Physiological RNA dynamics in RNA-Seq analysis

Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.

[1]  Dmitri D. Pervouchine,et al.  A benchmark for RNA-seq quantification pipelines , 2016, Genome Biology.

[2]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[3]  Donald Sharon,et al.  A single-molecule long-read survey of the human transcriptome , 2013, Nature Biotechnology.

[4]  Günter P. Wagner,et al.  A model based criterion for gene expression calls using RNA-seq data , 2013, Theory in Biosciences.

[5]  Davis J. McCarthy,et al.  Count-based differential expression analysis of RNA sequencing data using R and Bioconductor , 2013, Nature Protocols.

[6]  Charles C. Kim,et al.  Trimming of sequence reads alters RNA-Seq gene expression estimates , 2016, BMC Bioinformatics.

[7]  Dmitri D. Pervouchine,et al.  The human transcriptome across tissues and individuals , 2015, Science.

[8]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[9]  J. Harrow,et al.  Assessment of transcript reconstruction methods for RNA-seq , 2013, Nature Methods.

[10]  A. Routh,et al.  Parallel ClickSeq and Nanopore sequencing elucidates the rapid evolution of defective-interfering RNAs in Flock House virus , 2017, PLoS pathogens.

[11]  S. Sugano,et al.  Analysis of RNA decay factor mediated RNA stability contributions on RNA abundance , 2015, BMC Genomics.

[12]  Karl G. Kugler,et al.  Genome interplay in the grain transcriptome of hexaploid bread wheat , 2014, Science.

[13]  Jie Zhou,et al.  RNA-seq differential expression studies: more sequence or more replication? , 2014, Bioinform..

[14]  Ting Chen,et al.  Modeling RNA degradation for RNA-Seq with applications. , 2012, Biostatistics.

[15]  M. Stephens,et al.  RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.

[16]  M. Borodovsky,et al.  Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm , 2014, Nucleic acids research.

[17]  Mariella G. Filbin,et al.  Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma , 2016, Nature.

[18]  S. Ranade,et al.  Stem cell transcriptome profiling via massive-scale mRNA sequencing , 2008, Nature Methods.

[19]  Kay Nieselt,et al.  Global Transcriptional Start Site Mapping Using Differential RNA Sequencing Reveals Novel Antisense RNAs in Escherichia coli , 2014, Journal of bacteriology.

[20]  R. Ebrahimpour,et al.  Prediction of Gene Co-Expression by Quantifying Heterogeneous Features , 2015 .

[21]  R. Lister,et al.  Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis , 2008, Cell.

[22]  A. Conesa,et al.  Differential expression in RNA-seq: a matter of depth. , 2011, Genome research.

[23]  Asha A. Nair,et al.  Impact of RNA degradation on fusion detection by RNA-seq , 2016, BMC Genomics.

[24]  Dale N. Richardson,et al.  Deciphering the Plant Splicing Code: Experimental and Computational Approaches for Predicting Alternative Splicing and Splicing Regulatory Elements , 2012, Front. Plant Sci..

[25]  Javad Zahiri,et al.  Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data , 2017, Plant Biotechnology Reports.

[26]  Gilles Celeux,et al.  Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models , 2015, Bioinform..

[27]  Jürg Bähler,et al.  Post-transcriptional control of gene expression: a genome-wide perspective. , 2005, Trends in biochemical sciences.

[28]  Lennart Opitz,et al.  Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching , 2013, BMC Bioinformatics.

[29]  Robert J. White,et al.  Transcription by RNA polymerase III: more complex than we thought , 2011, Nature Reviews Genetics.

[30]  Yixing Han,et al.  Advanced Applications of RNA Sequencing and Challenges , 2015, Bioinformatics and biology insights.

[31]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[32]  Katharina J. Hoff,et al.  Current methods for automated annotation of protein-coding genes. , 2015, Current opinion in insect science.

[33]  C. Pikaard,et al.  Multisubunit RNA polymerases IV and V: purveyors of non-coding RNA for plant gene silencing , 2011, Nature Reviews Molecular Cell Biology.

[34]  Thomas Shafee,et al.  Transcriptomics technologies , 2017, PLoS Comput. Biol..

[35]  Nagarjun Vijay,et al.  Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA‐seq experiments , 2013, Molecular ecology.

[36]  M. Gerstein,et al.  The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing , 2008, Science.

[37]  C. Vogel,et al.  Computational challenges, tools, and resources for analyzing co‐ and post‐transcriptional events in high throughput , 2015, Wiley interdisciplinary reviews. RNA.

[38]  K. Nieselt,et al.  Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori. , 2015, Methods.

[39]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[40]  Kristin Reiche,et al.  The primary transcriptome of the major human pathogen Helicobacter pylori , 2010, Nature.

[41]  Yonghao Yu,et al.  Chemical genetic discovery of PARP targets reveals a role for PARP-1 in transcription elongation , 2016, Science.

[42]  D. Tollervey,et al.  The Many Pathways of RNA Degradation , 2009, Cell.

[43]  Achim Tresch,et al.  Comparative dynamic transcriptome analysis (cDTA) reveals mutual feedback between mRNA synthesis and degradation , 2012, Genome research.

[44]  Tuo Li,et al.  An Argonaute phosphorylation cycle promotes microRNA-mediated silencing , 2016, Nature.

[45]  Sheng Li,et al.  Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study , 2014, Nature Biotechnology.

[46]  David G Hendrickson,et al.  Differential analysis of gene regulation at transcript resolution with RNA-seq , 2012, Nature Biotechnology.

[47]  Sandrine Dudoit,et al.  Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.

[48]  David P. Kreil,et al.  The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance , 2014, Nature Biotechnology.

[49]  Ping Lin,et al.  Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size , 2017, BMC Systems Biology.

[50]  J. Carpten,et al.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.

[51]  B. Frey,et al.  Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing , 2008, Nature Genetics.

[52]  Kenta Nakai,et al.  Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis. , 2011, Genome research.

[53]  Larisa M Haupt,et al.  Review: Alternative Splicing (AS) of Genes As An Approach for Generating Protein Complexity , 2013, Current genomics.

[54]  R. Milo,et al.  Noise in gene expression is coupled to growth rate , 2015, Genome research.

[55]  Jan Gorodkin,et al.  MicroRNA discovery by similarity search to a database of RNA-seq profiles , 2013, Front. Genet..

[56]  Xuehui Huang,et al.  Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. , 2010, Genome research.

[57]  M. Wang,et al.  Alternative splicing at GYNNGY 5′ splice sites: more noise, less regulation , 2014, Nucleic acids research.

[58]  Thean-Hock Tang,et al.  Biases in small RNA deep sequencing data , 2013, Nucleic acids research.

[59]  Charlotte Soneson,et al.  A comparison of methods for differential expression analysis of RNA-seq data , 2013, BMC Bioinformatics.

[60]  A. Regev,et al.  Scaling single-cell genomics from phenomenology to mechanism , 2017, Nature.

[61]  Jennifer A. Doudna,et al.  Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection , 2016, Nature.

[62]  C. Mason,et al.  The impact of read length on quantification of differentially expressed genes and splice junction detection , 2015, Genome Biology.

[63]  B. Di Camillo,et al.  Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. , 2015, Briefings in functional genomics.

[64]  I. Amit,et al.  Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq , 2016, Cell.

[65]  Ido Golding,et al.  Genetic Determinants and Cellular Constraints in Noisy Gene Expression , 2013, Science.

[66]  I. Goodhead,et al.  Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution , 2008, Nature.

[67]  Kevin Struhl,et al.  Global Analysis of mRNA Isoform Half-Lives Reveals Stabilizing and Destabilizing Elements in Yeast , 2014, Cell.

[68]  D. Bartel,et al.  Expanded identification and characterization of mammalian circular RNAs , 2014, Genome Biology.

[69]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[70]  Christopher W. J. Smith,et al.  Alternative splicing: global insights , 2010, The FEBS journal.

[71]  W. Huber,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .

[72]  Xi Chen,et al.  Single-cell RNA-seq identifies a PD-1hi ILC progenitor and defines its development pathway , 2016, Nature.

[73]  S. Salzberg,et al.  Genome-wide annotation of microRNA primary transcript structures reveals novel regulatory mechanisms , 2015, Genome research.

[74]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

[75]  N. Lennon,et al.  Characterizing and measuring bias in sequence data , 2013, Genome Biology.

[76]  J. Gagneur,et al.  TT-seq maps the human transient transcriptome , 2016, Science.

[77]  Jeffrey G. Reifenberger,et al.  Direct RNA sequencing , 2009, Nature.

[78]  Mario Stanke,et al.  Simultaneous gene finding in multiple genomes , 2016, Bioinform..

[79]  M. Tress,et al.  Alternative Splicing May Not Be the Key to Proteome Complexity. , 2017, Trends in biochemical sciences.

[80]  Y. Gilad,et al.  RNA-seq: impact of RNA degradation on transcript quantification , 2014, BMC Biology.

[81]  B. Tian,et al.  RNA‐Seq methods for transcriptome analysis , 2017, Wiley interdisciplinary reviews. RNA.

[82]  Elise A. R. Serin,et al.  Learning from Co-expression Networks: Possibilities and Challenges , 2016, Front. Plant Sci..

[83]  James B. Brown,et al.  Diversity and dynamics of the Drosophila transcriptome , 2014, Nature.

[84]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[85]  Mariella G. Filbin,et al.  Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq , 2017, Science.

[86]  Ying Li,et al.  Measure transcript integrity using RNA-seq data , 2016, BMC Bioinformatics.

[87]  G. Barton,et al.  Erratum: How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? , 2016, RNA.

[88]  Yan Guo,et al.  Mining diverse small RNA species in the deep transcriptome. , 2015, Trends in biochemical sciences.

[89]  Marcel H. Schulz,et al.  A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome , 2008, Science.

[90]  Sara Ballouz,et al.  Guidance for RNA-seq co-expression network construction and analysis: safety in numbers , 2015, Bioinform..

[91]  Xuegong Zhang,et al.  mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data , 2015, Nature Communications.

[92]  S. Palumbi,et al.  SNP genotyping and population genomics from expressed sequences – current advances and future possibilities , 2015, Molecular ecology.

[93]  Ramana V. Davuluri,et al.  Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms , 2016, Briefings Bioinform..

[94]  G. Brewer,et al.  The regulation of mRNA stability in mammalian cells: 2.0. , 2012, Gene.

[95]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[96]  Nicolas Servant,et al.  A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis , 2013, Briefings Bioinform..

[97]  Franck Picard,et al.  SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence , 2016, Nucleic acids research.

[98]  John T. Lis,et al.  Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans , 2012, Nature Reviews Genetics.

[99]  D. Bechhofer,et al.  Global analysis of mRNA decay intermediates in Bacillus subtilis wild‐type and polynucleotide phosphorylase‐deletion strains , 2014, Molecular microbiology.

[100]  Charles C. Kim,et al.  Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq , 2016, BMC Bioinformatics.

[101]  Selene L. Fernandez-Valverde,et al.  Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica , 2015, BMC Genomics.

[102]  C. Vogel,et al.  Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation. , 2015, Molecular bioSystems.

[103]  Gary D Bader,et al.  Inferring interaction type in gene regulatory networks using co-expression data , 2015, Algorithms for Molecular Biology.

[104]  Mingyao Li,et al.  PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution , 2013, Nucleic acids research.

[105]  Günter P. Wagner,et al.  Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples , 2012, Theory in Biosciences.

[106]  Fatih Ozsolak,et al.  RNA sequencing: advances, challenges and opportunities , 2011, Nature Reviews Genetics.

[107]  H. Deising,et al.  New gene models and alternative splicing in the maize pathogen Colletotrichum graminicola revealed by RNA-Seq analysis , 2014, BMC Genomics.

[108]  N. Hacohen,et al.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.

[109]  Mark D. Robinson,et al.  Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage , 2016, Genome Biology.

[110]  Michael Gribskov,et al.  Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis. , 2016, Bioinformatics.

[111]  P. Hoen,et al.  Alternative mRNA transcription, processing, and translation: insights from RNA sequencing , 2015 .

[112]  Robert J. Weatheritt,et al.  The ribosome-engaged landscape of alternative splicing , 2016, Nature Structural &Molecular Biology.

[113]  Jeffrey T Leek,et al.  Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown , 2016, Nature Protocols.

[114]  John Hardy,et al.  An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks , 2017, BMC Systems Biology.

[115]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[116]  Mads Kærn,et al.  Noise in eukaryotic gene expression , 2003, Nature.

[117]  Matthew R Willmann,et al.  Genome-Wide Mapping of Uncapped and Cleaved Transcripts Reveals a Role for the Nuclear mRNA Cap-Binding Complex in Cotranslational RNA Decay in Arabidopsis[OPEN] , 2016, Plant Cell.

[118]  B. Johansson,et al.  The emerging complexity of gene fusions in cancer , 2015, Nature Reviews Cancer.

[119]  Huiyi Chen,et al.  Genome-wide study of mRNA degradation and transcript elongation in Escherichia coli , 2015, Molecular systems biology.

[120]  Olivier Elemento,et al.  Reversible methylation of m6Am in the 5′ cap controls mRNA stability , 2016, Nature.

[121]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[122]  Karla D. Passalacqua,et al.  Global mRNA decay analysis at single nucleotide resolution reveals segmental and positional degradation patterns in a Gram-positive bacterium , 2012, Genome Biology.

[123]  Mihaela Zavolan,et al.  Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data , 2015, Genome Biology.

[124]  J. McPherson,et al.  Coming of age: ten years of next-generation sequencing technologies , 2016, Nature Reviews Genetics.

[125]  R. Unger,et al.  Trade-off between Transcriptome Plasticity and Genome Evolution in Cephalopods , 2017, Cell.

[126]  Jin Billy Li,et al.  Reliable identification of genomic variants from RNA-seq data. , 2013, American journal of human genetics.

[127]  E. Young,et al.  Coupling mRNA Synthesis and Decay , 2014, Molecular and Cellular Biology.

[128]  Laura L. Elo,et al.  Comparison of software packages for detecting differential expression in RNA-seq studies , 2013, Briefings Bioinform..