\name{seqbias.fit}
\alias{seqbias.fit}
\title{Fitting seqbias models}
\description{Fits a seqbias module given a reference sequence and reads in BAM
format}
\usage{seqbias.fit(ref_fn, reads_fn, n = 1e5, L = 15, R = 15)}
\arguments{
\item{ref_fn}{filename of a reference sequence against which the reads are
aligned, in FASTA format.}
\item{n}{train on at most this many reads.}
\item{L}{consider at most L positions to the left of the read start.}
\item{R}{consider at most R positions to the right of the read start.}
}
\details{
A Bayesian network is trained on the first \code{n} unique reads in the provided
BAM file, predicting the posterior probability of a read beginning at a
position given the surrounding sequence. This is used to discern the
sequencing bias: how more or less likely a read is to fall on a particular
position.

The abundance of region can be more accurately assessed by normalizing
(dividing) each position by its predicted bias.
}
\value{A vector of reals giving the predicted sequencing bias for each
position.}
\note{
Both the BAM file and the FASTA file should be indexed, with,
'samtools index'
and,
'samtools faidx'
respectively.
}
\author{
Daniel Jones
\email{dcjones@cs.washington.edu}}
\seealso{