ANGSD: Analysis of next generation Sequencing Data

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Error estimation: Difference between revisions

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Notice that we for the more stringent test2 dataset have somewhat lower error rates. But we should really choose a much larger number of sites to do this analysis.
Notice that we for the more stringent test2 dataset have somewhat lower error rates. But we should really choose a much larger number of sites to do this analysis.
NB Currently the ordering of each line, can not be interpreted as the error rates along the genome, due to the threading. This will likely change in future versions.


==example==
==example==

Revision as of 22:21, 11 October 2012

Error estimation from polymorphic sites

The method for estimating typespecific errors is described in Kim2011, and is based on the counts of the 4 different nucleotides. This method should be applied to the sites that are variable and the measure for variability is the simple MAF estimator that is described in Li2010.

options

-numSites [int]

default 10000. This is the number of sites we want to use for this analysis.

-cutoff [float]

default 0.005. This means we only run the error estimation on sites with a MAF>0.005. This should be modified according to the number of samples in the dataset.

-eps [float]

default 0.001.This is a guess of the errorrate in the sample, this is used for the simple MAF estimator

-errors [filename]

This file should contain a guess of the typespecific errors. NB this is not implemented in the current version

extra options

To further refine what data should be used please see alleles counts.

Example

The simplest example is:

./angsd -bam smallBam.filelist -doCounts 1 -out test  -doError 1 -doMajorMinor 2 -nThreads 2 -minSites 1000 

Or a more elaborate example where we only want to estimate the typespecific errors for the "good" data:

./angsd -bam smallBam.filelist -doCounts 1 -out test2  -doError 1 -doMajorMinor 2 -nThreads 2 -minSites 1000 -minQ 20 -minMapQ 30

Output

#test
0.000000	0.005488	0.003847	0.003137\
0.006807	0.000000	0.001972	0.002396\
0.002190	0.001855	0.000000	0.008068\
0.002491	0.004268	0.005812	0.000000
#test2
0.000000	0.000071	0.003381	0.001254\
0.003989	0.000000	0.000000	0.002568\
0.002270	0.000000	0.000000	0.003650\
0.001451	0.004327	0.000974	0.000000

Notice that we for the more stringent test2 dataset have somewhat lower error rates. But we should really choose a much larger number of sites to do this analysis.

NB Currently the ordering of each line, can not be interpreted as the error rates along the genome, due to the threading. This will likely change in future versions.

example

Error estimation using an outgroup and an error free individual

-doAncError [int]
-anc [filename]

fasta file with the ancestral alleles

-ref [filename]

fasta file of a reference (error free) individual.

-doAncError 1
-doAncError 2

additional options

-minQ [int]

default 0. Minimum allowed base quality score

-minMapQ [int]

default 0. Minimum allowed mapping quality score