rework documentation

Move to GitHub pages. Edited all the pages, added individual application pages, added new pages.

Readme.md

@@ -4,164 +4,41 @@ A multi-threaded wrapper for processing multi-replicate, multi-condition ChIPSeq
 
 ## Description
 
-ChIPSeq and related methods, such as ATACSeq, is increasingly being used not just as 
-a means of discovery ("where is my factor binding?") but also as an assay for experimental 
-conditions ("how does my mutant affect factor X occupancy?"). Many traditional ChIPSeq 
-programs are not necessarily well equipped to handle multiple replicates and/or sample 
-conditions.
+ChIPSeq and related methods, such as ATACSeq and Cut-and-Run, is increasingly being
+used not just as a means of discovery ("where is my factor binding?") but also as an
+assay for experimental conditions ("how does my mutant affect factor X occupancy?").
+Many traditional ChIPSeq programs are not necessarily well equipped to handle
+multiple replicates and/or sample conditions.
 
-This is a wrapper application for processing ChIPSeq samples 
-comprised of multiple biological replicas and/or multiple conditions in a manner to 
-make comparisons as consistent and uniform as possible across samples. 
+This is a wrapper application for processing ChIPSeq samples comprised of multiple
+biological replicas and/or multiple conditions in a manner to make comparisons as
+consistent and uniform as possible across samples. 
 
-## Rationale
+## Documentation
 
-The venerable [Macs2](https://pypi.org/project/MACS2/) application provides a robust
-method of determining enrichment of ChIP fragments over input with a number of
-advantages: fragment-based pileup of ChIP signal versus simple counts, single
-base-pair resolution instead of sliding windows, estimation of local chromatin bias
-using multiple window sizes, and more. However, Macs2 does not natively deal with
-replicates, and comparing multiple conditions requires careful, manual execution 
-of each set with identical parameters and/or complicated intersections. 
+For full documentation, including overview, usage guide, examples, and installation
+guide, see the [Documentation pages](https://huntsmancancerinstitute.github.io/MultiRepMacsChIPSeq).
 
-This package aims to automate Macs2 ChIPSeq peak calling with support for multiple 
-replicas and conditions while supporting newer normalization methods. Importantly, it 
-will output normalized, processed bigWig enrichment files for subsequent genic analysis; 
-numerous analytical, comparative, and QC metric plots; and peak count tables ready for 
-quantitative differential analysis.
+## Installation
 
-## Overview
+This is a Perl package and may be built using the standard incantation.
 
-![Overview](MultiRepMacsChIPSeq.png)
+	perl Build.PL
+	./Build
+	./Build install
 
-Above is a graphical overview of the pipeline. Below is a description of the steps.
-Here, _samples_ are used to mean separate experimental conditions (antibody,
-treatment, genetic status, time point, whatever), while _replicates_ are used to
-indicate one or more biological samples (animals, culture, etc) of the same
-condition. Technical replicates could also be included.
+For detailed notes on accessory packages and dependencies, see the 
+[Installation guide](https://huntsmancancerinstitute.github.io/MultiRepMacsChIPSeq/Install.hmtl).
 
-- Generate exclusion list
-
-    Unless provided with an exclusion (black) list, an empirically-derived exclusion 
-    list is automatically generated from the Control reference files by combining all 
-    of the control files (without deduplication) and calling peaks. Exclusion lists 
-    are critical to removing potential false positives and removing a large source of 
-    duplicate alignments.
-
-- Calculate mappable size
-
-    Unless provided with an explicit, effective genome size, the effective mappable 
-    size is calculated empirically from all provided alignment files using 
-    [report_mappable_space](applications.md#report_mappable_spacepl). Having an 
-    accurate, experiment-based genome size should provide a more accurate statistical 
-    estimation of peak enrichment.
-
-- Filter alignments
-
-	Alignment files may be filtered for a variety of reasons, including overlapping 
-	exclusion intervals, unwanted chromosomes (mitochondrial, alternate haplotypes, 
-	unmapped contigs, and/or decoy sequences), duplicate alignments (optical or 
-	PCR-derived), secondary and supplemental alignments, and mapping quality. 
-
-	An optional step can include sub-sampling PCR-duplicates to a consistent level. 
-	See the [De-Duplication Evaluation](DeDuplicationEvaluation/ReadMe.md) for 
-	further details.
-
-- Generate replicate count tracks 
-
-    To facilitate generating count matrices later, point-data count bigWig files,
-    either shifted start positions (single-end) or fragment midpoints (paired-end),
-    are generated using [bam2wig](https://metacpan.org/pod/bam2wig.pl) for each
-    sample replicate. By default, replicates are depth-normalized and scaled to the
-    same target depth (by default the median observed depth of all provided Bam files). 
-
-    When independent replicate peaks are called too, fragment coverage tracks for
-    each replicate are also generated.
-
-- Independent-replicate peak calls
-
-    When indicated, independent peak calls may optionally be generated for each 
-    replicate using [Macs2](https://pypi.org/project/MACS2/). These replicate peaks 
-    are then intersected to generate a consensus peak call set for the sample using 
-    [intersect_peaks](applications.md#intersect_peakspl). By default, consensus 
-    peaks must be identified from (n - 1) replicates.
-
-- Generate replicate-mean fragment coverage and enrichment files
-
-    Generate per-sample mean fragment coverage for both ChIP and corresponding 
-    reference control using [bam2wig](https://metacpan.org/pod/bam2wig.pl). This 
-    averages sample replicates in a depth-normalized manner, ensuring that all 
-    replicates have the same weight when making peak calls.
-
-    [Macs2](https://pypi.org/project/MACS2/) is used to generate q-value and Log2 Fold
-    Enrichment tracks from the mean-replicate ChIP fragment coverage and lambda 
-    control files for each sample.
-
-- Call peaks
-
-    Use [Macs2](https://pypi.org/project/MACS2/) to call peaks from the mean-replicate
-    q-value tracks for each sample separately using the indicated threshold, minimum
-    peak size, and peak gap size for merging. The peak call parameters can be
-    explicitly specified for custom control. Broad, or gapped-peak, calls may also be
-    made if desired.
-
-- Intersect peaks
-
-    Use [intersect_peaks](applications.md#intersect_peakspl) to intersect the peaks
-    from each ChIP sample into a master list of peaks across all ChIP conditions,
-    as well as generate a variety of statistics regarding the peaks and their
-    overlap. This is done for both mean-replicate and independent-replicate samples. 
-    These statistics are plotted as QC plots by 
-    [plot_peak_figures](applications.md#plot_peak_figuresr).
-
-- Rescore merged peaks
-
-    Use [get_datasets](https://metacpan.org/pod/get_datasets.pl) to generate matrices
-    of log2 Fold Enrichment scores, q-value scores, coverage, and count data for the
-    merged list of peaks. Also, use
-    [get_relative_data](https://metacpan.org/pod/get_relative_data.pl) to collect the
-    profile of plots using both fragment coverage and log2 Fold Enrichment scores 
-    around the peak summit or midpoint.
-
-- Plot replicate and sample QC and heat maps
-
-    A variety of replicate and sample QC plots, correlation plots (Euclidean
-    distance, PCA, Pearson, and Spearman), intersection (UpSet plots, count and 
-    spatial-overlap intersection heat maps), and occupancy and enrichment heat maps
-    and line plots are generated using a custom R script,
-    [plot_peak_figures](applications.md#plot_peak_figuresr).
-
-- Generate report
-
-	Generate a Markdown report that summarizes the pipeline options, alignment
-	statistics, and peak call numbers, intersections, correlations, and results.
-	Includes a select number of plots to illustrate the report. If Pandoc is
-	available, the Markdown report will automatically be converted to a
-	self-contained HTML report.
-
-- Differential analysis
-
-    The merged peaks may then be evaluated for differential occupancy between two or
-    more samples using rigorous statistical analysis. For example,
-    [DESeq2](https://bioconductor.org/packages/DESeq2/) may be used to identify
-    significantly different peaks. Some basic R scripts are included to perform such
-    analysis on an ad hoc basis.
-
-# Usage and installation
-
-See the accompanying [Installation guide](INSTALL.md), [Usage Guide](Usage.md), and 
-[list of application menus](applications.md) for further information. Examples and 
-example scripts are provided in the [examples directory](examples/Readme.md).
-
-# AUTHOR
+## AUTHOR
 
     Timothy J. Parnell, PhD
-    Bioinformatics Shared Resource
+    Cancer Bioinformatics Shared Resource
     Huntsman Cancer Institute
     University of Utah
     Salt Lake City, UT, 84112
 
-# LICENSE
+## LICENSE
 
 This package is free software; you can redistribute it and/or modify
 it under the terms of the Artistic License 2.0. For details, see the

docs/DeDuplicationEvaluation/ReadMe.md

@@ -1,7 +1,7 @@
 # De-Duplication Evaluation
 
 The Multi-Replica Macs ChIPSeq Wrapper includes a novel application,
-[bam_partial_dedup](../applications.md#bam_partial_deduppl), for sub-sampling
+[bam_partial_dedup](applications/bam_partial_dedup), for sub-sampling
 duplicate alignments to a standard fractional level across all replicates and
 samples. The theory behind the application is to keep some fraction of duplicate
 alignments on the assumption that these may include biological duplicates, known to
@@ -40,7 +40,7 @@ package](https://github.com/HuntsmanCancerInstitute/UMIScripts). Reads were alig
 to the genome using Novocraft Novoalign (version 4.3).
 
 Alignment bam files were processed with either
-[bam_partial_dedup.pl](../applications.md#bam_partial_deduppl) or with
+[bam_partial_dedup.pl](applications/bam_partial_dedup) or with
 `bam_umi_dedup.pl` from the [UMIScripts
 package](https://github.com/HuntsmanCancerInstitute/UMIScripts). With partial
 de-duplication, all optical duplicates (optical threshold of 2500 pixels) were