Lectures 13: High throughput sequencing: Beyond the genome. Spring 2017 March 28, 2017

Transcription

1 Lectures 13: High throughput sequencing: Beyond the genome Spring 2017 March 28, 2017

2 cartoons- 5- rna- seq.html

3 Omics Transcriptome - the set of all mrnas present in a cell Proteome proteins Metabolome/physiome - metabolites Microbiome the collecson of microbes present in an organism or other locason Interactome In physics the - on suffix has tended to signify an elementary parscle: the photon, electron, proton, meson, etc., whereas - ome in biology has the opposite intellectual funcson, of direcsng a@enson to a holissc abstracson, an eventual goal From: Ome Sweet Omics. The ScienSst 15(7), 2001

4 Omics Biologists have high- throughput methods for probing each - ome: Transcriptome RNA- Seq Proteome mass spectrometry, protein arrays Microbiome next generason sequencing Interactome yeast- two- hybrid Regulome ChIP- Seq Lots of data for bioinformascs people to analyze!

5 RNA- seq: profiling the transcriptome Technique: sequence the total RNA produced by the cell

6 Read mapping

7 Pile- ups From: The ENCODE Project ConsorSum (2011) A User's Guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biol 9(4): e

8 Pile- ups gene model read depth Most reads fall into coding exons or UTRs

9 RNA- seq: profiling the transcriptome Technique: sequence the total RNA produced by the cell What is this good for?

10 RNA- seq: profiling the transcriptome Genome annotason (transcript assembly) Detect alternasve splicing Obtain gene/transcript expression levels and detecson of differensal expression Allele- specific expression Small- RNA transcriptome (different protocol than regular RNA- seq)

11 All the uses of RNA- seq seqblog.com/news/informason/rna- seq- blog- poll- results/

12 DifferenSal expression

13 RNA- seq protocol

14 Raw and Aligned Reads Raw data is a (large) set of sequences Typical file format is TTAATCTACAGAATAGATAGCTAGCATATATTT + hhhhhhhhhhhhhhhdhhhhhhhhhhhdrehdh Read idensfier Bases called Base quality codes Alignment to genome is done by efficient indexing Aligned reads in SAM 163 chr M2I25M Read idensfier Where this read matched Start and end posisons Codes for match: 16 matches, 2 extra,

15 Cataloging the transcriptome Transcriptomics involves studying expression at SpaSal resoluson: Sssues, individuals, locason Temporal resoluson: circadian, seasonal, lifesme

16 Inter- Genic Reads Many reads reflect unannotated genes: opportunity to discover new genes

17 RPKM A Simple NormalizaSon Different numbers of counts per sample (sequencing depth) Divide counts in a region of interest (a genomic region or a gene or an exon) by all counts (reads per million reads - RPM) Genes have different lengths: divide also by length of gene Obtain RPKM (reads per kilobase of exon per million reads) Some use FPKM (fragments/kb/mr)

18 ChIP- seq Seq.html

19

20 Comments on ChIP- seq Genome- wide mapping of transcripson factor binding sites ComputaSonal problems: Peak calling SSll need mosf finders, but makes the problem easier

21 Variants Apply the methodology to RNA: map RNA- binding sites in mrna that interact with specific RNA- binding proteins CLIP- Seq (cross- linking immunoprecipitason sequencing) RIP- Seq (RNA immunoprecipitason sequencing)

22 Other sequencing- based techniques Methyl- seq, BS- seq: methylason Chromosome conformason capture (3C- 4C- 5C- HiC): spasal organizason of chromosomes

23 Other sequencing- based techniques Methyl- seq, BS- seq: methylason Chromosome conformason capture (3C- 4C- 5C): spasal organizason of chromosomes seqfold: RNA secondary structure DNAase- seq And many more!

24 Read mapping All the sequencing- based techniques require read mapping as a first step. ExisSng alignment tools are not fast enough à need new algorithms!

25 Read mapping How is the problem of read mapping different than sequence alignment as we have considered it unsl now?