrics and show that compute jobs in NGS projects use more RAM than in non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat. <br>Hosting NGS projects imposes a large administrative burden at UPPMAX, due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We give a set of recommendations for e-infrastructures hosting NGS research projects. We provide anonymised versions of our storage, job and efficiency databases.
DOI: 10.5524/100423
title: Supporting data for "Optimized Distributed Systems Achieve Significant Performance Improvement on Sorted Merging of Massive VCF Files"

description: Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by genomic locations. In particular, merging a large number of Variant Call Format (VCF) files are frequently encountered in large scale whole genome sequencing or whole exome sequencing projects. Traditional single machine based methods become increasingly inefficient when processing hundreds or even thousands of VCF files due to the excessive computation time and I/O bottleneck. The distributed systems and the more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized working flow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. <br>
In this study, we custom design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase and Spark, to perform sorted merging of large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks which are conquered in an ordered, parallel and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI) based high performance computing (HPC) implementation and the popular VCFTools. <br>
Our experiments suggest that all three schemas either deliver a significant improvement in efficiency or render much better strong/weak scalabilities over traditional methods. We believe that our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

Please note that these files are also available from the AWS S3 at https://s3.amazonaws.com/xsun316/sample_results/result.tar.gz
DOI: 10.5524/100426
title: Supporting data for “Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building”

description: Advancements in portable scientific instruments provide promising avenues to expedite field work in order to understand the diverse array of organisms that inhabit our planet. Here, we tested the feasibility for <em>in situ</em> molecular analyses of endemic fauna using a portable laboratory fitting within a single backpack, in one of the worlds most imperiled biodiversity hotspots: the Ecuadorian Chocó rainforest. We utilized portable equipment, including the MinION nanopore sequencer (Oxford Nanopore Technologies) and the miniPCR (miniPCR), to perform DNA extraction, PCR amplification and real-time DNA barcoding of reptile specimens in the field.<br>We demonstrate that nanopore sequencing can be implemented in a remote tropical forest to quickly and accurately identify species using DNA barcoding, as we generated consensus sequences for species resolution with an accuracy of &gt;99% in less than 24 hours after collecting specimens. The flexibility of our mobile laboratory further allowed us to generate sequence information at Universidad Tecnológica Indoamérica in Quito for rare, endangered, and undescribed species. This includes the recently re-discovered Jambato toad, which was thought to be extinct for 28 years. Sequences generated on the MinION required as little as 30 reads to achieve high accuracy relative to Sanger sequencing and with further multiplexing of samples, nanopore sequencing can become a cost-effective approach for rapid and portable DNA barcoding.<br>Overall, we establish how mobile laboratories and nanopore sequencing can help to accelerate species identification in remote areas to aid in conservation efforts and be applied to research facilities in developing countries. This opens up possibilities for biodiversity research by promoting local research capacity building, teaching laymen and students about the environment, tackling wildlife crime or by promoting conservation via research focused eco-tourism.
DOI: 10.5524/100427
title: Supporting data for “A practical tool for Maximal Information Coefficient analysis”

description: The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual Information based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which coniugate computational efficiency with good bias/variance properties. Despite that, a complete software implementation of these two measures and of a statistical procedure to test the significance of each association is still missing. In this paper we present MICtools, a comprehensive and effective pipeline which combines TICe and MICe into a multi-step procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies. MICtools is implemented in Python, and is available for download at https://github.com/minepy/mictools
DOI: 10.5524/100431
title: Supporting data for "Whole-Genome De Novo Sequencing Reveals Unique Genes that Contributed to the Adaptive Evolution of the Mikado Pheasant"

description: The Mikado pheasant (Syrmaticus mikado) is a nearly endangered species indigenous to high-altitude regions of Taiwan. It possesses a unique position in evolution because of its geographic isolation. Currently, the genetic background and adaptive behaviors of the Mikado pheasant remain unclear. <br>We present the draft genome of the Mikado pheasant, which consists of 1.04 Gb of DNA and 15 972 annotated protein-coding genes. The Mikado pheasant displays expansion and positive selection of genes related to features that contribute to its adaptive evolution, such as energy metabolism, oxygen transport, hemoglobin binding, radiation response, immune response, and DNA repair. The major histocompatibility complex (MHC) region contains 39 putative genes within 227 kb of DNA. Compared with the chicken MHC, we not only found that TAPBP and the TAP1-TAP2 block are in inverse orientation, but also identified some genes undergoing rapid evolution. The complete mitochondrial genome was further sequenced, assembled, and compared against 4 other long-tailed pheasants. The results from molecular clock analysis suggest that ancestors of the Mikado pheasant migrated from the north to Taiwan about 3.47 million years ago. <br>This study provides a valuable genomic resource for the Mikado pheasant, insights into its adaptation to high altitude, and the evolutionary history of the genus Syrmaticus, which could potentially be useful for future studies investigating molecular evolution, genomics, and immunogenetics.
DOI: 10.5524/100433
title: Supporting data for "Chromosome-level reference genome of the Siamese fighting fish Betta splendens, a model species for the study of aggression"

description: Siamese fighting fish Betta splendens are notorious for their aggressiveness and accordingly have been widely used to study aggression. However, the lack of a reference genome has so far limited the understanding of the genetic basis of aggression in this species. Here we present the first reference genome assembly of the Siamese fighting fish.<br>We first sequenced and de novo assembled a 465.24 Mb genome for the B. splendens variety Giant, with a weighted average (N50) scaffold size of 949.03 Kb and an N50 contig size of 19.01 Kb, covering 99.93% of the estimated genome size. To obtain a chromosome-level genome assembly, we constructed one Hi-C library and sequenced 75.24 Gb reads using the BGISEQ-500 platform. We anchored approximately 93% of the scaffold sequences into 21 chromosomes and evaluated the quality of our assembly using the high contact frequency heatmap and BUSCO. We also performed comparative chromosome analyses between Oryzias latipes and B. splendens, revealing a chromosome conservation evolution in B. splendens. We predicted a total of 23,981 genes assisted by RNA-seq data generated from brain, liver, muscle and heart tissues of Giant, and annotated 15% repetitive sequences in the genome. Additionally, we resequenced other five B. splendens varieties and detected ~3.4M single-nucleotide variations (SNVs) and 27,305 indels.<br>We provide the first chromosome-level genome for the Siamese fighting fish. The genome will lay a valuable foundation for future research on aggression in B. splendens.
DOI: 10.5524/100439
title: Supporting data for "1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset"

description: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common strategy to assess the regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed and examined by the pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine learning algorithm, are able to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine learning models, large, well-curated datasets are needed. We released a dataset of 1399 annotated whole-slide images of lymph nodes, both with and without metastases, in total three terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five different medical centers to cover a broad range of image appearance and staining variations. Each whole-slide image has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases or isolated tumor cells. Furthermore, for 209 whole-slide images, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.
DOI: 10.5524/100450
title: Supporting data for "SV-plaudit: A cloud-based framework for manually curating thousands of structural variants"

description: SV-plaudit is a framework for rapidly curating structural variant (SVs) predictions. For each SV, we generate an image that visualizes the coverage and alignment signals from a set of samples. Images are uploaded to our cloud framework where users assess the quality of each image using a client-side web application. Reports can then be generated as a tab-delimited file or annotated VCF. As a proof of principle, nine researchers collaborated for one hour to evaluate 1,350 SVs each. We anticipate that SV-plaudit will become a standard step in variant calling pipelines and the crowd-sourced curation of other biological results.