1 Motivation

This package is designed to:

  • Replace the use of command-line utilities for most post-alignment processing, e.g. bedtools and deeptools
  • Be easy-to-use and easy-to-install, without requiring external dependencies, e.g. hitslib or the kent source utilities from the UCSC genome browser
  • Allow users to string together common analysis pipelines with simple, fast-running one-liners
  • Avoid code repetition by providing tested and validated code
  • Exploit the properties of basepair-resolution data to optimize performance and increase user-friendliness
  • Use process forking to make use of multicore processors
  • Maximize compatibility with Bioconductor’s rich ecosystem of analysis software, in addition to leveraging the traditional strengths of R in statistics and data visualization
  • Fully replace the bigWig R package

2 Features

  • Process and import bedGraph, bigWig, and bam files quickly and easily, with several pre-configured defaults for typical uses
  • Count and filter spike-in reads
  • Calculate spike-in normalization factors using several methods and options, including options for batch normalization
  • Count reads by regions of interest
  • Count reads at positions within regions of interest, at single-base resolution or in larger bins, and generate count matrices for heatmapping
  • Calculate bootstrapped signal (e.g. readcount) profiles with confidence intervals (i.e. meta-profiles)
  • Modify gene regions (e.g. extract promoters or genebody regions) using a single simple and straightforward function
  • Conveniently and efficiently call DESeq2 to calculate differential expression in a manner that is robust to global changes1 Avoid the default behavior of calculating genewise dispersion across all samples present, which is invalid if any experimental condition causes broad changes
    • Use non-contiguous genes in DESeq2 analysis, e.g. to exclude of specific sites/peaks from the analysis (not usually supported by DESeq2)
    • Efficiently generate results across a list of comparisons
  • Support for blacklisting throughout, and proper accounting of blacklisted sites in relevant calculations
  • Users interact with an intuitive and computationally efficient data structure (the “basepair resolution GRanges” object), which is already supported by a rich, user-friendly suite of tools that greatly simplify working with datasets and annotations

3 Coming Soon

Data processing:

  • Summarizing and plotting replicate correlations
  • Function to use random read sampling to assess if sequencing depth sufficient to stabilize arbitrary calculations (so a user can supply anonymous function to calculate things like rank expression, power analysis or differential expression by DESeq2, pausing indices, etc.)

Signal counting and analysis:

  • Two-stranded meta-profile calculations
  • Automated generation of a list of DESeq2 comparisons using all possible combinations; all possible permutations; or by defining a simple hierarchy of each-vs-one comparisons