pyLoopSage

Updated version of the stochastic loop extrusion model: LoopSage with capability to run incredibly fast, parallelized across CPU cores. This package is even more user-friendly and it can be installed via PyPI.

New features

• More user-friendly environment.
• Installable with pip install pyLoopSage.
• Parralelization of the stochastic simulation across multiple CPUs.
• Capability of modelling the whole chromosome.
• Visualization functions.
• Capability to run form terminal with a simple command loopsage -c config.ini.

The model

Stochastic Simulation

Before simulating 3D structures, a stochastic simulation takes place that it purely applies in the locations of cohesins. The purpose of it is to create realistic ensemble of cohesin locations, and recontruct their trajectories. Otherwise, a molecular simulation alone, could not have enough variability to reconstruct the experimental heatmaps with the same fixed positions. Therefore, the stochastic simulation can be seen as a generator of cohesin possible configurations.

We have a polymer chain with $N_{\text{beads}}$ number of monomers. In general we can scale by deault the granularity of the simulation so as to give reasonable results. Therefore if we have a region counted in genomic coordinates, we can assume that N_beads=(region[1]-region[0])//2000.

Let's assume that each cohesin $i$ can be represented of two coordinates $(m_{i},n_{i})$ we allow three moves in our simulation:

• Slide both locations randomly (linearly or as 1D random walk) or
• Rebind somewhere else.

In general a good working assumption is that the number of cohesins (or loop extrusion factors LEFs) is $N_{\text{lef}}=2N_{\text{CTCF}}$.

The main idea of the algorithm is to ensemble loop extrusion from a Boltzmann probability distribution, with Hamiltonian,

$$E = c_{\text{fold}}\sum_{i=1}^{N_{\text{coh}}}\log(n_i-m_i)+c_{\text{cross}}\sum_{i,j}K(m_i,n_i;m_j,n_j)+c_{\text{bind}}\sum_{i=1}^{N_{\text{coh}}}\left(L(m_i)+R(n_i)\right)$$

The first term corresponds to the folding of chromatin, and the second term is a penalty for the appearance of crosss. Therefore, we have the function, $K(m_{i},n_{i};m_{j},n_{j})$ which takes the value 1 when $m_{i} < m_{j} < n_{i} < n_{j}$ or $m_{i}=m_{j}$ or $m_{i}=n_{j}$.

These $L(\cdot), R(\cdot)$ functions are two functions that define the binding potential and they are orientation specific - so they are different for left and right position of cohesin (because CTCF motifs are orientation specific), therefore when we have a gap in these functions, it means presence of CTCF. These two functions are derived from data with CTCF binning and by running the script for probabilistic orientation. Moreover, by $N_{(\cdot)}$ we symbolize the normalization constants for each factor,

$$c_{\text{fold}}=-\dfrac{N_{\text{beads}}f}{N_{\text{lef}}\log(N_{\text{beads}}/N_{\text{lef}})},\quad c_{\text{bind}}=-\dfrac{2N_{\text{beads}}b}{N_{CTCF}},\quad c_{\text{cross}}=\kappa \times 10^4.$$

The parameters are defined in such a way that when $f=b=\kappa=1$, the three terms of the stochastic energy are balanced. To clarrify, this parametrization is slightly different than this of the original paper. We multiply with $N_{\text{beads}}$, so as to keep the input parameters $f,b,\kappa$ simpler close to 1. Therefore, a value $f=0.1$ leads to unfolded structures, and a value $f=2$ it leads to very fast propagating long loops.

And the energy difference can be expressed as the energy difference of each term,

$$\Delta E = \Delta E_{\text{fold}}+\Delta E_{\text{cross}}+\Delta E_{\text{bind}}.$$

In this manner we accept a move in two cases:

• If $\Delta E<0$ or,
• if $\Delta E>0$ with probability $e^{-\Delta E/kT}$.

And of course, the result - the distribution of loops in equilibrium - depends on temperature of Boltzmann distribution $T$.

Modelling with two families of cohesins

In this version of LoopSage it is possible to have two families of cohesins (LEFs) as well, with different folding coefficients (which is equivalent to different cohesin speeds). Therefore, we could write the energy of folding,

$$E_{\text{fold}} = c_{\text{fold,1}}\sum_{i=1}^{N_{\text{coh,1}}}\log(n_i-m_i)+c_{\text{fold,2}}\sum_{i=N_{\text{coh,1}}}^{N_{\text{coh,1}}+N_{\text{coh},2}}\log(n_i-m_i)$$

The coefficient of the first family of LEFs is set to one by default $f_1 = 1$, whereas the other one is set to zero $f_2 = 0$. Usually a small amount of fast cohesins with $f_2>f_1$ can reconstruct more long-range loops, because the cohesins have time to extrude. Contrary, leads in very stable small loops.

Molecular Simulation

Let us consider a system comprising $N_{\text{lef}}$ LEFs, as well as two matrices, $M$ and $N$, both of which possess dimensions $N_{\text{lef}}\times N_{\text{steps}}$. These matrices represent the respective constraints associated with each LEF. Consequently, we define a time-dependent force field as follows:

$$E(t) = E_{\text{bond}}+E_{\text{angle}}+E_{\text{rep}}+E_{\text{loop}}(t)$$

where,

• $E_{\text{bond}}$ corresponds to a typical harmonic bond force field that connects adjacent beads $i$ and $i+1$, with an equilibrium length of $x_{0}=0.1 \text{nm}$ and a Hook constant assumed to be $k=3\times 10^5 \text{kJ/(mol}\cdot \text{nm}^2)$.
• $E_{\text{angle}}$ a harmonic angle force that connects beads $i-1,i,i+1$, and has equilibrium angle $\theta_{0}=\pi$ and Hook constant $200 kJ/(mol\cdot nm^2)$. The strength of the angle force, can be tuned by the user.
• $E_{\text{rep}}$ which is a repelling forcefield of the form: $$E_{\text{rep}} = \epsilon\left(\frac{\sigma_{1}+\sigma_{2}}{r}\right)^{\alpha}$$ where $\epsilon=10 kJ/mol$ and $\sigma_{1}=\sigma_{2}=0.05 nm$. The power $\alpha$ is a parameter of the simulation, but it is set as $\alpha=3$ by default.
• $E_{\text{loop}}(t)$ represents a time-dependent LE force. This force reads the matrices $M$ and $N$, applying a distinct set of constraints $C_{t_i}=(m_j(t_i),n_j(t_i))$ at each time step $t_i$. Each LEF $j$ is subjected to specific constraints $m_{j,t_i}$ and $n_{j,t_i}$. The functional form of this force is also a harmonic bond force, with parameters $x_{0}=0.1 \text{nm}$ and a Hook constant assumed to be $k=5\times 10^4 \text{kJ/(mol}\cdot \text{nm}^2)$. The strength and equillibrium distance of the looping bonds can also be set in different way, if the user wishes.

For the implementation of this model in python, we used OpenMM and CUDA acceleration. To minimize the energy Langevin dynamics were used, in temperature of $T_{L}=310 K$, friction coefficient $\gamma = 0.05 psec^{-1}$ and time step $t_{s}=100 fsec$. Note that the temperature of molecular dynamics simulation is independent from the temperature of stochastic simulation and they represent different physical realities.

In general the user can run simulation in two different ways:

1. Energy minimization (EM): It means that for each sample of cohesin positions $C_{t_i}=(m_j(t_i),n_j(t_i))$ start from a different initial structure (usually 3D random walk) and we apply the forcefield. For each structure we start from a different initial condition. In general, it is suggested to run the model in this way because it is faster, less prone to errors and the structures are not correlated to each other.
2. Molecular Dynamics (MD): In this case we have only one initial structure, we minimize the energy according to the forcefield only once and then we run a molecular dynamics simulation over time. This creates a continuous trajectory of structures, and it is cool for visualization pruposes. It is also biophysically more correct, in the sense that loop extrusion should be time-dependent, and the structure at time $t_i$ has to me correlated with structures at time $t_{i\pm1}$. It is a little bit more prone to error, and you may need to change the simulation frequence and step in case of instability (smaller frequency and more steps to stabilize it).

Installation

Can be easily installed with pip install pyLoopSage. To have CUDA acceleration, it is needed to have cuda-toolkit installed in case that you use nvidia drivers (otherwise you can use OpenCL or parallelization across CPU cores).

🐳 Running LoopSage with Docker

To use LoopSage in a fully containerized and reproducible way, you can build and run it using Docker. This is a very efficient way when you want to use CUDA.

Step 1: Build the Docker Image

Clone the repository and build the image:

docker build -t pyloopsage-cuda .

The Dockerfile can be found in the GitHub repo of pyLoopSage.

Step 2: Run the Simulation

Use the following command to run your simulation:

docker run --rm -it --gpus all \
-v "$PWD/config.ini:/app/config.ini:ro" \
-v "$PWD/tmp:/app/output" \
-v "$HOME/Data:/home/blackpianocat/Data:ro" \
pyloopsage-cuda \
python -m loopsage.run -c /app/config.ini

What this does:

• --rm: Automatically removes the container after it finishes.
• --gpus all: It detects the gpus of the system.
• -it: Runs with an interactive terminal.
• -v "$PWD/config.ini:/app/config.ini:ro": Mounts your local config.ini as read-only inside the container.
• -v "$PWD/tmp:/app/output": Maps the tmp/ directory for outputs.
• -v "$HOME/Data:/home/blackpianocat/Data:ro": Mounts your full data directory so LoopSage can access input files.
• The final command runs LoopSage with your config file.

You do not need to manually stop or clean up anything—the container is temporary and self-destructs after it completes. The image (pyloopsage-cuda) remains available on your system and can be deleted anytime using:

docker rmi pyloopsage-cuda
docker system prune -a

Note: Install nvidia-container-toolkit in your system if you want to use the container with CUDA: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

How to use?

Python Implementation

The main script is stochastic_simulation.py. However, the implementation of the code is very easy and it can be described in the following lines,

# Definition of Monte Carlo parametersimportloopsage.stochastic_simulationaslpsN_steps, MC_step, burnin, T, T_min=int(4e4), int(5e2), 1000, 2.5, 1.0mode='Metropolis'# Simulation Strengthsf, b, kappa=1.0, 1.0, 1.0# Definition of regionregion, chrom= [15550000,16850000], 'chr6'# Definition of dataoutput_name='../HiChIP_Annealing_T1_MD_region'interaction_file='/home/skorsak/Data/HiChIP/Maps/hg00731_smc1_maps_2.bedpe'sim=lps.StochasticSimulation(region,chrom,interaction_file,out_dir=output_name,N_beads=1000)
Es, Ms, Ns, Bs, Ks, Fs, ufs=sim.run_energy_minimization(N_steps,MC_step,burnin,T,T_min,mode=mode,viz=True,save=True)
sim.run_EM('CUDA')

Behind the scenes, the class still walks through the same steps as before: it sets the main simulation parameters (N_beads, N_coh, kappa, f, b, or sensible defaults if you'd rather not specify them, though it's worth double-checking they suit your system), along with the Monte Carlo parameters (N_steps, MC_step, burnin, T), before initializing LoopSage(). Calling sim.run_energy_minimization() then launches the stochastic Monte Carlo simulation, which yields a set of cohesin constraints (Ms, Ns) — generated in one of two modes, Annealing or Metropolis. These constraints are passed on to the molecular simulation stage, where either EM_LE() or MD_LE() turns them into a trajectory of 3D structures and an average contact heatmap. MD_LE() goes a step further and produces an actual trajectory together with a .dcd video of the simulation evolving over time, though it's memory-hungry — a bond has to be defined at every timestep, which can make it impractical for larger systems. For those cases, EM_LE() is the better choice, since it achieves the same goal without the heavy memory footprint.

Input data

Firstly, we need to define the input files from which LoopSage would take the information to construct the potential. We define also the specific region that we would like to model. Therefore, in the code script above we define an interaction_file from which information about the CTCF loops or CTCF binding sites is imported. The format is auto-detected from the file extension, and three alternatives are supported: .bedpe, .bed, and .narrowPeak.

1. .bedpe (paired loop anchors)

This is the richest format, since each line already describes a candidate loop between two anchors. The .bedpe file must be in the following format:

chr1	903917	906857	chr1	937535	939471	16	3.2197903072213415e-05	0.9431392038374097
chr1	979970	987923	chr1	1000339	1005916	56	0.00010385804708107556	0.9755733944997329
chr1	980444	982098	chr1	1063024	1065328	12	0.15405319074060866	0.999801529750033
chr1	981076	985322	chr1	1030933	1034105	36	9.916593137693526e-05	0.01617512105347667
chr1	982171	985182	chr1	990837	995510	27	2.7536240913152036e-05	0.5549511180231224
chr1	982867	987410	chr1	1061124	1066833	71	1.105408615726611e-05	0.9995462969421808
chr1	983923	985322	chr1	1017610	1019841	11	1.7716275555648395e-06	0.10890923034907056
chr1	984250	986141	chr1	1013038	1015474	14	1.7716282101935205e-06	0.025665007111095667
chr1	990949	994698	chr1	1001076	1003483	34	0.5386388489931403	0.9942742844900859
chr1	991375	993240	chr1	1062647	1064919	15	1.0	0.9997541297643132

where the last two columns represent the probabilities for the left and right anchor respectively to be tandem right. If the probability is negative it means that no CTCF motif was detected in this anchor.

Alternativelly, it is possible to import a .bedpe file without the last two columns (CTCF orientation). In this case, CTCF would act as an orientation independent barrier. This might affect slightly the results, but it is an easier option, if you do not want to run a CTCF motif finding script.

2. .bed (single CTCF sites, e.g. ChIP-seq intervals)

If you only have unpaired CTCF binding intervals rather than loop calls, you can pass a .bed file instead. LoopSage will still build orientation-aware L/R binding vectors from it, but since there is no anchor-pairing information in a .bed file, no loop-adjacency matrix (J) can be constructed - CTCF sites contribute individually rather than as loop anchors. Expected columns: chrom, start, end, name, score, strand, optionally followed by a probability column (probability the site's best motif hit is reverse-oriented) and an orientation call (>, <, or .).

3. .narrowPeak (single CTCF peaks, e.g. MACS2/HiChIP peaks)

Similarly, a .narrowPeak file of individual peaks can be used in place of a .bedpe/.bed file, with the same caveat: no J loop matrix is built, only L/R. Expected columns: chrom, start, end, name, score, optionally followed by a forward-orientation probability column and an orientation call.

Extracting CTCF motif orientation

To generate the orientation/probability columns for any of the three formats above (.bedpe, .bed, or .narrowPeak), use the motif-finding scripts from BlackPianoCat/motif_finder:

• motif_finder_bedpe.py for .bedpe loop files
• bed_motif_finder.py for .bed interval files
• motif_finder_peaks.py for .narrowPeak peak files

Run the appropriate script with --prob to get the probability columns shown above (e.g. python motif_finder_bedpe.py -i loops.bedpe -g genome.fa -m CTCF.pfm -o loops_with_motif.bedpe --prob). See that repo's README for full usage details on each script.

You also need to download the reference genome from: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa

Running LoopSage from command-line

Running LoopSage from the command line comes down to a single command.

loopsage -c config.ini

This command runs the model using the parameters specified in a config.ini file. Here's an example of what that file might look like:

[Main]
; Input Data and Information
INTERACTION_FILE = /home/skorsak/Data/HiChIP/Maps/hg00731_smc1_maps_2.bedpe
REGION_START = 15550000
REGION_END = 16850000
CHROM = chr6
OUT_PATH = ../HiChIP_Annealing_T15_MD_region
; Simulation Parameters
N_BEADS = 1000
N_STEPS = 40000
MC_STEP = 500
BURNIN = 1000
T_INIT = 1.5
T_FINAL = 0.01
METHOD = Metropolis
; Molecular Dynamics
PLATFORM = CUDA
INITIAL_STRUCTURE_TYPE = rw
SIMULATION_TYPE = EM TOLERANCE = 1.0

Visualization with PyVista

There are many tools for visualization of polymer structures. A very good one is UCSF chimera: https://www.cgl.ucsf.edu/chimera/. Usually these visualization tools work well for proteins, but we can use them for chromatin as well.

LoopSage offers its own visualization which relies in the pyvista python library. To visualize a structure, you can run some very simple commands, which call LoopSage functions,

importloopsage.vizualization_toolsasvzimportloopsage.utilsasutsV=uts.get_coordinates_cif('/home/skorsak/Projects/mine/LoopSage/HiChIP_Annealing_T15_EM_region/ensemble/EMLE_1.cif')
vz.viz_structure(V)

The output should be something like that,

In case that you would like to create a continuous video from the enseble of structures, you can use the following command, which would generate an interactive video in gif format which would show how the structure changes in 3D. The command includes quaternion Kabsch aligment as well.

importloopsage.vizualization_toolsasvzvz.interactive_plot('/home/skorsak/Projects/mine/LoopSage/HiChIP_Annealing_T15_EM_region/ensemble')

Output Files

In the output files, simulation produces one folder with 4 subfolders. In subfolder plots, you can find plots that are the diagnostics of the algorithm. One of the most basic results you should see is the trajectories of cohesins (LEFs). this diagram should look like that,

In this diagram, each LEF is represented by a different colour. In case of Simulated Annealing, LEFs should shape shorter loops in the first simulation steps, since they have higher kinetic energy due to the high temperature, and very stable large loops in the final steps if the final temperature $T_f$ is low enough. Horizontal lines represent the presence of CTCF points. In case of Metropolis, the distribution of LEFs should look invariant in respect to computational time,

Good cohesin trajectory diagrams should be like the ones previously shown, which means that we do not want to see many unoccupied (white) regions, but we also do not like to see static loops. If the loops are static then it is better to choose higher temperature, or bigger number of LEFs. If the loops are too small, maybe it is better to choose smaller temperature.

Now, to reassure that our algorithm works well we need to observe some fundamental diagnostics of Monte Carlo algorithms. Some other important diagnostics can be seen in the following picture,

In graph A, we can see the plot of the energy as a function of simulation time. In Metropolis after some steps, the simulation should reach equillibrium after the defined burnin period (blue vertical line). In case of Simulated Annealing, the energy should decrease as function of time because the temperature decreases, and thus the energy should not be conserved. Autocorrelations (B), show us if the thermodyncamic ensembles that we obtained are autocorrelated or not. The Monte Carlo step (sampling frequency) should be big enough so as to have small autocorrelations (<0.5). The averaged heatmap (C), shows the simulated heatmap, after averaging inversed all-versus-all distances of the region of interest. Finally, (D) shows the Pearson correlation between projected 1D signal of heatmap of experimental and simulated data.

In the output folder there are another three subfolders:

• ensembles has the ensembles of 3D structures in .cif format (it can open with vizualization software Chimera: https://www.cgl.ucsf.edu/chimera/) or pyvista,
• heatmaps with the inversed all-versus-all distance heatmap of each one of these structures.
• other here are some numpy arrays and some computed statistics. Numpy arrays like Ms and Ns have the degrees of freedoms of LEFs over time, then Fs, Ks, Es they have folding, corssing energy and total energy over time. Ts is the temperature over time. And finally in other.txt you can see the statistics of the simulation and the input parameters. In correlations.txt you can find a file with Pearson, Spearmann and Kendau tau correlations between estimated and experimental data. We provide an optimistic simulations where zeros of the signal are taken into account, and a pessimistic one where the zeros are not taken into account.

An example, illustrated with Chimera software, simulated trajectory of structures after running Simulated Annealing and molecular dynamics.

Long-table of LoopSage arguments

General Settings

Argument	Description	Type	Default
PLATFORM	Simulation platform: CPU, CUDA, or OpenCL	str	CPU
DEVICE	CUDA/OpenCL device index (from 0)	str	None
OUT_PATH	Output folder path	str	../results
SAVE_MDT	Save simulation metadata	bool	True

Input Data

Argument	Description	Type	Default
INTERACTION_FILE	Loop/peak file (.bedpe, .bed, or .narrowPeak). Required.	str	None
LEF_TRACK_FILE	bigWig track biasing LEF loading toward enriched regions	str	None
BW_FILES	bigWig file(s) acting as extrusion barriers	list	None
SMOOTHING_INPUT	Smooth noisy/sparse loop-peak signal before use	bool	False
COMP_BW_FILE	bigWig track for A/B compartment bias	str	None
COMP_BED_FILE	BED file for A/B compartment bias (alt. to COMP_BW_FILE)	str	None
DATA_LOSS_MODE	0 = 1D binding potentials (L/R); 1 = 2D loss interpolated from input loops	int	0
CONTRASTIVE_BINDING	If True, penalizes loop-free regions so extrusion is confined within TADs; if False, extrusion is uniform everywhere	bool	False
REGION_START	Region start coordinate	int	None
REGION_END	Region end coordinate	int	None
CHROM	Chromosome of the modeled region	str	None
GENOME	Reference genome for chromosome sizes (e.g. hg38)	str	hg38
FLOAT_LIST	Extra custom numeric settings	list	None
STRING_LIST	Extra custom text settings	list	None

Stochastic Simulation Parameters

Argument	Description	Type	Default
N_BEADS	Number of simulation beads	int	None
N_STEPS	Monte Carlo steps	int	40000
MC_STEP	Sampling frequency (reduces autocorrelation)	int	200
BURNIN	Steps discarded before equilibrium	int	1000
T_INIT	Initial temperature	float	2.0
T_FINAL	Final temperature	float	1.0
METHOD	'Metropolis' or 'Annealing'	str	'Annealing'
LEF_RW	Cohesins slide as random walk (vs. one direction)	bool	True
LEF_DRIFT	LEFs bounce back off each other	bool	False
N_LEF	Number of loop extrusion factors	int	None
N_LEF2	Number of second-family LEFs	int	0
CROSS_LOOP	Penalize mi<mj<ni<nj crossings (vs. only mj=ni); pair with LEF_DRIFT if False	bool	True
BETWEEN_FAMILIES_PENALTY	Penalize crossings between LEF families	bool	True

Energy Coefficients

Argument Name	Description	Type	Default Value
FOLDING_COEFF	Folding coefficient.	float	1.0
FOLDING_COEFF2	Folding coefficient for the second family of LEFs.	float	0.0
CROSS_COEFF	LEF crossing coefficient.	float	1.0
BIND_COEFF	CTCF binding coefficient.	float	1.0
BW_STRENGTHS	List of strengths of the energy (floats) corresponding to each BW file. This equivalent to the r parameter in the LoopSage paper.	list	None

Molecular Dynamics Simulation

Argument Name	Description	Type	Default Value
INITIAL_STRUCTURE_TYPE	Choose from: rw, confined_rw, self_avoiding_rw, helix, circle, spiral, sphere.	str	rw
INTEGRATOR_STEP	Step of the integrator.	Quantity	100 femtosecond
FORCEFIELD_PATH	Path to XML file with forcefield.	str	default_xml_path
ANGLE_FF_STRENGTH	Angle force strength.	float	200.0
LE_FF_LENGTH	Equilibrium distance of loop forces.	float	0.1
LE_FF_STRENGTH	Interaction Strength of loop forces.	float	50000.0
CONTINUOUS_TOP	True if the topological constraints are applied continuously during the simulation.	bool	False
EV_P	Probability that excluded volume is disabled. Enable it only in case of topoisomerase activity simulation.	float	0.0
EV_FF_STRENGTH	Excluded-volume strength.	float	100.0
EV_FF_POWER	Excluded-volume power.	float	3.0
FRICTION	Friction coefficient of the Langevin integrator.	float	0.1
TOLERANCE	Stopping condition for energy minimization.	float	0.001
SIM_TEMP	Temperature of the 3D simulation (EM or MD).	Quantity	310 kelvin
SIM_STEP	Amount of simulation steps for loop force adjustments.	int	2000

Visualization

Argument Name	Description	Type	Default Value
VIZ_HEATS	True to visualize the output average heatmap.	bool	True
SAVE_PLOTS	True to save diagnostic plots.	bool	True

Citation

Please cite the method and biological paper in case that you would like to use this model for your work,

• Korsak, Sevastianos, and Dariusz Plewczynski. "LoopSage: An energy-based Monte Carlo approach for the loop extrusion modelling of chromatin." Methods (2024).
• Jodkowska, K., Parteka-Tojek, Z., Agarwal, A., Denkiewicz, M., Korsak, S., Chiliński, M., Banecki, K., & Plewczynski, D. (2024). Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes. In bioRxiv (p. 2024.05.16.594268). https://doi.org/10.1101/2024.05.16.594268