cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100354
title: Supporting data for "Draft genome of the Gayal, Bos frontalis"

description: Gayal (Bos frontalis), also known as mithan or mithun, is a large and endangered semi-domesticated bovine that has a limited geographical distribution in hill-forests of China, Northeast India, Bangladesh, Myanmar, and Bhutan. The chromosome number of Gayal (2n=58) differs from gaur (Bos gaurus, 2n=56) and domesticated cattle (Bos indicus and Bos taurus, 2n=60). Many questions in Gayal such as origin, population history as well as genetic basis regarding local adaptation remain largely unresolved. De novo sequencing and assembly of whole Gayal genome provides an opportunity to address these issues.

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We report a high-depth sequencing, de novo assembly, and annotation of a female Gayal genome. Based on Illumina genomic sequencing platform, we have generated 350.38Gb raw data from 16 different insert size libraries. A total of 276.86Gb clean data is retained after quality control. The assembled genome is about 2.85Gb with scaffold and contig N50 sizes of 2.74Mb and 14.41kb, respectively. Repetitive elements account for 48.13% of the genome. Gene annotation has yielded 26,667 protein-coding genes, of which 97.18% have been functionally annotated. BUSCO assessment shows that our assembly captures 93% (3,183 of 4,104) of the core eukaryotic genes, and 83.1% of vertebrate universal single-copy orthologs.

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We provide a comprehensive de novo genome of the Gayal. This genetic resource is integral for inferring the origin of Gayal and performing comparative genomic studies to improve understanding of the speciation and divergence of Bovine species.
DOI: 10.5524/100355
title: Supporting data for "Comparative transcriptomics of five high-altitude vertebrates and their low-altitude relatives"

description: Species living at high altitude are subject to strong selective pressures due to inhospitable environments (e.g., hypoxia, low temperature, high solar radiation, and lack of biological production), making these species valuable models for comparative analyses of local adaptation. Studies that examined high-altitude adaptation identified a vast array of rapidly evolving genes that characterize the dramatic phenotypic changes in high-altitude animals. However, how high-altitude environment shapes gene expression programs remains largely unknown.
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We generated a total of 910 Gb high-quality RNA-seq data for 180 samples derived from six tissues of five agriculturally important high-altitude vertebrates (Tibetan chicken, Tibetan pig, Tibetan sheep, Tibetan goat and yak), and their cross-fertile relatives living in geographically neighboring low-altitude regions. Of these, ~75% reads could be aligned to their respective reference genomes, and on average ~70% of annotated protein coding genes in each organism showed FPKM expression values greater than 0.1. We observed a general concordance in topological relationships between the nucleotide alignments and gene expression-based trees. Tissue and species accounted for markedly more variance than altitude based on either the expression or the alternative splicing patterns. Cross-species clustering analyses showed a tissue-dominated pattern of gene expression, and a species-dominated pattern for alternative splicing. We also identified numerous differentially expressed genes were potentially involved in phenotypic divergence shaped by high-altitude adaptation.
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This data serves as a valuable resource for examining the convergence and divergence of gene expression changes between species as they adapt or acclimatize to high-altitude environments.
DOI: 10.5524/100315
title: The genome of the Chinese crocodile lizard; <em>Shinisaurus crocodilurus</em>

description: The Chinese crocodile lizard, Shinisaurus crocodilurus, is the only living representative of the monotypic family Shinisauridae under the order Squamata. It is an obligate semi-aquatic, viviparous, diurnal species restricted to specific portions of mountainous locations in southwestern China and northeastern Vietnam. However, in the past several decades, this species has undergone a rapid decrease in population size due to illegal poaching and habitat disruption, making this unique reptile species endangered and listed in CITES Appendix II since 1990. A proposal to uplist it to Appendix I was passed at the CITES CoP17 in 2016.<br>
To promote the conservation of this species, we sequenced the genome of a male Chinese crocodile lizard using a whole-genome shotgun strategy on the Illumina HiSeq 2000 platform. In total, we generated ~291Gb raw sequencing data (149X depth) from 13 libraries with insert sizes ranging from 250 bp to 40 kb. After filtering for PCR-duplicated and low-quality reads, ~137 Gb clean data (70X depth) were obtained for genome assembly. We yielded a draft genome assembly with a total length of 2.24 Gb and an N50 scaffold size of 1.47 Mb. The assembled genome was predicted to contain 20,150 protein-coding genes and up to 1,114 Mb (49.6%) of repetitive elements. <br>
The genomic resource of the Chinese crocodile lizard will contribute to deciphering the biology of this organism and provides an essential tool for conservation efforts. It also provides a valuable resource for future study of squamate evolution.
DOI: 10.5524/100327
title: High quality chimpanzee reference genome (Pan_tro_3.0) from hybrid assembly approach

description: The chimpanzee is arguably the most important species for the study of human origins. A key resource for these studies is a high quality reference genome assembly. The current iteration of the chimpanzee reference genome assembly (Pan_tro_2.1.4) is highly fragmented, with more than 183,000 contigs and incorporating over 159,000 gaps, with a genome wide contig N50 of 51 Kbp. <br>
In this work we produce an extensive and diverse array of sequencing datasets to rapidly assemble a new chimpanzee reference that surpasses previous iterations in bases represented and organized in large scaffolds. We show substantial improvements over the Pan_tro_2.1.4 version by several metrics: increased contiguity by &gt;750% and 300% on contigs and scaffolds, respectively; closure of 77% of gaps in the Pan_tro_2.1.4 assembly gaps spanning &gt;850 Kbp of novel coding sequence based on RNASeq data. We furthermore report over 2,700 genes that had putatively erroneous frame-shift predictions to human in Pan_tro_2.1.4 and show a substantial increase in the annotation of repetitive elements. <br>
We apply a simple 3-way hybrid approach to considerably improve the reference genome assembly for the chimpanzee, providing a valuable resource to study human origins. We furthermore produced extensive sequencing datasets that are all derived from the same cell line, generating a broad non-human benchmark dataset.
DOI: 10.5524/100332
title: Supporting data for "The sponge microbiome project"

description: Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal and environmental scales.
Samples from marine sponges (n=3569 specimens), seawater (n=370), marine sediments (n=65) and other environments (n=29) were collected from different locations across the globe. This dataset incorporates at least 269 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with a) a standard protocol using QIIME closed-reference picking resulting in 39,543 Operational Taxonomic Units (OTU) at 97% sequence identity, b) a de novo protocol using Mothur resulting in 518,246 OTUs, and c) a new high-resolution Deblur protocol resulting in 83,908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications and metadata are provided. 
This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host-specificity, convergent evolution, environmental drivers of microbiome structure and the sponge-associated rare biosphere.
DOI: 10.5524/100337
title: Supporting data for "Bayes Forest: a data-intensive generator of morphological tree clones"

description: Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, varying morphology of trees contribute differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere.<br>
Here, we present an algorithm for generating morphological tree clones based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e. morphological clones, similar (and not identical) in respect of tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multi-purpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target, experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm.<br>
Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research.
DOI: 10.5524/100338
title: Supporting data for "fastBMA: Scalable Network Inference and Transitive Reduction"

description: Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel and scalable implementation of Bayesian model av-eraging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminat-ing redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem.
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We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time-series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory efficient, parallel and distributed application that scales to human genome wide expression data. A 10,000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores).
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fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as one based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable timeframe. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA
Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).
DOI: 10.5524/100339
title: Supporting data for "Construction of the third generation Zea mays haplotype map"

description: Characterization of genetic variations in maize has been challenging, mainly due to deterioration of collinearity between individual genomes in the species. An international consortium of maize research groups combined resources to develop the maize haplotype version 3 (HapMap 3), built from whole genome sequencing data from 1,218 maize lines, covering pre-domestication and domesticated Zea mays varieties across the world.
A new computational pipeline was set up to process over 12 trillion bp of sequencing data, and a set of population genetics filters were applied to identify over 83 million variant sites.
We identified polymorphisms in regions where collinearity is largely preserved in the maize species. However, the fact that the B73 genome used as the reference only represents a fraction of all haplotypes is still an important limiting factor.
DOI: 10.5524/100341
title: Supporting data for "Lipidomics profiling reveals the role of glycerophospholipid metabolism in psoriasis"

description: Psoriasis is a common and chronic inflammatory skin disease complicated by genetic-environmental interactions. Although genomic, transcriptomic and proteomic analyses have been performed to investigate the pathogenesis of psoriasis, the role of metabolites in psoriasis, particularly of lipids, remains unclear. Lipids not only comprise the bulk of the cellular membrane bilayers but also regulate a variety of biological processes, such as cell proliferation, apoptosis, immunity, angiogenesis and inflammation. 
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In this study, an untargeted lipidomics approach was used to study the lipid profiles in psoriasis and identify lipid metabolite signatures for psoriasis through ultra-performance liquid chromatography-quadrupole tandem mass spectrometry. Plasma samples from 90 participants (45 healthy and 45 psoriasis patients) were collected and detected. Statistical analysis was applied to find different features between the two subjects. In addition, ELISA was performed to examine differential expression lipids in psoriatic patient plasma. We finally identified several differential expression lipids including LPAs, LysoPCs, PIs, PCs and PAs, among these metabolites, LPAs, LysoPCs and PAs were significantly increased, while PCs and PIs were down-regulated in psoriasis group.
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DOI: 10.5524/100342
title: Supporting data for "Taxonomic structure and functional association of foxtail millet root microbiome"

description: The root microbes play pivotal roles in plant productivity, nutrient uptakes and disease resistance. The root microbial community structure has been extensively investigated by 16S/18S/ITS amplicons and metagenomic sequencing in crops and model plants. However, the functional associations between root microbes and host plant growth are poorly understood. This work investigates the root bacterial community of foxtail millet (Setaria italica) and its potential effects on host plant productivity. <br>
We determined the bacterial composition of 2,882 samples from foxtail millet rhizoplane, rhizosphere and corresponding bulk soils from two well-separated geographic locations by 16S rRNA gene amplicon sequencing. We identified 16,109 OTUs, and defined 187 OTUs as shared rhizoplane core OTUs. The -diversity analysis revealed that microhabitat was the major factor shaping foxtail millet root bacterial community, followed by geographic locations. Large-scale association analysis identified the potential beneficial bacteria correlated with plant high productivity. Besides, the functional prediction revealed specific pathways enriched in foxtail millet rhizoplane bacterial community.
DOI: 10.5524/100344
title: Benchmark data sets, software results and reference data for the first CAMI challenge.

description: In just over a decade, metagenomics has developed into a powerful and productive method in microbiology and microbial ecology. The ability to retrieve and organize bits and pieces of genomic DNA from any natural context has opened a window into the vast universe of uncultivated microbes. Tremendous progress has been made in computational approaches to interpret this sequence data but none can completely recover the complex information encoded in metagenomes. A number of challenges stand in the way. Simplifying assumptions are needed and lead to strong limitations and potential inaccuracies in practice. Critically, methodological improvements are difficult to gauge due to the lack of a general standard for comparison. Developers also face a substantial burden to individually evaluate existing approaches, which consumes time and computational resources, and may introduce unintended biases.<br><br>
The Critical Assessment of Metagenome Interpretation (CAMI) is a community-led initiative that tackles these problems by aiming for an independent, comprehensive and bias-free evaluation of methods. In the first CAMI challenge running from March to July 2015, it provided three simulated benchmark metagenome datasets of different organismal complexities and sizes. These were generated from around ~700 newly sequenced genomes and ~600 circular elements (plasmids, viruses, other circular elements) not included in public databases during the challenge. These are now available here, together with gold standards for assembly, genome and taxonomic binning and taxonomic profiling, the underlying genome sequences, NCBI and ARB reference sequences snapshots from before the challenge and the reference NCBI taxonomy used. In addition, 3 test (toy) data sets are provided that were simulated from public genomes before the challenge. For the most realistic evaluation of reference based methods on the challenge data sets, usually taxonomic binners and profilers, the provided reference sequences or other sequence collections from before challenge should be used as references, as by now all underlying genomes have been deposited at NCBI or EBI.
DOI: 10.5524/100348
title: Ginseng genome examination for ginsenoside biosynthesis

description: Ginseng, which contains ginsenosides characterized as bioactive compounds, has been regarded as an important traditional medicine for several millennia. Howerver, the genetic background of ginseng remains poorly understood partly because of the plant's large and complex genome composition.
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We report the entire genome sequence of Panax ginseng using next-generation sequencing. The 3.5 Gb nucleotide sequence contained more than 60% repeats and encoded 42,006 predicted genes. Twenty-two transcriptome datasets and mass spectrometry images of ginseng roots were adopted to precisely quantify the functional genes. Thirty-one genes were identified to be involved in the mevalonic acid pathway. Eight of these genes were annotated as 3-hydroxy-3-methylglutaryl-CoA reductases, which displayed diverse structures and expression characteristics. A total of 225 UDP-glycosyltransferase (UGTs) were identified, and these UGTs accounted for one of the largest gene families of ginseng. Tandem repeats contributed to the duplication and divergence of UGTs. Molecular modeling of UGTs in the 71, 74, and 94 families revealed a regiospecific conserved motif located at the N-terminus. Molecular docking predicted that this motif captured ginsenoside precursors.
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The panorama of ginseng genome represents a valuable resource for understaning and improving the breeding, cultivation, and synthesis biology of this key herb.
DOI: 10.5524/100351
title: Supporting data for "A dataset of images and morphological profiles of 30,000 small-molecule treatments using the Cell Painting assay"

description: Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.<br> This microscopy data set includes 919,265 five-channel fields of view representing 30,616 tested compounds, available at The Cell Image Library repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.<br> Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The data set can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically-perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics. <br>The current version of the dataset was generated using updated CellProfiler pipelines to improve the quality of cell and nucleus segmentations. To evaluate the quality of segmentations, 30 wells were randomly sampled across seven plate maps from the bioactive compound collection, and one site was randomly sampled per well, producing a test set of 210 five-channel images. Based on this set, two expert CellProfiler users produced an improved segmentation pipeline that was used to reprocess all the 406 plates. The updated pipeline also produces more measurements per cell (n=1783) compared to the previous version. This version of the dataset contains 7 fewer plates 406 compared to 413). Six of these (PlateIDs = 26782, 26783, 26784, 26791, 26792, 26796) correspond to plates that had later been reimaged and should not have been included in analysis. One set (25723) had several inconsistencies in the files that could not be resolved, and was therefore excluded.
DOI: 10.5524/100352
title: Supporting data for "Lightning-fast genome variant detection with GROM"

description: Current human whole genome sequencing projects produce massive amounts of data, often creating significant computational challenges. Different approaches have been developed for each type of genome variant and method of its detection, necessitating users to run multiple algorithms to find variants. <br>
We present GROM (Genome Rearrangement OmniMapper), a novel comprehensive variant detection algorithm accepting aligned read files as input and finding SNVs, indels, structural variants (SVs), and copy number variants (CNVs). We show that GROM outperforms state-of-the-art methods on seven validated benchmarks using two whole genome sequencing (WGS) datasets. Additionally, GROM boasts lightning fast run times, analyzing a 50x WGS human dataset (NA12878) on commonly available computer hardware in 11 minutes, more than an order of magnitude (up to 72 times) faster than tools detecting a similar range of variants.<br>
Addressing the needs of big data analysis, GROM combines in one algorithm SNV, indel, SV, and CNV detection providing superior speed, sensitivity, and precision.