KAS-Analyzer

A new computational framework for exploring KAS-seq data

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Here we introduce KAS-Analyzer, a newly designed, comprehensive methodological framework aimed at simplifying the analysis and interpretation of KAS-seq data. In addition to standard analyses, KAS-Analyzer offers many novel features specifically tailored for KAS-seq data, including, but not limited to: calculation of termination length and index, identification of single-stranded transcribing (SST) enhancers, high-resolution mapping of R-loops, and time-course differential RNA polymerase activity analysis.

KAS-seq is a kethoxal-assisted single-stranded DNA sequencing (KAS-seq) approach, based on the fast and specific reaction between N3-kethoxal and guanines in ssDNA. KAS-seq allows rapid (within 5 min), sensitive and genome-wide capture and mapping of ssDNA produced by transcriptionally active RNA polymerases or other processes in situ using as few as 1,000 cells. KAS-seq can also enable definition of a group of enhancers that are single-stranded. Overall, KAS-seq facilitates fast and accurate analysis of transcription dynamics and enhancer activities in both low-input and high-throughput manner. Strand-specific KAS-seq (spKAS-seq) was further developped to identify in vivo genome-wide R-loops with strand specificity using as few as 50,000 cells by mapping the exposed single-stranded DNA (ssDNA) caused by RNA-DNA duplex R-loop formation.

KAS-seq:

Strand-specific KAS-seq (spKAS-seq):

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Citation:

1. Tong, Wu, Ruitu Lyu, Qiancheng You, and Chuan He. Kethoxal-assisted single-stranded DNA sequencing captures global transcription dynamics and enhancer activity in situ. Nature methods 17, no. 5 (2020): 515-523.

2. Ruitu, Lyu, Tong Wu, Allen C. Zhu, Diana C. West-Szymanski, Xiaocheng Weng, Mengjie Chen, and Chuan He. KAS-seq: genome-wide sequencing of single-stranded DNA by N3-kethoxal–assisted labeling. Nature protocols (2022): 1-19.

3. Wu, Tong, Ruitu Lyu, and Chuan He. spKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity. Science Advances 8.48 (2022): eabq2166.

4. Lyu, Ruitu, Tong Wu, Gayoung Park, Yu-Ying He, Mengjie Chen, and Chuan He. KAS-Analyzer: a novel computational framework for exploring KAS-seq data. Bioinformatics Advances 3, no. 1 (2023): vbad121.

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Documentation:

Our documentation contains more details on the individual command line tool and usages

Please see also the FAQ, which we update regularly.

If you have any questions about the KAS-Analyzer, please post your questions to github discussions.

For more specific troubleshooting, feedback, and suggestions, please post to github issue or send emails to the authors: lvruitu@gmail.com.

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Installation

Install by cloning KAS-Analyzer git repository on github:

You can install KAS-Analyzer using command line (linux) by cloning git repository on github:

git clone https://github.com/Ruitulyu/KAS-Analyzer.git
cd KAS-Analyzer
bash ./setup.sh
source ~/.bashrc
	
# If anaconda or miniconda was not installed on your system. #OPTIONAL.
KAS-Analyzer install -conda

# Make sure you have Mamba installed. If not, you can install it in your base Conda environment with the following command:
conda install mamba -c conda-forge

# Install 'KAS-Analyzer' environment using mamba. 
mamba env create -f ./environment.yml

# Decompress the genomic annotation files.
find ./ -type f -name '*.xz' -exec xz -d {} +

# or
KAS-Analyzer install -KAS-Analyzer

# Activate conda 'KAS-Analyzer' environment.
conda activate KAS-Analyzer

# Deactivate conda 'KAS-Analyzer' environment.
conda deactivate

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Quick start

Download test data

Users can download example KAS-seq data in HEK293T cells from Gene Expression Omnibus (GEO):

Note: install sra-tools using "conda install -c bioconda sra-tools" if fastq-dump is not available in your system.

wget https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR10349532/SRR10349532 ./ &
wget https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR10349533/SRR10349533 ./ &
wget https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR10349534/SRR10349534 ./ &
wget https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR10349535/SRR10349535 ./ &

mv SRR10349532 HEK293T_KAS-Input.rep1.sra
mv SRR10349533 HEK293T_KAS-Input.rep2.sra
mv SRR10349534 HEK293T_KAS-seq.rep1.sra
mv SRR10349535 HEK293T_KAS-seq.rep2.sra
        
fastq-dump HEK293T_KAS-Input.rep1.sra & 
fastq-dump HEK293T_KAS-Input.rep2.sra &
fastq-dump HEK293T_KAS-seq.rep1.sra &
fastq-dump HEK293T_KAS-seq.rep2.sra &	

Trimming of adapter and poor quality sequence

Note: the "nohup" command is optional, which means "no hang up" and writes the screen output to "nohup.out" file.
nohup KAS-Analyzer trim -a illumina -t 10 -1 HEK293T_KAS-Input.rep1.fastq.gz &
nohup KAS-Analyzer trim -a illumina -t 10 -1 HEK293T_KAS-Input.rep2.fastq.gz &
nohup KAS-Analyzer trim -a illumina -t 10 -1 HEK293T_KAS-seq.rep1.fastq.gz &
nohup KAS-Analyzer trim -a illumina -t 10 -1 HEK293T_KAS-seq.rep2.fastq.gz &

Read alignment of KAS-seq and Input control data

nohup KAS-Analyzer KAS-seq -t 10 -i /absolute path/hg19_Bowtie2Index/hg19 -o HEK293T_KAS-Input.rep1 -s hg19 -1 HEK293T_KAS-Input.rep1_trimmed.fq.gz &
nohup KAS-Analyzer KAS-seq -t 10 -i /absolute path/hg19_Bowtie2Index/hg19 -o HEK293T_KAS-Input.rep2 -s hg19 -1 HEK293T_KAS-Input.rep2_trimmed.fq.gz &
nohup KAS-Analyzer KAS-seq -t 10 -i /absolute path/hg19_Bowtie2Index/hg19 -o HEK293T_KAS-seq.rep1 -s hg19 -1 HEK293T_KAS-seq.rep1_trimmed.fq.gz &
nohup KAS-Analyzer KAS-seq -t 10 -i /absolute path/hg19_Bowtie2Index/hg19 -o HEK293T_KAS-seq.rep2 -s hg19 -1 HEK293T_KAS-seq.rep2_trimmed.fq.gz &

Here, the user can generate a read alignment summary report:

# Run from "Summary" directory.
KAS-Analyzer statistics -o HEK293T_KAS-seq_statistics -s /absolute path/Summary/ &

Example summary report:

Samples	Raw reads	Clean reads	Mapped reads	Deduplicated reads	Mapping ratios	Duplication ratios
KAS-seq.rep1	39,249,037	39,194,455	38,608,066	31,874,927	98.50%	17.44%
KAS-seq.rep2	37,235,447	37,195,072	36,623,472	30,659,014	98.46%	16.29%
Input.rep1	45,182,939	45,162,826	44,306,192	41,261,883	98.10%	6.87%
Input.rep2	39,911,067	39,886,186	39,065,103	36,158,950	97.94%	7.44%

KAS-seq peaks calling

Call merged KAS-seq peaks with two KAS-seq replicates:

# Run from "Bed_files" directory.	 
nohup KAS-Analyzer peakscalling -t HEK293T_KAS-seq.rep1.ext150.bed,HEK293T_KAS-seq.rep2.ext150.bed -c HEK293T_KAS-Input.rep1.ext150.bed,HEK293T_KAS-Input.rep2.ext150.bed -o HEK293T_KAS-seq -g hg19 &

Call KAS-seq peaks with KAS-seq data individually:

# Run from "Bed_files" directory. 
nohup KAS-Analyzer peakscalling -t HEK293T_KAS-seq.rep1.ext150.bed -c HEK293T_KAS-Input.rep1.ext150.bed -o HEK293T_KAS-seq.rep1 -g hg19 &
nohup KAS-Analyzer peakscalling -t HEK293T_KAS-seq.rep2.ext150.bed -c HEK293T_KAS-Input.rep2.ext150.bed -o HEK293T_KAS-seq.rep2 -g hg19 &

Quality control

Fingerprint plot of KAS-seq data:

# Run from "Bam_files" directory.
nohup KAS-Analyzer fingerprint -t 10 -s hg19 -o HEK293T_KAS-seq_fingerprint -l labels.txt -k KAS-seq_data.txt &

Example "labels.txt" and "KAS-seq_data.txt" :

KAS-seq.rep1                            HEK293T_KAS-seq.rep1_rmdup.bam             
KAS-seq.rep2                            HEK293T_KAS-seq.rep2_rmdup.bam
Input.rep1                              HEK293T_Input.rep1_rmdup.bam
Input.rep2       ---labels.txt          HEK293T_Input.rep2_rmdup.bam        ---KAS-seq_data.txt

Example fingerprint plot:

Fraction of reads in peaks (FRiP) scores:

nohup KAS-Analyzer FRiP -o HEK293T_KAS-seq_FRiP -p peaks_files.txt -l labels.txt -k KAS-seq.txt &

Example "peaks_files.txt", "labels.txt", and "KAS-seq.txt" :

HEK293T_KAS-seq.rep1_peaks.broadPeak                                 KAS-seq.rep1                            HEK293T_KAS-seq.rep1.ext150.bed
HEK293T_KAS-seq.rep2_peaks.broadPeak     ---peaks_files.txt          KAS-seq.rep2     ---labels.txt          HEK293T_KAS-seq.rep2.ext150.bed     ---KAS-seq.txt

Example fraction of reads in peaks (FRiP) scores:

Calculate library complexity metrics for KAS-seq data, including PCR Bottlenecking Coefficient and Non-Redundant Fraction (NRF):

# Run from "Bam_files" directory.        
nohup KAS-Analyzer complexity -o HEK293T_KAS-seq_complexity -l labels.txt -k KAS-seq.txt &

Example "labels.txt" and "KAS-seq.txt" :

KAS-seq.rep1                              HEK293T_KAS-seq.rep1_sorted.bam
KAS-seq.rep2      ---labels.txt           HEK293T_KAS-seq.rep2_sorted.bam      ---KAS-seq.txt

Example library complexity metrics of KAS-seq data in HEK293T cells:

Samples	PBC	Bottlenecking level	NRF	Complexity
KAS-seq.rep1	0.88	None	0.84	Ideal
KAS-seq.rep2	0.89	None	0.85	Ideal
Input.rep1	0.96	None	0.95	Ideal
Input.rep2	0.95	None	0.95	Ideal

Calculate the correlation coefficient and pvalue, generate scatterplot for replicates of KAS-seq data:

# Run from "Bam_files" directory.
nohup KAS-Analyzer correlation -m pearson -t 10 -s hg19 -r bin -p heatmap -o KAS-seq -l labels.txt -k KAS-seq.txt &

Example "labels.txt" and "KAS-seq.txt" :

KAS-seq.rep1                             HEK293T_KAS-seq.rep1.bam
KAS-seq.rep2     ---labels.txt           HEK293T_KAS-seq.rep2.bam     ---KAS-seq.txt

Example scatterplot between two replicates of KAS-seq data:

Generate KAS-seq read density files that can be viewed in the UCSC genome browser:

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer UCSC -k KAS-seq.txt -n UCSC_track_names.txt &

Example "KAS-seq.txt" and "UCSC_track_names.txt" :

HEK293T_KAS-seq.rep1.ext150.bg                             KAS-seq.rep1
HEK293T_KAS-seq.rep2.ext150.bg     ---KAS-seq.txt          KAS-seq.rep2     ---UCSC_track_names.txt

Example snapshot of KAS-seq data custom tracks from UCSC Genome Browser:

Normalization of KAS-seq data

Using the number of uniquely mapped reads:

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer normalize -m ratios -k HEK293T_KAS-seq_data.txt -r ratios.txt -b -s hg19 &

Example "HEK293T_KAS-seq_data.txt" and "ratios.txt" :

HEK293T_KAS-seq.rep1.ext150.bg                                           1.2
HEK293T_KAS-seq.rep1.ext150.bg      ---HEK293T_KAS-seq_data.txt          1.4       ---ratios.txt

Using Reads Per Kilobase per Million mapped reads (RPKM):

# Run from "Bam_files" directory.
nohup KAS-Analyzer normalize -m RPKM -k HEK293T_KAS-seq_data.txt -b -s hg19 &

Example "HEK293T_KAS-seq_data.txt":
Note: the bam files need to be deduplicated.

HEK293T_KAS-seq.rep1.rmdup.bam
HEK293T_KAS-seq.rep2.rmdup.bam      ---HEK293T_KAS-seq_data.txt

Generate summary plots of KAS-seq signals

Generate metagene profile of KAS-seq read density on gene coding regions (TSS, genebody or TES):

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer profile -t 10 -s hg19 -o HEK293T_KAS-seq_profile -r genebody -c red,blue,green,purple -l labels.txt -k KAS-seq.normalized.bigWig.txt &

Generate heatmap of KAS-seq read density on gene coding regions (TSS, genebody or TES):

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer heatmap -t 10 -s hg19 -o HEK293T_KAS-seq_heatmap -r genebody -q -c Reds -l labels.txt -k KAS-seq.normalized.bigWig.txt &

Example heatmap and metagene profile of KAS-seq signals on gene coding regions:

Calculate transcription-related metrics

Calculate pausing index (PI) of RNA Pol II:

# Run from "Bam_files" directory.
nohup KAS-Analyzer index -o HEK293T_pausing_index -t 10 -s hg19 -i pausing -l labels.txt -k KAS-seq.txt &

Calculate elongation index (EI) of RNA Pol II:

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer KASindex -o HEK293T_elongation_index -t 10 -s hg19 -r gene -l labels.txt -k KAS-seq.txt &

Calculate termination index (TI) of RNA Pol II:

# Run from "Bam_files" directory.
nohup KAS-Analyzer index -o HEK293T_termination_index -t 10 -s hg19 -i termination -l labels.txt -k KAS-seq.txt &

Calculate termination length (TL) of RNA Pol II:

# Run from "Bam_files" directory.
nohup KAS-Analyzer termilength -o HEK293T_termination_length -t 10 -g hg19 -p peaks.txt -l labels.txt -k KAS-seq.txt &

Identify single-stranded transcribing enhancers (SST enhancers)

# Run from "BedGraph_files" directory.
nohup KAS-Analyzer SST_enhancer -o HEK293T_SST_enhancers -t 10 -s hg19 -e H3K27ac_enhancers.bed -p HEK293T_KAS-seq_peaks.broadPeak -k HEK293T_KAS-seq.rep1.ext150.nor.bigWig,HEK293T_KAS-seq.rep1.ext150.nor.bigWig &

For more details on the above tools, and other features implemented in KAS-Analyzer, such as "identification of genome-wide R-loops" (only for spKAS-seq) and "differential RNA Pols activity analysis" (two-conditions or time-course KAS-seq data), please check the tutorial.

This tool suite is developed by the Dr. Ruitu Lyu at Prof. Chuan He's lab of the University of Chicago.

Documentation | FAQ