Asses Mapping Biases and Evaluating Read Reliability
AMBER computes sequence read mapping bias, ancient DNA damage patterns, fragment length distribution and genome breadth of coverage directly from a BAM-file. No external packages except for python3, numpy, matplotlib and pysam are required.
git clone https://github.com/tvandervalk/AMBER.git
cd AMBER
chmod +x AMBER
If matplotlib,numpy and/or pysam are not yet installed:
python -m pip install -U pysam
python -m pip install -U matplotlib
python -m pip install -U numpy
To assess your ancient samples, a tab seperated file with the bamfiles to be analyzed has to be supplied. The First column of the file should contain sample name, second column should contain path to bamfile. A maximum of 6 bamfiles can be analysed and plotted in the same run (however it is recommended to analyse just one bamfile at the time).
sampleA /path/to/sampleA.bam
sampleB /path/to/sampleB.bam
sampleC /path/to/sampleC.bam
....
A text file containing per line the name of a chromosomes/scaffolds or contigs that should be excluded from the analysis. For example:
contig001
contig002
chrX
chrY
./AMBER
--bamfiles (default = empty) Tab seperated file containing the sample names and path to bamfiles to be included in the analysis, required
--output (default = amber) Name of the output files generated by AMBER, optional
--exclude (default = empty) A text file containing per line the name of chromosomes/scaffolds or contigs that should be excluded from the analysis, optional
--errorbars (default = not set) plot the 95% confidence intervals in the mistmatch rate plot
--counts (default = not set) Plot the data as counts instead of fractions
