Compute postmortem damage patterns and decontaminate ancient genomes
##############Description
PMDtools implements a likelihood framework incorporating postmortem damage (PMD), base quality scores and biological polymorphism to identify degraded DNA sequences that are unlikely to originate from modern contamination. Using the model, each sequence is assigned a PMD score, for which positive values indicate support for the sequence being genuinely ancient. For details of the method, please see the main paper in PNAS.
PMDtools takes SAM-formatted input, and requires an MD tag with alignment information. The MD tag is featured in the output of many aligners but can otherwise be added e.g. using the SAMtools fillmd/calmd tool (Li, Handsaker et al. 2009).
No external packages except for python 2.6 are required, but for manipulating BAM files, SAMtools is recommended. Extra plotting requires R.
Questions can be addressed to [email protected].
##############Basic usage To restrict to sequences with a PMD score of at least 3, enter:
samtools view -h mybam.bam | python pmdtools.0.60.py --threshold 3 --header | samtools view -Sb - > mybam.pmds3filter.bam
To compute deamination-derived damage patterns separating CpG and non-CpG sites, enter:
samtools view mybam.bam | python pmdtools.0.60.py --platypus --requirebaseq 30 > PMD_temp.txt
R CMD BATCH plotPMD.v2.R
cp PMD_plot.frag.pdf PMD.plot.MYBAM.pdf
PMDtools also computes damage patterns from sequence libraries in which damage has been repaired, e.g. using uracil–DNA–glycosylase and endonuclease VIII. This is done by restricting to nucleotides in a CpG context, for which deamination of Cytosine results in Thymine. Enter:
samtools view mybam.bam | python pmdtools.0.60.py --deamination --range 30 --CpG
For a full list of options, enter
python pmdtools.py --help
##############Citation Please cite: P Skoglund, BH Northoff, MV Shunkov, A Derevianko, S Pääbo, J Krause, M Jakobsson (2014) Separating ancient DNA from modern contamination in a Siberian Neandertal, Proceedings of the National Academy of Sciences USA doi:10.1073/pnas.1318934111