deletions | additions       diff --git a/ipynb/analyses/~$hypo-hyper-overlap.xlsx b/ipynb/analyses/~$hypo-hyper-overlap.xlsx  deleted file mode 100644  index 1194b64..0000000  Binary files a/ipynb/analyses/~$hypo-hyper-overlap.xlsx and /dev/null differ diff --git a/notes/hyper-splitting.md b/notes/hyper-splitting.md  index e69de29..8617c17 100644  --- a/notes/hyper-splitting.md  +++ b/notes/hyper-splitting.md 
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With a good two weeks hands-off of the the array data it took a bit of time to get back on target. Following up from last time (per my instruction) I began to delve into how the hypomethylated versus hypermethylated DMLs played out with respect to genomic features. Still not convinced I am convoluting the analysis I went [through the motions](http://nbviewer.ipython.org/github/sr320/paper-Temp-stress/blob/master/ipynb/Array-feature-overlap-05.ipynb). ~[bu-html](http://goo.gl/EuCIIN). ~[excel](https://github.com/sr320/paper-Temp-stress/blob/master/ipynb/analyses/hypo-hyper-overlap.xlsx) :(  ---  As a reminder most of the DMLs are Hypos. The first thing I did was split the `*.sig.bedgraph` files to `hypo` and `hyper`. These live in `tenacle` ie `/Users/sr320/data-genomic/tentacle/2014.07.02.2M_sig.hypo.bedGraph`.   ---  Looking at DEGs, there really did not seem to be a pattern (confirm with stats).   I considered the overlap based on total number of DMLs (hypo and hyper separately).      ---  Interesting and maybe not unexpected? was that there is a clear pattern based on gene function.     When splitting DMLs, a housekeeping genes were hyper more? hypermethylated, whereas environmental response genes were (on percentage basis) more likely to be hypomethylated. That being said it seems that the fact that both were more hypomethyated when you just look at the #s (recalling that overall there was more DMLs that were hypo v hyper.   --  Slow story short... this is becoming a descriptive paper with clean RNA-seq data but a loss to how the array data relates. Hindsigting there are serval hypotheses.  * Stress demethylates response genes to allow for spuriousness  * Stress regulates gene expression via promoter methylation  * Stress randomly alters methylation - to add noise  * Methylation status is not impacted directly by stress but rather a side effect of gene activity.   One place I could go next is to identify gene products that significantly altered the number of isoforms post stress? This would be interesting regardless, though not readily evident how it would work .  diff --git a/notes/splicing-around.md b/notes/splicing-around.md  new file mode 100644  index 0000000..1636a5d  --- /dev/null  +++ b/notes/splicing-around.md 
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Here I am going to see to what degree I can identify differential splicing events that occur upon acute heat stress with the ultimate goal of determing if there is a relationship with differentiall splicing and DMLs. [As the Tophat suite was used for RNA-seq](http://onsnetwork.org/halfshell/2015/01/08/rna-seq-tophat-via-iplant/), I will start exploring the `cuffdiff` output. Note all output from `cuffdiff` can be [found here](http://owl.fish.washington.edu/halfshell/index.php?dir=BS-heat%2FCuffdiff2_heat-b-2014-12-20-22-27-15.4%2F).   ---  Based on the very nice documentation at the first place I would look would be `splicing.diff`.     Having a gander at this file, there are in fact some features that appear to be significant. To make myself feel better about what I am looking at I will visualize in IGV.     ----  If my notebook holds up I should be able to refer to [this post](http://onsnetwork.org/halfshell/2015/02/26/heating-up-the-beds/) (found via IGV tag) to recreate...    Once IGV is open I hope to simply paste locus field (ie `scaffold1391:350297-393525` into search bar. Actually there is some [fancy formulas that would allow me to directly linkout from Excel](https://www.broadinstitute.org/software/igv/ControlIGV).  ----    ---    ---    ---    ---    ---    ---    ----    ---    ---    ---    ---  So there was a good chunk and cut and pastes, some things to ponder, look at, and certainly come back to.   Some closing help.   That IGV session file is @ `/Users/sr320/data-genomic/tentacle/igv_session_041615.xml`  and   Those significant splice locales  ```  scaffold1391:350297-393525 scaffold1501:189-2280 scaffold1546:22946-41272 scaffold157:93056-102166 scaffold157:288396-298950 scaffold1583:636568-663717 scaffold1630:57333-61806 scaffold1643:190932-200778 scaffold1670:360106-365501 scaffold1750:71251-77856 scaffold1009:677703-719650 scaffold1009:990592-1008075 scaffold193:111771-117728 scaffold198:1032767-1055090 scaffold198:1084454-1102022 scaffold211:954418-990421 scaffold351:641567-648889 scaffold102:1297353-1322657 scaffold383:99975-117650 scaffold38366:25577-54928 scaffold1024:1037898-1043721 scaffold395:85069-105025 scaffold399:120007-128313 scaffold41228:55316-64219 scaffold41858:126164-135361 scaffold42366:124115-157800 scaffold42892:55315-57225 scaffold42904:154963-177485 scaffold43208:19552-46201 scaffold43940:65971-85144 scaffold452:65648-77493 scaffold527:16020-64639 scaffold588:178668-183438 scaffold616:53220-63262 scaffold828:110697-114691 scaffold942:369690-377892 scaffold1160:333463-390372 scaffold1213:118721-121110 scaffold128:428337-438047 scaffold1282:23471-48121 scaffold13:4222-16204 scaffold1322:265134-304245  ```