Fasta

This option creates a fasta.gz file from a sequencing data file (BAM file). The function uses genome information in the BAM header to determine the length and chromosome names. For the sites without data an "N" is written.

<classdiagram type="dir:LR">

[Single BAM file{bg:orange}]->[Sequence data|Random base (-doFasta 1);Consensus base (-doFasta 2);Highest EBD (-doFasta 3); write iupac (-doFasta 4)]

[sequence data]->doFasta[fasta file{bg:blue}]

</classdiagram>

<classdiagram type="dir:LR">

[Multiple BAM files{bg:orange}]->[Sequence data|Random base (-doFasta 1);Consensus base (-doFasta 2);write iupac (-doFasta 4)]

[sequence data]->doFasta[fasta file{bg:blue}]

</classdiagram>

This can be used as input for the ANGSD analysis:

1. Error estimation
2. ABBA-BABA

The iupac output code was kindly provided by Kristian Ullrich.

Brief Overview

./angsd -dofasta 	-> Tue Sep 26 17:02:07 2017
--------------
abcWriteFasta.cpp:
	-doFasta	0
	1: use a random (non N) base (needs -doCounts 1)
	2: use the most common (non N) base (needs -doCounts 1)
	3: use the base with highest ebd (under development) 
	4: output iupac codes (under development) 
	-basesPerLine	50	(Number of bases perline in output file)
	-explode	0	 print chromosome where we have no data (0:no,1:yes)
	-rmTrans	0	 remove transitions as different from -ref bases (0:no,1:yes)
	-ref	(null)	 reference fasta, only used with -rmTrans 1
	-seed	0	 use non random seed of value 1

This function will dump a fasta file, the full header information from the SAM/BAM file will be used. This means that a fasta will be generated for the entire chromosome even if '-r/-rf -sites' is used.

Options

-doFasta 1

sample a random base at each position. N's or filtered based are ignored. The "-doCounts 1" options for allele counts is needed in order to determine the most common base. If multiple individuals are used the four bases are counted across individuals.

-doFasta 2

use the most common base. In the case of ties a random base is chosen among the bases with the same maximum counts. N's or filtered based are ignored. The "-doCounts 1" options for allele counts is needed in order to determine the most common base. If multiple individuals are used the four bases are counted across individuals.

-doFasta 3

use the base with thie highest effective depth (EBD). This only works for one individual

-basesPerLine [INT]

Number of bases perline in output fasta file (default is 50)

-explode [INT]

0 (default) only output chromosomes with data. 1: write out all chromosomes

-rmTrans [INT]

0 (default) all sites are used. 1: Remove transition. Here transitions are determined using a fasta file such as a reference genome.

-ref [fileName]

a fasta file used to determine if a site is a transitions (needed when using -rmTrans 1 is used)

-seed [INT]

Use a seed in order to replicate results ( relevant when using random sample -dofasta 1 )

For filters see Filters

Output

Output is a fasta file, a normal looking fast file. Nothing special about this. For -doFasta 1, sometimes its big letters sometime small letters. This is due to the results being copied directly from the sequencing data. So small/big letters correspond to which strand for the original data. For the consensus fasta all letters are capital letters.

Example

Create a fasta file bases from a random samples of bases.

./angsd -i bams/smallNA07056.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam -doFasta 1

EBD

For four bases we have 4 different EBD, each EBD is the product of the mapping quality and scores for the base under consideration. The EBD is the effective base depth, as defined by [1]:

$EBD_{b}=\sum _{i=1}^{N_{b}}(phred(mapq_{i})*phred(qscore_{i})),\qquad phred(q)=10^{-q/10}\qquad b\in \{A,C,G,T\}$

where $b$ is a certain base, $N_{b}$ is the number of reads with base $b$

Ancestral fasta

If you have outgroup species map to your reference genome and you want to use them to make a fasta file with ancestral alleles. You can use one or more outgroup individuals e.g. for human you could have a four outgroup bam file from a chimp, a bonobo, a gorrilla and a orangotan. Assuming you want to make a fasta file where the alleles is the same for all outgroup species then you can use a command like

./angsd -b fourOutgroup.bamlist -out myFasta -doCounts 1 -snp_pval 0.01 -domaf 1 -domajorminor 1 -gl 2 -rmSNPs 1 -minind 4 -setMinDepthInd 10 -explode 1

-b fourOutgroup.bamlist contains the bam files for four outgroup individuals.

-out myFasta the output name

-doCounts 1 counts bases accross individuals to determine the concensus allele

-snp_pval 0.01 p-value threshold for defining a SNP. A lower threshold will need more evidence to call a SNPs.

-domaf 1 estimate allele frequency (use to call SNPs) with that the major and minor alleles inferred from data

-domajorminor 1 infer the major and minor allele from data

-gl 2 use genotype likelihoods based on the GATK model

-rmSNPs 1 remove polymorphic sites. instead of keeping sites that are polymorphic then we remove them such that all outgroups have the same allele.

-minind 4 remove site where you don't have data for all four individuals

-setMinDepthInd 10 require at least 10 read for each individual

-explode 1 make the fasta file for the whole genome not just the chromosomes/scaffolds were you have data

The sites that are polymorphic or do not have enough data will be labeled as 'N' in the fasta file