scholarly journals Identification and Comparative Analysis of Differential Gene Expression in Soybean Leaf Tissue under Drought and Flooding Stress Revealed by RNA-Seq

2016 ◽  
Vol 7 ◽  
Author(s):  
Wei Chen ◽  
Qiuming Yao ◽  
Gunvant B. Patil ◽  
Gaurav Agarwal ◽  
Rupesh K. Deshmukh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Differential Gene Expression ◽  
Leaf Tissue ◽  
Rna Seq ◽  
Flooding Stress ◽  
Soybean Leaf ◽  
Differential Gene

scholarly journals Differential Gene Expression by RNA-Seq Analysis of the Primo Vessel in the Rabbit Lymph

2019 ◽  
Vol 12 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Jun-Young Shin ◽  
Sang-Heon Choi ◽  
Da-Woon Choi ◽  
Ye-Jin An ◽  
Jae-Hyuk Seo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Rna Seq ◽  
Differential Gene

scholarly journals Differential Gene Expression in Ovaries of Qira Black Sheep and Hetian Sheep Using RNA-Seq Technique

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0120170 ◽  
Author(s):  
Han Ying Chen ◽  
Hong Shen ◽  
Bin Jia ◽  
Yong Sheng Zhang ◽  
Xu Hai Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Rna Seq ◽  
Black Sheep ◽  
Differential Gene

scholarly journals Effect of method of deduplication on estimation of differential gene expression using RNA-seq

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3091 ◽  
Author(s):  
Anna V. Klepikova ◽  
Artem S. Kasianov ◽  
Mikhail S. Chesnokov ◽  
Natalia L. Lazarevich ◽  
Aleksey A. Penin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Cancer Genomics ◽  
False Negative ◽  
Read Length ◽  
Rna Seq ◽  
Negative Results ◽  
Paired Samples ◽  
Highly Expressed Genes ◽  
Differential Gene

BackgroundRNA-seq is a useful tool for analysis of gene expression. However, its robustness is greatly affected by a number of artifacts. One of them is the presence of duplicated reads.ResultsTo infer the influence of different methods of removal of duplicated reads on estimation of gene expression in cancer genomics, we analyzed paired samples of hepatocellular carcinoma (HCC) and non-tumor liver tissue. Four protocols of data analysis were applied to each sample: processing without deduplication, deduplication using a method implemented in samtools, and deduplication based on one or two molecular indices (MI). We also analyzed the influence of sequencing layout (single read or paired end) and read length. We found that deduplication without MI greatly affects estimated expression values; this effect is the most pronounced for highly expressed genes.ConclusionThe use of unique molecular identifiers greatly improves accuracy of RNA-seq analysis, especially for highly expressed genes. We developed a set of scripts that enable handling of MI and their incorporation into RNA-seq analysis pipelines. Deduplication without MI affects results of differential gene expression analysis, producing a high proportion of false negative results. The absence of duplicate read removal is biased towards false positives. In those cases where using MI is not possible, we recommend using paired-end sequencing layout.

scholarly journals SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis

2015 ◽  
Author(s):  
Benjamin K Johnson ◽  
Matthew B Scholz ◽  
Tracy K Teal ◽  
Robert B Abramovitch
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Quality Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Bacterial Rna ◽  
Analysis Workflow ◽  
Differential Gene ◽  
Reference Counting

Summary: SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. The workflow is implemented in Python for file management and sequential execution of each analysis step and is available for Mac OS X, Microsoft Windows, and Linux. To promote the use of SPARTA as a teaching platform, a web-based tutorial is available explaining how RNA-seq data are processed and analyzed by the software. Availability and Implementation: Tutorial and workflow can be found at sparta.readthedocs.org. Teaching materials are located at sparta-teaching.readthedocs.org. Source code can be downloaded at www.github.com/abramovitchMSU/, implemented in Python and supported on Mac OS X, Linux, and MS Windows. Contact: Robert B. Abramovitch ([email protected]) Supplemental Information: Supplementary data are available online

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