\name{read.bismark}
% TODO: alias required?
\title{
Parsing output from the Bismark alignment suite.
}
\description{
Parsing output from the Bismark alignment suite.
}
loci = NULL,
colData = NULL,
rmZeroCov = FALSE,
strandCollapse = TRUE,
BPPARAM = bpparam(),
BACKEND = getRealizationBackend(),
dir = tempfile("BSseq"),
replace = FALSE,
chunkdim = NULL,
level = NULL,
verbose = getOption("verbose"))
}
\arguments{
\item{files}{The path to the files created by running Bismark's methylation
extractor, one sample per file.
Files ending in \code{.gz} or \code{.bz2} will be automatically
We strongly recommend you use the 'genome wide cytosine report' output files.
See section 'File formats' for further details.}
\item{loci}{\code{NULL} (default) or a \linkS4class{GenomicRanges} instance
containing methylation loci (all with width equal to 1).
If \code{loci = NULL}, then \code{read.bismark()} will perform a first pass over the Bismark file to identify candidate loci.
If \code{loci} is a \linkS4class{GenomicRanges} instance, then these form the candidate loci.
In either case, the candidate loci will be filtered if
\code{rmZeroCov = TRUE} and collapsed if \code{strandCollapse = TRUE} to form the final set of methylation loci that form the \code{\link[SummarizedExperiment]{rowRanges}} of the returned \linkS4class{BSseq} object.
See section 'Efficient use of \code{read.bismark()}' for further details.}
\item{colData}{An optional \linkS4class{DataFrame} describing the samples.
Row names, if present, become the column names of the \linkS4class{BSseq}
object. If \code{NULL}, then a \linkS4class{DataFrame} will be created with
\code{files} used as the row names.}
\item{rmZeroCov}{A \code{logical(1)} indicating whether methylation loci that
have zero coverage in all samples be removed.
For several reasons, the default \code{rmZeroCov = FALSE} is recommended even in cases where you ultimately want to remove such loci.
See section 'Efficient use of \code{read.bismark()}' for further details.}
\item{strandCollapse}{A \code{logical(1)} indicating whether strand-symmetric
methylation loci (i.e. CpGs) should be collapsed across strands.
This is only applicable for stranded methylation loci, e.g., loci extracted
from 'genome wide cytosine reports' (see section 'File formats' for further details).}
determining the parallel back-end to be used during evaluation.
Currently supported are \linkS4class{SerialParam} (Unix, Mac, Windows),
(Unix, Mac, and Windows, limited to single-machine clusters), and
\linkS4class{BatchJobsParam} (Unix, Mac, Windows, only with the in-memory
realization backend).
See sections 'Parallelization and progress  monitoring' and 'Realization
backends' for further details.}
\item{BACKEND}{\code{NULL} or a single string specifying the name of the
realization backend.
When the backend is set to \code{NULL}, the \code{M} and \code{Cov} assays
are realized in memory as ordinary matrices, otherwise these are realized with the given \code{BACKEND}.
See section 'Realization backends' for further details.}
\item{dir}{\strong{Only applicable if \code{BACKEND == "HDF5Array"}.}
The path (as a single string) to the directory where to save the HDF5-based
\linkS4class{BSseq} object. The directory will be created so should not
already exist, unless \code{replace} is set to \code{TRUE}.}
\item{replace}{\strong{Only applicable if \code{BACKEND == "HDF5Array"}.}
If directory \code{dir} already exists, should it be replaced
with a new one? The content of the existing directory will be lost!}
\item{chunkdim}{\strong{Only applicable if \code{BACKEND == "HDF5Array"}.}
The dimensions of the chunks to use for writing the data to
disk. By default, \code{\link[HDF5Array]{getHDF5DumpChunkDim}()} using the
dimensions of the returned \linkS4class{BSseq} object will be used. See
\item{level}{\strong{Only applicable if \code{BACKEND == "HDF5Array"}.}
The compression level to use for writing the data to disk. By
information.}
\item{verbose}{A \code{logical(1)} indicating whether progress messages
should be printed (default \code{TRUE}).}
}

\section{File formats}{
The format of each file is automatically detected using the internal function \code{bsseq:::.guessBismarkFileType()}.
Files ending in \code{.gz}, \code{.bz2}, \code{.xz}, or \code{.zip} will be automatically decompressed to \code{\link[base]{tempdir}()}.
\subsection{Supported file formats}{
Bismark's 'genome wide cytosine report' (\url{https://github.com/FelixKrueger/Bismark/tree/master/Docs#the-genome-wide-cytosine-report-optional-is-tab-delimited-in-the-following-format-1-based-coords}) and 'coverage' (\url{https://github.com/FelixKrueger/Bismark/tree/master/Docs#the-coverage-output-looks-like-this-tab-delimited-1-based-genomic-coords}) formats are both supported.
If setting \code{loci = NULL}, then we strongly recommend using the 'genome wide cytosine report' output format because this includes strand information for each locus.
The 'coverage' output does not contain strand information and so the \code{\link[BiocGenerics]{strand}} of the returned \linkS4class{BSseq} object will be set to \code{*} unless stranded \code{loci} are supplied.
}

\subsection{Unsupported file formats}{
Neither the 'bedGraph' output format (\url{https://github.com/FelixKrueger/Bismark/tree/master/Docs#the-bedgraph-output-optional-looks-like-this-tab-delimited-0-based-start-1-based-end-coords}) nor the 'bismark_methylation_extractor' output format (\url{https://github.com/FelixKrueger/Bismark/tree/master/Docs#the-bismark_methylation_extractor-output-is-in-the-form-tab-delimited-1-based-coords}) are supported.
The former does not include the required counts of methylated and unmethylated reads hile the is an intermediate file containing read-level, rather than locus-level, data on methylation.
}
\subsection{One-based vs. zero-based genomic co-ordinates}{
The genomic co-ordinates of the Bismark output files may be zero-based or one-based depending on whether the \code{--zero_based} argument was used when running Bismark's methylation extractor.
Furthermore, the default co-ordinate counting system varies by version of Bismark.
\pkg{bsseq} makes no assumptions about the basis of the genomic co-ordinates and it is left to the user to ensure that the appropriate basis is used in the analysis of their data.

Since Bioconductor packages typically use one-based co-ordinates, we strongly recommend that your Bismark files are also one-based.
}
}

We recommend the following to achieve fast and efficient importing of Bismark files:

\itemize{
\item Specify the set of methylation loci via the \code{loci} argument.
\item Use Bismark files in the 'coverage' output format.
\item Leave \code{rmZeroCov = FALSE}.
\item Use a \code{BPPARAM} with a moderate number of workers (cores).
\item Use \code{BACKEND = "HDF5Array"}.
}

Each point is discussed below.
\subsection{Specifying \code{loci}}{
Specifying the set of methylation loci via the \code{loci} argument means that \code{read.bismark()} does not need to first parse all \code{files} to identify the set of candidate loci.
Provided that \code{rmZeroCov = FALSE}, this means that each file is only read once.
This may be a considerable saving when there are a large number of \code{files}.

\strong{If you are unsure whether the below-described shortcuts apply to your data, leave \code{loci = NULL} and let \code{read.bismark()} identify the set of candidate loci from \code{files}.}

You may wish to use the \code{\link{findLoci}()} function to find all methylation loci of interest in your reference genome (e.g., all CpGs) and then pass the result via the \code{loci} argument.

Alternatively, if all \code{files} are 'genome wide cytosine reports' for samples aligned to the same reference genome, then all \code{files} contain the exact same set of methylation loci.
In this case, you may wish to first construct \code{loci} using the internal function \code{bsseq:::.readBismarkAsFWGRanges()} applied to a single file, e.g., \code{loci = bsseq:::.readBismarkAsFWGRanges(files[1], rmZeroCov, strandCollapse)}.
}

\subsection{Using the 'coverage' Bismark files}{
It will generally be faster to parse Bismark files in the 'coverage' output format than those in the 'genome wide cytosine report' format This is because the former only includes loci with non-zero coverage and so the file size is often considerably smaller, particularly for shallowly sequenced samples (e.g., those from single-cell bisulfite sequencing).
}

\subsection{Leaving \code{rmZeroCov = FALSE}}{
If you set \code{rmZeroCov = TRUE}, then \code{read.bismark()} must first parse all the \code{files} to identify which loci have zero coverage in all samples and then filter these out from the set of candidate loci.
\strong{This will happen even if you supply \code{loci} with a \linkS4class{GenomicRanges} of candidate loci.}

Furthermore, any coverage-based filtering of methylation loci is best left until you have constructed your final \linkS4class{BSseq} object.
In our experience, the final \linkS4class{BSseq} object is often the product of combining multiple \linkS4class{BSseq} objects, each constructed with a separate call to \code{read.bismark()}.
In such cases, it is premature to use \code{rmZeroCov = TRUE} when running each \code{read.bismark()}; regretably, combining these objects will often then lead to an inefficiently stored \linkS4class{BSseq} object.
}

\subsection{Using a \code{BPPARAM} with a moderate number of workers (cores)}{
Each file can be processed on its own, so you can process in parallel as many files as you have workers. However, if using the HDF5Array backend, then writing to the HDF5 file cannot be performed in parallel and so becomes the bottleneck. Nonetheless, by using a moderate number of workers (2 - 10), we can ensure there is processed data available to write to disk as soon as the current write is completed.
}

\subsection{Using \code{BACKEND = "HDF5Array"}}{
By using the HDF5Array realization backend from \pkg{HDF5Array}, we reduce the amount of data that is kept in-memory at any one time.
Once each file is parsed, the data are written to the HDF5 file and are no longer needed in-memory.
When combined with multiple workers (cores), this means that each file will only need to read and retain in-memory 1 sample's worth of data at a time.

Conversely, if you opt for all data to be in-memory (via \code{BACKEND = NULL}), then each worker will pass each file's data back to the main process and memory usage will steadily accumulate to often unreasonable levels.
}
}

\section{Realization backends}{
The \code{read.bismark()} function creates a \linkS4class{BSseq} object with two assays, \code{M} and \code{Cov}.
The choice of \emph{realization backend} controls whether these assays are stored in-memory as an ordinary \link[base]{matrix} or on-disk as a \linkS4class{HDF5Array}, for example.
The choice of realization backend is controlled by the \code{BACKEND} argument, which defaults to the current value of \code{DelayedArray::\link[DelayedArray]{getRealizationBackend}()}.

\code{read.bismark()} supports the following realization backends:

\itemize{
\item \code{NULL} (in-memory): This stores each new assay in-memory using an ordinary \link[base]{matrix}.
\item \code{HDF5Array} (on-disk): This stores each new assay on-disk in a HDF5 file using an \linkS4class{HDF5Matrix} from \pkg{HDF5Array}.
}

Please note that certain combinations of realization backend and parallelization backend are currently not supported.
For example, the \linkS4class{HDF5Array} realization backend is currently only compatible when used with a single-machine parallelization backend (i.e. it is not compatible
\strong{multiple} machines).
% TODO: Test this
\code{BSmooth()} will issue an error when given such incompatible realization and parallelization backends.

Additional arguments related to the realization backend can be passed via the \code{...} argument.
These arguments must be named and are passed to the relevant \linkS4class{RealizationSink} constructor.
For example, the \code{...} argument can be used to specify the path to the HDF5 file to be
used by \code{BSmooth()}.
Please see the examples at the bottom of the page.
}

\section{Parallelization, progress monitoring, and logging}{
\code{read.bismark()} now uses the \pkg{BiocParallel} package to implement
parallelization. This brings some notable improvements:

\itemize{
\item Imported files can now be written directly to an on-disk
realization backend by the worker. This dramatically reduces memory
usage compared to previous versions of \pkg{bsseq} that required all
results be retained in-memory.
\item Parallelization is now supported on Windows through the use of a
\linkS4class{SnowParam} object as the value of \code{BPPARAM}.
\item Detailed and extensive job logging facilities.
}

All parallelization options are controlled via the \code{BPPARAM} argument.
In general, we recommend that users combine multicore (single-machine)
parallelization with an on-disk realization backend (see section,
'Realization backend'). For Unix and Mac users, this means using
a \linkS4class{MulticoreParam}. For Windows users, this means using a

A useful feature of \pkg{BiocParallel} are progress bars to monitor the
status of long-running jobs, such as \code{BSmooth()}. Progress bars are
controlled via the \code{progressbar} argument in the

\pkg{BiocParallel} also supports extensive and detailed logging facilities.
}

\value{
}

\seealso{
\itemize{
alignment suite.
\item \code{\link{read.umtab}()} for parsing legacy (old) formats from the BSmooth alignment suite.
\item \code{\link{collapseBSseq}()} for collapsing (aggregating) data from sample's with multiple Bismark methylation extractor files (e.g., technical replicates).
}
}

\examples{
# Run read.bismark() on a single sample to construct a matrix-backed BSseq
# object.
infile <- system.file("extdata/test_data.fastq_bismark.bismark.cov.gz",
package = "bsseq")
colData = DataFrame(row.names = "test_data"),
rmZeroCov = FALSE,
strandCollapse = FALSE,
verbose = TRUE)
# This is a matrix-backed BSseq object.
sapply(assays(bsseq, withDimnames = FALSE), class)
bsseq

\dontrun{
# Run read.bismark() on a single sample to construct a HDF5Array-backed BSseq
# object (with data written to 'test_dir')
test_dir <- tempfile("BSseq")
colData = DataFrame(row.names = "test_data"),
rmZeroCov = FALSE,
strandCollapse = FALSE,
BACKEND = "HDF5Array",
dir = test_dir,
verbose = TRUE)
# This is a HDF5Array-backed BSseq object.
sapply(assays(bsseq, withDimnames = FALSE), class)
# The 'M' and 'Cov' assays are in the HDF5 file 'assays.h5' (in 'test_dir').
sapply(assays(bsseq, withDimnames = FALSE), path)
}
}

\author{
Peter Hickey \email{peter.hickey@gmail.com}
}

% TODO: Mention and link to findLoci().