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Welcome to GENOVA!

The increase in interest for Hi-C methods in the chromatin community has led to a need for more user-friendly and powerful analysis methods. The few currently available software packages for Hi-C do not allow a researcher to quickly summarize and visualize their data. An easy to use software package, which can generate a comprehensive set of publication-quality plots, would allow researchers to swiftly go from raw Hi-C data to interpretable results.

Here, we present GENome Organisation Visual Analytics (GENOVA): a software suite to perform in-depth analyses on various levels of genome organisation, using Hi-C data. GENOVA facilitates the comparison between multiple datasets and supports the majority of mapping-pipelines.

Quick, reliable, easy and flexible.

Support

We have provided a quite lengthy vignette, so please read that first. If there are still unanswered questions, please use the issue-tracker.


Changelog

[0.9.98] - 27-02-2019

Github-hash: 9951098

Added

  • ATA: outputs also the used TADs.bed
  • HiC_matrixplot: smoothing of the regions with no data (e.g. the white stripes) has been implemented. This is done by filling these bins with the result of a Nadaraya/Watson normalization of the kernel. Set smoothNA to true to use and smoothBandwidth to tweak
  • HiC_matrixplot: If chip.yMax is NULL, a warning will be given for the used yMaxes.
  • PE-SCAn: A maximal distance can be given with maxDist.
  • PE-SCAn: verbosity can be limited off with verbose = F
  • PE-SCAn: Catches errors when no matrices are found by cov2d.
  • PE-SCAn: A treshold for the minimum amount of BED-entries per chromosome is added: minComparables.
  • PE-SCAn: Shifted bed-entries bigger than their chromosome are now fixed (they "wrap around" to the start)
  • PE-SCAn: Returns not only the O/E matrix, but a list with a O/E score-matrix (if shift != 0), otherwise an observed score-matrix, the underlying signal and background-matrices and the shift used.
  • visualise.PESCAn.ggplot: several checks to see if the input-data is comparable (including shift).
  • visualise.PESCAn.ggplot: colorscales for O/E and O are better (i.e. no log2-divergent scale for shift==0 matrices).
  • cis.compartment.plot: a second experiment-object can be used. This is plotted in the lower-left corner. Note: only the first matrix is invisibly returned.
  • cis.compartment.plot: smoothing of the regions with no data (e.g. the white stripes) has been implemented. This is done by filling these bins with the result of a Nadaraya/Watson normalization of the kernel. Set smoothNA to true to use and smoothBandwidth to tweak
  • getTADstats: a new function to get some stats on ATA-results. Still quite buggy.
  • saddle: Forces user for either CS or chip.
  • README now contains actual text.

Changed

  • HiC_matrixplot: bed.col and bw.col are merged to chip.col. This argument can take a vector of four colours, parallel to the chip-argument.
  • HiC_matrixplot: yMax is renamed to chip.ymax.
  • visualise.PESCAn.ggplot: works woth new output of PE-SCAn
  • PE-SCAn: cov2d loops over a vectorised list without duplicates, which speeds up the code.
  • PE-SCAn: cleaned up cov2d's return-code
  • quantifyAPA: the enrichmentType argument lets you choose between pixel/mean(backgroundRegions) and the fraction of loops with more than 50% higher signal than the background.
  • visualise.compartmentStrength: at least one bin is used (bugfix).
  • saddle: extra effort to use the CS is taken by matching the CS-vector and the eigen-vector. This will fix problems due to big centromeres or shaky/different centromere-calls.
  • vignette: fixed typo's