Parallel solution for local RNA secondary structure analysis
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README.md

ParasoR

Build Status

Features


ParasoR can compute these features for RNA sequences even if they are longer than human genome sequences with computer clusters.

  • Base pairing probability (bpp)
  • Stem probability
  • Accessibility
  • Structure profiles (probability and motif sequence)
  • γ-centroid structure (in not parallel) or credible structures having base pairs (bpp >= 1/(1+γ)) with the color code of stem probability.

* γ-centroid structure or credible structures with the color code of structure profiles. * Color: Exterior (light green), Stem (red), Bulge (orange), Multibranch (green), Hairpin (violet), Internal (blue).

In addition, ParasoR simulates structure arrangements caused by a single point mutation.

Requirements

  • C++11

We already tested ParasoR running with Apple LLVM version 6.0 and GCC 4.8.1.

How to install

git clone https://github.com/carushi/ParasoR
cd ParasoR
git checkout tags/v1.1.0
./configure
make
make install

Or download from "Download ZIP" button and unzip it.

As a default, 'double' option is valid for the precision of floating point. You can also change an option for the precision of floating point like

make VAR=LONG
# use long double.
make VAR=SHORT
# use float.

If you have a trouble about automake setting, please try to type as below.

cd src
make -f _Makefile

or

 autoreconf -ivf

Example

We prepared a shell script 'check.sh' for test run. This script runs by the commands as follows.

cd script/
sh check.sh
cat ../doc/pre.txt
# stem probability based on previous algorithm (Rfold model)
cat ../doc/stem.txt
# stem probability based on ParasoR algorithm
python test.py
# Output numerical error between the result of ParasoR with single core and multiple core

For more sample, please visit our wiki.

Reference

Citation

  • Kawaguchi R. et al. (2016) Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome. BMC Bioinformatics, 17:203.

Algorithm

  • Kiryu H. et al. (2008) Rfold: an exact algorithm for computing local base pairing probabilities. Bioinformatics, 24 (3), 367–373.
  • Hamada M. et al. (2009) Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics, 25 (4), 465-473.
  • Kiryu H. et al. (2011) A detailed investigation of accessibilities around target sites of siRNAs and miRNAs. Bioinformatics, 27 (13), 1789-97.
  • Fukunaga T. et al. (2014) CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol., 15 (1), R16.

Implementation

  • Hamada M. et al. (2009) Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics, 25(4), 465–473.
  • Gruber AR. et al. (2008) The Vienna RNA websuite. Nucleic Acids Res., 36 (Web Server issue), W70–W74.

Energy model

  • Turner DH. et al. (2010) NNDB: the nearest neighbour parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res., 38(Database issue), D280–D282.
  • Andronescu M. et al. (2010) Computational approaches for RNA energy parameter estimation. RNA, 16(12), 2304–2318.