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RELICS ✨: Regulatory Element Location Identification in CRISPR screens

RELICS is an analysis method for discovering functional sequences from tiling CRISPR screens. The current version (v.2.0) of RELICS uses a Bayesian hierarchical model and considers the overlapping effects of multiple guides, can jointly analyze multiple pools per replicate, and estimates the number of functional sequences supported by the data.

Briefly; RELICS splits the region of interest into segments. It then iteratively places one functional sequence at a time, while considering all previously placed functional sequences. RELICS is a semi-supervised method and takes a set of positive control sgRNAs as input. Note that RELICS currently does not use non-targeting sgRNAs and only analyzes one chromosome at a time. We are working on several extensions and if you have any requests, please feel free to reach out to us!

This work is continuously being improved. Please ask questions or post issues.


RELICS requires R version 3.5.1 or higher

Obtain source code

Clone source code to your desired location with the following command: git clone Alternatively, download the repository.

Install requirements

You will need the following packages to run RELICS. If you don't have them, install them using the following commands. Installations will take about 5 minutes on a standard laptop.

R packages

ggplot2 (for plotting)


gridExtra (for combining multiple plots in one figure)


poibin (for calculating the Poisson-Binomial)


extraDistr (for calculating the Dirichlet-Multinomial)


gtools (for calculating combinations)


Bioconductor packages

if (!requireNamespace("BiocManager", quietly = TRUE))


IRanges (for handling genomic coordinates)


GenomicRanges (for handling genomic coordinates)


Input data format

RELICS requires an input file containing the sgRNA targets and the corresponding counts in the different pools. The required columns must have the following information: chromosome, sgRNA target start, sgRNA target end, sgRNA label, ...(sgRNA counts in different pools)...

The columns specifying chromosome, guide target start, guide target end and label are mandatory and must be labelled chrom, start, end, and label respectively.

For the columns containing sgRNA counts, names are necessary but the names do not matter as the user will index the columns by number, not by name.

Below is part of the example file in the RELICS_tutorial folder. It's a subset from the CD69 CRSIPRa screen by Simeonov et al.. It contains 2 replicates from a FACS experiment. Input pools (back) were sorted into no CD69 expression (baseline), low, medium and high expression.

chrom label start end CD69back_1 CD69back_2 CD69baseline_1 CD69baseline_2 CD69low_1 CD69low_2 CD69medium_1 CD69medium_2 CD69high_1 CD69high_2
chr12 chr 9913351 9913371 788 926 3492 968 2349 1087 355 923 110 1023
chr12 chr 9913413 9913433 2656 3361 4779 1579 8695 3036 3177 7693 730 9960
chr12 chr 9913414 9913434 1099 1089 2102 565 3705 1172 1054 2669 265 4727
chr12 CD69_promoter 9913429 9913449 504 412 2185 238 580 445 103 570 49 342

Quickstart with example data in RELICS_tutorial

Create a directory for the output files. In this case we recommend creating a folder in the tutorial file: CD69_tutorial_output

After that, navigate to the RELICS_tutorial folder. In an interactive R session:


1. Source the script


# if you moved into the RELICS_tutorial folder:
# source('../Code/RELICS.v2.R')

2. Set up the analysis specification file

Several parameters must be specified by the user before running RELICS. The tutorial walkthrough below describes the most important parameters required to get the analysis going. In the process you will analyze a subset of the CD69 CRISPRa screen from Simeonov et al..

Option 1: Modify the given template in the RELICS_tutorial folder (Example_analysis_specifications.txt)

There is a template specification file already set up for the example data. It contains the main flags required to run RELICS. The meaning of the different flags are discussed in the next section.

Option 2: Set the flags within R

It is also possible to set the parameters directly within R. The following steps demonstrate how to set up parameters for the example specification file. At the end of this section, you will be able to run RELICS on the example data.

  1. Flags are set up in a list object
relics.parameters <- list()
  1. Set the output name of the analysis (dataName). Be sure to choose a different name from the existing file so that you don't overwrite the example, and you can compare and check that you got the same flags.
relics.parameters$dataName <- 'CD69_tutorial_analysis'
  1. Specify the data file.
relics.parameters$DataInputFileLoc <- './Example_data/CD69_data_example.csv'
  1. RELICS uses a Dirichlet-Multinomial and jointly analyzes all pools from each replicate. In the specification file the replicates are separated by a semicolon (;) and referred to by column position in the data file. Within R, the replicates are each an element within a list()

Example: 5,7,9,11,13;6,8,10,12,14 represents two replicates. In this example, the pools of each replicate are altering.

Note 1: The input type is a string, such as the example shown above.

Note 2: Analysis across multiple replicates has not been implemented yet, so jointly analyzing all pools (5,7,9,11,13,6,8,10,12,14) is not advised!

repl.pools <- list()
repl.pools[[1]] <- c(5, 7, 9, 11, 13)
repl.pools[[2]] <- c(6, 8, 10, 12, 14)

relics.parameters$repl_groups <- repl.pools
  1. The label column assigns each sgRNA to a category. These categories are used in the beginning to specify the training sets. By default a set of sgRNAs overlapping known functional sequences (FSs) have to be provided to the FS0_label flag. In this case these are guides overlapping the CD69 promoter. All other sgRNAs are used to train the background parameters. The counts of the sgRNAs overlapping each FS detected are iteratively added to the set of counts used to determine the Dirichlet parameters of the FS.

As an option, it is also possible to specify the sgRNA labels to be used as the background. This is done with the background_label flag.

In both cases the flags are given either as string or as vector of strings.

relics.parameters$FS0_label <- 'CD69_promoter' # use all sgRNAs that overlap the CD69 promoter are used to initially train the FS parameters

# option: specify the background parameters
# relics.parameters$background_label <- c('chr', 'exon') # specify what sgRNAs to use to initially train the background parameters
  1. Specify the number of functional sequences to look for and the number of functional sequences you expect to find. RELICS will look for a total of max_fs_nr functional sequences and eight their signal according to the prior which is specified by the expected_fs_nr. We recommned setting the max_fs_nr to at least expected_fs_nr + 3. For larger expected_fs_nr we recommend setting max_fs_nr to 4/3*expected_fs_nr.
# specify the expected number of functional sequences and how many to look for in total
relics.parameters$max_fs_nr <- 15
relics.parameters$expected_fs_nr <- 5 # expected based on previous findings by Simeonov et al., 2017 and Fiaux et al., 2020
  1. Specify the CRISPR system used. Depending on the CRISPR system used, the area of effect (AoE) is different. By default RELICS assumes that the AoE follows a normal distribution. For more details see section Area of Effect below.
relics.parameters$crisprSystem <- 'CRISPRa' # other options: CRISPRcas9, CRISPRi, dualCRISPR
  1. Give the location of the output directory by setting the out_dir flag. Either reference to full path or the path from the current working directory. In this example we will do the latter and assume you are in the RELICS_tutorial folder. We recommend you create a new file in which the results are saved. Note, RELICS will NOT create non-existent files for you. In this example, first create the CD69_tutorial_output folder, then set the flag:
relics.parameters$out_dir <- 'CD69_tutorial_output'
  1. RELICS now explicityl models the count-dispersion relationship. This drastically imporves performance and helps reduce the number of false positives. See the section Count-Dispersion modeling below for details on how to best estimate nr_disp_bins and repl_spline_df:
# specify the number of bins to group the guide counts into and the degrees of freedom of the spline function for each replicate
relics.parameters$nr_disp_bins <- 15
relics.parameters$repl_spline_df <- list(repl_1 = 3, repl_2 = 3)


Once you have set up your parameters you can run RELICS by directly giving it the list we set up above, or by first saving it to a .txt file. In the latter case, the flags and their values should be separated by a colon (:, see Example_analysis_specifications.txt). The CD69 example provided should take about 10 minutes to run on a typical desktop computer. RELICS will look for 15 functional sequences as specified above and report the results in the finalFS_k15 files.

RELICS(input.parameter.list = relics.parameters)

# option: use the parameter file instead
# RELICS('Example_analysis_specifications.txt')

4. Output files

RELICS will return several output files. They all start with the dataName specified in step 2 above. All output files are one-indexed except for the .bedgraph and .bed files which are zero-indexed. By default, RELICS will give you the genome segments that were used, as well as the files associated with finding the last functional sequence before convergence:

  • {dataName}_segmentInfo.csv: This file contains the segments used by RELICS. It contains the information of chromosome, start and end location of the segment, as well as the label of the segment.
Column name Column description
chrom chromosome of the region
start region start
end region end
label highest overlapping label according to the label hierarchy
  • {dataName}_countsVSdispersion_repl: The plot of the count-dispersion modeling. It is recommended to check these plots and make sure that the dispersion for each replicate has been well modeled.

  • {dataName}_FS0_seg_llRt.bedgraph: Bedgarph file of the log-likelihood ratio of each segment containing a functional sequence. This can be a helpfuls representation of the data as it can indicate the number of potential functional sequences. However, a high signal doe not necessarily mean RELICS will detect a functional sequence because the information used to generate this track is reduced and does not correspond to the full RELICS analysis.

  • {dataName}_FS0_guideLLR.csv: Contains the log-likeohood ration of each guide overlapping a functional sequence. This can be helpful when sleecting guides for validation.

In all subsequent file names, the pattern _kX_ refers to X functional sequences detected.

  • {dataName}_finalFS_kX_summaryStatPlots.bedgraph: Plots of the model log-likelihood progression as more FS are detected as well as the per-FS contribution to the model. The top row is adjusted to account for the prior, the bottowm row contains the raw lieklihoods.

  • {dataName}_final_kX_cs_pp.bedgraph: This file contains the sum of the credible sets of posterior probabilities across all functional sequences detected.

  • {dataName}_finalFS_kX_FS_locations.bed: This file contains all genome segments part of the functional sequences detected.

  • {dataName}_finalFS_kX_cs_pp.csv: This file contains the credible set functional sequence probabilities of all functional sequences detected. Each column corresponds to a genome segment, ordered as in {dataName}_segmentInfo.csv. Each row corresponds to the functional sequence probabilities of a particular functional sequence. The first row corresponds to FS0, the second to FS1 etc.

  • {dataName}_finalFS_kX_model_lls.csv: This file contains the per-functional sequence log-likelihood contribution to the model improvement as well as the model lig-likelihood progression. The raw likelihoods are also given, which have not yet been weighted by the prior given for the number of FS.

Column name Column description
FS the functional sequence
fs_ll_contrib contribution of each FS to the model
raw_fs_ll_contrib raw contribution of each FS to the model
model_ll model log-likelihood progression
raw_model_ll raw model log-likelihood progression
cs_raw_pp sum of the credible set posterior probability
  • {dataName}_finalFS_kX_alphas.csv: This file contains the dirichlet sorting parameters for the background (alpha0) and for functional sequences (alpha1) for all replicates.

  • {dataName}_finalFS_kX_disp.csv: This file contains the per-guide assigned dispersions.

Count-dispersion modeling

RELICS explicitly models the relationship between guide counts and their dispersion (variance). It has been observed that the dispersion of biological count data changes with increasing count size (see Fig. 1 in Love, Huber, Anders., 2014). We use a spline function to account for the wide range of possible count-dispersion relationships. To estimate the correct spline function we first sort and bin the guides according to their total per-replicate counts. Using differen degrees of freedom (number of points that a spline can use to fit to the data) we compute the the best fit for the number of bins followed by calculating the dispersion for each individual guide. Based on the plotted output the best fit can then be used to the RELICS analysis.

The spline parameters have to be estimated for each replicate. While usually there isn't much difference between replicates we have observed cases where it's beneficial to use different degrees of freedom across different replciates.

We proivde a function to help users determine their correct settings. spline_fitting takes the same parameters as the main analysis function (RELICS) so you'll only have to set up the input list once. By default spline_fitting will return the fit to 3, 5, 10 and 15 degrees of freedom for a given bin size. We generally recommend bin sizes around 20 to 100. Increasing the number of bins can help in cases where some bins have very high counts.

First, source the spline modling script and the RELICS code.


# if you moved into the RELICS_tutorial folder:
# source('../Code/estimate_spline_fitting.R')
# use the same parameter list used for the RELICS analysis
analysis.specs$nr_disp_bins <- 15 
spline_fitting(input.parameter.list = analysis.specs, repl = 1) # plot the splie fits for replicate 1

CD69 example spline fitting

In the example above it seems having 3 degrees of freedom with 15 bins leads to the best result. The spline does not overfit to the data. In general, if two splines fit approximately equelly well it's better to pick the one with less degrees of freedom to avoid overfitting. Note that here we've plotted the 1/dispersion values. This helps with the resolution to choose the best fit. If you are interested in seeing what the final dispersion values look like you can set the plot.true.disp flag to TRUE.

# use the same parameter list used for the RELICS analysis
spline_fitting(input.parameter.list = relics.parameters,repl = 1, plot.true.disp = TRUE)

CD69 example spline fitting_trueDisp

It is also possible to disable the dispersion modeling. Set the mean-variance type to independent and disbale the disperison modeling.

relics.parameters$mean_var_type <- 'independent'
relics.parameters$model_dispersion <- FALSE

Advanced flags

Name the pools

In the output files it can be convenient to have the pools named.

# specify the names of the different pools. Helps with naming hyperparameters in the output files
all.repl.names <- list()
all.repl.names[[1]] <- c("back", "baseline", "low", "medium", "high")
all.repl.names[[2]] <- c("back", "baseline", "low", "medium", "high")
relics.parameters$pool_names <- all.repl.names

Specify guide efficiency

RELICS performs better if guide efficeincies are provided. They can be given in a separate .csv file as long as the order is the same as in the input data. An easier option is to directly add them to the input file and specify the column within

relics.parameters$guide_efficiency_loc <- relics.parameters$DataInputFileLoc
relics.parameters$guide_efficiency_cols <- c(21)

As additional output file RELICS will give the computed guide-efficeincy coefficient used in the logistic model. By default RELICS will recompute these parameters after each FS placement. This can help in the case of noisy guide scores, which will allow RELICS to downweight their importance on the results. However, if you trust your guide efficeincy scores than you can also fix them to their input values.

relics.parameters$fixed_ge_coeff <- TRUE

Recording additional information

By default RELICS only returns the files from section 4. However, it's possible to get additional info such as the raw posterior probabilities and raw credible sets. All these results have not been weighted by the prior hence they are still raw.

relics.parameters$record_pp <- TRUE # returns .csv with all posterior probabilities for each FS and bedgraph of combined prosteriors
relics.parameters$record_cs_pp <- TRUE # returns .csv with all credible set posterior probabilities for each FS and bedgraph of combined credible set prosteriors

Record output at each iteration

By default, RELICS only outputs files once it has discovered all functional sequences. However, it is possible to get the same output for every functional sequence detected. This can be useful when data sets take a long time to run. The intermediate files can already reveal where the first set of functional sequences are located while RELICS still searches for more. To record all intermediate files set the record.all.fs to TRUE when running RELICS.

RELICS(input.parameter.list = relics.parameters, record.all.fs = TRUE)

Set the label hierarchy

RELICS combines information of guides which overlap with their guide effect. This can lead to scenarios where guides with different labels overlap. By default the label with fewer occurrences in the data set is chosen. However, it is also possible for the user to specify the hierarchy by explicitly setting the labelHierarchy flag.

The rightmost label has highest priority. Using the example below: if a region has overlapping guides labeled as both promoter overlapping (CD69_promoter) as well as targeting guides with unknown effect (chr), then the region will be assigned the label with higher priority in the hierarchy - in this case being CD69_promoter.

If specifying the labelHierarchy, all labels should be provided. Guides with labels that were not included will not be properly used the analysis. In this case this means also specifying the exon label for guides overlapping CD69 exons.

relics.parameters$labelHierarchy <- c('chr', 'exon', 'CD69_promoter')

Compute the likelihood that an FS spans multiple segments

By default, RELICS considers functional sequences to be up to 10 genome segments. In the case of 100bp segments that would correspond to 1kb. It is possible to either increase or decrease this functional sequence length by adjusting the nr_segs flag. Note, by increasing the number, RELICS' runtime will increase as it will consider all possible functional sequence sizes from 1 up to nr_segs.

relics.parameters$nr_segs <- 10 # default is 10

RELICS uses a truncated geometric distribution for modeling the probability of a functional sequence of length x. By default RELICS uses p = 0.1. To adjust that, for either being more or less restrictive for having longer functional sequences, use the geom_p flag.

relics.parameters$geom_p <- 0.1 # default is 0.1

Modify stopping criteria

Usually RELICS discovers the specified max_fs_nr functional sequences. However, it is possible that the data does not contain as many functional sequences. In this case RELICS will terminate early. Specifically, RELICS will terminate if the largest credible set sum is below min_cs_sum (default: 0.01). This can be adjusted by changing the threshold

relics.parameters$min_cs_sum <- 0.01

Modify credible set parameters

RELICS by default computes a relative 90% credible set across 10 genome segments. This can be adjusted with cs_threshold and cs_sw_size. Restricting the number of segments considered in a credible set can lead to narrower credible sets while changing the threshold adjusts the number of segments that are included.

relics.parameters$cs_threshold <- 0.9
relics.parameters$cs_sw_size <- 10

Modify segment length

By default RELICS segments the data into ~100bp segments. This can be adjusted with the seg_dist flag

relics.parameters$seg_dist <- 100 # default is 100

Recomputing hyperparameters

By default RELICS computes the hyperparameters based on the guides labelled as FS0 and then keeps the hyperparameters the same throughout the rest of the analysis. However, it's also possible to recompute the hyperparameters after each placement of a functional sequence. This could help in a case where the signal at FS0 is a lot stronger than all other functional sequences.

relics.parameters$fix_hypers <- FALSE

Changing the Are of Effect (AoE)

By default RELICS assumes a normal AoE. This means that base pairs further away from a guide target site are less likely to be perturbed. By default we assume that for Cas9 only half the cell containing a guide have a perturbation more than 10bp away from the target site. For CRISPRi and CRISPRa that we assume that this is the case for 200bp away from the target site. In both cases this is modeled using a normal distribution with adjusted standard deviations. We also specify a max. range beyond which it's unlikely that a base pair is affected crisprEffectRange.

# for a Cas9 system
relics.parameters$normal_areaOfEffect_sd <- 8.5
relics.parameters$crisprEffectRange <- 21

# for a CRISPRi or CRISPRa system
relics.parameters$normal_areaOfEffect_sd <- 170 
relics.parameters$crisprEffectRange <- 415

It is also possible to nor model the AoE and assume a uniform parturbation instead where it's equally likely for any base pair to be perturbed

relics.parameters$areaOfEffect_type <- 'uniform'

Input data format contd. (for backward compatibility)

Instead of providing one joint file containing both coordinates and counts it is also possible to supply them separately. In this case the format is the following:

  1. A guide information file, containing information about all the simulated guides (chromosome, start, end, label).
  2. A counts file, containing the counts for each guide in each pool.

The counts file contains only the counts for each guide for each experiment. Column names are necessary but the names do not matter as the user will index the columns by number, not by name.

Example count file: 2 replicates from a FACS experiment. Input pools was sorted into high, medium and low expression

repl1_input repl1_high repl1_med repl1_low repl2_input repl2_high repl2_med repl2_low
11 9 12 11 152 119 189 102
68 81 39 67 360 339 280 821
96 89 109 17 3 4 5 0
104 97 116 38 190 198 194 23

The guide information file contains all remaining info about the guides such as targeting position and type of guide (positive control, negative control, exon targeting etc.). Non-targeting controls should be specified by setting chrom, start, and end to NA. The columns specifying chromosome, guide target start, guide target end and label are mandatory and must be labelled chrom, start, end, and label respectively.

chrom start end label
chr8 128704468 128704488 chr
chr8 128704469 128704489 chr
NA NA NA neg
chr8 128704482 128704502 exon

Row 1 in the count file should correspond to the guide in row 1 in the info file. If the two files are given then that has to be specified accordingly in the input

relics.parameters$CountFileLoc <- 'location/of/count/file'
relics.parameters$sgRNAInfoFileLoc <- 'location/of/info/file'
RELICS(input.parameter.list = relics.parameters, data.file.split = TRUE)


Regulatory Element Location Identification in CRISPR screens







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