scholarly journals Read trimming is not required for mapping and quantification of RNA-seq reads

2019 ◽  
Author(s):  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Read Mapping ◽  
Genome Wide ◽  
Sequencing Quality ◽  
Total Data ◽  
Order Of Magnitude ◽  
Gene Expression Quantification ◽  
The Impact ◽  
Genome Wide Gene Expression

AbstractRNA sequencing (RNA-seq) is currently the standard method for genome-wide gene expression profiling. RNA-seq reads often need to be mapped to a reference genome before read counts can be produced for genes. Read trimming methods have been developed to assist read mapping by removing adapter sequences and low-sequencing-quality bases. It is however unclear what is the impact of read trimming on the quantification of RNA-seq gene expression, an important task in the analysis of RNA-seq data. In this study, we used a benchmark RNA-seq dataset generated in the SEQC project to assess the impact of read trimming on mapping and quantification of RNA-seq reads. We found that adapter sequences can be effectively removed by the read aligner via its ‘soft-clipping’ procedure and many low-sequencing-quality bases, which would be removed by read trimming tools, were rescued by the aligner. Accuracy of gene expression quantification from using untrimmed reads was found to be comparable to or slightly better than that from using trimmed reads, based on expression of >900 genes measured by real-time PCR. Total data analysis time was reduced by up to an order of magnitude when read trimming was not performed. Our study suggests that read trimming is a redundant process in the quantification of RNA-seq expression data.

scholarly journals Read trimming is not required for mapping and quantification of RNA-seq reads at the gene level

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Data Analysis ◽  
Pearson Correlation ◽  
Rna Seq ◽  
Genome Wide ◽  
Gene Level ◽  
Sequencing Quality ◽  
Total Data ◽  
Order Of Magnitude ◽  
Gene Expression Quantification ◽  
The Impact

Abstract RNA sequencing (RNA-seq) is currently the standard method for genome-wide expression profiling. RNA-seq reads often need to be mapped to a reference genome before read counts can be produced for genes. Read trimming methods have been developed to assist read mapping by removing adapter sequences and low-sequencing-quality bases. It is however unclear what is the impact of read trimming on the quantification of RNA-seq data, an important task in RNA-seq data analysis. In this study, we used a benchmark RNA-seq dataset and simulation data to assess the impact of read trimming on mapping and quantification of RNA-seq reads. We found that adapter sequences can be effectively removed by read aligner via ’soft-clipping’ and that many low-sequencing-quality bases, which would be removed by read trimming tools, were rescued by the aligner. Accuracy of gene expression quantification from using untrimmed reads was found to be comparable to or slightly better than that from using trimmed reads, based on Pearson correlation with reverse transcriptase-polymerase chain reaction data and simulation truth. Total data analysis time was reduced by up to an order of magnitude when read trimming was not performed. Our study suggests that read trimming is a redundant process in the quantification of RNA-seq expression data.

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

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.

Assessing the Impact of Tissue Devitalization Time on Genome-wide Gene Expression Analysis in Ovarian Tumor Samples

2008 ◽  
Vol 17 (4) ◽  
pp. 200-206 ◽  
Author(s):  
Catherine I. Dumur ◽  
Sherjeel Sana ◽  
Amy C. Ladd ◽  
Andrea Ferreira-Gonzalez ◽  
David S. Wilkinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Ovarian Tumor ◽  
Gene Expression Analysis ◽  
Genome Wide ◽  
The Impact ◽  
Genome Wide Gene Expression

scholarly journals Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data

2021 ◽  
Author(s):  
David Chisanga ◽  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Gene Expression ◽  
Gene Annotation ◽  
Expression Data ◽  
Refseq Gene ◽  
Rna Seq ◽  
Sequencing Data ◽  
Microarray Expression Data ◽  
Sequencing Quality ◽  
Gene Expression Quantification ◽  
Expression Quantification

Abstract Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

scholarly journals Impact of gene annotation choice on the quantification of RNA-seq data

2021 ◽  
Author(s):  
David Chisanga ◽  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Gene Expression ◽  
Gene Annotation ◽  
Expression Data ◽  
Rna Seq ◽  
Microarray Expression Data ◽  
Refseq Annotation ◽  
Sequencing Quality ◽  
Gene Expression Quantification ◽  
Microarray Expression ◽  
Expression Quantification

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from $>$800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

scholarly journals The impact of protein quantity during energy restriction on genome-wide gene expression in adipose tissue of obese humans

2017 ◽  
Vol 41 (7) ◽  
pp. 1114-1120 ◽  
Author(s):  
I P G Van Bussel ◽  
E M P Backx ◽  
C P G M De Groot ◽  
M Tieland ◽  
M Müller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Energy Restriction ◽  
Genome Wide ◽  
The Impact ◽  
Genome Wide Gene Expression

scholarly journals Genome-wide gene expression analysis in the amphioxus, Branchiostoma belcheri after poly (I: C) challenge using strand-specific RNA-seq

Oncotarget ◽  
2017 ◽  
Vol 8 (65) ◽  
pp. 108392-108405 ◽  
Author(s):  
Qi-Lin Zhang ◽  
Zheng-Qing Xie ◽  
Ming-Zhong Liang ◽  
Bang Luo ◽  
Xiu-Qiang Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Rna Seq ◽  
Genome Wide ◽  
Branchiostoma Belcheri ◽  
Genome Wide Gene Expression

scholarly journals Differential DNA Methylation and Gene Expression Between ALV-J-Positive and ALV-J-Negative Chickens

2021 ◽  
Vol 8 ◽  
Author(s):  
Yiming Yan ◽  
Huihua Zhang ◽  
Shuang Gao ◽  
Huanmin Zhang ◽  
Xinheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cancer Progression ◽  
Economic Losses ◽  
Effective Vaccine ◽  
Rna Seq ◽  
Genome Wide ◽  
Avian Leukosis Virus Subgroup ◽  
Genome Wide Gene Expression ◽  
Integration Analysis

Background: Avian leukosis virus subgroup J (ALV-J) is an oncogenic virus that causes serious economic losses in the poultry industry; unfortunately, there is no effective vaccine against ALV-J. DNA methylation plays a crucial role in several biological processes, and an increasing number of diseases have been proven to be related to alterations in DNA methylation. In this study, we screened ALV-J-positive and -negative chickens. Subsequently, we generated and provided the genome-wide gene expression and DNA methylation profiles by MeDIP-seq and RNA-seq of ALV-J-positive and -negative chicken samples; 8,304 differentially methylated regions (DMRs) were identified by MeDIP-seq analysis (p ≤ 0.005) and 515 differentially expressed genes were identified by RNA-seq analysis (p ≤ 0.05). As a result of an integration analysis, we screened six candidate genes to identify ALV-J-negative chickens that possessed differential methylation in the promoter region. Furthermore, TGFB2 played an important role in tumorigenesis and cancer progression, which suggested TGFB2 may be an indicator for identifying ALV-J infections.

scholarly journals mtDNA eQTLs and the m1A 16S rRNA modification explain mtDNA tissue-specific gene expression pattern in humans

2018 ◽  
Author(s):  
Tal Cohen ◽  
Chen Mordechai ◽  
Alal Eran ◽  
Dan Mishmar
Keyword(s):  
Gene Expression ◽  
Mitochondrial Dna ◽  
16S Rrna ◽  
Gene Expression Pattern ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Dependent Manner ◽  
Rna Seq ◽  
Genome Wide ◽  
The Impact

Expression quantitative trait loci (eQTLs) are instrumental in genome-wide identification of regulatory elements, yet were overlooked in the mitochondrial DNA (mtDNA). By analyzing 5079 RNA-seq samples from 23 tissues we identified association of ancient mtDNA SNPs (haplogroups T2, L2, J2 and V) and recurrent SNPs (mtDNA positions 263, 750, 1438 and 10398) with tissue-dependent mtDNA gene-expression. Since the recurrent SNPs independently occurred in different mtDNA genetic backgrounds, they constitute the best candidates to be causal eQTLs. Secondly, the discovery of mtDNA eQTLs in both coding and non-coding mtDNA regions, propose the identification of novel mtDNA regulatory elements. Third, we identified association between low m1A 947 MT-RNR2 (16S) rRNA modification levels and altered mtDNA gene-expression in twelve tissues. Such association disappeared in skin which was exposed to sun, as compared to sun-unexposed skin from the same individuals, thus supporting the impact of UV on mtDNA gene expression. Taken together, our findings reveal that both mtDNA SNPs and mt-rRNA modification affect mtDNA gene expression in a tissue-dependent manner.

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