Inferring sex chromosome and autosomal ploidy in NGS data
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README.md

2016_project_6

Inferring sex chromosome and autosomal ploidy in NGS data

Slide show here: https://docs.google.com/presentation/d/1OB2d_mu5zC742N_NKfzHjVpUm4BFtm5lUzniLLI--OQ/edit?usp=sharing

Publication Links

List of Goals: Assess X/Y ploidy and correct for misalignment

  1. Extract input chromosomes - recommend chrX, chrY, chr19 - from BAM (can input any autosome)

  2. Infer sex chromosome ploidy from WGS data relative to autosomal ploidy

  • XX
  • XY
  • XXY
  • X0
  • And all other combinations Use A. Quality B. Read Depth C. Allele Balance D. Ampliconic/Palindromic/CNV filter

Typical expectations for heterozygous calls under different sex chromosome complements:

Genotype X_call Y_call
XX het none
XY hap hap
X0 hap none or partial_hap
XXY het or hap hap
XYY hap hap
XXX het none

Note: Half of 47,XXY are paternal in origin -> do not expect het sites: http://humupd.oxfordjournals.org/content/9/4/309.full.pdf

Expectations for depth under different sex chromosome complements:

Genotype X_depth Y_depth
XX 2x 0x
XY 1x 1x
X0 1x 0x (or partial)
XXY 2x 1x
XYY 1x 2x
XXX 3x 0x
  1. IF - If we infer there are no Y chromosomes in the sample, conduct re-mapping to increase confidence in X-linked alleles. Strip reads from X and Y Remap all X & Y reads to the X chromosome only Remove X and Y from the input BAM file Merge the empty Y and the remapped X chromosome into the BAM

  2. Assessment of 1000 genomes high coverage data Compare SNV and CNV variant calling in 1000 genomes high coverage before/after running this pipeline Test how different alignment algorithms, parameters, and reference sequences affect variant calling in different regions of the X and Y Compare variant calling with the "Gold Standard" reference individual

Other goals: Because I think we have to address this if we want to get a really good handle on #2 given the extremely high copy number variable regions on X and Y - the ampliconic regions. Likely we will masking them out to infer #2, which will be easiest, but then we can have an extended goal to see characterize variations in these regions.

Known problems and complications

  • High sequence identity between X and Y
  • Higher amount of sequence repeats

Group Members

Name email github ID
Madeline Couse mhcouse@gmail.com @Madelinehazel
Bruno Grande bgrande@sfu.ca @brunogrande
Eric Karlins karlinser@mail.nih.gov @ekarlins
Tanya Phung tnphung@ucla.edu @tnphung
Phillip Richmond phillip.a.richmond@gmail.com @Phillip-a-Richmond
Tim Webster timothy.h.webster@asu.edu @thw17
Whitney Whitford whitney.whitford@auckland.ac.nz @whitneywhitford
Melissa A. Wilson Sayres melissa.wilsonsayres@asu.edu @mwilsonsayres