Information and Statistical Analysis Pipeline for High-Throughput RNA Sequencing Data

2019 ◽  
pp. 199-208
Author(s):  
Shinji Nakaoka ◽  
Keita Matsuyama
Keyword(s):  
Statistical Analysis ◽  
Rna Sequencing ◽  
High Throughput ◽  
Sequencing Data ◽  
Analysis Pipeline

scholarly journals TagSeqTools: a flexible and comprehensive analysis pipeline for NAD tagSeq data

2020 ◽  
Author(s):  
Huan Zhong ◽  
Zongwei Cai ◽  
Zhu Yang ◽  
Yiji Xia
Keyword(s):  
Rna Sequencing ◽  
Comprehensive Analysis ◽  
Enzymatic Reactions ◽  
Computational Tool ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Oxford Nanopore ◽  
Long Read ◽  
Identification And Characterization

AbstractNAD tagSeq has recently been developed for the identification and characterization of NAD+-capped RNAs (NAD-RNAs). This method adopts a strategy of chemo-enzymatic reactions to label the NAD-RNAs with a synthetic RNA tag before subjecting to the Oxford Nanopore direct RNA sequencing. A computational tool designed for analyzing the sequencing data of tagged RNA will facilitate the broader application of this method. Hence, we introduce TagSeqTools as a flexible, general pipeline for the identification and quantification of tagged RNAs (i.e., NAD+-capped RNAs) using long-read transcriptome sequencing data generated by NAD tagSeq method. TagSeqTools comprises two major modules, TagSeek for differentiating tagged and untagged reads, and TagSeqQuant for the quantitative and further characterization analysis of genes and isoforms. Besides, the pipeline also integrates some advanced functions to identify antisense or splicing, and supports the data reformation for visualization. Therefore, TagSeqTools provides a convenient and comprehensive workflow for researchers to analyze the data produced by the NAD tagSeq method or other tagging-based experiments using Oxford nanopore direct RNA sequencing. The pipeline is available at https://github.com/dorothyzh/TagSeqTools, under Apache License 2.0.

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi Moradi Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data

2017 ◽  
Vol 2 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Rashmi Tripathi ◽  
Pavan Chakraborty ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Rna Sequencing ◽  
High Throughput ◽  
Sequencing Data ◽  
Non Coding Rnas

scholarly journals Assessing Study Reproducibility through M2RI: A Novel Approach for Large-scale High-throughput Association Studies

2020 ◽  
Author(s):  
Zeyu Jiao ◽  
Yinglei Lai ◽  
Jujiao Kang ◽  
Weikang Gong ◽  
Liang Ma ◽  
...  
Keyword(s):  
Sample Size ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Association Studies ◽  
Structural Mri ◽  
Data Sets ◽  
Sequencing Data ◽  
Novel Approach ◽  
Magnetic Resonance Imaging Mri

AbstractHigh-throughput technologies, such as magnetic resonance imaging (MRI) and DNA/RNA sequencing (DNA-seq/RNA-seq), have been increasingly used in large-scale association studies. With these technologies, important biomedical research findings have been generated. The reproducibility of these findings, especially from structural MRI (sMRI) and functional MRI (fMRI) association studies, has recently been questioned. There is an urgent demand for a reliable overall reproducibility assessment for large-scale high-throughput association studies. It is also desirable to understand the relationship between study reproducibility and sample size in an experimental design. In this study, we developed a novel approach: the mixture model reproducibility index (M2RI) for assessing study reproducibility of large-scale association studies. With M2RI, we performed study reproducibility analysis for several recent large sMRI/fMRI data sets. The advantages of our approach were clearly demonstrated, and the sample size requirements for different phenotypes were also clearly demonstrated, especially when compared to the Dice coefficient (DC). We applied M2RI to compare two MRI or RNA sequencing data sets. The reproducibility assessment results were consistent with our expectations. In summary, M2RI is a novel and useful approach for assessing study reproducibility, calculating sample sizes and evaluating the similarity between two closely related studies.

scholarly journals Comparison of high-throughput single-cell RNA sequencing data processing pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

scholarly journals RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data

2009 ◽  
Vol 25 (19) ◽  
pp. 2615-2616 ◽  
Author(s):  
Nicole Cloonan ◽  
Qinying Xu ◽  
Geoffrey J. Faulkner ◽  
Darrin F. Taylor ◽  
Dave T. P. Tang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
High Throughput ◽  
Sequencing Data ◽  
Mapping Strategy

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi M. Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput:   datasets with over a million cells are becoming commonplace.   The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods,  led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data.  The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities.  Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users.  We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals LSTrAP-Cloud: A User-friendly Cloud Computing Pipeline to Infer Co-functional and Regulatory Networks

2020 ◽  
Author(s):  
Qiao Wen Tan ◽  
William Goh ◽  
Marek Mutwil
Keyword(s):  
Rna Sequencing ◽  
Gene Function ◽  
Regulatory Networks ◽  
Large Scale ◽  
Function Prediction ◽  
Sequencing Data ◽  
Gene Function Prediction ◽  
Analysis Pipeline ◽  
User Friendly

AbstractAs genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes that they might be interested in. LSTrAP-Cloud is based on Google Colaboratory and provides user-friendly tools that process and quality-control RNA sequencing data streamed from the European Sequencing Archive. LSTRAP-Cloud outputs a gene co-expression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters and transcription factors involved in the synthesis, transport and regulation of nicotine can be identified using our pipeline.

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi Moradi Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput:   datasets with over a million cells are becoming commonplace.   The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods,  led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data.  The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities.  Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users.  We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

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