scholarly journals Differential Expression Analysis of ZIKV Infected Human RNA Sequence Reveals Potential Biomarkers

2018 ◽  
Author(s):  
Almas Jabeen ◽  
Nadeem Ahmad ◽  
Khalid Raza
Keyword(s):  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Zika Virus ◽  
Biological Process ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Viral Outbreak ◽  
Potential Biomarkers

Zika virus (ZIKV) is considered to be an emerging viral outbreak due to its link to diseases like microcephaly, Guillain-Barre Syndrome in human. In this paper, we identify differentially expressed genes (DEGs) using RNA-seq data. In this study, we adopted the RNA-seq analysis pipeline to quantify RNA-seq data into read counts. Our analysis uncovers the significant DEGs which may be involved in the altered biological process somehow. Here, we report the list of significant DEGs, out of which three genes are found to be highly differentially expressed. In addition, our analysis also predicts other moderate DEGs, low DEGs whose differential expression was induced due to ZIKV infections.

scholarly journals A comparative study of techniques for differential expression analysis on RNA-Seq data

2014 ◽  
Author(s):  
Zong Hong Zhang ◽  
Dhanisha J. Jhaveri ◽  
Vikki M. Marshall ◽  
Denis C. Bauer ◽  
Janette Edson ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
False Positives ◽  
Sequencing Depth ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Next Generation Sequencing Technology

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

scholarly journals Differential Expression Analysis for RNA-Seq Data

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Rashi Gupta ◽  
Isha Dewan ◽  
Richa Bharti ◽  
Alok Bhattacharya
Keyword(s):  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Low Cost ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Ratio Method ◽  
Rna Seq ◽  
Huge Data ◽  
Next Generation Sequencing Ngs ◽  
Short Time

RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.

An automated RNA-Seq analysis pipeline to identify and visualize differentially expressed genes and pathways in CHO cells

2015 ◽  
Vol 31 (5) ◽  
pp. 1150-1162 ◽  
Author(s):  
Chun Chen ◽  
Huong Le ◽  
Chetan T. Goudar
Keyword(s):  
Differentially Expressed Genes ◽  
Cho Cells ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Analysis Pipeline

scholarly journals Transcriptome Analysis of Orbital Adipose Tissue in Active Thyroid Eye Disease Using Next Generation RNA Sequencing Technology

2018 ◽  
Vol 12 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Bradford W. Lee ◽  
Virender B. Kumar ◽  
Pooja Biswas ◽  
Audrey C. Ko ◽  
Ramzi M. Alameddine ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Thyroid Eye Disease ◽  
Differentially Expressed ◽  
Eye Disease ◽  
Healthy Controls ◽  
Rna Seq ◽  
Next Generation

Objective: This study utilized Next Generation Sequencing (NGS) to identify differentially expressed transcripts in orbital adipose tissue from patients with active Thyroid Eye Disease (TED) versus healthy controls. Method: This prospective, case-control study enrolled three patients with severe, active thyroid eye disease undergoing orbital decompression, and three healthy controls undergoing routine eyelid surgery with removal of orbital fat. RNA Sequencing (RNA-Seq) was performed on freshly obtained orbital adipose tissue from study patients to analyze the transcriptome. Bioinformatics analysis was performed to determine pathways and processes enriched for the differential expression profile. Quantitative Reverse Transcriptase-Polymerase Chain Reaction (qRT-PCR) was performed to validate the differential expression of selected genes identified by RNA-Seq. Results: RNA-Seq identified 328 differentially expressed genes associated with active thyroid eye disease, many of which were responsible for mediating inflammation, cytokine signaling, adipogenesis, IGF-1 signaling, and glycosaminoglycan binding. The IL-5 and chemokine signaling pathways were highly enriched, and very-low-density-lipoprotein receptor activity and statin medications were implicated as having a potential role in TED. Conclusion: This study is the first to use RNA-Seq technology to elucidate differential gene expression associated with active, severe TED. This study suggests a transcriptional basis for the role of statins in modulating differentially expressed genes that mediate the pathogenesis of thyroid eye disease. Furthermore, the identification of genes with altered levels of expression in active, severe TED may inform the molecular pathways central to this clinical phenotype and guide the development of novel therapeutic agents.

scholarly journals Confronting false discoveries in single-cell differential expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jordan W. Squair ◽  
Matthieu Gautier ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Nicholas D. James ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Cell Type ◽  
Mouse Spinal Cord ◽  
Cell Type Specific ◽  
False Discoveries ◽  
Biological Differences

AbstractDifferential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

scholarly journals Effects of duplicated mapped read PCR artifacts on RNA-seq differential expression analysis based on qRNA-seq

2018 ◽  
Author(s):  
Anna C. Salzberg ◽  
Jiafen Hu ◽  
Elizabeth J. Conroy ◽  
Nancy M. Cladel ◽  
Robert M. Brucklacher ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Pcr Amplification ◽  
False Positives ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Gold Standard Method ◽  
Pcr Duplicates ◽  
Cdna Molecule

AbstractBest practices to handling duplicated mapped reads in RNA-seq analyses has long been discussed but a gold standard method has yet to be established, as such duplicates could originate from valid biological transcripts or they could be PCR-related artifacts. Here we used the NEXTflex™qRNA-SeqTM(aka Molecular Indexing™) technology to identify PCR duplicates via the random attachment of unique molecular labels to each cDNA molecule prior to PCR amplification. We found that up to 64.3% of the single end and 19.3% of the mouse paired end duplicates originated from valid biological transcripts rather than PCR artifacts. For single end reads, either removing or retaining all duplicates resulted in a substantial number of false positives (up to 47.0%) and false negatives (up to 12.1%) in the sets of significantly differentially expressed genes. For paired end reads, only the alignment retaining all duplicates resulted in a substantial number of false positives. This is the first effort to evaluate the performance of qRNA-seq using ‘real-world’ biomedical samples, and we found that PCR duplicate identification provided minor benefits for paired end reads but greatly improved the sensitivity and specificity in the determination of the significantly differentially expressed genes for single end reads.

scholarly journals Confronting false discoveries in single-cell differential expression

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Matthieu Gautier ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Nicholas D. James ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Experimental Manipulation ◽  
Differentially Expressed ◽  
Cell Type Specific ◽  
False Discoveries ◽  
Cell Data

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

scholarly journals RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage

2020 ◽  
Author(s):  
Margo Tuerlings ◽  
Marcella van Hoolwerff ◽  
Evelyn Houtman ◽  
Eka (H.E.D.) Suchiman ◽  
Nico Lakenberg ◽  
...  
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Subchondral Bone ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Joint Replacement Surgery ◽  
And Cartilage

ABSTRACTObjectiveThe aim of this study was to identify key determinants of the interactive osteoarthritis (OA) pathophysiological processes of subchondral bone and cartilage.MethodsWe performed RNA sequencing on macroscopically preserved and lesioned OA subchondral bone of patients that underwent joint replacement surgery due to OA (N=24 pairs; 6 hips, 18 knees, RAAK-study). Unsupervised hierarchical clustering and differential expression analyses were performed. Results were combined with previously identified, differentially expressed genes in cartilage (partly overlapping samples) as well as with recently identified OA risk genes.ResultsWe identified 1569 genes significantly differentially expressed between lesioned and preserved subchondral bone, including CNTNAP2 (FC=2.4, FDR=3.36×10−5) and STMN2 (FC=9.6, FDR=3.36×10−3). Among the identified genes, 305 were also differentially expressed and with same direction of effects in cartilage, including IL11 and CHADL, recently acknowledged OA susceptibility genes. Upon differential expression analysis stratifying for joint site, we identified 509 genes exclusively differentially expressed in subchondral bone of the knee, such as KLF11 and WNT4. These exclusive knee genes were enriched for involvement in epigenetic processes, characterized by for instance HIST1H3J and HIST1H3H.ConclusionTo our knowledge, we are the first to report on differential gene expression patterns of paired OA subchondral bone tissue using RNA sequencing. Among the most consistently differentially expressed genes with OA pathophysiology in both bone and cartilage were IL11 and CHADL. As these genes were recently also identified as robust OA risk genes they classify as attractive druggable targets acting on two OA disease relevant tissues.

scholarly journals powsimR: Power analysis for bulk and single cell RNA-seq experiments

2017 ◽  
Author(s):  
Beate Vieth ◽  
Christoph Ziegenhain ◽  
Swati Parekh ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Power Analysis ◽  
A Priori ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Flexible Tool ◽  
Power Analyses

AbstractPower analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

Sign in / Sign up

Export Citation Format

Share Document