1. RNA surveillance and degradation: the Yin Yang of RNA RNA Pol II AAAAAAAAAAA AAA production destruction RNA Ribosome

2. MODEL: * * * * AAAAA Exosome Degradation of hypomodified tRNA i Met Hypomodified tRNA i Met * * * * Polyadenylation by Trf4p * * * * AAAAA Mtr3p Rrp41p Rrp45p Rrp40p Rrp46p Rrp42p Rrp4p Rrp43p Rrp44p Csl4p * *- Hypothetical diagram of the exosome Rrp6p Trf4p Mtr4

3. Workflow Knockdown mMtr4 Library Construction PolyA-SeqMapping Remove Internal A AggregateNormalize Connect& Compare CollectVisualize

4. Next Gen sequencing PolyA-Seq Mtr4 TRAMP Complex Papd5 ZCCHC7 siRNA knockdown AAAA

5. Library creation for NGS

6. Map paired end reads to genome BWA (Burrows-Wheeler Aligner) Algorithm used to map each pair of reads to the genome Report each pair of reads as a single nucleotide position within the genome where polyadenylation detected in an RNA sample Average insert size 300 – Read size ~45 TTTp-5’ AAAA-3’ 3’-A

7. Raw reads vs Mapped reads Data type/kd typeRaw readsMapped readspositions Replicate Data Mtr415,135,07810,853,534651,551 Ctrl16,348,78011,708,310652,128 Rrp615,971,92612,388,266705,173 Original data Mtr4ND34,204,5341,124,968 CtrlND7,195,942582,256 Rrp6ND8,241,505597,672 Normalization of data: reads per million (rpm)

8. Analysis Starting with refseq database – Raw read counts converted to reads per million Reads at position/total reads in sample – Remove all non-coding RNAs – From each sample collect normalized reads mapping at the 3’ end +/- 50 bases of each refseq encoding protein – Dot Plot normalized reads on log scale, X axis=control and Y axis=mMtr4KD

9. mRNA polyadenylation does not change between Mtr4 and control KD R2=0.95141

10. Problems encountered Sequencing read depth very different in the original data – 34 mil mapped reads in one sample 8 mil in other Lack of 3 replicates for robust statistical analysis of data Removal of internal A – Seq reads that map to a oligoadenylate track in the genome – Algorithm developed misses many – Manual removal takes too much time.

11. Remove Internal A AAAAAAAA TTTTTTTTT

12. How to mine the data based on a hypothesis Hypothesis: PolyA+ RNAs of unknown identity will accumulate upon depletion of mMtr4 vs. the control. – How can the transcriptome be queried? – How detailed should a query be? Every pA position, or only those exhibiting greater than x number of raw/normalized reads? How do we find significant differences with one sample, or possibly two? How can repetitive elements be accounted for in the data?

13. Custom annotation to remove bias from existing annotations Data mapped with Bowtie to mouse genome mm10 build Mapped data from KD and control compared using cufflinks to explore gene expression differences using a custom annotation Custom annotation – 1000 base pair genes with 500 base pair overlap with next gene This did not work well

14. Problems with using custom annotation First real problem was the no computing could handle more than 5000 genes of the custom annotation at a time – One chromosome had 147K genes There was a problem with assignment when the reads overlapped – Cuffdiff would randomly assign the reads to only one of the genes. Overlaps split into two fasta files, but we could not capture differences in the data that we knew exists. – cuffdiff collects data from the entire 1000 bp gene and compares between 2 samples – This method leads to false negatives for pA data where the focus is on one or a few positions as a pA event.

15. What next? Mapping Map raw reads against mm10 assembly with Bowtie2/ Tophat Strand and 3’ end selection Select alignments on positive and negative strand Select 3’ read of paired reads to define site of polyadenylation Custom annotation preparation and count Run F-Seq to identify the mode of all peaks Normalize data then collect reads at mode (+/-5-10 nucleotides) Statistical test with DESeq, an R package Negative binominal model

16. F-Seq Tags to identify specific sequence features for different library preparations (ChIP-seq), (DNase-seq) and (pA-seq). Will summarize and display individual sequence data as an accurate and interpretable signal, by generating a continuous tag sequence density estimation.

17. Generating Peaks with FSeq 1. Estimate kernel density to estimate pdf 2. compute threshold – n w =nw/L. – x c, – Repeat step 2 k times – s SDs above the mean 2.1 threshold output module is modifiable

18. Magnitude of data: one sample both strands 51 million bases of Chromosome 12 12 thousand bases of Chromosome 12 Chromsome 12 is 121 million base pairs long

19. rRNA workflow Mapping Map raw reads against 13kb rDNA with Bowtie2 Strand and 3’ end selection Select alignments on positive strand Select 3’ read of a pair and 3’ end of a read Density estimation and visualization Density estimation with F-Seq, a peak calling tool

20. 18S28S5.8S pA reads intersecting 45S pre- rRNA

21. 18S 28S 5.8S

22. Accumulation of micro RNA processed 5’ leader upon depletion of Mtr4 Comparison of Mtr4 V. Control KD Abundant polyA found near 5’ end of annotated Mir322 Confirmed using molecular technique