FLEP-seq (full-length elongating and polyadenylated RNA sequencing) allows calculation of the kinetics of cotranscriptional splicing and detects polyadenylated transcripts with unspliced introns retained at specific positions posttranscriptionally.
This pipeline includes basecalling and preprocessing of the FLEP-seq data.
- Minimap2 (https://github.com/lh3/minimap2)
- SAMtools (http://www.htslib.org/)
- BLAST+ (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/) (require for Nanopore data analysis)
- Python 3.7 or above, and following packages:
- Pysam (https://github.com/pysam-developers/pysam)
- ont_fast5_api (https://github.com/nanoporetech/ont_fast5_api)
- pandas (https://pandas.pydata.org/)
- NumPy (https://numpy.org/)
- Matplotlib (https://matplotlib.org/)
- Joblib (https://github.com/joblib/joblib)
- click (https://click.palletsprojects.com/en/7.x/)
- R 3.5.2 or above, and following packages:
- Tidyverse (https://www.tidyverse.org/)
- optparse (https://cran.r-project.org/web/packages/optparse/index.html)
One-step installation
Install FLEP-seq pipeline, its dependencies, and other required packages in one step using conda or mamba:
mamba create -n flepseq minimap2 pysam click ont-fast5-api joblib r-tidyverse r-optparse samtools pandas
Below is the commands for FLEP-seq pipeline in one-step:
snakemake --profile lsf --use-conda
| File format | Information contained in file | File description |
|---|---|---|
| fast5 | nanopore raw signal | The standard sequencing output for Oxford Nanopore sequencers |
| File format | Information contained in file | File description |
|---|---|---|
| sequencing_summary.txt | basecalling summery file | Ooutput of Guppy basecaller |
| fastq | long-read fastq file | Read pass the quality threshold |
| *.read.info.txt | long-read fastq file | Generated by merge_read_info.R |
| *.read.splicing_kinetics.pdf | splicing kinetics | Generated by plot_intron_splicing_kinetics.R |
- Nanopore basecalling
You can use MinKNOW to perform real-time basecalling while sequencing, or use the GPU version of Guppy to speed up basecalling after sequencing. Both MinKNOW and Guppy are available via Nanopore community site (https://community.nanoporetech.com). Command-line for running Guppy basecalling is as follow:
$ guppy_basecaller -i raw_fast5_dir -s out_fast5_dir -c dna_r9.4.1_450bps_hac.cfg --recursive --fast5_out --disable_pings --qscore_filtering --device "cuda:all:100%"
- Convert FASTQ files to FASTA format
$ python fastqdir2fasta.py --indir path/to/fastq_pass --out all.fasta
- Use minimap2 to map reads to reference genome
$ minimap2 -ax splice --secondary=no genome.fasta all.fasta > tmp.sam
CAUTION: For the organisms with short introns, such as Arabidopsis, it might be better to use the parameter “-G” to set the max intron length, for example, “-G 12000”. You also can set “-t number_of_threads” to use more threads to speed up.
$ samtools sort -o mapped.bam tmp.sam
$ samtools index mapped.bam
$ rm tmp.sam
- (Optional) Remove rRNA and tRNA derived reads
$ python filter_rRNA_bam.py --inbam mapped.bam --inbed rRNAtRNAetc.bed --out clean.bam
$ samtools index clean.bam
- Find 3’ adapter in reads
$ python adapterFinder.py --inbam clean.bam --inseq all.fasta --out adapter.result.txt --threads 36
- Identify polyA tail and estimate its length
$ python PolyACaller.py --inadapter adapter.result.txt --summary sequencing_summary.txt --fast5dir fast5_dir --out polyA_tail.result.txt --threads 36
- Extract read information
This pipeline will produce a table containing intron retention information and Pol II position.
$ python extract_read_info.py --inbam clean.bam --inbed lib/exon_intron_pos.repr.bed --out read_info.result.txt
- Merge the above analysis results
$ Rscript merge_read_info.R --type Nanopore --inreadinfo read_info.result.txt --inadapter adapter.result.txt --inpolya polyA_tail.result.txt --out read.info.txt
- Analyze splicing kinetics
$ python prepare_data_for_splice_kinetics.py --inreadinfo read.info.txt --inbed lib/exon_intron_pos.repr.bed --out read.intron.pos.splicing.txt
$ Rscript plot_intron_splicing_kinetics.R --inrelpos read.intron.pos.splicing.txt --inreadinfo read.info.txt --inintron lib/select_introns.txt --out read.splicing_kinetics.txt --pdf read.splicing_kinetics.pdf
- Calculate intron retention ratio of polyadenylated transcripts
$ Rscript cal_polya_transcript_ir.R --inrelpos read.intron.pos.splicing.txt --inreadinfo read.info.txt --outrna mRNA.incompletely_spliced_ratio.txt --outintron intron.unspliced_ratio.txt
.
├── aligned_data
│ ├── flep_seq_demo.adapter.result.txt
│ ├── flep_seq_demo.polyA_tail.result.txt
│ ├── flep_seq_demo.read_info.result.txt
│ ├── flep_seq_demo.sorted.bam
│ └── flep_seq_demo.sorted.bam.bai
├── basecalled_data
│ └── flep_seq_demo.fasta
├── config.yml
├── genome_data
│ ├── exon_intron_pos.repr.bed
│ ├── rRNAtRNAetc.bed
│ └── select_introns.txt
├── guppy_out
├── pipeline.svg
├── README.md
├── requirements.txt
├── results
│ ├── flep_seq_demo.read.info.txt
│ ├── flep_seq_demo.read.intron.ir.stat
│ ├── flep_seq_demo.read.intron.pos.splicing.txt
│ ├── flep_seq_demo.read.rna.ir.stat
│ ├── flep_seq_demo.read.splicing_kinetics.pdf
│ └── flep_seq_demo.read.splicing_kinetics.txt
├── script
│ ├── adapterFinder.py
│ ├── ASCaller.py
│ ├── cal_polya_transcript_ir.R
│ ├── extract_read_info.generate_seq.py
│ ├── extract_read_info.py
│ ├── fastqdir2fasta.py
│ ├── filter_rRNA_bam.py
│ ├── lima_bam2fasta.py
│ ├── merge_read_info.R
│ ├── merge_sequencing_summary.py
│ ├── pacbio_find_polyA.py
│ ├── plot_intron_splicing_kinetics.R
│ ├── PolyACaller.py
│ ├── prepare_data_for_splice_kinetics.py
│ ├── run_guppy2.sh
│ ├── run_nanoplot.sh
│ ├── run_toulligqc.sh
│ └── split_files_into_dirs.py
└── snakefile
The demo data is available at figshare:
https://doi.org/10.6084/m9.figshare.20217632.v1
Long, Y., Jia, J., Mo, W. et al. FLEP-seq: simultaneous detection of RNA polymerase II position, splicing status, polyadenylation site and poly(A) tail length at genome-wide scale by single-molecule nascent RNA sequencing. Nat Protoc 16, 4355–4381 (2021). https://doi.org/10.1038/s41596-021-00581-7
Jia, J., Long, Y., Zhang, H. et al. Post-transcriptional splicing of nascent RNA contributes to widespread intron retention in plants. Nat. Plants 6, 780–788 (2020). https://doi.org/10.1038/s41477-020-0688-1