cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100353
title: Supporting data for "Large scale phylogenomic analysis resolves a backbone phylogeny in ferns"

description: Ferns originated about 360 million years ago is the sister group of seed plants. Despite remarkable progress in our understanding of fern phylogeny, with conflicting molecular evidences and different morphological interpretations, relationships among major fern lineages remain controversial.

<br>With the aim to obtain a robust fern phylogeny, we carried large scale phylogenomic analysis using high-quality transcriptome sequencing data which covered 69 fern species from 38 families and 11 orders. Both coalescent-based and concatenation-based methods were applied to both nucleotides and amino acids sequences in species tree estimation. Among the mainly consistent and strongly supported cladograms, coalescent-based method using nucleotides sequence yielded the most robust cladogram. 

<br>Our result confirmed that Equisetales is sister to the rest of ferns, and Dennstaedtiaceae is sister to eupolypods. Moreover, our result strongly supported some relationships new to the current view of fern phylogeny, including that Psilotaceae and Ophioglossaceae form a monophyletic clade which is sister to Marattiaceae; Gleicheniaceae and Hymenophyllaceae form a monophyletic clade which is sister to Dipteridaceae; and that Aspleniaceae is sister to the rest groups in eupolypods II. These results were interpreted with morphological traits, especially sporangia characters, and a new evolutionary route of sporangia annulus in ferns was suggested. This backbone phylogeny in ferns sets a foundation for further studies in biology and evolution in ferns, and therefore in plants.
DOI: 10.5524/100357
title: Multigenomic Entropy Based Score (MEBS): The molecular reconstruction of the sulfur cycle

description: The increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging. <br>
We describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare and infer complex metabolic pathways in large omic datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used to both: build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2,107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, Receiver Operator Characteristic plots and the Area Under the Curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC=0.985) hard to culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones and metagenomic environments such as hydrothermal vents, or deep-sea sediment. CONCLUSIONS: Our benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa.
DOI: 10.5524/100311
title: Supporting data for "De Novo PacBio long-read and phased avian genome assemblies correct and add to reference genes generated with intermediate and short reads"

description: Reference quality genomes provide a resource for studying gene structure, function, and evolution. However, often genes of interest are not completely or accurately assembled, leading to unknown errors in analyses or additional cloning efforts for the correct sequences. A promising solution is long-read sequencing. Here we tested PacBio-based long-read sequencing and diploid assembly for potential improvements to the Sanger-based intermediate-read zebra finch reference and Illumina-based short-read Annas hummingbird reference, two vocal learning avian species widely studied in neuroscience and genomics. With DNA of the same individuals used to generate the reference genomes, we generated diploid assemblies with the FALCON-Unzip assembler, resulting in contigs with no gaps in the megabase range, representing 150-fold and 200-fold improvements over the current zebra finch and hummingbird references, respectively. These long-read and phased assemblies corrected and resolved what we discovered to be numerous misassemblies in the references, including missing sequences in gaps, erroneous sequences flanking gaps, base call errors in difficult to sequence regions, complex repeat structure errors, and allelic differences between the two haplotypes. These improvements were validated by single long genome and transcriptome reads, and resulted for the first time in completely resolved protein-coding genes widely studied in neuroscience and specialized in vocal learning species. These findings demonstrate the impact of long reads, sequencing of previously difficult-to-sequence regions, and phasing of haplotypes on generating high quality assemblies necessary for understanding gene structure, function, and evolution.
DOI: 10.5524/100317
title: Supporting data for "Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients"

description: The inflammatory intestinal disorder Crohns disease (CD) has become a health challenge worldwide. The gut microbiota closely interacts with the host immune system, but its functional impact in CD is unclear. Except for studies on a small number of CD patients, analyses of the gut microbiota in CD have used 16S rDNA amplicon sequencing. Here we employed metagenomic shotgun sequencing to provide a detailed characterization of the compositional and functional features of the CD microbiota, comprising also unannotated bacteria, and investigated its modulation by exclusive enteral nutrition (EEN). <br>
Based on signature taxa, CD microbiotas clustered into two distinct metacommunities indicating individual variability in CD microbiome structure. Metacommunity-specific functional shifts in CD showed enrichment in producers of the pro-inflammatory hexa-acylated lipopolysaccharide variant and a reduction in the potential to synthesize short chain fatty acids. Disruption of ecological networks was evident in CD, coupled with reduction in growth rates of many bacterial species. Short-term EEN elicited limited impact on the overall composition of the CD microbiota, although functional changes occurred following treatment. 
The microbiotas in CD patients can be stratified into two distinct metacommunities with the most severely perturbed metacommunity exhibiting functional potentials that deviate markedly from that of the healthy individuals with possible implication in relation to CD pathogenesis.<br>
DOI: 10.5524/100318
title: Supporting data for "Population-wide Sampling of Retrotransposon Insertion Polymorphisms Using Deep Sequencing and Efficient Detection"

description: Active retrotransposons play important roles during evolution and continue to shape our genomes today, especially in genetic polymorphisms underlying a diverse set of diseases. However, studies of human retrotransposon insertion polymorphisms (RIPs) based on whole-genome deep sequencing at the population level have not been sufficiently undertaken, despite the obvious need for a thorough characterization of RIPs in the general population.<br>
Herein, we present a novel and efficient computational tool named Specific Insertions Detector (SID) for the detection of non-reference RIPs. We demonstrate that SID is suitable for high depth whole-genome sequencing (WGS) data using paired-end reads obtained from simulated and real datasets. We construct a comprehensive RIP database using a large population of 90 Han Chinese individuals with a mean 68× depth per individual. In total, we identify 9342 recent RIPs, and 8433 of these RIPs are novel compared with dbRIP, including 5826 Alu, 2169 long interspersed nuclear element 1 (L1), 383 SVA, and 55 long terminal repeats (LTR). Among the 9342 RIPs, 4828 were located in gene regions and five were located in protein-coding regions. We demonstrate that RIPs can, in principle, be an informative resource to perform population evolution and phylogenetic analyses. Taking the demographic effects into account, we identify a weak negative selection on SVA and L1 but approximately neutral selection for Alu elements based on the frequency spectrum of RIPs.<br>
SID is a powerful open-source program for the detection of non-reference RIPs. We built a non-reference RIP dataset that greatly enhanced the diversity of RIPs detected in the general population and should be invaluable to researchers interested in many aspects of human evolution, genetics, and disease. As a proof-of-concept, we demonstrate that the RIPs can be used as biomarkers in a similar way as single nucleotide polymorphisms (SNPs).
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DOI: 10.5524/100319
title: Supporting data for "The draft genome sequence of a desert tree Populus pruinosa"

description: Populus pruinosa is a large tree that grows in deserts and shows distinct differences in both morphology and adaptation from those of its sister species, P. euphratica. Here we present a draft genome sequence for P. pruinosa and examine genomic variations between the two species.
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A total of 60 Gb of clean reads from whole-genome sequencing of a P. pruinosa individual were generated using the Illumina HiSeq2000 platform. The assembled genome is 479.3 Mb in length, with an N50 contig size of 14.0 kb and a scaffold size of 698.5 kb. 45.47% of the genome is composed of repetitive elements. We predicted 35,131 protein-coding genes, of which 88.06% were functionally annotated. Gene family clustering revealed 224 unique and 640 expanded gene families in the P. pruinosa genome. Further evolutionary analysis identified numerous genes with elevated values for pairwise genetic differentiation between P. pruinosa and P.euphratica.
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We provide the genome sequence and gene annotation for P. pruinosa. A large number of genetic variations were recovered by comparison of the genomes between P. pruinosa and P. euphratica. These variations will provide a valuable resource for studying the genetic bases for the phenotypic and adaptive divergence of the two sister species.
DOI: 10.5524/100320
title: Supporting data for "Transcriptome Analysis of the Response of Burmese Python to Digestion"

description: Exceptional and extreme feeding behaviour makes the Burmese python (Python bivittatus) an interesting model to study physiological remodelling and metabolic adaptation in response to refeeding after prolonged starvation. In this study, we used transcriptome sequencing of five visceral organs during fasting as well as 24h and 48h after ingestion of a large meal to unravel the postprandial changes in Burmese pythons. We first used the pooled data to perform a de novo assembly of the transcriptome and supplemented this with a proteomic survey of enzymes in the plasma and gastric fluid. <br>
We constructed a high-quality transcriptome with 34,423 transcripts of which 19,713 (57%) were annotated. Among highly expressed genes (FPKM&gt;100 in one tissue) we found the transition from fasting to digestion was associated with differential expression of 43 genes in the heart, 206 genes in the liver, 114 genes in the stomach, 89 genes in the pancreas and 158 genes in the intestine. We interrogated the function of these genes to test previous hypotheses on the response to feeding. We also used the transcriptome to identify 314 secreted proteins in the gastric fluid of the python. <br>
Digestion was associated with an upregulation of genes related to metabolic processes, and translational changes therefore appears to support the postprandial rise in metabolism. We identify stomach-related proteins from a digesting individual and demonstrate that the sensitivity of modern LC-MS/MS equipment allows the identification of gastric juice proteins that are present during digestion.
DOI: 10.5524/100322
title: Supporting data for "High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization"

description: Atlases provide a framework for spatially-mapping information from diverse sources into a common reference space. Specifically, brain atlases allow annotation of gene expression, cell morphology, connectivity, and activity. In larval zebrafish, advances in genetics, imaging, and computational methods now allow the collection of such information brain-wide. However, due to technical considerations, disparate datasets may use different references and may not be aligned to the same coordinate space. Two recent larval zebrafish atlases exemplify this problem: Z-Brain, containing gene expression, neural activity and neuroanatomical segmentations, was acquired using immunohistochemical stains, while the Zebrafish Brain Browser (ZBB) was constructed from live scans of fluorescent reporters in transgenic larvae. Although different references were used, the atlases included several common transgenic patterns that provide potential 'bridges' for transforming each into the other's coordinate space. We tested multiple bridging channels and registration algorithms and found that the symmetric diffeomorphic normalization (SyN) algorithm improved live brain registration precision while better preserving cell morphology than B-spline based registrations. SyN also corrected for tissue distortion introduced during fixation. Multi-reference channel optimization provided a transformation that enabled Z-Brain and ZBB to be co-aligned with precision of approximately a single cell diameter and minimal perturbation of cell and tissue morphology. Finally, we developed software to visualize brain regions in 3-dimensions, including a virtual reality neuroanatomy explorer. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate reference templates and acquisition protocols, when sufficient information is present for bridging. Increased accuracy and interoperability of zebrafish digital brain atlases will facilitate neurobiological studies.
DOI: 10.5524/100326
title: Supporting data for "Connections between human gut microbiome and gestational diabetes mellitus"

description: The human gut microbiome can modulate metabolic health and affect insulin resistance, and may play an important role in the etiology of gestational diabetes mellitus (GDM). Here, we compared the gut microbial composition of 43 GDM patients and 81 healthy pregnant women via whole-metagenome shotgun sequencing of their fecal samples collected at 21-29 weeks, to explore associations between GDM and the composition of microbial taxonomic units and functional genes.
Metagenome-wide association study (MGWAS) identified 154,837 genes, which clustered into 129 metagenome linkage groups (MLGs) for species description, with significant relative abundance differences between the two cohorts. Parabacteroides distasonis, Klebsiella variicola, etc., were enriched in GDM patients, whereas Methanobrevibacter smithii, Alistipes spp., Bifidobacterium spp. and Eubacterium spp. were enriched in controls. The ratios of the gross abundances of GDM-enriched MLGs to control-enriched MLGs were positively correlated with blood glucose levels. Random Forest model shows fecal MLGs have excellent discriminatory power to predict GDM status.
DOI: 10.5524/100330
title: Supporting data for "Development and validation of a multi-locus DNA metabarcoding method to identify endangered species in complex samples"

description: DNA metabarcoding provides great potential for species identification in complex samples such as food supplements and traditional medicines. Such a method would aid CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora) enforcement officers to combat wildlife crime by preventing illegal trade of endangered plant and animal species. The objective of this research was to develop a multi-locus DNA metabarcoding method for forensic wildlife species identification and to evaluate the applicability and reproducibility of this approach across different laboratories.
 
A DNA metabarcoding method was developed that makes use of 12 DNA barcode markers that have demonstrated universal applicability across a wide range of plant and animal taxa, and that facilitate the identification of species in samples containing degraded DNA. The DNA metabarcoding method was developed based on Illumina MiSeq amplicon sequencing of well-defined experimental mixtures, for which a bioinformatics pipeline with user-friendly web interface was developed. The performance of the DNA metabarcoding method was assessed in an international validation trial by 16 laboratories, in which the method was found to be highly reproducible and sensitive enough to identify species present in a mixture at 1% dry weight content.
 
The advanced multi-locus DNA metabarcoding method assessed in this study provides reliable and detailed data on the composition of complex food products, including information on the presence of CITES-listed species. The method can provide improved resolution for species identification, while verifying species with multiple DNA barcodes contributes to an enhanced quality assurance.
DOI: 10.5524/100331
title: Supporting data for "The draft genome assembly of Rhododendron delavayi Franch. var. delavayi"

description: Rhododendron delavayi Franch is globally famous as an ornamental. Its distribution in southwest China covers several different habitats and environments. However, not much research had been conducted on Rhododendron spp. at molecular level, which hinders understanding of the nature of its wide adaptability to different environments, evolution, speciation and synthesis of secondary metabolites. Here, we report genome assembly and gene annotation of R. delavayi var. delavayi (the second genome sequenced in the Ericaceae), which will facilitate the study of the family, and have further applications in genome-assisted cultivar breeding.<br>
The final size of the assembled R. delavayi var. delavayi genome (695.09 Mb) was close to the 697.94 Mb estimated by k-mer analysis. A total of 336.83 gigabases (Gb) of raw Illumina HiSeq 2000 reads were generated from nine libraries (with insert sizes ranging from 170bp to 40kb), achieving a raw sequencing depth of 482.61×. After quality filtering, 246.06 Gb of clean reads were obtained, giving 352.55× coverage depth. Assembly using Platanus gave a total scaffold length of 695.09 Mb, with a contig N50 of 61.8 kb and a scaffold N50 of 637.83kb. Gene prediction resulted in the annotation of 32,938 protein-coding genes. The genome completeness was evaluated by CEGMA and BUSCO, and reached 95.97 % and 92.8 % respectively. The Gene annotation completeness was also evaluated by CEGMA and BUSCO, and reached 97.01 % and 87.4 %. Genome annotation revealed that 51.77 % of the R. delavayi genome is composed of transposable elements, and 37.48 % of long terminal repeat elements (LTRs).<br>
The de novo assembled genome of R. delavayi var. delavayi (hereinafter referred to as R. delavayi) is the second genomic resource of the family Ericaceae, and will provide a valuable resource for research on future comparative genomic studies in Rhododendron species. The availability of the R. delavayi genome sequence will hopefully provide a tool for scientists to tackle open questions regarding molecular mechanisms underlying environmental interactions in the genus Rhododendron, more accurately understand the evolutionary processes and systematics of the genus, facilitate the identification of genes encoding pharmaceutically important compounds, and accelerate molecular breeding to release elite varieties.
DOI: 10.5524/100334
title: Supporting data for "Positive and relaxed selection associated with flight evolution and loss in insect transcriptomes"

description: The evolution of powered flight is a major innovation that has facilitated the success of insects. Previously, studies of birds, bats, and insects have detected molecular signatures of differing selection regimes in energy-related genes associated with flight evolution and/or loss. Here, using DNA sequences from over 1,000 nuclear and mitochondrial protein-coding genes obtained from insect transcriptomes, we conduct a broader test of which gene categories display positive and relaxed selection at the origin of flight as well as with multiple independent losses of flight. 
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We detected a number of categories of nuclear genes more often under positive selection in the lineage leading to the winged insects (Pterygota), related to catabolic processes such as protease, as well as splicing-related genes. Flight loss was associated with relaxed selection signatures in splicing genes, mirroring the results for flight evolution. Similar to previous studies of flight loss in various animal taxa, we observed consistently higher non-synonymous-to-synonymous substitution ratios in mitochondrial genes of flightless lineages, indicative of relaxed selection in energy-related genes. While oxidative phosphorylation genes were not detected as being under selection with the origin of flight specifically, they were most often detected as being under positive selection in holometabolous insects as compared with other insect lineages. 
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DOI: 10.5524/100343
title: Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping"

description: In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection; hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline.<br>
We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results (&gt;97% accuracy) for root and shoot feature identification and localisation. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually-identified QTL were also discovered using our automated approach based on the deep learning detection to locate plant features. <br>
We have shown deep-learning-based phenotyping to have very good detection and localisation accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in QTL discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.