= SOP for converting LifeLines Geno Data  =

[[TOC()]]

This SOP applies to LL3.

Data is released to researcher 'per study' (i.e. an approved research request). 
* Per study a subset of the genotypes is created and made available to the researcher:
* Only individuals selected for study (e.g. 5000 out of total 17000)
* The identifiers 're-pseunomized' from 'marcel identifiers' to 'study identifiers' (so data can not be matched between studies).

== Expected outputs ==

User expects files in PLINK format:
* TPED/TFAM genotype files (chosen for internal use as easier to produce)
* BIM/BED/FAM genotype files (with missing value phenotype, monomorphic filtered)
* IDEM but then splitted per chromosome
* MAP/PED dosage files (with missing value phenotype, monomorphic filtered) 
* IDEM but then splitted per chromosome

== Available inputs ==

Complete genotype data is in: /target/gpfs2/lifelines_rp/releases/LL3/

Per study a text file is available contains the mapping + selection of identifiers:

== Procedure ==



* Data resides on /target/gpfs2/lifelines_rp/releases/LL3/BeagleImputedTriTyper (accessible from all our new VMs)
 * Convertor from TriTyper to PLINK resides on /target/gpfs2/lifelines_rp/releases/LL3
 * Correct Java version resides on /target/gpfs2/lifelines_rp/tools/jdk1.6.0_22/bin/
 * STEP 1: make the subset_molgenis<n>.txt file:
   * In every MOLGENIS<n> schema for a study that has geno data, there is a VW_DICT_GENO_PSEUDONYMS view
   * In this view, PA_IDs (LL IDs) are related to GNO_IDs ("Marcel" IDs, the LL_WGA numbers)
   * Export this view (tab separated, no enclosures, no headers) to molgenis<n>.txt and scp to cluster.gcc.rug.nl:/target/gpfs2/lifelines_rp/releases/LL3
   * Run the following command there: {{{ ./formatsubsetfile.sh molgenis<n>.txt }}}
   * Your file is now available as subset_molgenis<n>.txt and looks like:[[BR]]LL_WGA0001   STUDYPSEUDO1   0[[BR]]LL_WGA0002   STUDYPSEUDO2   0[[BR]]LL_WGA0003   STUDYPSEUDO3   0[[BR]]...
    * So: Geno individual ID's - TAB - Study pseudonyms - TAB - Phenotypes (can be all 0's as TFAM will be generated later by the user)
    * Items are TAB-separated and it doesn't end with a newline
 * STEP 2: run the convertor
  * Usage: {{{ /target/gpfs2/lifelines_rp/tools/jdk1.6.0_22/bin/java -jar TriToPlinkLifeLines.jar P BeagleImputedTriTyper/ study<n> subset_molgenis<n>.txt }}}
 * STEP 3: copy to correct location
  * {{{cp study<n>.tped ../../lifelines0<n>}}}
  * May take some time!

== Further Genodata ==

The commands above generate a single large file for the study in question. From this researchers would like some further file manipulation to be done: 
 * Supply the large genodata in binary format, using command:
     {{{ plink --tfile <data> --make-bed --out <data> }}}
     This should generate .bed, .bim and .fam files.
 * Supply the data also in separate files per chromosome. This can be done with the commands:
     {{{  plink --tfile <data> --make-bed --chr 1 --out <data_chr1> }}}
     {{{  plink --tfile <data> --make-bed --chr 2 --out <data_chr2> }}}
     {{{  ... }}}
   (a script file should take care of this series of commands)

Besides the genodata dosage information is also desired, both for the total (per-study) dataset and for that dataset split per chromosome.
 * Joeri has a tool for this. NB: it is slow, reimplementing it in a compiled language might be worthwhile.

[[Image(http://i.imgur.com/nLT2e.png)]]

A schematic overview of the two export paths described above.