Incomplete removal of ribosomal RNA can affect chromatin RNA-seq data analysis

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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Expression and Co-expression Analyses of WRKY, MYB, bHLH and bZIP Transcription Factor Genes in Potato (Solanum tuberosum) Under Abiotic Stress Conditions: RNA-seq Data Analysis

Potato Research ◽

10.1007/s11540-021-09502-3 ◽

2021 ◽

Author(s):

Ertugrul Filiz ◽

Firat Kurt

Keyword(s):

Transcription Factor ◽

Abiotic Stress ◽

Solanum Tuberosum ◽

Data Analysis ◽

Stress Conditions ◽

Bzip Transcription Factor ◽

Rna Seq ◽

Transcription Factor Genes

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An Easy, Cost‐Effective, and Scalable Method to Deplete Human Ribosomal RNA for RNA‐seq

Current Protocols ◽

10.1002/cpz1.176 ◽

2021 ◽

Vol 1 (6) ◽

Author(s):

Amber Baldwin ◽

Adam R. Morris ◽

Neelanjan Mukherjee

Keyword(s):

Ribosomal Rna ◽

Cost Effective ◽

Rna Seq

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Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

Scientific Reports ◽

10.1038/s41598-020-74567-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Li Tong ◽

◽

Po-Yen Wu ◽

John H. Phan ◽

Hamid R. Hassazadeh ◽

...

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Disease Outcome ◽

Rna Seq ◽

Next Generation Sequencing Technology ◽

Normalization Methods ◽

The Us ◽

Sequencing Quality ◽

Improved Accuracy ◽

The Impact

Abstract To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

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BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

Cell Systems ◽

10.1016/j.cels.2018.10.007 ◽

2018 ◽

Vol 7 (5) ◽

pp. 556-561.e3 ◽

Cited By ~ 61

Author(s):

Denis Torre ◽

Alexander Lachmann ◽

Avi Ma’ayan

Keyword(s):

Data Analysis ◽

Rna Seq ◽

Automated Generation

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RNA-Seq Data Analysis (Bowtie-TopHat-Cufflinks) v3 (protocols.io.x9qfr5w)

protocols.io ◽

10.17504/protocols.io.x9qfr5w ◽

2019 ◽

Author(s):

Kiichi Hirota

Keyword(s):

Data Analysis ◽

Rna Seq

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Biogenesis and Function of Small Non-Coding RNAs Derived from Eukaryotic Ribosomal RNA

10.20944/preprints201810.0596.v1 ◽

2018 ◽

Author(s):

Marine Lambert ◽

Abderrahim Benmoussa ◽

Patrick Provost

Keyword(s):

Ribosomal Rna ◽

Potential Role ◽

A Priori ◽

Biological Significance ◽

Rna Seq ◽

New Class ◽

Scientific Mind ◽

Non Coding Rnas ◽

And Function ◽

Rrna Sequences

The advent of RNA-sequencing (RNA-Seq) technologies has markedly improved our knowledge and expanded the compendium of small non-coding RNAs, most of which derive from the processing of longer RNA precursors. In this review article, we will discuss about the biogenesis and function of small non-coding RNAs derived from eukaryotic ribosomal RNA (rRNA), called rRNA fragments (rRFs), and their potential role(s) as regulator of gene expression. This relatively new class of ncRNAs remained poorly investigated and underappreciated until recently, due mainly to the a priori exclusion of rRNA sequences—because of their overabundance—from RNA-Seq datasets. The situation surrounding rRFs resembles that of microRNAs (miRNAs), which used to be readily discarded from further analyses, for more than five decades, because we could not believe that RNA of such a short length could bear biological significance. As if we had not yet learned our lesson not to restrain our investigative, scientific mind from challenging widely accepted beliefs or dogmas, and from looking for the hidden treasures in the most unexpected places.

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