- A set of secondary structures from the Pareto front,
- The list of known modules inserted inplace in the corresponding structures
- A set of positions of the nucleotides in contact with the protein represented by asterisks (only if the motifs_28-05-2021.json library is used!)
2/ The different models
==================================
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@@ -28,7 +29,8 @@ Biorseo can be used with two modules datasets (yet):
* Rna3Dmotifs (from the work of *Djelloul & Denise, 2008*)
* The RNA 3D Motif Atlas of BGSU's RNA lab (*Petrov et al, 2013*, see http://rna.bgsu.edu/rna3dhub/motifs/)
* CaRNAval 1.0 (*Reinhartz et al, 2018*)
* RNA-Bricks 2, RNAMC, CaRNAval 2.0, and others could theoretically be used, but are not supported (yet). You might write your own API.
* /data/modules/ISAURE/motifs_28-05-2021.json a library of motifs from RNA linked to a protein from Isaure Chauvot de Beauchêne of LORIA laboratory
(contact:isaure.chauvot-de-beauchene@loria.fr)
PATTERN MATCHING STEP
- Use **simple pattern matching**. Rna3Dmotifs modules are available with sequence information. We use regular expressions to find those known loops in your query. This is the approach of RNA-MoIP (*Reinharz et al, 2012*), we deal the same way with short components and wildcards.
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@@ -43,6 +45,8 @@ OBJECTIVE FUNCTIONS FOR THE MODULE INSERTION CRITERIA
***Function B** : weights a module by its number of components (strands) and penalizes it by the log^(_2) of its nucleotide size.
***Function C** : weights a module by its insertion site score (JAR3D or BayesPairing score).
***Function D** : weights a module by its number of components (strands) and insertion site score (JAR3D or BayesPairing score), and penalizes it by the log^(_2) of its nucleotide size.
***Function E** : weights a module by its nucleotides in contact with a protein, number of occurences and number of nucleotides in the module.
***Function F** : weights a module by its nucleotides in contact with a protein, number of occurences and number of nucleotides along the entire length of the RNA.
3/ Installation
==================================
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@@ -55,10 +59,10 @@ Check the file [INSTALL.md](INSTALL.md) for installation instructions.
- If you **might expect a pseudoknot, or don't know**:
* The most promising method is the use of direct pattern matching with Rna3Dmotifs and function A. But this method is sometimes subject to combinatorial explosion issues. If you have a long RNA or a large number of loops, don't use it. Example:
`./biorseo.py -i PDB_00304.fa -O resultsFolder/ --rna3dmotifs --patternmatch --func A --MEA`
* The use of the RNA 3D Motif Atlas placed by JAR3D and scored with function A is not subject to combinatorial issues, but performs a bit worse. It also returns less solutions. Example:
`./biorseo.py -i PDB_00304.fa -O resultsFolder/ --3dmotifatlas --jar3d --func A
`./biorseo.py -i PDB_00304.fa -O resultsFolder/ --3dmotifatlas --jar3d --func A --MEA
5/ List of Options
==================================
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@@ -68,9 +72,9 @@ Usage: You must provide:
2) a module type with --rna3dmotifs, --carnaval, --3dmotifatlas or --contacts
3) one module placement method in { --patternmatch, --jar3d, --bayespairing }
4) one scoring function with --func A, B, C, D, E ou F
5) one estimator betwenn --MEA or --MFE
If you are not using the Docker image:
5) --modules-path, --biorseo-dir and (--jar3d-exec or --bypdir)
6) --modules-path, --biorseo-dir and (--jar3d-exec or --bypdir)
Options:
-h [ --help ] Print this help message
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@@ -85,11 +89,15 @@ Options:
-b [ --bayespairing ] Use BayesPairing2 to place modules in the sequence (requires --rna3dmotifs or --3dmotifatlas)
-o [ --output=… ] File to summarize the results
-O [ --outputf=… ] Folder where to output result and temp files
-f [ --func=… ](A, B, C or D, default is B) Objective function to score module insertions:
-f [ --func=… ](A, B, C, D, E or F default is B) Objective function to score module insertions:
(A) insert big modules (B) insert light, high-order modules
(c) insert modules which score well with the sequence
(C) insert modules which score well with the sequence
(D) insert light, high-order modules which score well with the sequence.
C and D require cannot be used with --patternmatch.
C and D cannot be used with --patternmatch.
(E) and (F) insert modules with a lot of nucleotides and a lot of nucleotides in contact with a proteine, and a huge number of occurences.
(E) maximize the number of contact nucleotide inside the module, while (F) maximize the number of contact nucleotide along the entire length of the RNA.
--MEA Use Maximum Expected Accuracy for the second objective
--MFE Use Minimum Free Energy based on the formula of (*Legendre et al., 2018*) for the second objective
-c [ --first-objective=… ](default 1) Objective to solve in the mono-objective portions of the algorithm.
(1) is the module objective given by --func, (2) is the expected accuracy of the structure.
-l [ --limit=… ](default 500) Number of solutions in the Pareto set from which
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@@ -114,9 +122,9 @@ Options:
BiORSEO from outside the docker image. Use the FULL path.
Examples:
biorseo.py -i myRNA.fa -O myResultsFolder/ --rna3dmotifs --patternmatch --func B