ERNA: A graphic user interface-based tool for RNA sequencing data analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE53452 | GEO | 2014/06/01
SECONDARY ACCESSION(S): PRJNA232038
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-06-01 | E-GEOD-53452 | biostudies-arrayexpress
Project description:we performed RNA sequencing analysis using 7 human plasma samples and evaluated efficiency and accuracy of eRNA on miRNA-seq data analysis. We sequenced microRNAs from the 7 human tissue samples with replicates (A1/A8, B2/B9, C3/C10, D4/D11, E5/E12, F6/F13, G7/G14) using NEBNext Multiplex Small RNA library Prep Set from New England Biolab (NEB). After that, we identified miRNAs in these plasma samples against known human matured miRNAs.
2014-06-01 | E-GEOD-53451 | biostudies-arrayexpress
Project description:we performed RNA sequencing analysis using 10 tissue samples from human prostate and evaluated efficiency and accuracy of eRNA on mRNA-seq data analysis. We sequenced mRNAs from the 10 human tissue samples. After that, we identified mRNAs in these samples against known human genes.
2014-06-01 | E-GEOD-53450 | biostudies-arrayexpress
Project description:we performed RNA sequencing analysis using 7 human plasma samples and evaluated efficiency and accuracy of eRNA on miRNA-seq data analysis.
2014-06-01 | GSE53451 | GEO
Project description:we performed RNA sequencing analysis using 10 tissue samples from human prostate and evaluated efficiency and accuracy of eRNA on mRNA-seq data analysis.
2014-06-01 | GSE53450 | GEO
Project description:BackgroundRNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers.ResultsWe developed a standalone tool with graphic user interface (GUI)-based analytic modules, known as eRNA. The capacity of performing parallel processing and sample management facilitates large data analyses by maximizing hardware usage and freeing users from tediously handling sequencing data. The module miRNA identification" includes GUIs for raw data reading, adapter removal, sequence alignment, and read counting. The module "mRNA identification" includes GUIs for reference sequences, genome mapping, transcript assembling, and differential expression. The module "Target screening" provides expression profiling analyses and graphic visualization. The module "Self-testing" offers the directory setups, sample management, and a check for third-party package dependency. Integration of other GUIs including Bowtie, miRDeep2, and miRspring extend the program's functionality.ConclusionseRNA focuses on the common tools required for the mapping and quantification analysis of miRNA-seq and mRNA-seq data. The software package provides an additional choice for scientists who require a user-friendly computing environment and high-throughput capacity for large data analysis. eRNA is available for free download at https://sourceforge.net/projects/erna/?source=directory.
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Project description:Amplicon sequencing of the 16S rRNA gene is commonly performed for the assessment and comparison of microbiomes. Here, we introduce GenePiper, an open-source R Shiny application that provides an easy-to-use interface, a wide range of analytical methods, and optimized graphical outputs for offline microbiome data analyses.
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Project description:BackgroundWith the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large. They might be cumbersome to read and important information could be overseen quickly. To address this issue, a DQ assessment (DQA) tool with a graphical user interface (GUI) was developed and provided as a web application.MethodsThe aim was to provide an easy-to-use interface for users without prior programming knowledge to carry out DQ checks and to present the results in a clearly structured way. This interface serves as a starting point for a more detailed investigation of possible DQ irregularities. A user-centered development process ensured the practical feasibility of the interactive GUI. The interface was implemented in the R programming language and aligned to Kahn et al.'s DQ categories conformance, completeness and plausibility.ResultsWith DQAgui, an R package with a web-app frontend for DQ assessment was developed. The GUI allows users to perform DQ analyses of tabular data sets and to systematically evaluate the results. During the development of the GUI, additional features were implemented, such as analyzing a subset of the data by defining time periods and restricting the analyses to certain data elements.ConclusionsAs part of the MIRACUM project, DQAgui is now being used at ten German university hospitals for DQ assessment and to provide a central overview of the availability of important data elements in a datamap over 2 years. Future development efforts should focus on design optimization and include a usability evaluation.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-09-01 | E-GEOD-55160 | biostudies-arrayexpress
Project description:BackgroundMicrobiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.ResultsWe benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108-103 cells, 0.4-80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden's index. In the skin dataset, the Decontam prevalence filter and MicrobIEM's ratio filter effectively reduced common contaminants while keeping skin-associated genera.ConclusionsMicrobIEM's ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.
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