From RNA-seq reads to differential expression results

By Alicia Oshlack, Mark D. Robinson, Matthew D. Young. Genome Biology, 2010

754 citations recorded by the source corpus.

Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6a2465793fee97d6840bdfe707d9649386a3f4fa

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Citations in the corpus

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “Read counts are required for a wide range of count-based statistical methods for diﬀerential expression or diﬀerential binding analysis (Oshlack et al. , 2010).”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, methodology): “Compared to microarray techniques, RNA-Seq can be performed without prior knowledge of reference sequences and enables a wide range of applications including transcriptome de novo assembly [1-4], abundance estimation [5-9], and detection of alternative splicing events [10-12], all of which have revo”
Applications of next-generation sequencing to the study of biological invasions (2015, methodology): “The protocol for using transcriptomic data to quantify gene expression/differential expression is reviewed in Oshlack et al. (2010) but in brief, the mapped reads are assembled into ‘expression summaries’, which are normalized to account for size differences between libraries from different…”
A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. (2012, neutral): “Compared with the hybridization-based microarray technologies that have been the dominate approach to studying gene expression in the past decade, RNA-seq technologies offer several advantages including the lifted requirement of prior knowledge of the genome, a wider range of expression levels, more”
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis (2013, neutral): “lytical pipelines differ considerably [13].”
A comparison of methods for differential expression analysis of RNA-seq data (2013, background): “RNA-seq offers several advantages over microarrays for differential expression analysis, such as an increased dynamic range and a lower background level, and the ability to detect and quantify the expression of previously unknown transcripts and isoforms [3-5].”
Differential analysis of gene regulation at transcript resolution with RNA-seq (2012, background): “Choosing a model that adequately controls for variability in sequencing depth, biological noise and splicing structure has been the subject of debate 19 .”
Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, background): “Second, the total number of reads can vary across samples [2], and so a large difference in a gene’s read count between different conditions may simply be the result of differential coverage, rather than of differential expression.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, background): “One common approach to summarize RNA-seq data is to count the number of sequence reads mapping to each gene or genomic feature of interest [9– 12].”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “In each analysis, the linear modelling (Equation (1)) was carried out on the normalized log2(CPM).”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “It is becoming common-place for RNA-Seq to be used for similar experiments, so there is a pressing need for statistical methods that can provide the same ﬂexibility and rigor for complex RNA-Seq experiments (20).”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, methodology): “Additionally, we also consider the use of similarly preprocessed data in the analyses using the Trimmed Mean of M values (TMM) normalization [18], which was recently recommended in an extensive comparison of various normalization methods [19].”
Differential expression in RNA-seq: a matter of depth. (2011, background): “…that, in a mammalian genome, ∼ 700 million reads would be required to obtain accurate quantification of > 95% of expressed transcripts (Blencowe et al. 2009), but as yet, there has not been a systematic analysis on how sequencing coverage affects differential expression calls (Oshlack et al. 2010).”
Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data (2011, methodology): “The trimmed mean of M values (TMM) normalization (Robinson and Oshlack, 2010) aims to correct biases due to differences in samples sizes and expression patterns.”
RNA-seq differential expression studies: more sequence or more replication? (2014, methodology): “RNA-seq has been widely used for differential expression studies (Oshlack et al., 2010; Ozsolak and Milos, 2011).”
TopHat-Fusion: an algorithm for discovery of novel fusion transcripts (2011, background): “SAM format [28], enabling further downstream analyses [29], such as transcript assembly and differential gene expression.”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “Given the widespread use of bulk RNA-seq 37 , many powerful tools for processing high-throughput transcriptomic data already exist 38 .”