scholarly journals RNASeqR: an R package for automated two-group RNA-Seq analysis workflow

2019 ◽  
Author(s):  
Kuan-Hao Chao ◽  
Yi-Wen Hsiao ◽  
Yi-Fang Lee ◽  
Chien-Yueh Lee ◽  
Liang-Chuan Lai ◽  
...  
Keyword(s):  
R Package ◽  
Software Tools ◽  
Biological Research ◽  
Command Line ◽  
Rna Seq ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Fundamental Information ◽  
Biological Interpretation ◽  
Fast Light

RNA-Seq analysis has revolutionized researchers' understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).

scholarly journals rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Marieke Lydia Kuijjer ◽  
James J. Cai
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
R Package ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Experiment ◽  
Tissue Samples ◽  
Molecular Phenotypes ◽  
Public Datasets

Motivation: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and Implementation: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

RNA sequencing analysis for profiling activation of cancer-associated molecular pathways.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13032-e13032 ◽  
Author(s):  
Anton Buzdin ◽  
Andrew Garazha ◽  
Maxim Sorokin ◽  
Alex Glusker ◽  
Alexey Aleshin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Original Data ◽  
Tissue Expression ◽  
Molecular Pathways ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Healthy Human ◽  
Tissue Samples ◽  
Normal Tissues

e13032 Background: Intracellular molecular pathways (IMPs) control all major events in the living cell. They are considered hotspots in contemporary oncology because knowledge of IMPs activation is essential for understanding mechanisms of molecular pathogenesis in oncology. Profiling IMPs requires RNA-seq data for tumors and for a collection of reference normal tissues. However, there is a shortage now in such profiles for normal tissues from healthy human donors, uniformly profiled in a single series of experiments. Access to the largest dataset of normal profiles GTEx is only partly available through the dbGaP. In TCGA database, norms are adjacent to surgically removed tumors and may be affected by tumor-linked growth factors, inflammation and altered vascularization. ENCODE datasets were for the autopsies of normal tissues, but they can’t form statistically significant reference groups. Methods: Tissue samples representing 20 organs were taken from post-mortal human healthy donors killed in road accidents no later than 36 hours after death, blood samples were taken from healthy volunteers. Gene expression was profiled in RNA-seq experiments using the same reagents, equipment and protocols. Bioinformatic algorithms for IMP analysis were developed and validated using experimental and public gene expression datasets. Results: From original sequencing data we constructed the biggest fully open reference expression database of normal human tissues including 465 profiles termed Oncobox Atlas of Normal Tissue Expression (ANTE, original data: GSE120795). We next developed a method termed Oncobox for interrogating activation of IMPs in human cancers. It includes modules of expression data harmonization and comparison and an algorithm for automatic annotation of molecular pathways. The Oncobox system enables accurate scoring of thousands molecular pathways using RNA-seq data. Oncobox pathway analysis is also applicable for quantitative proteomics and microRNA data in oncology. Conclusions: The Oncobox system can be used for a plethora of applications in cancer research including finding differentially regulated genes and IMPs, and for discovery of new pathway-related diagnostic and prognostic biomarkers.

scholarly journals TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

AbstractBackgroundThe quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data.ResultsWe developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5’ and 3’ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.ConclusionsOur proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals animalcules: interactive microbiome analytics and visualization in R

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yue Zhao ◽  
Anthony Federico ◽  
Tyler Faits ◽  
Solaiappan Manimaran ◽  
Daniel Segrè ◽  
...  
Keyword(s):  
16S Rrna ◽  
Microbial Communities ◽  
R Package ◽  
Command Line ◽  
Data Generation ◽  
Sequencing Data ◽  
Shotgun Metagenomics ◽  
Microbiome Analysis ◽  
Link Type ◽  
R Shiny

Abstract Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. Results To address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. Conclusions We present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html.

scholarly journals scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

2020 ◽  
Vol 36 (10) ◽  
pp. 3115-3123 ◽  
Author(s):  
Teng Fei ◽  
Tianwei Yu
Keyword(s):  
Single Cell ◽  
Differential Expression Analysis ◽  
Distance Matrix ◽  
Real Data ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

2021 ◽  
Author(s):  
Federico Agostinis ◽  
Chiara Romualdi ◽  
Gabriele Sales ◽  
Davide Risso
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Bioconductor Project ◽  
Single Cell Rna Sequencing

Summary: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals CoGe LoadExp+: A web-based suite that integrates next-gen sequencing data analysis workflows and visualization

2017 ◽  
Author(s):  
Jeffrey W. Grover ◽  
Matthew Bomhoff ◽  
Sean Davey ◽  
Brian D. Gregory ◽  
Rebecca A. Mosher ◽  
...  
Keyword(s):  
Downstream Processing ◽  
Biological Research ◽  
Chromatin Binding ◽  
Rna Seq ◽  
Sequencing Data ◽  
Skill Levels ◽  
Web Based ◽  
Sequencing Data Analysis ◽  
Bioinformatics Workflows ◽  
Variant Identification

AbstractTo make genomic and epigenomic analyses more widely available to the biological research community, we have created LoadExp+, a suite of bioinformatics workflows integrated with the web-based comparative genomics platform, CoGe. LoadExp+ allows users to perform transcriptomic (RNA-seq), epigenomic (bisulfite-seq), chromatin-binding (ChIP-seq), variant identification (SNPs), and population genetics analyses against any genome in CoGe, including genomes integrated by users themselves. Through LoadExp+’s integration with CoGe’s existing features, all analyses are available for visualization and additional downstream processing, and are available for export to CyVerse’s data management and analysis platforms. LoadExp+ provides easy-to-use functionality to manage genomics and epigenomics data throughout its entire lifecycle and facilitates greater accessibility of genomics analyses to researchers of all skill levels. LoadExp+ can be accessed at https://genomevolution.org.

scholarly journals Animalcules: Interactive Microbiome Analytics and Visualization in R

2020 ◽  
Author(s):  
Yue Zhao ◽  
Anthony Federico ◽  
Tyler Faits ◽  
Solaiappan Manimaran ◽  
Stefano Monti ◽  
...  
Keyword(s):  
16S Rrna ◽  
Microbial Communities ◽  
R Package ◽  
Vital Role ◽  
Command Line ◽  
Data Generation ◽  
Sequencing Data ◽  
Shotgun Metagenomics ◽  
Microbiome Analysis ◽  
R Shiny

Abstract Background: Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data.Results: To address some of these challenges, we have developed Animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis are enhanced in the toolkit, while new methods such as biomarker identification are introduced. Powerful interactive and dynamic figures generated by Animalcules enable users to understand their data and discover new insights. Animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface.Conclusions: We present Animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. Animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/animalcules.html).

scholarly journals TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: (i) full-length RNA-seq for detection of splicing patterns and (ii) high-throughput 5′ and 3′ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts. We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the most commonly used community gene models, TAIR10 and Araport11 for A.thaliana and SacCer3 for S.cerevisiae. In particular, we identify multiple transient transcripts missing from the existing annotations. Our new annotations promise to improve the quality of A.thaliana and S.cerevisiae genome research. Conclusions Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

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