Analysis workflow of publicly available RNA-sequencing datasets.
Ontology highlight
ABSTRACT:
SUBMITTER: Sanchis P
PROVIDER: S-EPMC8061314 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline starts with a pre-processing module, which converts raw current intensities into multiple types of processed data including FASTQ and BAM, providing metrics of the quality of the run, quality-filtering, demultiplexing, base-calling and mapping. In a second step, the pipeline performs downstream analyses of the mapped reads, including prediction of RNA modifications and estimation of polyA tail lengths. Four direct RNA MinION sequencing runs can be fully processed and analyzed in 10 h on 100 CPUs. The pipeline can also be executed in GPU locally or in the cloud, decreasing the run time fourfold. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow simplifies direct RNA sequencing data analyses, facilitating the study of the (epi)transcriptome at single molecule resolution.
| S-EPMC7089958 | biostudies-literature
Project description:IntroductionCardiac presentation of autoimmune sarcoidosis, known as cardiac sarcoidosis (CS), is a poorly understood disease with high mortality and low diagnosis rate. While CS is an immunological syndrome, little is known about how cardiac parenchymal and stromal cells mediate its pathogenesis. Moreover, while most current sarcoidosis research is based on research in pulmonary sarcoidosis (PS), it remains unclear how much both presentations of sarcoidosis overlap. To tackle these concerns, we leveraged publicly available sarcoidosis transcriptomic datasets.MethodsTwo publicly available bronchoalveolar lavage single-cell RNA sequencing datasets were integrated to analyze PS relative to control. Additionally, two publicly available cardiac single-nucleus RNA sequencing datasets were integrated to analyze CS relative to control. Following integration, we ran cell-cell communication, transcription factor, and differential expression analyses on parenchymal, stromal, and immune subsets identified in our analysis.ResultsOur analysis revealed that there was an expansion of stromal and immune cells in PS and CS. We also observed upregulation of Th17.1 and attenuated activation transcriptional profiles in the immune cells of CS and PS relative to control. Additionally, we found upregulation of pro-inflammatory and pro-fibrotic transcriptional profiles in the cardiac stromal cells of CS relative to control. We also found that cardiomyocytes exhibited upregulated cardiac stress and proliferation transcriptional profiles in CS relative to control.ConclusionsOur integrative transcriptomic analysis shows that despite tissue-specific differences, there are shared transcriptional trends between CS and PS. It also shows that stromal and parenchymal populations exhibit transcriptional trends that could explain their pathogenic role in CS.
| S-EPMC10419306 | biostudies-literature
Project description:Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.
| S-EPMC6511704 | biostudies-literature
Project description:In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.
| S-EPMC4060771 | biostudies-literature
Project description:Methods for analyzing the terminal sequences of proteins have been refined over the previous decade; however, few studies have evaluated the quality of the data that have been produced from those methodologies. While performing global N-terminal labelling on bacteria, we observed that the labelling was not complete and investigated whether this was a common occurrence. We assessed the completeness of labelling in a selection of existing, publicly available N-terminomics datasets and empirically determined that amine-based labelling chemistry does not achieve complete labelling and potentially has issues with labelling amine groups at sequence-specific residues. This finding led us to conduct a thorough review of the historical literature that showed that this is not an unexpected finding, with numerous publications reporting incomplete labelling. These findings have implications for the quantitation of N-terminal peptides and the biological interpretations of these data.
| S-EPMC6631386 | biostudies-literature
Project description:Assembly of cell fusing agent virus from publicly available sequencing datasets
| PRJEB33801 | ENA
Project description:The endocannabinoid system (ECS) and the dopaminergic system (DAS) are two major regulators of basal ganglia function. During Huntington's disease (HD) pathogenesis, the expression of genes in both the ECS and DAS is dysregulated. The purpose of this study was to determine the changes that were consistently observed in the ECS and DAS during HD progression in the central nervous system (CNS) and in the periphery in different models of HD and human HD tissue. To do this, we conducted a meta-analysis of differential gene expression in the ECS and DAS using publicly available microarray data. The consolidated data were summarized as observed changes in gene expression (OCGE) using a weighted sum for each gene. In addition, consolidated data were compared to previously published studies that were not available in the gene expression omnibus (GEO) database. The resulting data confirm gene expression changes observed using different approaches and provide novel insights into the consistency between changes observed in human tissue and various models, as well as disease stage- and tissue-specific transcriptional dysregulation in HD. The major implication of the systems-wide data presented here is that therapeutic strategies targeting the ECS or DAS must consider the dynamic changes in gene expression over time and in different body areas, which occur during HD progression and the interconnectedness of the two systems.
| S-EPMC4317235 | biostudies-literature
Project description:The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.
| S-EPMC8869561 | biostudies-literature
Project description:Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.
| S-EPMC10884505 | biostudies-literature
Project description:Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.
| S-EPMC7327722 | biostudies-literature