Bork A Berghoff1, Torgny Karlsson2, Thomas Källman3,4, E Gerhart H Wagner5, Manfred G Grabherr3,4. 1. Institut für Mikrobiologie und Molekularbiologie, Justus-Liebig-Universität, Giessen, Germany. 2. Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. 3. Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. 4. Bioinformatics Infrastructure for Life Sciences (BILS), Science for Life Laboratories, Uppsala University, Uppsala, Sweden. 5. Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Abstract
BACKGROUND: Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. RESULTS: Here, we present a novel method, moose2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli, and show how moose2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/. CONCLUSIONS: The proposed RNA-seq normalization method, moose2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data are handled adequately to minimize variations between replicates.
BACKGROUND: Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. RESULTS: Here, we present a novel method, moose2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli, and show how moose2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/. CONCLUSIONS: The proposed RNA-seq normalization method, moose2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data are handled adequately to minimize variations between replicates.
Entities:
Keywords:
DNA damage; Gene expression; Normalization; RNA-seq; Stress response; Transcriptomics
Authors: Roger D Canales; Yuling Luo; James C Willey; Bradley Austermiller; Catalin C Barbacioru; Cecilie Boysen; Kathryn Hunkapiller; Roderick V Jensen; Charles R Knight; Kathleen Y Lee; Yunqing Ma; Botoul Maqsodi; Adam Papallo; Elizabeth Herness Peters; Karen Poulter; Patricia L Ruppel; Raymond R Samaha; Leming Shi; Wen Yang; Lu Zhang; Federico M Goodsaid Journal: Nat Biotechnol Date: 2006-09 Impact factor: 54.908
Authors: Charles Y Lin; Jakob Lovén; Peter B Rahl; Ronald M Paranal; Christopher B Burge; James E Bradner; Tong Ihn Lee; Richard A Young Journal: Cell Date: 2012-09-28 Impact factor: 41.582
Authors: Jo Vandesompele; Katleen De Preter; Filip Pattyn; Bruce Poppe; Nadine Van Roy; Anne De Paepe; Frank Speleman Journal: Genome Biol Date: 2002-06-18 Impact factor: 13.583
Authors: Daniel Edelmann; Florian H Leinberger; Nicole E Schmid; Markus Oberpaul; Till F Schäberle; Bork A Berghoff Journal: Microorganisms Date: 2021-04-27