rna surveillance and degradation the yin yang of rna n.
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RNA surveillance and degradation: the Yin Yang of RNA. AAAAAAAAAAA. RNA Pol II. production. RNA. destruction. AAA. Ribosome. *. *. *. *. *. *. *. *. *. *. *. *. MODEL:. Mtr4. Polyadenylation by Trf4p. Trf4p. AAAAA. Hypomodified tRNA i Met. *. Rrp46p. Csl4p. Rrp43p.

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RNA surveillance and degradation: the Yin Yang of RNA

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

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

    3. Workflow

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

    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 AAAA-3’ 3’-A TTTp-5’

    7. Raw reads vs Mapped reads 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.


    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?

    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 • nw=nw/L. • xc, • Repeat step 2 ktimes • 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

    20. pA reads intersecting 45S pre-rRNA 18S 5.8S 28S

    21. pA reads intersecting 45S pre-rRNA 18S 5.8S 28S

    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