RIP – T RANSCRIPT E XPRESSION L EVELS

O UTLINE RNA Immuno-Precipitation (RIP) NGS on RIP & its alternatives Alternate splicing Transcription as a graph Distribution of tags in exons Pipeline on RIP-seq dataset

RNA I MMUNO -P RECIPITATION (RIP) Global identification of multiple RNA targets of RNA-Binding Proteins (RBPs) Identify proteins associated with RNAs in RNP complexes Identify subsets of RNAs that are functionally- related and potentially co-regulated

H OW IS RIP PERFORMED ?

S EQUENCING ON RIP RIP-Chip Noisy May miss out rare transcripts RIP-RT-PCR PCR introduces mutations RIP tilting-arrays Very expensive Too sensitive to ‘transcriptional noise’

NGS ON RIP RIP-Seq A more complete and unbiased assessment of the global population of RNAs associated with a RNP complex Minimize sequencing bias and high backgrounds known to the previously-mentioned methods

A LTERNATE S PLICING A simple example Regions with the numbers of reads – Exon1: chr1: (5,1); – Exon2: chr1: (2,0); – Exon3: chr1: (3,0); Splice reads – chr1, , ,chr1, , ,3.0; – chr1, , ,chr1, , ,2.0; Exon1(5)Exon2(2)Exon3(3) Exon_Num(Tags)

A LTERNATE S PLICING A less ideal example Regions with the numbers of reads – Exon1: chr4: (29,0); – Exon2: chr4: (8,0); – Exon3: chr4: (116,1); Splice reads – chr4, , ,chr4, , ,3.0; – chr4, , ,chr4, , ,2.0; Exon1(29)Exon2(8)Exon3(116)

T RANSCRIPTION AS A G RAPH From RNA-seq data, check the overlap of the tags If a region has more than one tag, we call it an enriched region Nodes Using the splice reads, we will connect the enriched regions Edges

T RANSCRIPTION AS A G RAPH Represent transcriptome in a topologically sorted acyclic graph Some Observed Errors (RME005) Out-of-range edges in graphs Self-looping nodes Default action: Ignore them

D ISTRIBUTION OF T AGS IN E XONS rQuant – Courtesy of Regina Bohnert (FML, Tubingen)

RNA- SEQ RIP- SEQ The previous results are from RNA-seq Will we have similar observations on RIP-seq datasets? And possibly link the observations to transcription expression levels in transcriptome

P IPELINE ON RIP- SEQ DATASET 1. Dataset RME005 is used 2. Use TopHat / Eland to map RNA back to genome 3. Generate transcription-graphs for each transcript with alternate splicing 4. Express the paths of all transcriptions in the graph using a set of linear equations 5. Use R to solve the linear equations

A N EXAMPLE FROM RME005 There are two transcripts Path1: Exon1 -> Exon2 -> Exon4 Path2: Exon1 -> Exon3 -> Exon4 Exon1 - Exon4 have length L1 - L4, and have reads with number N1 - N4 S1-S4 are the numbers of splice reads Exon1Exon2Exon3 Exon4 N1 N4N3 N2 S1 S2 S3 S4

A SSUMPTIONS The transcript expression levels are: Path1: x1 Path2: x2 The read length = constant The reads are uniformly sampled from the transcripts Use density of reads instead of read_coverage Differentiate reads on both long & short exons

E QUATIONS FOR LINEAR PROGRAMMING Objective function: minimize the sum of d_i Constraints N1/L1 = x1 + x2 + d1 - d2 S1/R = x1 + d3 - d4 N2/L2 = x1 + d5 - d6 S2/R = x1 + d7 - d8 S3/R = x2 + d9 - d10 N3/L3 = x2 + d11 - d12 S4/R = x2 + d13 - d14 N4/L4 = x1 + x2 + d15 - d16 x1, x2 >= 0 d_i >= 0 The solution should be the values of x1, x2 and all d_i N1 N4N3N2 S1 S2 S3 S4

A NOTHER PROBLEM An implicit assumption on enriched regions in RME005 RIP is known to be ~10% efficient Noise will overwhelm true RNP-targets Should use total-RNA as control dataset True-positive regions from RIP should be relatively enriched with tags than

H ANDLING THE ASSUMPTION Obtain RNA-seq from the same source of transcriptome Directly compare both RNA-seq and RIP-seq data RIP-chip discriminate enriched region with >4-fold than RNA-chip data Maybe 4-fold is the magic number ? Current tag distribution observed by Dr Li Guoliang Non-uniform as opposed to what rQuant has observed on RNA-seq

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