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
- 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.
In addition, ParasoR simulates structure arrangements caused by a single point mutation.
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
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.
- Kawaguchi R. et al. (2016) Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome. BMC Bioinformatics, 17:203.
- 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.
- 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.
- 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.