GC-Content Normalization for RNA-Seq Data

Risso, Davide; Schwartz, Katja; Sherlock, Gavin; Dudoit, Sandrine

doi:10.1186/1471-2105-12-480

Cited by 777 publications

(666 citation statements)

References 38 publications

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“…Counts were generated from alignment files in BAM format using a custom Python To correct for possible GC or length bias, data were normalized using the EDASeq R 467 package (Risso, 2011). Differential expression analysis and gene ontology (GO) 468 term and pathway enrichment analyses were conducted using the EdgeR package 469 (Robinson et al, 2010).…”

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confidence: 99%

Trehalose 6-Phosphate Regulates Photosynthesis and Assimilate Partitioning in Reproductive Tissue

et al. 2018

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confidence: 99%

Trehalose 6-Phosphate Regulates Photosynthesis and Assimilate Partitioning in Reproductive Tissue

et al. 2018

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“…It has been shown that these abundance measures are prone to biases correlated with the nucleotide composition [14,17] and length of the transcript [1,18]. Several within and between sample correction and normalisation procedures have recently been developed to address these biases either as nucleotide composition effects [17] or various combinations of nucleotide, length or library preparation biases [14,15].…”

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confidence: 99%

Development in Rice Genome Research Based on Accurate Genome Sequence

2014

The Role of Bioinformatics in Agriculture

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Background: RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. Results: Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. Conclusions: This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.

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“…Various tools are able to correct for this type of bias, like EDASeq (Risso et al 2011) and cqn (Hansen et al 2012) where the GC bias or length bias are included as covariates.…”

Section: Normalizationmentioning

confidence: 99%

“…It is lane-dependent and probably introduced during the library preparation step (Risso et al 2011).…”

Section: Normalizationmentioning

confidence: 99%

Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

Mazzoni

Kadarmideen

2016

Systems Biology in Animal Production and Health, Vol. 2

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The use of RNA sequencing (RNA-Seq) technologies is increasing mainly due to the development of new next-generation sequencing machines that have reduced the costs and the time needed for data generation.Nevertheless, microarrays are still the more common choice and one of the reasons is the complexity of the RNA-Seq data analysis. Furthermore, numerous biases can arise from RNA-Seq technology, and these biases have to be identified and removed properly in order to obtain accurate results.Nowadays, many tools have been developed which allow to perform each step without high-level programming skills. However, each step of the pipeline needs to be performed carefully and requires a good knowledge of both the technology and the algorithms.In this comprehensive review, we describe the fundamental steps of the pipeline for RNA-Seq analysis to identify differentially expressed genes: raw data quality control, trimming and filtering procedures, alignment, postmapping quality control, counting, normalization and differential expression test.For each step, we present the most common tools and we give a complete description of their main characteristics and advantages focusing on the statistics that they perform and the assumptions that they make about the data.The choice of the right tool can have a big impact on the final results. Until now, no gold standard has been established for this type of analysis. 62In conclusion, this review can be useful for both educational purposes as well as for less experienced practitioners of animal genomic research. In the absence of a commonly accepted standard procedure, the general overview presented in this review can help to make the best choices during the implementation of an RNA-Seq pipeline.

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GC-Content Normalization for RNA-Seq Data

Cited by 777 publications

References 38 publications

Trehalose 6-Phosphate Regulates Photosynthesis and Assimilate Partitioning in Reproductive Tissue

Trehalose 6-Phosphate Regulates Photosynthesis and Assimilate Partitioning in Reproductive Tissue

Development in Rice Genome Research Based on Accurate Genome Sequence

Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

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