Mapping and differential expression analysis from short-read RNA-Seq data in model organisms

Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.

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

[2]  Nuno A. Fonseca,et al.  Tools for mapping high-throughput sequencing data , 2012, Bioinform..

[3]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[4]  Michael D. Wilson,et al.  The Evolutionary Landscape of Alternative Splicing in Vertebrate Species , 2012, Science.

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

[6]  C. Ponting,et al.  G&T-seq: parallel sequencing of single-cell genomes and transcriptomes , 2015, Nature Methods.

[7]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[8]  A. Subramanian,et al.  A fundamental study of the PCR amplification of GC-rich DNA templates , 2008, Comput. Biol. Chem..

[9]  Yi Liu,et al.  Single-Cell Gene Expression Profiling , 2022 .

[10]  Heng Li,et al.  A survey of sequence alignment algorithms for next-generation sequencing , 2010, Briefings Bioinform..

[11]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[12]  R. Durbin,et al.  Mapping Quality Scores Mapping Short Dna Sequencing Reads and Calling Variants Using P

, 2022 .

[13]  Pradeep S Rajendran,et al.  Single-cell dissection of transcriptional heterogeneity in human colon tumors , 2011, Nature Biotechnology.

[14]  T. Nilsen,et al.  Expansion of the eukaryotic proteome by alternative splicing , 2010, Nature.

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

[16]  Zhong Wang,et al.  Next-generation transcriptome assembly , 2011, Nature Reviews Genetics.

[17]  Ning Leng,et al.  EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments , 2013, Bioinform..

[18]  N. Kyrpides,et al.  Direct Comparisons of Illumina vs. Roche 454 Sequencing Technologies on the Same Microbial Community DNA Sample , 2012, PloS one.

[19]  S. Salzberg,et al.  Versatile and open software for comparing large genomes , 2004, Genome Biology.

[20]  J. Rinn,et al.  Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs , 2010, Nature biotechnology.

[21]  Kevin S. Smith,et al.  High-Resolution Transcriptome Analysis with Long-Read RNA Sequencing , 2014, PloS one.

[22]  Ruiqiang Li,et al.  SOAP: short oligonucleotide alignment program , 2008, Bioinform..

[23]  Enno Ohlebusch,et al.  Replacing suffix trees with enhanced suffix arrays , 2004, J. Discrete Algorithms.

[24]  Steven J. M. Jones,et al.  Slider—maximum use of probability information for alignment of short sequence reads and SNP detection , 2008, Bioinform..

[25]  Anja M. Billing,et al.  Genome-wide identification and functional analysis of Apobec-1-mediated C-to-U RNA editing in mouse small intestine and liver , 2014, Genome Biology.

[26]  Bernd Weisshaar,et al.  Exploiting single-molecule transcript sequencing for eukaryotic gene prediction , 2015, Genome Biology.

[27]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[28]  C. Libert,et al.  Predominant contribution of cis-regulatory divergence in the evolution of mouse alternative splicing , 2015, Molecular systems biology.

[29]  Michael Brudno,et al.  SHRiMP: Accurate Mapping of Short Color-space Reads , 2009, PLoS Comput. Biol..

[30]  R. Doerge,et al.  Statistical Applications in Genetics and Molecular Biology A Two-Stage Poisson Model for Testing RNA-Seq Data , 2011 .

[31]  N. Gemmell,et al.  The Accuracy, Feasibility and Challenges of Sequencing Short Tandem Repeats Using Next-Generation Sequencing Platforms , 2014, PloS one.

[32]  T. Hashimshony,et al.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.

[33]  Peter F. Stadler,et al.  Fast Mapping of Short Sequences with Mismatches, Insertions and Deletions Using Index Structures , 2009, PLoS Comput. Biol..

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

[35]  Momiao Xiong,et al.  Canonical correlation analysis for RNA-seq co-expression networks , 2013, Nucleic acids research.

[36]  Robert H Singer,et al.  Single-Cell Gene Expression Profiling , 2002, Science.

[37]  Knut Reinert,et al.  RazerS 3: Faster, fully sensitive read mapping , 2012, Bioinform..

[38]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[39]  Suzanne Mate,et al.  No assembly required: Full-length MHC class I allele discovery by PacBio circular consensus sequencing. , 2015, Human immunology.

[40]  Rona S. Gertner,et al.  Single cell RNA Seq reveals dynamic paracrine control of cellular variation , 2014, Nature.

[41]  N. Wray,et al.  Purification of Neural Precursor Cells Reveals the Presence of Distinct, Stimulus-Specific Subpopulations of Quiescent Precursors in the Adult Mouse Hippocampus , 2015, The Journal of Neuroscience.

[42]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[43]  Wing Hung Wong,et al.  SeqMap: mapping massive amount of oligonucleotides to the genome , 2008, Bioinform..

[44]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology High-Dimensional Regression and Variable Selection Using CAR Scores , 2011 .

[45]  Siu-Ming Yiu,et al.  SOAP2: an improved ultrafast tool for short read alignment , 2009, Bioinform..

[46]  Russell E. Durrett,et al.  Assembly and diploid architecture of an individual human genome via single-molecule technologies , 2015, Nature Methods.

[47]  Alyssa C. Frazee,et al.  Ballgown bridges the gap between transcriptome assembly and expression analysis , 2015, Nature Biotechnology.

[48]  Jürgen Jänes,et al.  A comparative study of RNA-seq analysis strategies , 2015, Briefings Bioinform..

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

[50]  Zong-Hong Zhang,et al.  Global transcriptome profiles of Camellia sinensis during cold acclimation , 2013, BMC Genomics.

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

[52]  Marcus Droege,et al.  The Genome Sequencer FLX System--longer reads, more applications, straight forward bioinformatics and more complete data sets. , 2008, Journal of biotechnology.

[53]  Robert Tibshirani,et al.  Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data , 2013, Statistical methods in medical research.

[54]  Derek Y. Chiang,et al.  MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery , 2010, Nucleic acids research.

[55]  A. W. van der Vaart,et al.  Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. , 2013, Biostatistics.

[56]  Susan R. Wilson,et al.  Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing , 2012, BMC Genomics.

[57]  Lior Pachter,et al.  Sequence Analysis , 2020, Definitions.

[58]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[59]  Lior Pachter,et al.  Identification of novel transcripts in annotated genomes using RNA-Seq , 2011, Bioinform..

[60]  Cole Trapnell,et al.  Computational methods for transcriptome annotation and quantification using RNA-seq , 2011, Nature Methods.

[61]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013 .

[62]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[63]  S. Salzberg,et al.  StringTie enables improved reconstruction of a transcriptome from RNA-seq reads , 2015, Nature Biotechnology.

[64]  Siu-Ming Yiu,et al.  SOAPsplice: Genome-Wide ab initio Detection of Splice Junctions from RNA-Seq Data , 2011, Front. Gene..

[65]  J. Rinn,et al.  Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs , 2010, Nature Biotechnology.

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

[67]  N. Friedman,et al.  Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data , 2011, Nature Biotechnology.

[68]  Tieliu Shi,et al.  Comprehensively identifying and characterizing the missing gene sequences in human reference genome with integrated analytic approaches , 2013, Human Genetics.

[69]  David Stoddart,et al.  Single-nucleotide discrimination in immobilized DNA oligonucleotides with a biological nanopore , 2009, Proceedings of the National Academy of Sciences.

[70]  Michael Farrar,et al.  Sequence analysis Striped Smith – Waterman speeds database searches six times over other SIMD implementations , 2007 .

[71]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[72]  Peter J. Bickel,et al.  The Developmental Transcriptome of Drosophila melanogaster , 2010, Nature.

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

[74]  K. Reinert,et al.  RazerS--fast read mapping with sensitivity control. , 2009, Genome research.

[75]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[76]  James R. Knight,et al.  Genome sequencing in microfabricated high-density picolitre reactors , 2005, Nature.

[77]  R. Tibshirani,et al.  Normalization, testing, and false discovery rate estimation for RNA-sequencing data. , 2012, Biostatistics.

[78]  Esko Ukkonen,et al.  Two Algorithms for Approximate String Matching in Static Texts , 1991, MFCS.

[79]  Eric T. Wang,et al.  Alternative Isoform Regulation in Human Tissue Transcriptomes , 2008, Nature.

[80]  Scott A. Rifkin,et al.  Imaging individual mRNA molecules using multiple singly labeled probes , 2008, Nature Methods.

[81]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

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

[83]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[84]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[85]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[86]  Gavin Sherlock,et al.  Extensive and coordinated control of allele-specific expression by both transcription and translation in Candida albicans , 2014, Genome research.

[87]  D. J. Wheeler,et al.  A Block-sorting Lossless Data Compression Algorithm , 1994 .

[88]  J. Lupski,et al.  The complete genome of an individual by massively parallel DNA sequencing , 2008, Nature.

[89]  Steven J. M. Jones,et al.  High quality SNP calling using Illumina data at shallow coverage , 2010, Bioinform..

[90]  Denis C. Bauer,et al.  A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data , 2014, bioRxiv.

[91]  S. Turner,et al.  Real-Time DNA Sequencing from Single Polymerase Molecules , 2009, Science.

[92]  Colin Echeverría Aitken,et al.  Real-time tRNA transit on single translating ribosomes at codon resolution , 2010, Nature.

[93]  Thomas J. Hardcastle,et al.  baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data , 2010, BMC Bioinformatics.

[94]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[95]  Michael Q. Zhang,et al.  Updates to the RMAP short-read mapping software , 2009, Bioinform..

[96]  David R. Kelley,et al.  Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks , 2012, Nature Protocols.

[97]  Philip M. Kim,et al.  Paired-End Mapping Reveals Extensive Structural Variation in the Human Genome , 2007, Science.

[98]  Yan Guo,et al.  Alternative applications for distinct RNA sequencing strategies , 2015, Briefings Bioinform..

[99]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[100]  Heng Li.  Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[101]  Matthew D. Young,et al.  From RNA-seq reads to differential expression results , 2010, Genome Biology.

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

[103]  Kenneth K. Lopiano,et al.  RNA-seq: technical variability and sampling , 2011, BMC Genomics.

[104]  C. Mason,et al.  Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data , 2013, Genome Biology.

[105]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[106]  Michael Q. Zhang,et al.  Using quality scores and longer reads improves accuracy of Solexa read mapping , 2008, BMC Bioinformatics.

[107]  Giovanni Manzini,et al.  Opportunistic data structures with applications , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[108]  Muwang Li,et al.  Alternative splicing and trans-splicing events revealed by analysis of the Bombyx mori transcriptome. , 2012, RNA.

[109]  Jeff H. Chang,et al.  The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq , 2011 .

[110]  Mark Akeson,et al.  Replication of Individual DNA Molecules under Electronic Control Using a Protein Nanopore , 2010, Nature nanotechnology.

[111]  Bin Ma,et al.  ZOOM! Zillions of oligos mapped , 2008, Bioinform..

[112]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[113]  W. Huber,et al.  Differential expression analysis for sequence count data , 2010 .

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

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

[116]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[117]  Mark D. Robinson,et al.  Moderated statistical tests for assessing differences in tag abundance , 2007, Bioinform..

[118]  T Laver,et al.  Assessing the performance of the Oxford Nanopore Technologies MinION , 2015, Biomolecular detection and quantification.

[119]  Alex A. Pollen,et al.  Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.

[120]  K. Hansen,et al.  Biases in Illumina transcriptome sequencing caused by random hexamer priming , 2010, Nucleic acids research.

[121]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.