Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

@article{Wagner2012MeasurementOM,
  title={Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples},
  author={G{\"u}nter P. Wagner and Koryu Kin and Vincent J. Lynch},
  journal={Theory in Biosciences},
  year={2012},
  volume={131},
  pages={281-285}
}
Measures of RNA abundance are important for many areas of biology and often obtained from high-throughput RNA sequencing methods such as Illumina sequence data. These measures need to be normalized to remove technical biases inherent in the sequencing approach, most notably the length of the RNA species and the sequencing depth of a sample. These biases are corrected in the widely used reads per kilobase per million reads (RPKM) measure. Here, we argue that the intended meaning of RPKM is a… 

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols.

TLDR
In this review, typical scenarios in which RPKM and TPM are misused are illustrated and it is hoped to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

TLDR
A simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays, and finds that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding and DNA methylation are negatively correlated withRNA stability.

Comparison of alternative approaches for analysing multi-level RNA-seq data

TLDR
The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments and improved sample comparability and delivered a robust prediction of subtle gene expression changes.

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

TLDR
It is shown that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability, and an R package is provided to help users integrate transcript- level abundance estimates from common quantification pipelines into count-based statistical inference engines.

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.

TLDR
It is illustrated that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets.

COEX-Seq: Convert a Variety of Measurements of Gene Expression in RNA-Seq

TLDR
A web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA- Seq.

Deconvolution of expression for nascent RNA-sequencing data (DENR) highlights pre-RNA isoform diversity in human cells

TLDR
DEConvolution of Expression for Nascent RNA sequencing data (DENR) is the first computational tool to enable abundance quantification of pre-RNA isoforms based on nascentRNA sequencing data, and it reveals high levels ofPre- RNA isoform diversity in human cells.

A model based criterion for gene expression calls using RNA-seq data

TLDR
A statistical model is suggested that considers the number of transcripts detected in a RNA-seq study as a mixture of two distributions: one is a exponential distribution for transcripts from inactive genes, and a negative binomial distribution for actively transcribed genes.

A protocol to evaluate RNA sequencing normalization methods

TLDR
This study tested various RNASeq normalization procedures and concluded that transcripts per million (TPM) was the best performing normalization method based on its preservation of biological signal as compared to the other methods tested.

Unit-Free and Robust Detection of Differential Expression from RNA-Seq Data

TLDR
A unified statistical model for joint detection of differential gene expression and between-sample normalization is proposed and is able to reliably normalize the data and detect differential gene expressions in some cases when more than 50% of the genes are differentially expressed in an asymmetric manner.
...

References

SHOWING 1-9 OF 9 REFERENCES

Mapping and quantifying mammalian transcriptomes by RNA-Seq

TLDR
Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.

Statistical inferences for isoform expression in RNA-Seq

TLDR
The results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods and statistical inferences are obtained from the posterior distribution by importance sampling.

RNA-Seq: a revolutionary tool for transcriptomics

TLDR
The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.

RNA sequencing: advances, challenges and opportunities

TLDR
Recent developments in RNA-seq methods have provided an even more complete characterization of RNA transcripts, including improvements in transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization and detection of alternative splicing events.

Function of alternative splicing.

Transcriptomic analysis of avian digits reveals conserved and derived digit identities in birds

Morphological characters are the result of developmental gene expression. The identity of a character is ultimately grounded in the gene regulatory network directing development and thus whole-genome

Measurement and Meaning in Biology

TLDR
This review presents the basic ideas of measurement theory and shows how it applies to theoretical as well as empirical work, and considers examples of empirical and theoretical evolutionary studies whose meaningfulness have been compromised by violations of measurement-theoretic principles.

Theories of Meaningfulness

Contents: R.D. Luce, Foreword. Introduction and Historical Background. Intuitive Theories of Meaningfulness. Axiomatic Set Theory. Axiomatic Generalizations of the Erlanger Program. Representational

Theory Biosci

  • Theory Biosci