Filters

We allow for filtering at many different levels.

1. Read level, MapQ, unique mapped reads etc
2. Base level, qscore
3. Sequencing depth
4. Regions (using BAM indexing (active lookup))
5. Single sites (passive lookup, also allows for forcing major and minor) -sites
6. Filtering based on downstream analysis. minimum MAF, LRT for SNP calling etc.
7. Trimming out the ends of the reads
8. etc

It follows that some filters will select a subset of data, and some of the filters will discard certain sites. If multiple filters has been chosen, the analysis will be limited to the chain of filters.

Filters for reads in Bam files

We allow for filtering and manipulation a the read level. These filters include minimum mapping and base qualtity, paired reads and others. Additionally specific regions can be analysed. All of the filters for bam files are described in Input#BAM_files.

Selected Sites

For analysing specfic regions see Input#BAM_files. If you are interested in running your analysis at individual sites that are distributed throughout the entire genome, it might be faster to simply to loop over the entire data, but only analyse the data at specific positions. This can be done by supplying the -sites argument. With this approach we also allows for the forcing of major/minor alleles using external information.

Allele frequencies

-minMaf [float]

only work with sites with a maf above [float]

Requires -doMaf.

Polymorphic sites

-SNP_pval [float]

only work with sites with a p-value less than [float]

Requires -doMaf.

Number of non missing individuals

-minInd [int]

only work with sites with information from at least [int] individuals

Extra

-setMinDepth [int]

Discard site if total sequencing depth (all individuals added together) is below [int] Requires -doCounts

-setMaxDepth [int]

Discard site if total sequencing depth (all individuals added together) is above [int] -doCounts

-geno_minDepth [int]

Only call genotypes if the depth is as least [int] for that individuals

This requires -doCounts and -doGeno

Examples

First we do a run with no filters

./angsd  -doMaf 2 -doMajorMinor 1 -out TSK -bam bam.filelist -GL 1 -r 1:

gunzip -c TSK.mafs.gz | head

chromo	position	major	minor	unknownEM	nInd
1	13999919	A	C	0.000006	1
1	13999920	G	A	0.000006	1
1	13999921	G	A	0.000006	1
1	13999922	C	A	0.000006	1
1	13999923	A	C	0.000006	1
1	13999924	G	A	0.000006	1
1	13999925	G	A	0.000006	1
1	13999926	A	C	0.000006	1
1	13999927	G	A	0.000006	1

Now we do a filter with MAF cutoff of 1\%

./angsd -doMaf 2 -doMajorMinor 1 -out TSK -bam bam.filelist -GL 1 -r 1: -minMaf 0.01

gunzip -c TSK.mafs.gz | head

chromo	position	major	minor	unknownEM	nInd
1	14000003	G	A	0.032285	9
1	14000013	G	A	0.058291	9
1	14000019	G	T	0.013709	9
1	14000023	C	A	0.025033	9
1	14000170	C	T	0.031133	10
1	14000176	G	A	0.028189	10
1	14000200	C	A	0.075946	7
1	14000202	G	A	0.257007	7
1	14000774	G	T	0.030039	10

Similar if we only want sites with information for atleast 5 samples

./angsd -doMaf 2 -doMajorMinor 1 -out TSK -bam bam.filelist -GL 1 -r 1: -minInd 5

gunzip -c TSK.mafs.gz | head

chromo	position	major	minor	unknownEM	nInd
1	13999972	G	A	0.000003	5
1	13999973	C	A	0.000002	5
1	13999974	G	A	0.000002	5
1	13999975	C	A	0.000002	5
1	13999976	C	A	0.000002	5
1	13999977	A	C	0.000000	5
1	13999978	C	A	0.000000	5
1	13999979	T	A	0.000000	5
1	13999980	G	A	0.000001	5

If we are interested in all sites with a p-value of 10^(-6) of being variable

./angsd -doMaf 2 -doMajorMinor 1 -out TSK -bam bam.filelist -GL 1 -r 1: -SNP_pval 1e-6

gunzip -c TSK.mafs.gz | head

chromo	position	major	minor	unknownEM	pu-EM	nInd
1	14000873	G	A	0.282476	0.000000e+00	10
1	14001018	T	C	0.259890	7.494005e-14	9
1	14001867	A	G	0.272099	6.361578e-14	10
1	14002422	A	T	0.377890	0.000000e+00	9
1	14003581	C	T	0.194393	5.551115e-16	9
1	14004623	T	C	0.259172	2.424727e-13	10
1	14007493	A	G	0.297176	5.114086e-07	9
1	14007558	C	T	0.381770	0.000000e+00	8
1	14007649	G	A	0.220547	1.054967e-11	9