RNA-Seq as a Discovery Tool Julia Salzman

Deciphering the Genome

Power of RNA-Seq: Quantification and Discovery Salzman, Gawad, Wang Lacayo, Brown, 2012 RNA Isoform specific gene expression Gene fusions Overlooked RNA structural variants

Paired-end RNA-Seq Matched sequences are obtained for each library molecule CTTC…..GAAG GGAC…..GCCT Data: millions of 70-150+ bp A/C/G/T sequences

Part 1: Isoform Specific Expression

Example: Paired-end Data Aligned Some reads are informative about isoform-specific expression

Paired-end RNA-Seq for RNA Isoform Specific Gene Expression Exon 4 Exon 1 Since the size distribution of library molecules is known, inferred insert lengths can be used to increase statistical power and inference Rnpep Goal: estimate the expression of each isoform? Nontrivial : we only observe fragments of sequences

Insert Length Distributions Insert lengths of entire library (pooled) can be calculated and used to precisely estimate the distribution of sizes of cDNA in the library: 100 200 300 Base pairs Sequenced molecule length

Paired-end RNA-Seq Model Compute genome-wide insert length distribution 100 200 300 Base pairs Sequenced molecule length Mapped to Isoform 1  length 150 Mapped to Isoform 2  length 90 Salzman, Jiang, Wong 2011

Using PE for quantification is statistically more powerful PE model is a statistical improvement over naïve models and has optimal information reduction “Information” gain using PE Sequencing Overall, using “mate pair” information, more power, but sometimes experimental artifacts can effect results

Paired-end Size Distributions are Foundation for Tophat and other PE-RNA Seq Algorithms Summary and Problems: rely on a reference assume uniformity of size distributions in library over look biases’ Rep.1 Rep.2

Paired-End RNA-Seq for Gene Fusions in Ovarian Tumors (2009) Paired-end sequencing of poly-A selected RNA from 12 late stage tumors– genome wide search Top hit of our novel algorithm : ESRRA-C11orf20 C11orf20 ESRRA Fusion Isoform-specific estimation: ESRRA and the fusion are expressed at roughly equal magnitude (Salzman, Jiang, Wong)

Part 2: Gene Fusions

Recurrent Gene Fusions in Cancer A handful of recurrent fusions in solid tumors PAX8 -PPARγ fusion (thyroid cancer) EML4-ALK fusion (non small cell lung cancer) TMPRSS2-ERG family fusion (prostate cancer) Not Genome-wide More to be learned by unbiased study of RNA

Fusion Discovery 2 flavors Totally “de novo” discovery Search for any RNA fragments out of order with respect to the reference genome– not necessarily coinciding with exon boundaries Noisy Discovery with a reference database Discover fusions at annotated exon boundaries (protein coding) and better statistical checks Misses some fusions

Reference Approach Search for gene fusions with exon A in gene 1 spliced to exon B of gene 2 Exon A Exon B

Algorithm (with respect to reference) Remove all PE reads consistent with the reference Identify gene pairs PE reads where (read1, read2) map to (gene1, gene2) Find PE reads of the form: (gene A, gene A-B junction) Exon A Exon B

Paired-End RNA-Seq for Gene Fusions in Ovarian Tumors Paired-end sequencing of poly-A selected RNA from 12 late stage tumors– genome wide search Top hit of our algorithm : ESRRA-C11orf20 C11orf20 ESRRA Fusion Isoform-specific estimation: ESRRA and the fusion are expressed at roughly equal magnitude (Salzman, Jiang, Wong) Salzman et al, 2011

Part 3: Exploratory Analysis of RNA Rearrangements

Exploratory analysis: biological “noise” in RNA-Seq Data Wildtype genome: DNA Canonical transcript Locally rearranged DNA Scrambled transcript Is exon scrambling present in rRNA-depleted RNA?

Bioinformatic Analysis Thousands of exon scrambling events in RNA from human leukocytes and cancer samples Wildtype genome: DNA Canonical transcript Inconsistent with the reference genome!

Potential Biological Mechanisms for RNA Rearrangements DNA Rearrangement RNA rearrangement Trans-splicing Template switching PCR artifact

Analysis of Leukocyte Data Exons in ‘scrambled’ (non-increasing) order with respect to canonical exon order Thousands of genes with evidence of exon scrambling Naïve estimate of fractional abundance of scrambled read rate: all read rate (per transcript)

100s of Transcripts with High Fractions of Scrambled Isoforms Canonical Isoform 100s of genes < 25% Scrambled Isoform > 75% 100s of transcripts from B cells, stem cells and neutrophils have >50% copies from scrambled isoform

What Models Can Explain Exon Scrambling in RNA?

Model 1 to Explain RNA Exon Scrambling

Model 1 Prediction Can be made statistically precise Model 1 is statistically inconsistent with vast majority of data A subset of genes have evidence of tandem duplication in mRNA Against Model 1 For Model 1 2000- 1000- 100 - Transcripts with evidence

Alternative Model Model and data are consistent

Mining RNA-Seq Data for Evidence Consistent with Circular RNA? In poly-A depleted samples, expect to see strong evidence of scrambled exons (circular RNA) In poly-A selected samples, expect to see little evidence of scrambled exons (circular RNA)

Poly-A Depleted Samples Enriched for Scrambled Exons Align all reads to a custom database

Summary of RNA-Seq for NGS RNA-Seq can be used for discovery Tophat and other fusion/splicing algorithms gives a broad picture May have significant noise Miss important features of RNA expression

(feel free to contact me for the algorithm to identify circular RNA!) Currently, all published/downloadable algorithms will miss identifying circular RNA! (feel free to contact me for the algorithm to identify circular RNA!) In poly-A depleted samples, expect to see strong evidence of scrambled exons (circular RNA) In poly-A selected samples, expect to see little evidence of scrambled exons (circular RNA)