cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100346
title: Supporting data for "Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies."

description: Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). <br>
We trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.<br>
We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.
DOI: 10.5524/100310
title: Supporting data for "Comprehensive analysis of microorganisms accompanying human archaeological remains"

description: Metagenome analyses has become a common source of information about microbial communities that occupy a wide range of niches, including archaeological specimens. It has been shown that vast majority of DNA extracted from ancient samples come from bacteria (presumably modern contaminants). However, characterization of microbial DNA accompanying human remains has never been done systematically for a wide range of different samples.<br>We used metagenomic approaches to perform comparative analyses of microorganism communities present in 161 archaeological human remains. DNA samples were isolated from the teeth of human skeletons dated from 100 AD to 1,200 AD. The skeletons were collected from seven archaeological sites in Central Europe and stored under different conditions. The majority of identified microbes were ubiquitous environmental bacteria that most likely contaminated the host remains not long ago. We observed that the composition of microbial communities was sample-specific and not correlated with its temporal or geographical origin. Additionally, traces of bacteria and archaea typical for human oral/gut flora as well as potential pathogens were identified in two-thirds of the samples. The genetic material of human-related species, in contrast to the environmental species that accounted for the majority of identified bacteria, displayed DNA damage patterns comparable with endogenous human aDNA, which suggested that these microbes might have accompanied the individual before death. <br>Our study showed that the microbiome observed in an individual sample is not reliant on the method or duration of sample storage. Moreover, shallow sequencing of DNA extracted from ancient specimens and subsequent bioinformatics analysis allowed both the identification of ancient microbial species, including potential pathogens, and their differentiation from contemporary species that colonized human remains more recently.<br>
DOI: 10.5524/100312
title: Supporting data for "Multi-locus and long amplicon sequencing approach to study microbial diversity at species level using the MinION portable nanopore sequencer"

description: The miniaturised and portable DNA sequencer MinIONTM has demonstrated great potential in different analyses such as genome-wide sequencing, pathogen outbreak detection and surveillance, human genome variability, and microbial diversity. In this study, we tested the ability of the MinIONTM platform to perform long amplicon sequencing in order to design new approaches to study microbial diversity using a multi-locus approach.<br>
After compiling a robust database by parsing and extracting the rrn bacterial region from more than 67,000 complete or draft bacterial genomes, we demonstrated that the data obtained during sequencing of the long amplicon in the MinIONTM device using R9 and R9.4 chemistries was sufficient to study two mock microbial communities in a multiplex manner and to almost completely reconstruct the microbial diversity contained in the HM782D and D6305 mock communities.<br>
Although nanopore-based sequencing produces reads with lower per-base accuracy compared with other platforms, we presented a novel approach consisting of multi-locus and long amplicon sequencing using the MinION MkIb DNA sequencer and R9 and R9.4 chemistries that help to overcome the main disadvantage of this portable sequencing platform. Furthermore, the nanopore sequencing library constructed with the last releases of pore chemistry (R9.4) and sequencing kit (SQK-LSK108) permitted to retrieve the higher level of 1D read accuracy sufficient to characterize the microbial species present in each mock community analysed. Improvements in nanopore chemistry, such as minimising base-calling errors and new library protocols able to produce rapid 1D libraries, will provide more reliable information in near future. Such data will be useful for more comprehensive and faster specific detection of microbial species and strains in complex ecosystems.
DOI: 10.5524/100313
title: Supporting data for "An expanded mammal mitogenome dataset from Southeast Asia"

description: Southeast (SE) Asia is one of the most biodiverse regions in the world and it holds approximately 20% of all mammal species. Despite this, the majority of SE Asias genetic diversity is still poorly characterised. The growing interest in using environmental DNA (eDNA) to assess and monitor SE Asian species, in particular threatened mammals - has created the urgent need to expand the available reference database of mitochondrial barcode and complete mitogenome sequences. We have partially addressed this need by generating 72 new mitogenome sequences reconstructed from DNA isolated from a range of historical and modern tissue samples.
DOI: 10.5524/100314
title: Supporting data for "Large-scale phenomics analysis of a T-DNA tagged mutant population."

description: Rice, Oryza sativa L., is one of the most important crops in the world. With the rising world population, feeding people in a more sustainable and environment-friendly way becomes increasingly important. Therefore, rice research community needs to share resources to better understand functions of rice genes that are the foundation for future agricultural biotechnology development, and one way to achieve this goal is via the extensive study of insertional mutants.<br>We have constructed a large rice insertional mutant population in a japonica rice variety, Tainung 67. The collection contains about 93,000 mutant lines, among them 85% with phenomics data and 65% with flanking sequence data. We screened the phenotypes of 12 individual plants for each line grown under field conditions according to 68 subcategories and 3 quantitative traits. Both phenotypes and integration sites are searchable in the database at Taiwan Rice Insertional Mutants Database (http://trim.sinica.edu.tw).<br>Detailed analyses of phenomics data, T-DNA flanking sequences and whole-genome sequencing data for rice insertional mutants can lead to the discovery of novel genes. In addition, studies of mutant phenotypes can reveal relationships among varieties, cultivation locations, and cropping seasons.
DOI: 10.5524/100316
title: Supporting data for "16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model"

description: 16GT is a variant caller for Illumina whole-genome and whole-exome sequencing data. It uses a new 16-genotype probabilistic model to unify SNP and indel calling in a single variant calling algorithm. In benchmark comparisons with five other widely used variant callers on a modern 36-core server, 16GT demonstrated improved sensitivity in calling SNPs, and it provided comparable sensitivity and accuracy for calling indels as compared to the GATK HaplotypeCaller. 16GT is available at https://github.com/aquaskyline/16GT.
DOI: 10.5524/100321
title: Supporting data for "Draft genome of the Antarctic dragonfish, Parachaenichthys charcoti"

description: The Antarctic bathydraconid dragonfish, Parachaenichthys charcoti, is an Antarctic notothenioid teleost endemic to the Southern Ocean.
The Southern Ocean has cooled to ?1.8C over the past 30 million years, and the seawater had retained cold temperature and isolated oceanic environment by Antarctic Circumpolar Current (ACC). Notothenioids dominate Antarctic fish, making up 90% biomass and all notothenioids have undergone molecular and ecological diversification to survive in this cold environment. Therefore, they are considered an attractive Antarctic fish model for evolutionary and ancestral genomic studies. Bathydraconidae is a speciose family of the Notothenioidei, the dominant taxonomic component of Antarctic teleosts. To understand the process of evolution of Antarctic fish, we select a typical Antarctic bathydraconid dragonfish, P. charcoti. Here, we have sequenced, de novo assembled and annotated a comprehensive genome from P. charcoti. <br>
The draft genome of P. charcoti is 709 Mb in size. The N50 contig length is 6,145 bp and its N50 scaffold length 178,362 kb. The genome of P. charcoti is predicted to contain 32,712 genes, 18,455 of which have been assigned preliminary functions. A total of 8,951 orthologous groups common to seven species fish were identified, while 333 genes were identified in P. charcoti only; 2,519 orthologous group were also identified in both P. charcoti and N. coriiceps, another Antarctic fish. Four gene ontology (GO) terms were statistically overrepresented among the 333 genes unique to P. charcoti, according to GO enrichment analysis.<br>
The draft P. charcoti genome will broaden our understanding of the evolution of Antarctic fish in their extreme environment. It will provide a basis for further investigating the unusual characteristics of Antarctic fishes.
DOI: 10.5524/100323
title: Supporting data for "Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines."

description: Processing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of pre-configured bioinformatics software and pipelines on any computational platform.<br>
We present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simple as running a single bioinformatics tool. This is achieved using a meta-script that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipeline output is post-processed by integration with the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser. <br>
Our goal is to enable easy access to NGS data analysis pipelines for non-bioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.<br>
DOI: 10.5524/100325
title: Supporting data for "A recurrence based approach for validating structural variation using long-read sequencing technology"

description: Although numerous algorithms have been developed to identify structural variation (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. This is significant, as the accurate identification of structural variation is still an outstanding but important problem in genomics. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as visual inspection of each region.<br>
Here we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long read sequencing data. We assessed the performance of VaPoR on SVs in both simulated and real genomes and report a high-fidelity rate for overall accuracy across different levels of sequence depths. We show that VaPoR can interrogate a much larger range of SVs while still matching existing methods in terms of false positive validations and providing additional features considering breakpoint precision and predicted genotype. We further show that VaPoR can run quickly and efficiency without requiring a large processing or assembly pipeline.<br>
VaPoR provides a long read based validation approach for genomic SVs that requires relatively low read depth and computing resources and thus will provide utility with targeted or low-pass sequencing coverage for accurate SV assessment. The VaPoR Software is available at: https://github.com/mills-lab/vapor.
DOI: 10.5524/100333
title: Supporting data for "Genome assembly and transcriptome resource for river buffalo, Bubalus bubalis (2n=50)"

description: Water buffalo is a globally important species for agriculture and local economies. A de novo assembled, well annotated, reference sequence for the water buffalo is an important prerequisite for studying the biology of this species, and necessary to manage genetic diversity and to use modern breeding and genomic selection techniques. However, no such genome assembly has been previously reported. There are two species of domestic water buffalo, the river (2n=50) and the swamp (2n=48) buffalo. Here we describe a draft quality reference sequence for the river buffalo created from Illumina GA and Roche 454 short read sequences using the MaSuRCA assembler. The assembled sequence is 2.83 Gb, consisting of 366,983 scaffolds with a scaffold N50 of 1.41 Mb and contig N50 of 21,398 bp. Annotation of the genome was supported by transcriptome data from 30 tissues, and identified 21,711 predicted protein coding genes. Searches for complete mammalian BUSCO gene groups found 98.6% of curated single copy orthologs present among predicted genes, which suggests a high level of completeness of the genome. The annotated sequence is available from NCBI at accession GCA_000471725.1.
DOI: 10.15468/dl.wf2n8c
title: Occurrence Download

description: A dataset containing 1054 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pristimantis gaigei (Dunn, 1931)"
 ]
}

 The dataset includes 1054 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002023-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5524/100278
title: Supporting data for "MinION nanopore sequencing of environmental metagenomes: a synthetic approach"

description: Environmental metagenomic analysis is typically accomplished by assigning taxonomy and/or function from whole genome sequencing (WGS) or 16S amplicon sequences. Both of these approaches are limited by read length and other technical and biological factors. A nanopore-based sequencing platform, MinION, produces reads that are 10000 bp in length, potentially providing for more precise assignment, thereby alleviating some of the limitations inherent in determining metagenome composition from short reads. We tested the ability of sequence data produced by MinION (R7.3 flow cells) to correctly assign taxonomy in single bacterial species runs and in three types of low complexity synthetic communities: a mixture of DNA using equal mass from four species, a community with one relatively rare (1%) and three abundant (33% each) components, and a mixture of genomic DNA from 20 bacterial strains of staggered representation. Taxonomic composition of the low-complexity communities was assessed by analyzing the MinION sequence data with three different bioinformatic approaches: Kraken, MG-RAST, and One Codex. Long read sequences generated from libraries prepared from single strains using the SQKMAP005 kit and chemistry, run on the original MinION device, yielded as few as 224 to as many as 3,497 bidirectional high-quality (2D) reads with an average overall study length of 6,000 bp. For the single-strain analyses, assignment of reads to the correct genus by different methods ranged from 53.1% to 99.5%, assignment to the correct species ranged from 23.9% to 99.5%, and the majority of mis-assigned reads were to closely related organisms. A synthetic metagenome sequenced with the same setup yielded 714 high quality 2D reads of approximately 5,500 bp that were up to 98% correctly assigned to the species level. Synthetic metagenomes from MinION libraries generated using the SQKMAP006 kit and chemistry yielded 899-3,497 2D reads with lengths averaging 5,700 bp with up to 98% assignment accuracy at the species-level. The observed community proportions for equal and rare synthetic libraries were close to the known proportions, deviating from 0.1 10% across all tests. For a 20-species mock community with staggered contributions, a sequencing run detected all but 3 species (each included at &lt;0.05% of DNA in the total mixture); 91% of reads were assigned to the correct species, 93% of reads were assigned to the correct genus, and &gt;99% of reads were assigned to the correct family.
DOI: 10.5524/100285
title: Example use case of SIC with the ndmg pipeline (SIC:ndmg)

description: Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools which drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries.
Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ''science in the cloud'' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results which will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.
We introduce and document an example use case of SIC with the ndmg pipeline, thus entitled SIC:ndmg. We have developed a capability which enables users to launch a cloud instance and run a container which performs an analysis of a cohort of structural and diffusion magnetic resonance imaging scans by (i) downloading the required data from a public repository in the cloud, (ii) fully processing each subject's data to estimate a connectome for each subject's associated graph statistics, and, optionally, (iii) plot quality control figures of various multivariate graph statistics.
DOI: 10.5524/100288
title: Supporting data for "iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D"

description: Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity.
We present an open-source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in three dimensions (3D). Users can explore networks (i) in 3D using a desktop; (ii) in stereoscopic 3D using 3D-vision glasses and a desktop; or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edgebundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation, or visualize their own networks (e.g. disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets or features without affecting other parts of the code.
Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field.
DOI: 10.5524/100289
title: Supporting data for "Enhancing Knowledge Discovery from Cancer Genomics Data with Galaxy"

description: The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. <br>To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.