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binless

Resolution-independent normalization of Hi-C data

Refer to the CHANGELOG for the latest changes

Installation

The easy way

Let devtools do the installation for you. Type the following in an R shell

install.packages("devtools")
devtools::install_github("3DGenomes/binless",subdir="binless")

Installation should take about 10 minutes.

Manual installation

You can also install it manually as follows:

  • Download the latest release here
  • Unpack it in a clean folder on your machine, and hit R CMD INSTALL binless in a shell.
  • If it complains that some packages are not installed, you must install them in R using install.packages

Requirements

The minimum required R version is 3.2.0 Binless uses the following packages: data.table, Hmisc, foreach, doParallel, MASS, matrixStats, ggplot2, dplyr, Matrix, quadprog, scales, utils

Also, you will need some common C++ libraries: the GNU Scientific Library (GSL), boost, OpenMP and Eigen. If you use a mac and homebrew, type brew install gsl libomp boost eigen in a terminal and that should be it.

Binless has been developed and tested on a MacBook Pro (2015) and on a CentOS 7 linux workstation with 128Gb RAM and 32 cores. Resource usage can go from modest (fast binless will run on a laptop for loci <10Mb) to huge (fast binless on human chromosome 1 at 5kb base resolution requires about 500Gb of RAM).

How does it work?

In the example/ folder, we provide plots and files to perform a normalization, taken from publicly available data (Rao et al., 2014). Alternatively you can use your own data. Start with something not too large, for example 1Mb. If you want a quick and dirty overview, skip to the Fast binless section. Otherwise, read on.

Preprocessing

Try out the file preprocessing.R, executing each line step by step (e.g. using rstudio). Binless takes mapped read pairs as input, in TADbit .tsv format (see below for file format descriptions). Binless normalization is performed on multiple datasets simultaneously. All .tsv files are pre-processed and put into a single CSnorm object that will hold all relevant information you need. At this point, you will also be able to visualize your data using our base-resolution (arrows) representation.

Optimized binless

See optimized_binless.R. The resolution-independent normalization is performed on the CSnorm object you built at the previous step. Once normalized, datasets can be combined, and signal and difference detection can be performed. This is the full-blown version of the algorithm, with statistically significant output. Note that this is a beta version, so check for updates regularly.

Fast binless

See fast_binless.R. Here, we implemented a fast approximation with fixed fusion penalty and approximate decay and bias terms. You can either use a CSnorm object produced at the preprocessing stage, or directly provide the binned raw matrix. This is a fast and approximate version of the full algorithm, so you will not get statistically significant output. But you can try out a whole chromosome ;)

Chromosome-wide normalization

Binless is meant for locus-specific normalization. If however you need binless on a whole chromosome, the script chromosome_binless.R can be used to combine optimized and fast binless in order to get a good first guess. See the paper for the procedure used to estimate the required parameters.

Base-resolution (arrow) plots

If you just want to see how your data looks like with the arrow plots and are not interested in binless normalization, check out the short tutorial arrow_plot.R.

Questions? Problems?

Check out the FAQ in the GitHub issues tracker. If you don't find anything, just file an issue by clicking on the "New issue" button. You can use it to report bugs, ask questions or suggest new features. We are looking forward to your feedback!

Citation

If you use this software, please acknowledge the following paper

Spill YG, Castillo D, Vidal E, Marti-Renom MA, "Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution", Nature Communications, 10:1938 (2019) doi:10.1038/s41467-019-09907-2

File format description

TADbit .tsv: tab-separated text file containing paired-end mapped reads for a single experiment. All lines starting with # will be discarded. The order of the reads is unimportant. It has the following columns

  1. id: ID of the mapped pair (alphanumeric without spaces)
  2. chr1: chromosome of read 1 (alphanumeric without spaces)
  3. pos1: position of the 5' end of read 1 (integer)
  4. strand1: whether the read maps on the forward (1) or reverse (0) direction
  5. length1: how many bases were mapped (integer)
  6. re.up1: upstream restriction site closest to pos1 (integer <= pos1)
  7. re.dn1: downstream restriction site closest to pos1 (integer > pos1)
  8. chr2
  9. pos2
  10. strand2
  11. length2
  12. re.up2
  13. re.dn2

Binned raw matrix (used for fast binless): tab, comma or space-separated text file containing multiple datasets. The first line is a header that must start with "name" "bin1" "pos1" "bin2" "pos2" "distance" "observed" "nobs". Optionally, more columns can be added but make sure their column names are different.

  1. name: The name of the dataset. Put in "" if you use spaces
  2. bin1: the label for the bin on the x axis. For example "[begin1,end1)"
  3. pos1: The position in bases of the center of the bin on the x axis
  4. bin2: The label for the bin on the y axis
  5. pos2: The position on the y axis
  6. distance: The average distance (in bases) between the two bins
  7. observed: How many paired-end reads were mapped within (bin1,bin2)
  8. nobs: The number of observables, i.e. four times the number of cut site intersections in that bin

Note that name will be converted to R factors, so you an also provide them as integers starting at 1 (i.e. use 1 for the first dataset, 2 for the second etc.). Also, you must have pos2 >= pos1, and the data must be sorted by name, pos1 and pos2, in that order.