pipeline of analyzing Ribo-nano

---

---

0.Preparation

packages install

• Python version Python 3.8.18 and above

• recommand using conda to install all packages

  • conda create -n Ribo_nano_env python >= 3.8
  • conda activate Ribo_nano_env
  • conda install -c bioconda -c conda-forge matplotlib scipy numpy pandas pysam seaborn cycler bowtie cutadapt hdf5 htseq jedi samtools bedtools star subread gffread sh adjusttext bzip2 pigz six tqdm svg-stack

• This command will create a environment: Ribo_nano_env

• FLEPSeq2 environment is required https://github.com/ZhaiLab-SUSTech/flep_seq2_polya_analysis

---

1 Nanopore bascalling and obtain the treanscript termination intermediates

1.1 Basecalling

guppy_basecaller -i {fast5_dir} -s {pass_fast5_dir} -c {model} --recursive --fast5_out --disable_pings --qscore_filtering --device {params_cuda}

• {fast5_dir}: directory of raw fast5

• {pass_fast5_dir}: directory of output fast5

• {model}: type of sample (DNA or RNA) experimental apparatus ,reagents. ex. dna_r9.4.1_450bps_hac.cfg

• {params_cuda}: GPU and cuda version. ex. "cuda:all:100%"

• tips: Parameters may be different for different versions of guppy, so please pay attention to modify them.

1.2 Transfer fastq to fasta

python fastqdir2fasta.py --indir {fastq_dir} --out {fasta_file}

• {fastq_dir}: directory of fastq, generated by bascalling.
• {fasta_file}: output fasta file, recommand to store this in new a directory.

1.3 Mapping process

minimap2 -t {threads} -ax splice --secondary=no -G 12000 {genome} {fasta_file} -o {mediate_sam}
samtools view -@ {threads} -F 2308 -hb {mediate_sam} {mediate_bam}
samtools sort -@ {threads} -O bam -o {sort_bam} {mediate_bam}
samtools index -@ {threads} {sort_bam}

• {threads}: The number of threads

• {genome}: The genome of fasta format

• tips: Parameters may be different for different versions of samtools. The sorted bam will generate, and you can delete the sam file after checking that the output file is correct.

1.4 Find 3'linker

python adapterFinder.py --inbam {input_bam} --inseq {fasta_file} --out {adapter} --threads {threads} --mode 1

• {adapter}: File contain the adapter's relative location and other infromation on coressponding read.

1.5 Identify and estimate the poly(A) tail

python PolyACaller.py --inadapter {adapter} --summary {sequencing_summary}  --fast5dir {pass_fast5_dir} --out {polyA_tail} --threads {threads}

• {sequencing_summary}: Sequencing summary file is generated by basecalling step.
• {polyA_tail}: Output result of poly(A) tail result.

1.6 Extract read information

python extract_read_info.py --inbed {bed} --inbam {sort_bam} --out {read_info}

• {read_info}: Extraction of information based on read and relative position of exons and introns on the genome.

---

2. Analyzing Ribo-nano (monosome~polysome n+) by jupyterlab

• run main1_jupyterlab_code.ipynb in the jupyterlab enviroment

---

3. Analyzing Ribo-seq profiling by jupyterlab

• run main2_jupyterlab_code.ipynb in the jupyterlab enviroment

---

Detailed information about this project

• URL: url
  • Please cite this article when using this software