Athurva Gore/LabNotes/2009-8-28: Difference between revisions
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>Ajgore (New page: {{ AGLabEntry|2009-8-27|2009-8-29 }} =To Do= * Forms for Tech Transfer Office * Invoice for Zymo =Removing Reads with Multiple SNPS= ==DF699== * Took results from removal of reads w/SNPs...) |
>Ajgore |
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* Removed reads that had more than one SNP. | * Removed reads that had more than one SNP. | ||
* 9386 hits, 1103 misses | * 9386 hits, 1103 misses | ||
* Don't do any better than just removing those with SNPs in the last 15 bp. | |||
** Probably not the approach to take. | |||
=Remaining options= | |||
* Considered: | |||
** Removing reads w/multiple SNPs | |||
** Removing reads w/SNPs near the end (removes many bad SNPs, but also some good SNPs) | |||
** Trimming reads w/SNPs near the end (removes many bad SNPs as well; retains more good and bad though) | |||
* Still remaining: | |||
** Reducing quality score of SNPs - probably won't see any better results than above | |||
** New Idea: Split reads into two sections; first 25, last 15? | |||
* For now, we will go with removing reads with SNPs near the end | |||
=IPS and Cancer= | |||
* For Proposal, preliminary data is needed of the difference between DF699 and fibroblast. | |||
** Want this data to be high quality | |||
* Started with results that removed reads w/ SNPs withing 15 bp of the end | |||
* Then ran pileup2variantsPair with min coverage of 8 and min quality of 20. | |||
* Used a chi-squared test and removed SNPs that scored lower than 6. | |||
* Found: | |||
** 193 SNPs | |||
** 145 not at dbSNP locations (the above 48 are probably just miscalls in one case due to biased capture) | |||
*** 128 Loss-of-Heterozygosity | |||
*** 13 Gain-of-Heterozygosity | |||
*** 4 Doubles | |||
** If these GOH mutations are investigated, only 5 are nonsynonymous | |||
*** 3 of these appear in the COSMIC set | |||
** Decided to check both LOH and GOH mutations to see if any are valid. | |||
*** SIFT: Does it report ref/call? Or does it report in same order? | |||
*** None are classified as purely damaging by SIFT; one is damaging but with low confidence | |||
* Need to investigate data itself, check over SNPs. | |||
* Decided to compare to a filter where SNPs had a minimum coverage of 4 instead | |||
** Will pass just GOH to SIFT, check to see if analysis for presentation was correct |
Latest revision as of 22:01, 28 August 2009
[edit]
To Do[edit]
- Forms for Tech Transfer Office
- Invoice for Zymo
Removing Reads with Multiple SNPS[edit]
DF699[edit]
- Took results from removal of reads w/SNPs in the last 15 bp
- Removed reads that had more than one SNP.
- 9386 hits, 1103 misses
- Don't do any better than just removing those with SNPs in the last 15 bp.
- Probably not the approach to take.
Remaining options[edit]
- Considered:
- Removing reads w/multiple SNPs
- Removing reads w/SNPs near the end (removes many bad SNPs, but also some good SNPs)
- Trimming reads w/SNPs near the end (removes many bad SNPs as well; retains more good and bad though)
- Still remaining:
- Reducing quality score of SNPs - probably won't see any better results than above
- New Idea: Split reads into two sections; first 25, last 15?
- For now, we will go with removing reads with SNPs near the end
IPS and Cancer[edit]
- For Proposal, preliminary data is needed of the difference between DF699 and fibroblast.
- Want this data to be high quality
- Started with results that removed reads w/ SNPs withing 15 bp of the end
- Then ran pileup2variantsPair with min coverage of 8 and min quality of 20.
- Used a chi-squared test and removed SNPs that scored lower than 6.
- Found:
- 193 SNPs
- 145 not at dbSNP locations (the above 48 are probably just miscalls in one case due to biased capture)
- 128 Loss-of-Heterozygosity
- 13 Gain-of-Heterozygosity
- 4 Doubles
- If these GOH mutations are investigated, only 5 are nonsynonymous
- 3 of these appear in the COSMIC set
- Decided to check both LOH and GOH mutations to see if any are valid.
- SIFT: Does it report ref/call? Or does it report in same order?
- None are classified as purely damaging by SIFT; one is damaging but with low confidence
- Need to investigate data itself, check over SNPs.
- Decided to compare to a filter where SNPs had a minimum coverage of 4 instead
- Will pass just GOH to SIFT, check to see if analysis for presentation was correct