Athurva Gore/LabNotes/2010-3-24: Difference between revisions
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=IPS Mutation Paper= | =IPS Mutation Paper= | ||
==Indel Calling== | ==Indel Calling== | ||
===Data to assemble=== | |||
{| {{table border="1" cellpadding="4" cellspacing="0" style="border:\#c9c9c9 1px solid; margin: 1em 1em 1em 0; border-collapse: collapse;"}} | {| {{table border="1" cellpadding="4" cellspacing="0" style="border:\#c9c9c9 1px solid; margin: 1em 1em 1em 0; border-collapse: collapse;"}} | ||
| align="center" style="background:#f0f0f0;"|'''Flowcell''' | | align="center" style="background:#f0f0f0;"|'''Flowcell''' | ||
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| HL037||||||s3 (NimbleGen)||s8 (NimbleGen) | | HL037||||||s3 (NimbleGen)||s8 (NimbleGen) | ||
|} | |} | ||
===Procedure=== | |||
* Merged CV-iF, CV-F, DF6, and FS | |||
** Running mapping+velvet script on CV-iF and CV-F | |||
* Still need to merge NimbleGen data | |||
==CNV-Seq== | |||
* Can we use Exome data to find CNV variation? | |||
** Theoretically, bias in probeset should be uniform | |||
** May not be true in practice | |||
* Once CV-F and CV-iF data is finished sorting (should be a couple hours), try CNV algorithm on it | |||
** See what results we get | |||
* Perhaps rather than trying to compare windows, we should compare the number of reads that map to each exon... | |||
** Variable size, can we model it properly? | |||
=BSPP Neural Network= | =BSPP Neural Network= |
Latest revision as of 23:22, 24 March 2010
Plan for Today[edit]
- Indel calling - run scripts
- CV-iF/CV-F and DF6/FS Exome data
- Nimblegen data (whatever is available)
- BSPP Neural Network
- Assemble Cpg97k data, BS_FirstExon data; see how this NN looks
IPS Mutation Paper[edit]
Indel Calling[edit]
Data to assemble[edit]
Flowcell | CV-F | CV-iF | DF6 | FS |
HL020 | s3, s4 | s1, s2 | ||
HL022 | s1, s2 | s3, s4 | ||
HL023 | s5, s6 | s7, s8 | ||
HL025 | s6 | s7 | ||
HL026 | s1 | s5 | s6 | s7 |
HL036 | s1 (NimbleGen) | s4 (NimbleGen) | ||
HL037 | s3 (NimbleGen) | s8 (NimbleGen) |
Procedure[edit]
- Merged CV-iF, CV-F, DF6, and FS
- Running mapping+velvet script on CV-iF and CV-F
- Still need to merge NimbleGen data
CNV-Seq[edit]
- Can we use Exome data to find CNV variation?
- Theoretically, bias in probeset should be uniform
- May not be true in practice
- Once CV-F and CV-iF data is finished sorting (should be a couple hours), try CNV algorithm on it
- See what results we get
- Perhaps rather than trying to compare windows, we should compare the number of reads that map to each exon...
- Variable size, can we model it properly?