Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensivein silicoassessment of RNA-seq experiments

2012 ◽  
Vol 22 (3) ◽  
pp. 620-634 ◽  
Author(s):  
Nagarjun Vijay ◽  
Jelmer W. Poelstra ◽  
Axel Künstner ◽  
Jochen B. W. Wolf
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Transcriptome Assembly ◽  
Rna Seq ◽  
Gene Expression Quantification ◽  
Differential Gene ◽  
Expression Quantification ◽  
Challenges And Strategies

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.

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