cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100569
title: Supporting data for "Digital Expression Explorer 2: a repository of uniformly processed RNA sequencing data"

description: RNA-seq is an indispensable tool in the study of gene regulation. While the technology has brought with it better transcript coverage and quantification, there remain considerable barriers-to-entry for the computational biologist to analyze large data sets. There is a real need for a repository of uniformly processed RNA-seq data that is easy to use. <br>To address these obstacles, we developed Digital Expression Explorer 2 (DEE2), a web-based repository of RNA-seq data in the form of gene-level and transcript-level expression counts. DEE2 contains over 5.3 trillion assigned reads from 580,000 RNA-seq data sets including species E. coli, yeast, Arabidopsis, worm, fruit fly, zebrafish, rat, mouse and human. Base-space sequence data downloaded from NCBI Sequence Read Archive underwent quality control prior to transcriptome and genome mapping using open-source tools. Uniform data processing methods ensure consistency across experiments, facilitating fast and reproducible meta-analyses. <br>The web interface allows users to quickly identify data sets of interest using accession number and keyword searches. The data can also be accessed programmatically using a specifically designed R package. We demonstrate that DEE2 data is compatible with statistical packages such as edgeR or DESeq. Bulk data are also available for download. DEE2 can be found at DEE2.io
DOI: 10.5524/100574
title: Supporting data for "DamMet: ancient methylome mapping accounting for errors, true variants and postmortem DNA damage"

description: Recent computational advances in ancient DNA research have opened access to the detection of ancient DNA methylation footprints at the genome-wide scale. The most commonly used approach infers the methylation state of a given genomic region, based on the amount of nucleotide mis-incorporations observed at CpG dinucleotide sites. However, this approach overlooks a number of confounding factors, including the presence of sequencing errors and true variants. The scale and distribution of the inferred methylation measurements are also variable across samples, precluding direct comparisons. <br>Here, we present DamMet, an open-source software retrieving maximum likelihood estimates of regional CpG methylation levels from ancient DNA sequencing data. It builds on a novel statistical model of post-mortem DNA damage for dinucleotides, accounting for sequencing errors, genotypes, and differential postmortem cytosine deamination rates at both methylated and unmethylated sites. In order to validate DamMet, we extended gargammel, a sequence simulator for ancient DNA data, by introducing methylation-dependent features of post-mortem DNA decay. This new simulator provides direct validation of DamMet prediction. Additionally, the methylation levels inferred by DamMet were found to be correlated to those inferred by epiPALEOMIX and both on par and directly comparable to those measured from whole genome bisulphite sequencing experiments of fresh tissues. <br> DamMet provides genuine estimates for local DNA methylation levels in ancient individual genomes. The returned estimates are directly cross-sample comparable and the software is available as an open source C++ program hosted at https://gitlab.com/KHanghoj/DamMet along with a manual and tutorial.
DOI: 10.5524/100580
title: Supporting data for "Ultra-deep, long-read nanopore sequencing of mock microbial community standards"

description: Long sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition.<br>We sequenced two commercially-available mock communities containing ten microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the ten individual species isolates were also sequenced with Illumina technology.<br> We generated 14 and 16 Gbp from two GridION flowcells and 150 and 153 Gbp from two PromethION flowcells for the evenly-distributed and log-distributed communities respectively. Read length N50 ranged between 5.3 Kbp and 5.4 Kbp over the four sequencing runs. Basecalls and corresponding signal data are made available (4.2 TB in total).<br> Alignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning.<br> We present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current
 aboratory and software pipelines.
DOI: 10.5524/100582
title: Supporting data for "Metabolic responses of rice cultivars with different tolerance to combined drought and heat stress under field conditions"

description: Rice is susceptible to both drought and heat stress, in particular during flowering and grain filling, when both grain yield and quality may be severely compromised. However, under field conditions, these two stresses rarely occur separately. Under well-watered conditions, plants avoid heat stress by transpirational cooling, while this is not possible under drought conditions. Although investigating combined drought and heat stress is clearly more agronomically relevant than analyzing the effects of the single stresses, only a few studies of this stress combination, in particular under field conditions, have been published. <br>Three rice cultivars differing in drought and heat tolerance were grown in the field under control and drought conditions in three consecutive years. Drought was applied either during flowering or during early grain filling, and resulted in simultaneous heat stress, leading to reduced grain yield and quality. Analysis by gas chromatographymass spectrometry (GC-MS) showed distinct metabolic profiles for the three investigated organs (flag leaves, flowering spikelets, developing seeds). The metabolic stress responses of the plants also strongly differed between cultivars and organs. Correlation analysis identified potential metabolic markers for grain yield and quality under combined drought and heat stress from both stress-regulated metabolites and from metabolites with constitutive differences between the cultivars. <br>GC-MS resolved metabolic responses to combined drought and heat stress in different organs of field-grown rice. The metabolite profiles can be used to identify potential marker metabolites for yield stability and grain quality that are expected to improve breeding efforts towards climate change resilient rice.
DOI: 10.5524/100583
title: Supporting data for "A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification"

description: An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and models their relationships with disease status are available and these tools have their limitations in simulating the multi-omics data. <br>We developed a multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., SNPs and copy number variations (CNVs)), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA-seq), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as meQTLs (SNPs influencing methylation), eQTLs (SNPs influencing gene expression), and eQTM (methylation influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model, and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network-based multi-omics analysis method, ATHENA, was applied to both the real and simulated data and the results were compared. <br>Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, CNVs, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data.
DOI: 10.5524/100587
title: Supporting data for "Over-Optimization of Academic Publishing Metrics: Observing Goodhart's Law in Action"

description: The academic publishing world is changing significantly, with ever-growing numbers of publications each year and shifting publishing patterns. However, the metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades. Moreover, recent studies indicate that these metrics have become targets and follow Goodhart's Law, according to which "when a measure becomes a target, it ceases to be a good measure.'' In this study, we analyzed over 120 million papers to examine how the academic publishing world has evolved over the last century, with a deeper look into the specific field of biology. Our study shows that the validity of citation-based measures is being compromised and their usefulness is lessening. In particular, the number of publications has ceased to be a good metric as a result of longer author lists, shorter papers, and surging publication numbers. Citation-based metrics, such citation number and h-index, are likewise affected by the flood of papers, self-citations, and lengthy reference lists. Measures such as a journal's impact factor have also ceased to be good metrics due to the soaring numbers of papers that are published in top journals, particularly from the same pool of authors. Moreover, by analyzing properties of over 2600 research fields, we observed that citation-based metrics are not beneficial for comparing researchers in different fields, or even in the same department. Academic publishing has changed considerably; now we need to reconsider how we measure success.
DOI: 10.5524/100589
title: Supporting data for "The genome assembly and annotation of yellowhorn (Xanthoceras sorbifolium Bunge)"

description: Yellowhorn (Xanthoceras sorbifolium Bunge), a deciduous shrub or small tree native to north China, is of great economic values. Seeds of yellowhorn are rich in oil containing unsaturated long chain fatty acids that have been used for producing edible oil and nervonic acid capsule. However, the lack of a high-quality genome sequence hampers the understanding of its evolution and gene functions.
<br>
In this study, a whole-genome of yellowhorn was sequenced and assembled by integration of Illumina sequencing, PacBio single-molecule real-time sequencing, 10X Genomics link-reads, Bionano optical maps and Hi-C. The yellowhorn genome assembly was 439.97 Mb, which comprised of 15 pseudo-chromosomes covering 95.42% (419.84 Mb) of the genome. The repetitive fractions accounted for 56.39% of yellowhorn genome. The genome contained 21,059 protein coding genes. Of them, 18,503 (87.46%) genes were functionally annotated at least one term by searching against the other databases. Transcriptomic analysis showed that 341, 113, 100, 135 and 125 genes were specifically expressed in leaf, hermaphrodite flower, shoot, staminate flower and young fruit, respectively. Phylogenetic analysis suggested that yellowhorn diverged from the common ancestral of Dimocarpus longan approximately 58.63 million years ago.
<br>The availability and subsequent annotation of yellowhorn genome, as well as the identification of tissue-specific functional genes, provides a valuable reference for plant comparative genomics, evolutionary studies and molecular design breeding.
DOI: 10.5524/100591
title: Supporting data for "PRSice-2: Polygenic Risk Score Software for Large-Scale Data"

description: Polygenic Risk Score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. Here we introduce PRSice-2, an efficient and scalable software for automating and simplifying polygenic risk score analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice and alternative polygenic score software, LDpred and lassosum, while having comparable predictive power. This combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated; for example, when incorporated into high-dimensional or gene-set based analyses.
DOI: 10.5524/100593
title: Supporting data for "A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia"

description: Clinical trials in Alzheimers disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging, by training machine learning tools in a high specificity regime. A multimodal signature of Alzheimer's dementia was first extracted from ADNI1. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N=235). The signature was optimized to predict progression to dementia over three years with low sensitivity (55.1%) but high specificity (95.6%), resulting in only moderate accuracy (69.3%) but high positive predictive value (80.4%, adjusted for a "typical" 33% prevalence rate of true progressors). These results were replicated in ADNI2 (N=235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity, 85.1% accuracy). We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.
DOI: 10.5524/100597
title: Supporting data for "Disordered gut microbiota and alterations in metabolic patterns are associated with atrial fibrillation"

description: With the establishment of the heart-gut axis concept, accumulating studies suggest that the gut microbiome plays an important role in the pathogenesis of cardiovascular diseases. Yet, little evidence has been reported in characterizing the gut microbiota shift in atrial fibrillation. We include the result of the global alterations that occur in the intestinal microbiota in a cohort of 50 atrial fibrillation patients and 50 matched controls based on a strategy of metagenomic and metabolomic analyses. The alterations include a dramatic elevation in microbial diversity, and a specific perturbation of gut microbiota composition. Overgrowth of Ruminococcus, Streptococcus and Enterococcus, as well as reduction of Faecalibacterium, Alistipes, Oscillibacter, and Bilophila were detected in atrial fibrillation patients. A gut microbial function imbalance and correlated metabolic pattern changes were observed with atrial fibrillation in both fecal and serum samples. The differential gut microbiome signatures could be used to identify atrial fibrillation patients. Our findings characterize the disordered gut microbiota and microbial metabolite profiles in atrial fibrillation. Intervention strategies targeting intestinal microbiome composition to counteract the progression of atrial fibrillation are highly suggested.
DOI: 10.5524/100601
title: Supporting data for "TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer"

description: Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes expression profiles to identify similarly altered signatures. TuBA's predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. TuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.
DOI: 10.5524/100602
title: Supporting data for "A chromosome-scale assembly of the major African malaria vector Anopheles funestus"

description: Anopheles funestus is one of the three most consequential and widespread vectors of human malaria in tropical Africa. However, the lack of a high-quality reference genome has hindered the association of phenotypic traits with their genetic basis in this important mosquito. Here we present a new high-quality An. funestus reference genome (AfunF3) assembled using 240x coverage of long-read single-molecule sequencing for contigging, combined with 100x coverage of short-read Hi-C data for chromosome scaffolding. The assembled contigs total 446 Mbp of sequence and contain substantial duplication due to alternative alleles present in the sequenced pool of mosquitos from the FUMOZ colony. Using alignment and depth-of-coverage information, these contigs were deduplicated to a 211 Mbp primary assembly, which is closer to the expected haploid genome size of 250 Mbp. This primary assembly consists of 1,053 contigs organized into 3 chromosome-scale scaffolds with an N50 contig size of 632 kbp and an N50 scaffold size of 93.811 Mbp, representing a 100-fold improvement in continuity versus the current reference assembly, AfunF1. This highly contiguous and complete An. funestus reference genome assembly will serve as an improved basis for future studies of genomic variation and organization in this important disease vector.
DOI: 10.5524/100605
title: Supporting data for "PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning"

description: Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods that can simultaneously identify phages and plasmids in metagenomic data, and the existing tools identifying either phages or plasmids have not yet presented satisfactory performances. <br>We present PPR-Meta, a three-class classifier that allows simultaneous identification of both phage and plasmid fragments from metagenomic assemblies. PPR-Meta consists of several modules for predicting sequences of different lengths. Using deep learning, a novel network architecture, referred to as the Bi-path Convolutional Neural Network, is designed to improve the performance for short fragments. PPR-Meta demonstrates much better performance than currently available similar tools individually for phage or plasmid identification, while testing on both artificial contigs and real metagenomic data. PPR-Meta is freely from GitHub. <br>To the best of our knowledge, PPR-Meta is the first tool that can simultaneously identify phage and plasmid fragments efficiently and reliably. The software is optimized and can be easily run on a local PC by non-computer professionals. We developed PPR-Meta to promote the research on mobile genetic elements and horizontal gene transfer.