RNA surveillance and degradation: the Yin Yang of RNA
RNA Pol II
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AAA
production
destruction
RNA
Ribosome
MODEL:
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*
AAAAA
Exosome
Degradation of hypomodified tRNAi
Met
Hypomodified tRNAiMet
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**
*
Polyadenylationby Trf4p
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*
AAAAAMtr3p Rrp41p
Rrp45p
Rrp40p
Rrp46p
Rrp42p
Rrp4p
Rrp43p
Rrp44p
Csl4p
*
*- Hypothetical diagram of the exosome
Rrp6p
Trf4p
Mtr4
Workflow
Next Gen sequencing PolyA-Seq
Mtr4
TRAMP Complex
Papd5
ZCCHC7
siRNA knockdown
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AAAA
AAAA
AAAAAA
AA
AAAA
AAAA
Library creation for NGS
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
Raw reads vs Mapped readsData type/kd type Raw reads Mapped reads positions
Replicate Data
Mtr4 15,135,078 10,853,534 651,551
Ctrl 16,348,780 11,708,310 652,128
Rrp6 15,971,926 12,388,266 705,173
Original data
Mtr4 ND 34,204,534 1,124,968
Ctrl ND 7,195,942 582,256
Rrp6 ND 8,241,505 597,672
Normalization of data: reads per million (rpm)
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
mRNA polyadenylation does not change between Mtr4 and control KD
R2=0.95141
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.
Remove Internal A
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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?
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
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.
What next?
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.
Generating Peaks with FSeq• 1. Estimate kernel density to estimate pdf
• 2. compute threshold– nw=nw/L.– xc,– Repeat step 2 k times– s SDs above the mean
• 2.1 threshold output module is modifiable
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
rRNA workflow
18S 28S5.8S
pA reads intersecting 45S pre-rRNA
pA reads intersecting 45S pre-rRNA
18S 28S5.8S
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