A resource of single-cell gene expression profiles in a planarian Dugesia japonica

The freshwater planarian Dugesia japonica maintains an abundant heterogeneous cell population called neoblasts, which include adult pluripotent stem cells. Thus, it is an excellent model organism for stem cell and regeneration research. Recently, many single-cell RNA sequencing (scRNA-seq) databases of several model organisms, including other planarian species, have become publicly available; these are powerful and useful resources to search for gene expression in various tissues and cells. However, the only scRNA-seq dataset for D. japonica has been limited by the number of genes detected. Herein, we collected D. japonica cells, and conducted an scRNA-seq analysis. A novel, automatic, iterative cell clustering strategy produced a dataset of 3,404 cells, which could be classified into 63 cell types based on gene expression profiles. We introduced two examples for utilizing the scRNA-seq dataset in this study using D. japonica. First, the dataset provided results consistent with previous studies as well as novel functionally relevant insights, that is, the expression of DjMTA and DjP2X-A genes in neoblasts that give rise to differentiated cells. Second, we conducted an integrative analysis of the scRNA-seq dataset and time-course bulk RNA-seq of irradiated animals, demonstrating that the dataset can help interpret differentially expressed genes captured via bulk RNA-seq. Using the R package "Seurat" and GSE223927, researchers can easily access and utilize this dataset.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network.  It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented.  For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations.  We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent.  We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines.  Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website.

Characterization of circular RNAs with advanced sequencing technologies in human complex diseases

Circular RNAs (circRNAs) are one category of non-coding RNAs that do not possess 5' caps and 3' free ends. Instead, they are derived in closed circle forms from pre-mRNAs by a non-canonical splicing mechanism named "back-splicing." CircRNAs were discovered four decades ago, initially called "scrambled exons." Compared to linear RNAs, the expression levels of circRNAs are considerably lower, and it is challenging to identify circRNAs specifically. Thus, the biological relevance of circRNAs has been underappreciated until the advancement of next generation sequencing (NGS) technology. The biological insights of circRNAs, such as their tissue-specific expression patterns, biogenesis factors, and functional effects in complex diseases, namely human cancers, have been extensively explored in the last decade. With the invention of the third generation sequencing (TGS) with longer sequencing reads and newly designed strategies to characterize full-length circRNAs, the panorama of circRNAs in human complex diseases could be further unveiled. In this review, we first introduce the history of circular RNA detection. Next, we describe widely adopted NGS-based methods and the recently established TGS-based approaches capable of characterizing circRNAs in full-length. We then summarize data resources and representative circRNA functional studies related to human complex diseases. In the last section, we reviewed computational tools and discuss the potential advantages of utilizing advanced sequencing approaches to a functional interpretation of full-length circRNAs in complex diseases. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.

The Digital Brain Bank, an open access platform for post-mortem imaging datasets

Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

Existing Data Sources in Clinical Epidemiology: The Taiwan National Health Insurance Laboratory Databases

This paper provides an introduction to laboratory databases established by Taiwan National Health Insurance Administration (NHIA) since 2015 and released for research since June 2017. The National Health Insurance (NHI) is a government-run single-payer program introduced in 1995 that now covers more than 99% of 23 million Taiwanese citizens. To prevent duplication of medication prescriptions and laboratory test and examination prescriptions, contracted health care providers are required to upload the results of laboratory tests and reports of examinations to the NHIA. The cumulative number of laboratory test results was 5.64 billion from January 2015 to the end of August 2020 for 602 types of test. There are 35 variables for each laboratory test result stored in the databases that can be used for research. However, different hospitals might use different format in reporting the results. The researchers therefore have to develop algorithms to include and exclude incompatible records and to determine whether the results are positive or negative (normal or abnormal). The NHIA suggests that researchers release their source codes of algorithms so that other researchers can modify the codes to improve inter-study comparability. Through the unique personal identification number, the laboratory data can be linked to NHI inpatient and outpatient claims data for further value-added analyses. Non-Taiwanese researchers can collaborate with Taiwan researchers to access the NHI laboratory databases.

Existing Data Sources in Clinical Epidemiology: Laboratory Information System Databases in Denmark

Routine biomarker results from hospital laboratory information systems, covering hospitals and general practitioners, in Denmark are available to researchers through access to the regional Clinical Laboratory Information System Research Database at Aarhus University and the nationwide Register of Laboratory Results for Research. This review describes these two data sources. The laboratory databases have different geographical and temporal coverage. They both include individual-level biomarker results that are electronically transferred from laboratory information systems. The biomarker results can be linked to all other Danish registries at the individual level, using the unique identifier, the CPR number. The databases include variables such as the CPR number, date and time (hour and minute) of sampling, NPU code, and name of the biomarker, identification code for the laboratory and the requisitioner, the test result with the corresponding unit, and the lower and upper reference limits. Access to the two databases differs since they are hosted by two different institutions. Data cannot be transferred outside Denmark, and direct access is provided only to Danish institutions. It is concluded that access to data on routine biomarkers expands the detailed biological and clinical information available on patients in the Danish healthcare system. The full potential is enabled through linkage to other Danish healthcare registries.

Contents, Construction Methods, Data Resources, and Functions Comparative Analysis of Bacteria Databases

Many bacterial-related databases are developed to meet the researchers' needs of analysis and search for a number of bacterial information. However, these databases have different data resources, construction methods, data formats, and analysis tools. It's difficult for researchers to select appropriate databases and analysis tools to promote their researches. In the paper, we compared the contents, construction methods, data sources, update frequency, scope and scale of data, analysis tools, and features of nine famous bacterial databases: CARD, EffectiveDB, MBGD, MPD, PATRCI, PHI-base, VFDB, gcMeta and SILVA, and help researchers to better make better use of these databases. In addition, we also hope this review can help researchers develop a more comprehensive database and better tools to meet the needs of researchers.

ProtaBank: A repository for protein design and engineering data

We present ProtaBank, a repository for storing, querying, analyzing, and sharing protein design and engineering data in an actively maintained and updated database. ProtaBank provides a format to describe and compare all types of protein mutational data, spanning a wide range of properties and techniques. It features a user‐friendly web interface and programming layer that streamlines data deposition and allows for batch input and queries. The database schema design incorporates a standard format for reporting protein sequences and experimental data that facilitates comparison of results across different data sets. A suite of analysis and visualization tools are provided to facilitate discovery, to guide future designs, and to benchmark and train new predictive tools and algorithms. ProtaBank will provide a valuable resource to the protein engineering community by storing and safeguarding newly generated data, allowing for fast searching and identification of relevant data from the existing literature, and exploring correlations between disparate data sets. ProtaBank invites researchers to contribute data to the database to make it accessible for search and analysis. ProtaBank is available at https://protabank.org.

Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Time-course assays of signaling proteins in cancer cell lines under kinase inhibition

Causal conceptual framework for network analysis

Data shed light on causal protein networks that are specific to biological context

Resource for signaling biology and for benchmarking computational methods

Using PeptideAtlas, SRMAtlas, and PASSEL: Comprehensive Resources for Discovery and Targeted Proteomics

PeptideAtlas, SRMAtlas, and PASSEL are Web-accessible resources to support discovery and targeted proteomics research. PeptideAtlas is a multi-species compendium of shotgun proteomic data provided by the scientific community; SRMAtlas is a resource of high-quality, complete proteome SRM assays generated in a consistent manner for the targeted identification and quantification of proteins; and PASSEL is a repository that compiles and represents selected reaction monitoring data, all in an easy-to-use interface. The databases are generated from native mass spectrometry data files that are analyzed in a standardized manner including statistical validation of the results. Each resource offers search functionalities and can be queried by user-defined constraints; the query results are provided in tables or are graphically displayed. PeptideAtlas, SRMAtlas, and PASSEL are publicly available freely via the Web site http://www.peptideatlas.org. In this protocol, we describe the use of these resources, we highlight how to submit, search, collate and download data.