serena-rna-tool 3.0.0.0

Serena RNA toolset for ensemble analysis with data-nut-squirrel data archive implementation

System Requirments

This software was developed to run on Ubuntu 22.04 LTS and Python 3.9. Python 3.9 is used due to the fact that the NUPACK version I currenlty use 4.0.0.28 will not build with any python versions after 3.9.

Installation

The package is pip installable via Pypi.org as well via the Github repo:

To install from Pypi:

pip install serena-rna-tool

To install from cloned repo:

pip install .

Algorithms, Tools, and Framework

As of release 2.0.0 all tools are fully coded up and unit tested from the list of algorithms presented at the annual RNA design conference Eternacon9 at Stanford University. This includes all algorithms around determining the switchyness of a rna sequence. These utilize a new software framework I developed that enabled the processing and shuttling around of information on RNA stuctural ensembles. These ensembles are determined through thermodynamic modeling using University research software tools such as Nupack4 and Vienna2.

Framework

The entry point into the framework is the Sara2SecondaryStructure. It is called this due to the fact that Serena is a toolset that runs on the brains and logic of the Sara algorithm and its predicesor Sara2 that designed one of the two featured RNA sequences in the peer-reviwe journal PNAS. This object contains all the information known about a single secondary structure found in the ensemble of a RNA sequence. This includes the structure in dot parentheses notation, total free energy, stack energy, RNA sequence and number of nucleotides. The full ensemble of a RNA sequence with all its seperate secondary structures is then represented through the Sara2StructureList to start with.

I very purposfully chose my words when I said "full ensemble of a RNA sequence". This is because the shape RNA takes should not be thought of as the MFE structure only, as it currently taught in schoolm as the best approximation of the shape it will take. This is becuase the MFE is only the shape that it will exhibit at the lowest and strongest negative free energy. The RNA in actuallity will jump around and you really need to look at the entire ensemble over a large span of energy from the MFE toward the positive direction of energy.

One major reason for this is that Temperature is one of the variables that goes into the Partiton Function for the ensemble that is calculated. It is impossible to hold the temperature in a state that there is zero variation and as such when the temperature fluctuates even a fraction of a degree, the partition function will change. The partition function then will feed back into how the secondary structures for the ensemble that is generated. Any change that would affect the partition function then would change how the enemble would look. Taking this a step further, when you predict the fold of a RNA and thus the MFE structure using a package such as Nupack, you have to enter the temperature at time of the fold. This gives you a MFE tied to only that exact temperature and does represent the MFE at different temperatures. We can however do a single fold at the target temperature and analyze the ensemble out some span form MFE, as the ensemble structures are predictions of what structures will appear at what total energy levels for that sequence and one of the things that affects energy is Temperature. We can thus use this information to determine stability of the RNA, as well as the prefered beavhiours the RNA sequence wants to take. In order to make more sense of the ensemble i have found it is best to break it up into chunks to better understand the transitions of each energy level in 1 degree increments. You can then get a feel for how stable the RNA is even across any generalized stability. You can also see if there are echoes of the ability to be a RNA riboswitch and how well it would do if different oligos were present.

The Sara2StructureList is thus a list of each Sara2SecondaryStructure in the ensemble in order found. When populated the oject is able to give you inromation on min and max energy levels, mfe structure, num of structures in list, as well as teh list of strucures. Each 1 kcal energy group has a Sara2StructureList generated for it which is used to make a unique SingleEnsembleGroup. The SingleEnsembleGroup is an object that is used to hold increasing amounts of information about chuncks of the ensemble and includes information used for switch analyis. Each SingleEnsembleGroup is then feed into a single MultipleEnsembleGroups which is an object that holds all the ensemble groups information broken down by kcal range for those structures.

A Sara2StructureList object is then feed into the Ensemble Variation Algotithm to determine general stability of a RNA sequence across the the descret range of teh ensemble. The value generated is a unit of measurment and Ensemble Variation or EV is the unit. A MultipleEnsembleGroups object is fed into the Local Minima Variation algorithm to get results for that various flavors of LMV that represent different aspect of the stabilty of the ensemble using the ensemble variation metric and unit of measure. To see examples of this implementation check out the unit tests for the nupack interface as it has teh entire process coded up.

How to call

After installation you can access Serena's EV and LMV tools from python via:

from serena.ensemble_variation import RunEnsembleVariation

or

from serena.local_minima_variation import RunLocalMinimaVariation

How to use

To start lets make an assumption that you have aquired one or more RNA sequences, and this/these sequences have also allowed you to derive some secondary structure in dot paren notation. You are also assumed to have aquired some total free energy to associate each secondary structures with. You may or may not have stack energies and that is not important right now for how the code is written. This info you have about the RNA sequence is loaded into Serena via the Sara2SecondaryStructure.

from serena.utilities.ensemble_structures import Sara2SecondaryStructure
rna_sequence='ACGUACAUGAC'
secondary_structure='((.......))'
total_free_energy=-30
stack_free_energy=-33

struc = Sara2SecondaryStructure(sequence=rna_sequence,
                        structure=secondary_structure,
                        free_energy=total_free_energy,
                        stack_energy=stack_free_energy
                        )

Now you add this structure to a Sara2StructureList. After you package up each secondary structure you can then add it to a Sara2StructureList and this can be feed into many different algorithms and other containers. You do this with the "add_structure" function once you initialize the Sara2StructureList.

from serena.utilities.ensemble_structures import Sara2StructureList
structure_list = Sara2StructureList()
structure_list.add_structure(structure=struc)

You can then feed this into the EnsembleVariation class that hold the algorithms for Ensemble Variation (EV). For this though you need a reference Sara2SecondaryStructure that has been packaged up. For this example the MFE structure for the ensemble was chosen.

from serena.utilities.ensemble_variation import EnsembleVariation, EV
e_v = EnsembleVariation()
ensemble_ev:EV = e_v.ensemble_variation_algorithm(kcal_group_structures_list=structure_list,
                                ref_structure=mfe_structure)

Links:

Documentation