1. First up, there is a get_data.sh script which downloads a bunch of
files including truth sets, BED files and some BAMs.  These may be
very large.  Just edit the script accordingly or look at the URLs and
have a browse around manually.

Once it's run you'll never need to download them again.  You should
also obtain your own copy of GRCh38 as I couldn't work out which of
the many ones at GIAB are appropriate.  This didn't seem to matter for
chr1 or chr20 though and I expect it's mainly the patches which are
different.


2. The two evaluation scripts are run_mpileup_HG002.sh and
run_mpileup_HG005.sh.  For consistency, rapid turnaround tuning,
setting of parameters and general code improvement should be done with
HG002.  Avoid any chromosomes you may wish to evaluate on later.

When finally finished tuning, use another chromosome (eg chr20) of
HG005 to avoid over-fitting to specific regions or specific samples
and instrument runs.

I usually name my temp outputs starting in underscore for ease of
removal, but that's just personal preference.  So _pb here is the
output from the caller and evaluation.

An example of running a small region for training.

    ./run_mpileup_HG002.sh pb_50x.bam chr1:10000000-20000000 _pb -X pacbio-ccs

For final evaluation.

    BCFTOOLS=bcftools.devel ./run_mpileup_HG005.sh illumina_300x.bam chr20:20000000-21000000 _i_dev -L999 -X illumina
    ./run_mpileup_HG005.sh illumina_300x.bam chr20:20000000-21000000 _i_new -L999 -X illumina

Note: we cannot be quite so pure for all instrument types as HG002 is
by far the most widely studied sample and some data sets are only
available on that sample.


3. After running the mpileup scripts and getting a bunch of output
directories you can plot them in gnuplot.

    set ylabel "FNr"
    set yrange [0:10]
    set title "PacBio 50x indels"
    
    set xlabel "FPr"
    a=8;b=10;t="ALL";plot "<grep ".t." _i_dev/plot" using a:b with linespoints title "dev" lw 2 ps .6, "<grep ".t." _i_new/plot" using a:b with linespoints title "indels-cns" lw 2 ps .6
    
    set xlabel "GTr"
    a=9;b=10; ... plot as above


4. Finally if we've produced both current and new sets of calls, we
will have two directories of output each containing their own .isec
directories with the intersection of their results and the truth set.

- 0000.vcf will be false negatives; variants only found in truth set
- 0001.vcf will be false positives; variants only found in our calls
- 000[23].vcf will be true variants, truth & our call versions

Note these will have been decomposed already into separate alleles, so
GT 1/2 will be two entries, one of which may be true and one which may
be false (positive or negative).  This makes counting easier so the
final numbers reflect the genotype as well as the position.

(Only variant evaluation tools may operate differently.)

Obviously there will be many common mistakes between both old and new
outputs.  False positives that we're always going to call as they're
simply down to misalignments or contamination, and false negatives
we'll never call as the data is plain absent.  However we can
data-mine our false positives and false negatives to find problems
that we fixed and new problems that we called.  For example to
identify changes in the false positives we could do this:

    # Bgzip the VCFs as bcftools isec annoyingly can't read plain VCF
    d=_old/bcftools.isec
    (cd $d;
     bgzip < 0003.vcf > 0003.vcf.gz; tabix -f 0003.vcf.gz;
     bgzip < 0000.vcf > 0000.vcf.gz; tabix -f 0000.vcf.gz;
     bgzip < 0001.vcf > 0001.vcf.gz;tabix -f 0001.vcf.gz)

    d=_new/bcftools.isec
    (cd $d;
     bgzip < 0003.vcf > 0003.vcf.gz; tabix -f 0003.vcf.gz;
     bgzip < 0000.vcf > 0000.vcf.gz; tabix -f 0000.vcf.gz;
     bgzip < 0001.vcf > 0001.vcf.gz;tabix -f 0001.vcf.gz)

    # Produce a new isec output
    bcftools isec -p _fp _{old,new}/0001.vcf.gz

_{old,new}/0001.vcf is the false positives our two runs produces.
After intersecting those, we now have:

- _fp/0000.vcf:  old false positives we have removed
- _fp/0001.vcf:  new false positives we have acquired

Similarly on _{old,new}/0000.vcf to identify cured / caused
false-negatives.  This provides a way to data mine things much more
carefully.  We can further drill down on these cured/caused variants
by subdividing into deletions vs insertions, or filtering to high
quality only.
