cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100883
title: Supporting data for "Multi-Stage Malaria Parasites Recognition by Deep Learning"

description: Malaria, a mosquito-borne infectious disease affecting humans and other animals, is widespread in the tropical and subtropical regions. Microscopy is the most common method in diagnosing the malaria parasite from stained blood smears. However, this procedure is time-consuming, error-prone, and requires a well-trained professional. Moreover, the recognition of a malaria parasite through a microscope is still a challenging process, especially in distinguishing multiple stages of parasites.<br>In this paper, we demonstrate a novel deep learning approach for multi-stage malaria parasites recognition in blood smear images using Deep Transfer Graph Convolutional Network (DTGCN). In addition, this paper is the first application of Graph Convolutional Network (GCN) on multi-stage malaria parasite recognition in blood smear images. The proposed DTGCN model is based on unsupervised learning by transferring knowledge learned from source images that contain the discriminative morphology characteristics of multistage malaria parasites. This transferred information guarantees the effectiveness of the target parasite recognition. This approach firstly learns the identical representations from the source to establish the topological correlations between source class centers and the unlabelled target samples. In this stage, the GCN is implemented to extract graph feature representations for the multistage malaria parasites recognition. The proposed method showed higher accuracy and effectiveness in publicly available microscopy images of multi-stage malaria parasites compared to a wide range of state-of-the-art approaches. Furthermore, this method is also evaluated on a large-scale dataset of unseen malaria parasites and the Babesia dataset. Code and dataset are available in GitHub under an OSI compliant license (MIT).
DOI: 10.5524/100895
title: Supporting data for "BIGwas - Single-command Quality Control and Association Testing for multi-cohort and biobank-scale GWAS/PheWAS data"

description: Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving one million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process one million GWAS samples. Here we present BIGwas, a portable, fully automated QC and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high performance compute system (HPC) with just one command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes around 16 days on a small HPC system with only seven compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with one million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource.
DOI: 10.5524/100899
title: Supporting data for "Annotation of yellow genes in Diaphorina citri, vector for the Huanglongbing disease"

description: Huanglongbing (HLB), also known as citrus greening disease, is caused by the bacterium <i>Candidatus</i> Liberibacter asiaticus (CLas) and represents a serious threat to global citrus production. This bacteria is transmitted by the Asian citrus psyllid, <i>Diaphorina citri</i> (Hemiptera) and there are no effective <i>in planta</i> treatments for CLas. Therefore, one strategy is to manage the psyllid population. Manual annotation of the <i>D. citri</i> genome can identify and characterize gene families that could serve as novel targets for psyllid control. The yellow gene family represents an excellent target as yellow genes are linked to development and immunity due to their roles in melanization. Combined analysis of the genome with RNA-seq datasets, sequence homology, and phylogenetic trees were used to identify and annotate nine <i>yellow</i> genes for the <i>D. citri</i> genome. Phylogenetic analysis shows a unique duplication of <i>yellow-y</i> in <i>D. citri</i>, with life stage specific expression for these two genes. Genomic analysis also indicated the loss of a gene vital to the process of melanization, <i>yellow-f</i>, and the gain of a gene which seems to be unique to hemipterans, <i>yellow 9</i>. We suggest that <i>yellow 9</i> or the gene <i>yellow 8 (c)</i>, which consistently groups closely to <i>yellow-f</i>, may take on this role. Manual curation of genes in <i>D. citri</i> has provided an in-depth analysis of the yellow family among hemipteran insects and provides new targets for molecular control of this psyllid pest. Manual annotation of the yellow gene family was done as part of a collaborative community annotation project.
DOI: 10.5883/ds-ricket
title: Torix Rickettsia are widespread and diverse in arthropods

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.6084/m9.figshare.12801107
title: BOLD contaminant pool dataset

description: This dataset describes 3,817 cytochrome c oxidase I gene sequences deemed to be contaminants by the Barcode of Life Data System (BOLD). These contaminants are from a barcoding project totalling 184,585 arthropods.<br>Contaminants are defined as sequences which do not match the initial arthropod taxa assigned morphologically. Contaminants include: spreadsheet 1, Rickettsia; spreadsheet 2, Bacteria; spreadsheet 3, all contaminants detected including bacteria, human and arthropod homologues.
DOI: 10.6084/m9.figshare.12801140
title: Rickettsia PhyloFlash and Kraken analysis of arthropod whole genome projects from Sequence Read Archive

description: Insights into the balance of <i>Rickettsia</i> groups within arthropod symbioses were obtained through searching for <i>Rickettsia</i> presence in Illumina datasets associated with arthropod whole genome sequence (WGS) projects in the SRA (60,409 records as of the 20th May 2019). To reduce the bias from over-represented laboratory model species (e.g. <i>Drosophila</i> spp., <i>Anopheles</i> spp.) a single dataset per species was examined, and where multiple data sets existed for a species, that with the largest read count was retained.<br>This data set was screened with phyloFlash which finds, extracts and identifies <i>16S rRNA</i> sequences. Reconstructed full <i>16S rRNA</i> sequences affiliated to <i>Rickettsia</i> were extracted and compared to sequences derived from the targeted screen phylogenetically (see sections above) to assess group representation within the genus. The microbial composition of all SRA datasets that did not result in a reconstructed <i>Rickettsia 16S rRNA</i> with phyloFlash were re-evaluated using Kraken2, a k-mer based taxonomic classifier for short DNA sequences. A cut-off of at least 40k reads assigned to <i>Rickettsia</i> taxa was applied for reporting potential infections (theoretical genome coverage of ~ 1 – 4X assuming an average genome size of ~1.5Mb). As Rickettsia-infected protists have previously been reported, phyloFlash was also used to identify reads aligned to protists to account for potential positives attributed to protists as opposed to insects.<br>
DOI: 10.5524/100882
title: Supporting data for "Driftage: a multi-agent system framework for concept drift detection"

description: The amount of data and behavior changes in society happens at a swift pace in this interconnected world. Consequently, machine learning algorithms lose accuracy because they do not know these new patterns. This change in the data pattern is known as concept drift. There exist many approaches for dealing with these drifts. Usually, these methods are costly to implement as (i) they require knowledge of drift detection algorithms, (ii) software engineering strategies, and (iii) continuous maintenance concerning new drifts. This paper proposes to create a new framework using multi-agent systems to simplify the implementation of concept drift detectors considerably and divide concept drift detection responsibilities between agents, enhancing explainability of each part of drift detection. As a case study, we illustrate our strategy using a muscle activity monitor of electromyography. We show a reduction in the number of false-positive drifts detected, improving detection interpretability, and enabling concept drift detectors interactivity with other knowledge bases.
DOI: 10.5524/100902
title: Supporting data for "Annotation of segmentation pathway genes in the Asian citrus psyllid, Diaphorina citri"

description: Insects have a segmented body plan that is established during embryogenesis when the anterior-posterior (A-P) axis is divided into repeated units by a cascade of gene expression. The cascade is initiated by protein gradients created by translation of maternally provided mRNAs, localized at the anterior and posterior poles of the embryo. Particular combinations of these proteins activate specific gap genes to divide the embryo into distinct regions along the A-P axis. Gap genes then activate pair-rule genes, which are usually expressed in part of every other segment. The pair-rule genes, in turn, activate expression of segment polarity genes in a portion of each segment. The segmentation genes are generally conserved among insects, although there is considerable variation in how they are deployed. We annotated 25 segmentation gene homologs in the Asian citrus psyllid, <i>Diaphorina citri</i>. We identified most of the genes that were expected to be present in <i>D. citri</i> based on known phylogenetic distribution in other insects were identified and annotated. Two exceptions were <i>eagle</i> and <i>invected</i>, which are present in at least some hemipterans, but were not found in <i>D. citri</i>. Many of the segmentation pathway genes are likely to be essential for <i>D. citri</i> development and thus may be useful targets for gene-based pest control methods.
DOI: 10.5524/100918
title: Supporting data for "DeePhage: distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach"

description: Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their different roles in interactions with bacterial hosts and regulations of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. Findings: DeePhage utilizes a one-hot encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on five-fold cross validation reaches as high as 89%, nearly 10% and 30% higher than that of two similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time than PhagePred and PHACTS by 245 and 810 times under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease. Conclusions: DeePhage is a novel tool developed to rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via the Zhu Lab webpage or the GitHub repository.
DOI: 10.5524/100984
title: Supporting data for "A high-quality assembled genome and its comparative analysis decode the adaptive molecular mechanism of Chinese No.1 cotton variety CRI-12"

description: <i>Gossypium hirsutum</i> L. is the most widely cultivated cotton species, and a high-quality reference genome is necessary for researching the molecular mechanism of agronomic traits in cotton. Here, we adopted the PacBio platform and Hi-C sequencing technologies to assemble a new upland cotton genome of the Chinese No.1 cotton variety CRI-12. We generated a high-quality assembled CRI-12 genome of 2.31 Gb with a contig N50 of 19.65 Mb, which was superior to reported genomes. Comparisons between CRI-12 and other reported genomes revealed 7,966 structural variations (SVs) and 7,378 presence/absence variations (PAVs). The distribution of the haplotypes among A-genome (<i>G. arboreum</i>), D-genome (<i>G. raimondii</i>), and AD-genome (<i>G. hirsutum</i> and <i>G. barbadense</i>) suggested that many haplotypes were lost and recombined in the process of polyploidization. More than half of the haplotypes that correlated with different tolerances were located on chromosome D13, suggesting that this chromosome may be important for wide adaptation. In addition, DNA methylation may provide adaptive advantages in environmental adaptation by whole-genome bisulfite sequencing (WGBS) analysis. This research provides a new reference genome for molecular biology research on <i>Gossypium hirsutum</i> L. and decodes the broad environmental adaptation mechanism in the Chinese No.1 cotton variety CRI-12.
DOI: 10.5524/100977
title: Supporting data for "Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package"

description: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonisable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonisation and submission of sequence data and contextual information to public biorepositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the National Center for Biotechnology (NCBI)'s BioSample database.
DOI: 10.5524/100928
title: Supporting data for "Female reproductive tract microbiota influence egg production in layer chickens"

description: The microbiota of the female reproductive tract is increasingly recognized as having fundamental roles in animal reproduction. To explore the relative contribution of reproductive tract microbiomes on egg production in chickens, we investigated the microbiota in multiple reproductive and digestive tract sites from 128 female layer (egg-producing) chickens in comparable environments. <br>We identified substantial differences between the diversity, composition, and predicted function of site-associated microbiota. Reproductive tract microbiota were more profoundly affected in egg production than in the digestive tract. We identified four reproductive tract microbial species, <i>Bacteroides fragilis, B. salanitronis, B. barnesiae, </i> and <i>Clostridium leptum</i>, which were related to immune function and potentially contribute to enhanced egg production. <br>These findings provide insights into the diverse microbiota characteristics of reproductive and digestive tracts, and may help design strategies for controlling and manipulating chicken reproductive tract microbiota to improve egg production.
DOI: 10.5524/100934
title: Supporting data for "Genomic features of <i>Mycobacterium avium</i> subsp. <i>hominissuis</i> isolated from pigs in Japan."

description: <i>Mycobacterium avium</i> subsp. <i>hominissui</i>s (MAH) is one of the most important agents causing non-tuberculosis mycobacterial infection in humans and pigs. Genome analysis on MAH of human isolates has been proceeding, however, those of pigs are limited despite its potential source of infection to human. In the current study, we obtained 30 draft genome sequences of MAH of pigs reared in Japan. The 30 draft genomes consisted of 4,848,678 5,620,788 bp length, 4,652 5,388 coding genes and 46 75 (Med: 47) tRNAs. All isolates had restriction modification associated genes and 185 222 predicted virulence genes. Two isolates had tRNA arrays and one isolate had a clustered regularly interspaced short palindromic repeat (CRISPR) region. Our results will be useful for evaluation of the ecology of MAH by providing a foundation for genome-based epidemiological studies.
DOI: 10.5524/100940
title: Supporting data for "A high-quality Genome and Comparison of Short versus Long Read Transcriptome of the Palaearctic duck <i>Aythya fuligula</i> (Tufted Duck)"

description: The tufted duck is a non-model organism that suffers high mortality in highly pathogenic avian influenza outbreaks. It belongs to the same bird family (Anatidae) as the mallard, one of the best-studied natural hosts of low-pathogenic avian influenza viruses. Studies in non-model bird species are crucial to disentangle the role of the host response in avian influenza virus infection in the natural reservoir. Such endeavour requires a high-quality genome assembly and transcriptome. <br>This study presents the first high-quality, chromosome-level reference genome assembly of the tufted duck using the Vertebrate Genomes Project pipeline. We sequenced RNA (cDNA) from brain, ileum, lung, ovary, spleen and testis using Illumina short-read and PacBio long-read sequencing platforms, which was used for annotation. We found 34 autosomes plus Z and W sex chromosomes in the curated genome assembly, with 99.6% of the sequence assigned to chromosomes. Functional annotation revealed 14,099 protein-coding genes that generate 111,934 transcripts, which implies an average of 7.9 isoforms per gene. We also identified 246 small RNA families. <br>This annotated genome contributes to continuing research into the host response in avian influenza virus infections in a natural reservoir. Our findings from a comparison between short-read and long-read reference transcriptomics contribute to a deeper understanding of these competing options. In this study, both technologies complemented each other. We expect this annotation to be a foundation for further comparative and evolutionary genomic studies, including many waterfowl relatives with differing susceptibilities to avian influenza viruses.
DOI: 10.5524/100941
title: Supporting data for "Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2"

description: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. <br>We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. <br>We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses.
DOI: 10.5524/100954
title: Supporting data for "Chromosome-level assembly of the common vetch reference genome (Vicia sativa)"

description: <i>Vicia sativa L. </i> (Common Vetch, n= 6) is an annual, herbaceous, climbing legume that is distributed in tropical, sub-tropical and temperate climates. Originating in the Fertile Crescent of the Middle East, <i>V. sativa</i> is now widespread and grows in the Mediterranean basin, West, Central and Eastern Asia, North and South America. <i>V. sativa</i> is of economic importance as a forage legume in countries such as Australia, China, and the USA and contributes valuable nitrogen to agricultural rotation cropping systems. To accelerate precision genome breeding and genomics-based selection of this legume, we here present a chromosome-level reference genome sequence for <i>V. sativa. </i> <br>We applied a combination of long-read Oxford Nanopore sequencing, short-read Illumina sequencing, and high-throughput chromosome conformation data (CHiCAGO and Hi-C) analysis to construct a chromosome-level genome of <i>V. sativa</i>. The chromosome-level assembly of six pseudo-chromosomes has a total genome length of 1.9 gigabases (Gb) with a median contig length of 684 kb. Benchmarking Universal Single-Copy Orthologs (BUSCO) of the assembly demonstrated a very high completeness of 98% of the dicotyledonous orthologs. RNA-seq analysis and gene modeling enabled the annotation of 58,415 protein-coding genes. <br>The high-quality chromosome-level genome assembly of <i>V. sativa</i> will provide novel insights into vetch genome evolution and be a valuable resource for genomic breeding, genetic diversity and for understanding adaption to diverse arid environments