You will need to install iCount first. Please, follow these instructions.

iCount provides all commands needed to process FASTQ files with iCLIP sequencing data and generate BED files listing identified and quantified cross-linked sites.

iCount uses and generates a number of files. We suggest you run this tutorial in an empty folder:

$ mkdir tutorial_example
$ cd tutorial_example


All steps provided in this tutorial are included in the tutorial.sh script, which you can obtain by running:

$ iCount examples
$ ls examples

hnRNPC.sh  hnRNPC_reduced.sh  tutorial.sh

Preparing a genome index

iCLIP sequencing reads must be mapped to a reference genome. The user can prepare its own FASTA genome sequence and GTF genome annotation files.

Another option is to download a release from ensembl. You can use the command releases to get a list of available releases supported by iCount:

$ iCount releases --source ensembl

There are 30 releases available: 88,87,86,85,84,83,82,81,80,79,78,77,76,75,74,73,

You can then use the command species to get a list of species available in a release:

$ iCount species --source ensembl -r 88

There are 87 species available: ailuropoda_melanoleuca,anas_platyrhynchos,


Current version of iCount is tested to work with human and rat genomes only.

Let’s download the human genome sequence from release 88:

$ iCount genome --source ensembl homo_sapiens -r 88 --chromosomes 21 MT

Downloading FASTA file into: /..././homo_sapiens.88.chr21_MT.fa.gz
Fai file saved to : /..././iCount/homo_sapiens.88.chr21_MT.fa.gz.fai


Processing the entire genome is computationally very expensive. For this reason, we are limiting the tutorial example to chromosomes 21 and MT.

And the annotation of the human genome from release 88:

$ iCount annotation --source ensembl homo_sapiens -r 88

Downloading GTF to: /..././homo_sapiens.88.gtf.gz

The next step is to generate a genome index that is used by STAR mapper. Let’s call the index hs88 and use ensembl’s GTF annotation on genes:

$ mkdir hs88  # folder should be empty
$ iCount indexstar homo_sapiens.88.chr21_MT.fa.gz hs88 \
--annotation homo_sapiens.88.gtf.gz --genome_sasparsed 2 --genome_saindexnbases 13

Building genome index with STAR for genome homo_sapiens.88.fa.gz
<timestamp> ..... Started STAR run
<timestamp> ... Starting to generate Genome files
<timestamp> ... starting to sort  Suffix Array. This may take a long time...
<timestamp> ... sorting Suffix Array chunks and saving them to disk...
<timestamp> ... loading chunks from disk, packing SA...
<timestamp> ... Finished generating suffix array
<timestamp> ... starting to generate Suffix Array index...
<timestamp> ..... Processing annotations GTF
<timestamp> ..... Inserting junctions into the genome indices
<timestamp> ... writing Genome to disk ...
<timestamp> ... writing Suffix Array to disk ...
<timestamp> ... writing SAindex to disk
<timestamp> ..... Finished successfully


A subfolder hs88 will be created in current working directory. You can specify alternative relative or absolute paths, e.g., indexes/hs88.


Changing the parameters genome_sasparsed and genome_saindexnbases results into lower memory requirements but longer run times.

We are now ready to start mapping iCLIP data to the human genome!

Preparing iCLIP data for mapping

Let’s process one of the hnRNP C sequencing data files from the original iCLIP publication:

$ wget http://icount.fri.uni-lj.si/data/20101116_LUjh03/\
SLX-2605.CRIRUN_501.s_4.sequence.reduced.txt.gz -O hnRNPC.fq.gz


In the tutorial, we are using a subset of the file [23 MB]. If you want to use the entire file, then download it:

$ wget http://icount.fri.uni-lj.si/data/20101116_LUjh03/\
SLX-2605.CRIRUN_501.s_4.sequence.txt.gz -O hnRNPC.fq.gz

This is a single file that contains five iCLIP experiments. Each experiment is marked with a unique barcode sequence at the very beginning of the sequencing reads. Part of the barcode are also so-called randomer nucleotides that are used to identify unique cDNA molecules after mapping.

We can extract the sample assignment and randomer sequence with the command demultiplex. The command expects the adapter sequence AGATCGGAAGAGCGGTTCAG, followed by the sample barcodes, in our case five, expected to be present in the sequencing file:

$ mkdir demultiplexed  # make sure that folder exists
NNNCAATNN NNNACCTNN NNNGGCGNN --out_dir "demultiplexed"

Allowing max 1 mismatches in barcodes.
Demultiplexing file: hnRNPC.fq.gz
Saving results to:
Trimming adapters (discarding shorter than 15)...


Position of a randomer nucleotide in barcode is indicated with the letter N.

This should have generated six files in subfolder demultiplexed:

$ ls -lh demultiplexed

total 37424
-rw-r--r-- 1 <user> <group>  758K <timestamp> demux_NNNACCTNN.fastq.gz
-rw-r--r-- 1 <user> <group>  2.9M <timestamp> demux_NNNCAATNN.fastq.gz
-rw-r--r-- 1 <user> <group>  8.0M <timestamp> demux_NNNGGCGNN.fastq.gz
-rw-r--r-- 1 <user> <group>  421K <timestamp> demux_NNNGGTTNN.fastq.gz
-rw-r--r-- 1 <user> <group>  4.8M <timestamp> demux_NNNTTGTNN.fastq.gz
-rw-r--r-- 1 <user> <group>  1.4M <timestamp> demux_nomatch.fastq.gz


Reads that cannot be assigned to any of the specified sample barcodes (for the given number of allowed mismatches) are stored in a separate file named demux_nomatch.fastq.gz. You should have a look at such reads and try to understand why they do not conform to expectations.

Mapping sample reads to the genome

Let’s focus on iCLIP experiment with barcode NNNGGCGNN and process it further. Same steps should be taken to process each experiment.

First, create a folder to store the mapping results:

$ mkdir mapping_NNNGGCGNN

Then, map the reads in the selected FASTQ file using STAR and the genome index we have generated at the very beginning of this tutorial:

$ iCount mapstar demultiplexed/demux_NNNGGCGNN.fastq.gz hs88 mapping_NNNGGCGNN \
--annotation homo_sapiens.88.gtf.gz

Mapping reads from demultiplexed/demux_NNNGGCGNN.fastq.gz
<timestamp> ..... Started STAR run
<timestamp> ..... Loading genome
<timestamp> ..... Processing annotations GTF
<timestamp> ..... Inserting junctions into the genome indices
<timestamp> ..... Started mapping
<timestamp> ..... Started sorting BAM
<timestamp> ..... Finished successfully

This should have generated a file Aligned.sortedByCoord.out.bam in folder mapping_NNNGGCGNN:

$ ls -lh mapping_NNNGGCGNN

total 842M
-rw-r--r-- 1 <user> <group>   15M Nov 15 05:28 Aligned.sortedByCoord.out.bam
-rw-r--r-- 1 <user> <group>  1.6K Nov 15 05:28 Log.final.out
-rw-r--r-- 1 <user> <group>   15K Nov 15 05:28 Log.out
-rw-r--r-- 1 <user> <group>  364B Nov 15 05:28 Log.progress.out
-rw-r--r-- 1 <user> <group>   51K Nov 15 05:28 SJ.out.tab

Quantifying cross-linked sites

Command xlsites reads a BAM file and generates a BED file with identified and quantified cross-linked sites:

$ iCount xlsites mapping_NNNGGCGNN/Aligned.sortedByCoord.out.bam \
NNNGGCGNN_cDNA_unique.bed  NNNGGCGNN_cDNA_multiple.bed NNNGGCGNN_cDNA_skipped.bam \
--group_by start --quant cDNA

This will generate a BED file where interaction strength is measured by the number of unique cDNA molecules (randomer barcodes are used for this quantification).

You may generate a BED files where interaction strength is determined by the number of reads:

$ iCount xlsites mapping_NNNGGCGNN/Aligned.sortedByCoord.out.bam \
NNNGGCGNN_reads_unique.bed  NNNGGCGNN_reads_multiple.bed NNNGGCGNN_reads_skipped.bam \
--group_by start --quant reads

By comparing the ration of cDNA vs reads counts we can estimate the level of over-amplification. Ideally, this ratio should be close to one.

Identifying significantly cross-linked sites

The peak finding analysis expects an annotation file with information about the segmentation of the genome into regions of different types, such as intergenic, UTR3, UTR5, ncRNA, intron, CDS regions.

Command segment can read the annotation obtained from ensembl and generate a new annotation file with genome segmentation:

$ iCount segment homo_sapiens.88.gtf.gz hs88seg.gtf.gz \

Calculating intergenic regions...
Segmentation stored in hs88seg.gtf.gz

Command peaks reads a genome segmentation GTF file, a BED file with cross-linked sites and generates a BED file with subset of significantly cross-linked sites:

$ iCount peaks hs88seg.gtf.gz NNNGGCGNN_cDNA_unique.bed peaks.bed \
--scores scores.tsv

Loading annotation file...
874 out of 31150 annotation records will be used (30276 skipped).
Loading cross-links file...
Calculating intersection between annotation and cross-link file...
Processing intersections...
Peaks caculation finished. Writing results to files...
BED6 file with significant peaks saved to: peaks.bed
Scores for each cross-linked position saved to: scores.tsv


P-value and FDR scores of all cross-linked sites can be stored by providing the parameter --scores.

Identifying clusters of significantly cross-linked sites

Command clusters reads a BED file with cross-linked sites and generates a BED file with clusters of cross-linked sites:

$ iCount clusters peaks.bed clusters.bed

Merging cross links form file peaks.bed
Done. Results saved to: clusters.bed

Annotating sites and summary statistics

Command clusters reads genome segmentation GTF file, a BED file with cross-linked sites and generates a file, where each site is annotated. By default it will annotate according to the biotype:

$ iCount annotate hs88seg.gtf.gz NNNGGCGNN_cDNA_unique.bed annotated_sites_biotype.tab

Calculating overlaps between cross-link and annotation_file...
Writing results to file...
Done. Output saved to: annotated_sites_biotype.tab

You can specify other attributes from annotation to use. For example, we can determine, which genes are annotated to each site:

$ iCount annotate --subtype gene_id hs88seg.gtf.gz NNNGGCGNN_cDNA_unique.bed \

Calculating overlaps between cross-link and annotation_file...
Writing results to file...
Done. Output saved to: annotated_sites_genes.tab

A summary of annotations can be generated with the command summary:

$ iCount summary hs88seg.gtf.gz NNNGGCGNN_cDNA_unique.bed summary.tab \

Calculating intersection between cross-link and annotation...
Extracting summary from data...
Done. Results saved to: summary.tab