LEC 15 Transcriptomics PDF

LEC 15 Transcriptomics BIOC 3265-Principles of Bioinformatics Dr. A. T Alleyne- UWI Cave Hill 1  At the end of this lecture, you should be able to: 1. Describe the components of the transcriptome...

LEC 15 Transcriptomics BIOC 3265-Principles of Bioinformatics Dr. A. T Alleyne- UWI Cave Hill 1  At the end of this lecture, you should be able to: 1. Describe the components of the transcriptome 2. Explain the C value paradox 3. Explain the impact of splicing on translation Learning 4. Describe the Non-sense Mediated Decay (NMD) pathway Outcomes 5. Discuss the steps involved in transcriptomics 6. Describe RNA seq technique 7. Differentiate between DNA microarrays and RNA seq 8. Compare transcriptomics with other “- omics” techniques 2 GENE EXPRESSION Regulated by RNA A genome-wide exploration of RNAs offers a global picture, while removing inadequate nature single gene-based studies when linking phenotypes to genotypes. 3 Gene expression 4 5  The transcriptome ─ the complete set of transcripts in a cell, at a specific developmental stage or physiological condition. ─Total cell RNA composition. 6  The transcriptome mRNA up less than 4% of the total cell RNA because not every gene is transcriptionally active in every cell. Transcriptome analysis is also called expression profiling. Transcriptomes of different diseased states, tissues, and single cells can be linked to gene function. 7 8 Types of RNA Transcriptome v genome Central dogma assumption But this assumption was based on bacterial systems: a linear relationship between genes and protein content 10  Genome sizes C-value is the amount of DNA in a haploid nucleus C-values are measured in picograms Why Onion has more DNA than Humans? C- value, C-value Paradox and What causes C- value paradox? - YouTube Comparative genome sizes 11  C value paradox the amount of DNA in a haploid genome (the C-value) does not directly correspond to the complexity of an organism C values can be extremely variable. Mammals have 30,000 to 50,000 genes, but their genome size (or C- value) is 3 x 109 bp. 12 C-VALUE Constant or characteristic value Measured in picograms Genome carry non-genic DNA that is used for regulation, non-gene functional activities 13 Splicing and alternate splicing inclusion or skipping of individual “cassette” exons, 14 Transcriptome complexity- Alternate splicing Enables cells to generate vast protein diversity from a limited number of genes. 15  Exon skipping : An exon is either spliced out or retained in the mature mRNA. Mutual exclusive of exons: Only one of two exons is retained in the mature mRNA. Alternative 5ʹ-splice site or 3ʹ- splice site: Exons are joined at different splice sites. Intron retention: An intron is not spliced out and remains in the mature mRNA. Alternative promoter or polyadenylation: Different transcription start or end sites are used. 16 ~20,000 genes ~83% have AS 17  mRNA Translation and Decay A large fraction of all mRNAs is degraded by the Non-sense Mediated Decay (NMD) pathway Highly conserved- selectively degrades RNAs with truncating mutations that prematurely terminate. mRNAs that possess a stop codon located more then 55 bp upstream of the last exon/exon boundary are targeted by the NMD pathway for degradation (Post- translational regulation of gene expression) The NMD pathway reduces errors in gene expression by eliminating mRNA transcripts that contain premature stop codons. NMD controls gene expression and regulation of several biological processes, such as mammalian development, cell differentiation and survival. 18  Nonsense-mediated mRNA decay (NMD) is a translation-dependent surveillance mechanism that rapidly degrades transcripts with premature stop codons (PTCs. NMD avoids the production of a faulty, truncated protein that could exert deleterious functions in the cell 19 Non-sense Mediated Decay (NMD) pathway 20  Transcriptomics aims to: Catalogue all species of transcript, mRNAs, non-coding RNAs and small RNAs; Transcriptomics Determine the transcriptional structure of goals genes, start sites, 5ʹ and 3ʹ ends, splicing patterns and other post-transcriptional modifications; Transcriptome assembly Build new or improved profile of transcribed regions (“gene models”) of the genome 21  Differential Gene Expression Quantitative evaluation and comparison Transcriptomics of transcript levels, usually between different groups goals Meta transcriptomics Transcriptome analysis of a community of different species (e.g., gut bacteria, hot springs, soil) Gain insights on the functioning and activity rather than just who is present 22  Genomics and transcriptomics use the same types of biomolecules Genomics and transcriptomics  Incubation of fluorescently labeled cDNA Hybridization with custom-made microarrays high throughput and inexpensive Transcriptome Methods directly determine the cDNA sequence Sequence- Tag-based methods high throughput and precise, but expensive based 24 Hybridization “arrays”  Typical DNA microarray Experimental overview From Dawany et al. 2010Figure 6-DNA Microarray: Hybridization using a) two channel and b) single- 26 channel microarray platforms  DNA MICROARRAYS ADVANTAGES Rapid Method and data analysis well described and supported Robust Convenient for directed and focussed studies DISADVANTAGES Closed system approach Difficult to correlate with absolute transcript number Sensitive to alternative splicing ambiguities or anomalies 27  Complementary DNAs (cDNAs) generated from the RNA of interest are directly sequenced using next-generation sequencing technologies. has been used successfully to precisely quantify transcript levels, confirm or revise previously annotated 5' and 3' ends of genes, and map exon/intron boundaries useful for new gene RNA seq This Photo by Unknown Author is licensed under CC BY-SA discoveries 28 29  1 Convert long RNAs into a library of cDNA RNA or DNA fragmentation 2 Add sequencing adaptors to cDNA Typical RNA high-throughput sequencing produces a short sequence read from each cDNA Seq steps 3 Align sequence with the reference genome or transcriptome to generate an expression profile for each gene from exonic reads, junction reads and poly(A) end-reads. 30  Summary of RNA sequencing ─ Sequences are aligned to a reference genome sequence to reconstruct the genome regions being transcribed. ─ Transcript presence are recorded with detection of fluorescent tags. ─ These data are then used to annotate the location of expressed genes, their relative expression levels, and any alternative splice variants Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T (2017) Transcriptomics technologies. PLOS Computational Biology 13(5): e1005457. PLOS Computational Biology: https://doi.org/10.1371/journal.pcbi.1005457 Transcriptomics technologies31 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005457 RNA Seq Advantages Bioinformatics tools and statistical analysis of RNA seq data 33  Classical parametric & non-parametric statistical tests for hypothesis testing Hierarchical clustering Clustering algorithms k-means and Self- Organising Maps Statistical Tests Classification e.g. Machine learning and Linear discriminant analysis Dynamic Bayesian Probabilistic Modelling Networks Markov Models 34 Transcriptome Shotgun Assembly(TSA) Sequence Database 35 RNA Seq: Principle and Workflow of RNA Sequencing - YouTube 36 References https://www.genome.gov/13014330#al- 1 http://www.genome.gov/26525202