This document provides a general workflow and overview of the tools we have used to analyse Nanopore RNA-seq data in prokaryotes, including:

Basecalling and demultiplexing of raw FAST5 reads using guppy
Trimming of reads using pychopper, cutadapt & samclip
Mapping of reads to the genome using minimap2
Gene abundance estimation using salmon
Detection of transcript boundaries using termseq_peaks
Read coverage analysis using bedtools

You can also have a look at a protocol recently published in Methods in Molecular Biology outlining different steps of Nanopore RNA-seq analysis.

Library preparation

Libraries for Nanopore sequencing were prepared from poly(A)-tailed (and rRNA-depleted and/or TEX-treated) RNAs according to the protocols provided by Oxford Nanopore for direct sequencing of native RNAs (SQK-RNA001, SQK-RNA002), direct cDNA native barcoding (SQK-DCS109 with EXP-NBD104) and PCR-cDNA barcoding (SQK-PCB109) with minor modifications.

Sequencing

Sequencing of DRS, cDNA and cDNA-PCR libraries were sequenced on a MinION Mk1B or Mk1C using R.94 flow cells and the recommended scripts in MinKNOW to generate FAST5 files.

Note: Live-basecalling in fast mode was enabled to monitor translocation speed and quality during a run.

Data analysis

Data management

We managed our folders in the following way:

microbepore/
└── data/
    ├── raw_FAST5
    ├── basecalled
    ├── FASTQ
        ├── normal
        ├── full_length
        ├── cutadapt
        └── cutadapt_SSP
    ├── summary
    ├── barcode
    ├── mapped
        ├── raw
        ├── adapter_trimmed
        └── trimmed
    ├── genome
    ├── salmon
    ├── pychopper
        ├── normal
        └── rescued
    ├── tss
        ├── raw
        └── trimmed
    ├── tts
        ├── raw
        └── trimmed
    ├── bed
    └── coverage_data
        ├── raw
        └── trimmed

Basecalling of raw reads using `guppy_basecaller`

After sequencing (and despite live-basecalling) all datasets in the raw_FAST5 📁 were re-basecalled using guppy (ont-guppy-for-mk1c v4.3.4) in high-accuracy mode (rna_r9.4.1_70bps_hac.cfg, dna_r9.4.1_450bps_hac.cfg) without quality filtering. The output files in FASTQ format were written to the basecalled 📂.

DRS & (PCR-)cDNA runs require different options.
Config file selection based on selected accuracy, flowcell version, library preparation kit are listed with guppy_basecaller --print_workflows

# files
input=microbepore/data/raw_FAST5/run_id # add run id
output_DRS=microbepore/data/FASTQ/normal/run_id # add run id
output_cDNA=microbepore/data/basecalled/run_id # add run id

# Basecalling of DRS files
guppy_basecaller \
--input_path ${input} \ # input path
--save_path ${output_DRS} \ # output path
-c rna_r9.4.1_70bps_hac.cfg  \ # config file: high accuracy RNA
--calib_detect \ # detect calibration spike-in
--reverse_sequence true \ # reverse since sequenced 3´-->5´
--u_substitution true \ # replace U´s with T´s
--compress_fastq \ # compress output
--fast5_out \ # output FAST5
--recursive \ # look for FAST5 recursively in path
--progress_stats_frequency 60 \ # output progress every minute
--chunks_per_runner 256 \ # options for Mk1C
--gpu_runners_per_device 4 \ # options for Mk1C
--num_callers 1 \ # options for Mk1C
-x auto # options for Mk1C

# Basecalling of cDNA files 
guppy_basecaller \
--input_path ${input} \
--save_path ${output_cDNA} \
-c dna_r9.4.1_450bps_hac.cfg \ # config file: high accuracy cDNA 
--compress_fastq \
--fast5_out \
--recursive \
--progress_stats_frequency 60 \
--chunks_per_runner 256 \
--gpu_runners_per_device 4 \
--num_callers 1 \
-x auto

With the selected options guppy produces fast5_pass, fast5_fail, fastq, summary and report files that are written to the FASTQ 📁. FASTQ are not grouped in pass and fail groups since --min_qscore is not enabled. Multiple FASTQs can be merged using cat microbepore/data/basecalled/run_id/*.fastq > microbepore/data/basecalled/run_id/run_id.fastq.

Sequencing summary files are also written to the FASTQ 📂 and are used during the quality control of the runs and reads. For better viewing they can be moved to the summary 📁 using mv microbepore/data/FASTQ/run_id/sequencing_summary.txt microbepore/data/summary/run_id.txt

Demultiplexing of basecalled reads using `guppy_barcoder`

Next, multiplexed cDNA libraries are demultiplexed in a separate step using guppy_barcoder.

# files
input=microbepore/data/basecalled/run_id # add run id
output=microbepore/data/FASTQ/normal/run_id # add run id

# Demultiplexing of (PCR-)cDNA files
guppy_barcoder \
--input_path ${input} \
--save_path ${output} \
--config configuration.cfg \
--barcode_kits SQK-PCB109 \
--progress_stats_frequency 60

Multiple FASTQs are written to the FASTQ 📁 and can be merged with e.g. cat microbepore/data/FASTQ/run_id/barcode01/*.fastq > microbepore/data/FASTQ/run_id/run_id_barcode01.fastq. Barcode summary files are written to the FASTQ 📂 and can be moved to the barcode 📂 for clarity using

Mapping of reads to the genome using `minimap2`

Files were mapped to the reference genome from Escherichia coli K-12 MG1655 (GenBank: U00096.3) using minimap2 (Release 2.18-r1015).
Genome FASTA and GFF3 files have been downloaded from GenBank. Output alignments in the SAM format were generated with -ax splice -k14 for Nanopore 2D cDNA-seq and -ax splice, -uf, -k14 for DRS with i) -p 0.99, to return primary and secondary mappings and ii) with --MD, to include the MD tag for calculating mapping identities. Alignment files were further converted to BAM files, sorted and indexed using [SAMtools(https://github.com/samtools/).
To analyse single reads in more detail with respect to the RNA type (mRNA, rRNA, other ncRNA, unspecified) they map to, BAM files were first converted back to FASTQ using bedtools v2.29.2. Next FASTQ files were remapped to a transcriptome file using minimap2 with the previously mentioned parameters to assign single read names with feature IDs. The transcript file was made using gffread with gffread microbepore/data/genome/NC_000913.3.gff -g microbepore/data/genome/NC_000913.3.fasta -w microbepore/data/genome/NC_000913.3.transcripts.fasta.

# files
input=microbepore/data/FASTQ/normal # input directory with all merged FASTQ files, 1 for each barcode or single DRS run
fasta=microbepore/data/genome/NC_000913.3.fasta # downloaded from GenBank
transcripts=microbepore/data/genomeNC_000913.3.transcripts.fasta # transcripts file made using gffread

# Mapping & Remapping - loop through all FASTQs
for file in ${input}/*/*.fastq
do
  
  # folder and filenames
  f_ex=${file##*/}
  foldername=$(echo ${f_ex} | cut -d"_" -f 1,2,3) # depending on how you name your files 
  filename=${f_ex%%.*}
  
  # make directories
  mkdir microbepore/data/mapped/raw # direct output to mapped folder for raw reads
  mkdir microbepore/data/mapped/raw/${foldername} # run_id
  output=microbepore/data/mapped/raw/${foldername}/${filename} # run_id/barcode_id
  mkdir ${output}

  if [[ $filename =~ "RNA" ]]; 
  then
  # align using minimap2
  minimap2 -ax splice -p 0.99 -uf -k14 --MD -t 8 ${fasta} ${file} > ${output}/${filename}.sam # DRS
  else
    minimap2 -ax splice -p 0.99 -k14 --MD -t 8 ${fasta} ${file} > ${output}/${filename}.sam # (PCR-)cDNA
  fi
 
  # convert to sorted.bam file
  samtools view -bS ${output}/${filename}.sam -o ${output}/${filename}.bam
  samtools sort ${output}/${filename}.bam -o ${output}/${filename}.sorted.bam
  samtools index ${output}/${filename}.sorted.bam
  
  # bam to fastq for remapping of mapped reads
  bedtools bamtofastq -i ${output}/${filename}.sorted.bam -fq ${output}/${filename}.remapped.fastq
  
  # map again
  if [[ $filename =~ "RNA" ]]; 
  then
  minimap2 -ax splice -p 0.99 -uf -k14 --MD -t 8 ${transcripts} ${output}/${filename}.remapped.fastq > ${output}/${filename}.remapped.sam
  else
    minimap2 -ax splice -p 0.99 -k14 --MD -t 8 ${transcripts} ${output}/${filename}.remapped.fastq > ${output}/${filename}.remapped.sam
 fi
 
  # convert to sorted.bam file
  samtools view -bS ${output}/${filename}.remapped.sam -o ${output}/${filename}.remapped.bam
  samtools sort ${output}/${filename}.remapped.bam -o ${output}/${filename}.remapped.sorted.bam
  samtools index ${output}/${filename}.remapped.sorted.bam

Gene abundance estimation using `salmon` in alignment-based mode

To estimate gene abundances salmon (v.1.4.0) was applied in alignment-based mode as described in https://salmon.readthedocs.io/en/latest/salmon.html#quantifying-in-alignment-based-mode. Transcripts per million (TPM) were re-calculated using the salmon-computed effective transcript length, after dropping reads mapping to rRNAs, that are variable between non-depleted and depleted RNA sets (compare custom Rscripts/salmon_analysis.R).

input=microbepore/data/mapped/raw # input directory with all remapped files

for file in ${input}/*/*/*remapped.sorted.bam
do
  
  # folder and filenames
  f_ex=${file##*/}
  foldername=$(echo ${f_ex} | cut -d"_" -f 1,2,3)
  filename=${f_ex%%.*}
  
  # create dir for quantification using salmon in alignment-based mode (e.g. used in conda environment)
  mkdir microbepore/data/salmon
  mkdir microbepore/data/salmon/${foldername}
  output=microbepore/data/salmon/${foldername}/${filename}
  mkdir ${output}
  
  conda activate salmon # activate conda environment
  
  # use conda in alignment-based mode
  salmon quant \
  -t ${transcripts} \
  -l A \
  -a ${file} \
  -o ${output} \
  --threads 8 
  
  conda deactivate
  
done

Trimming of reads using `pychopper`, `cutadapt` & `samclip`

Identification of full-length reads using `pychopper`

Full-length cDNA reads containing SSP and VNP primers in the correct orientation were identified using pychopper (v.2.5.0) with standard parameters using the default pHMM backend and autotuned cutoff parameters estimated from subsampled data. Save output in pychopper 📂.

# files
input=microbepore/data/FASTQ/normal # input directory with all merged FASTQ files, 1 for each barcode or single DRS run

# perform pychopper for all cDNA and (PCR)-cDNA files
for file in ${input}/*/*.fastq
do 

  # folder and filenames
  f_ex=${file##*/}
  foldername=$(echo $f_ex | cut -d"_" -f 1,2,3)
  filename=${f_ex%%.*}
  
  # make directories
  mkdir microbepore/data/pychopper/normal
  mkdir microbepore/data/pychopper/normal/${foldername}
  output=microbepore/data/pychopper/normal/${foldername}/${filename}
  mkdir ${output}

  # perform pychopper using precomputed q
  cdna_classifier.py \
  -r ${output}/${filename}_report.pdf \
  -t 8 \
  -u ${output}/${filename}_unclassified.fastq \
  -w ${output}/${filename}_rescued.fastq \
  -S ${output}/${filename}_stats.txt \
  $file \
  ${output}/${filename}_full_length_output.fastq
done

After a first round, a second round of pychopper was applied to the unclassified direct cDNA reads with DCS-specific read rescue enabled.

# files
input=microbepore/data/pychopper/normal # input directory with all merged FASTQ files, 1 for each barcode or single DRS run

# perform pychopper using the -x rescue option for DCS files
for file in ${input}/*unclassified.fastq # only use unclassified reads from first round as input
do 

  # folder and filenames
  filename_extended=${file##*/}
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
  
  # make directories
  mkdir ${dir}/data/pychopper/rescued
  mkdir ${dir}/data/pychopper/rescued/${foldername}
  output=microbepore/data/pychopper/rescued/${foldername}/${filename}
  mkdir ${output}
  
  # perfrom pychopper using -X option for native cDNA datasets
  cdna_classifier.py \
  -r ${output}/${filename}_report.pdf \
  -t 8 \
  -x rescue \
  -u ${output}/${filename}_unclassified.fastq \
  -w ${output}/${filename}_rescued.fastq \
  -S ${output}/${filename}_stats.txt \
  $file \
  ${output}/${filename}_full_length_output.fastq
done

Reads from rescued and normal folders were merged and used for subsequent steps.

# files
input=microbepore/data/pychopper/

# merge all full-length and rescued reads as full-length
for file in ${input}/normal/*/*/*full_length_output.fastq # both normal and rescued folders
do 
  filename_extended=${file##*/}
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=$(echo $filename_extended | cut -d"_" -f 1,2,3,4,5)

  keyword=$(echo $foldername | cut -d"_" -f 2) # get libary kit ID
  
  mkdir microbepore/data/FASTQ/full_length
  mkdir microbepore/data/FASTQ/full_length/${foldername}
  output=microbepore/data/FASTQ/full_length/${foldername}/${filename}
  mkdir ${output}
  
  if [[ $keyword =~ "PCB109" ]]; then
    cat $file ${input}/normal/${foldername}/${filename}/${filename}_rescued.fastq > ${output}/${filename}_full_length_all.fastq
  elif [[ $keyword =~ "DCS109" ]]; then
    cat $file ${input}/normal/${foldername}/${filename}/${filename}_rescued.fastq
    ${input}/rescued/${foldername}/${filename}_unclassified/${filename}_unclassified_full_length_output.fastq
    ${input}/rescued/${foldername}/${filename}_unclassified/${filename}_unclassified_rescued.fastq > ${output}/${filename}_full_length_all.fastq
  fi
done

For easier handling in the subsequent steps, DRS FASTQ files are also moved to the microbepore/data/FASTQ/full_length folder and adding *_full_length_all* to the filename.

Remove polyA-tails using `cutadapt`

To evaluate the influence of different trimming approaches on the accuracy of transcript boundary analysis, we applied additional 5´ and 3´ trimming steps using cutadapt v3.2.
To this end, polyA sequences were removed from the 3´ends:

# files
input=microbepore/data/FASTQ/full_length # input directory with all merged FASTQ files, 1 for each barcode or single DRS run

for file in ${input}/*/*/*_full_length_all.fastq
do 

  # folder and filenames
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
  
  mkdir microbepore/data/FASTQ/cutadapt
  mkdir microbepore/data/FASTQ/cutadapt/${foldername}
  output=microbepore/data/FASTQ/cutadapt/${foldername}/${filename}
  mkdir ${output}
  
  # cutadapt
  cutadapt \
    -a "A{10}" \ # trim polyAs longer than 10 bases from the 3´end
    -e 1 \ # allowed error rate
    -j 0 \ # auto-detect cores
    -o ${output}/${filename}.cutadapt.fastq \
    ${file}
done

Remove remaining SSP adapter using `cutadapt`

Remove remaining SSP sequences from the 5´ends of the cDNA reads using:

input=microbepore/data/FASTQ/cutadapt

# >  SSP adapter
for file in ${input}/*/*/*cutadapt.fastq
do 
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
  
  mkdir microbepore/data/FASTQ/cutadapt_SSP
  mkdir microbepore/data/FASTQ/cutadapt_SSP/${foldername}
  output=microbepore/data/FASTQ/cutadapt_SSP/${foldername}/${filename}
  mkdir ${output}

  cutadapt \
    -g "TTTCTGTTGGTGCTGATATTGCTGGG" \
    -e 1 \
    -j 0 \
    -o ${output}/${filename}.cutadapt_SSP.fastq \
    ${file}
done

Mapping of trimmed reads, removing clips using `samclip`

Finally, trimmed reads were mapped using minimap2 as described before. Reads with more than 10 clipped bases on either side were removed from the alignments using samclip (v.0.4.0).

Step: Align

input=microbepore/data/FASTQ/cutadapt_SSP
fasta=microbepore/data/genome/NC_000913.3.fasta # downloaded from GenBank

# map (pychopper) > polyA_trimmed > SSP trimmed fastqs
for file in ${input}/*/*/*fastq
do 
  filename_extended=${file##*/}
  foldername=$(echo ${filename_extended} | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}

  mkdir microbepore/data/mapped/adapter_trimmed
  mkdir microbepore/data/mapped/adapter_trimmed/${foldername}
  output=microbepore/data/mapped/adapter_trimmed/${foldername}/${filename}
  mkdir ${output}

  ## align using minimap2
  if [[ $filename =~ "RNA" ]]; 
  then
  # align using minimap2
  minimap2 -ax splice -p 0.99 -uf -k14 --MD -t 8 ${fasta} ${file} > ${output}/${filename}.sam
  else
    minimap2 -ax splice -p 0.99 -k14 --MD -t 8 ${fasta} ${file} > ${output}/${filename}.sam
  fi
done

Step: Remove clipping > 10 bases

input=microbepore/data/mapped/adapter_trimmed
fasta=microbepore/data/genome/NC_000913.3.fasta # downloaded from GenBank
transcripts=microbepore/data/genomeNC_000913.3.transcripts.fasta # transcripts file made using gffread

# remove reads with more than 10 bases that are clipped on either side. 
for file in ${input}/*/*/*.sam
do 
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
  
  if [[ $keyword =~ "sam" ]]; then
    echo ${foldername}
    echo ${filename}
    echo ${keyword}
    
    mkdir microbepore/data/mapped/trimmed
    mkdir microbepore/data/mapped/trimmed/${foldername}
    output=microbepore/data/mapped/trimmed/${foldername}/${filename}
    mkdir ${output}
  
    # remove mapped reads with a Maximum clip length to allow (10, 5 is default)
    samclip --max 10 --ref ${fasta} < ${file} > ${output}/${filename}.clipped.sam
    
    # convert to sorted.bam file
    samtools flagstat ${output}/${filename}.clipped.sam > ${output}/${filename}.clipped.stats.txt
    samtools view -bS ${output}/${filename}.clipped.sam -o ${output}/${filename}.clipped.bam
    samtools sort ${output}/${filename}.clipped.bam -o ${output}/${filename}.clipped.sorted.bam
    samtools index ${output}/${filename}.clipped.sorted.bam
    
    ## remap fastq converted reads
  bedtools bamtofastq -i ${output}/${filename}.clipped.sorted.bam -fq ${output}/${filename}.remapped.fastq
  
  ## map again
  if [[ $filename =~ "RNA" ]]; 
  then
  # align using minimap2
  minimap2 -ax splice -p 0.99 -uf -k14 --MD -t 8 ${transcripts} ${file} > ${output}/${filename}.remapped.sam
  else
    minimap2 -ax splice -p 0.99 -k14 --MD -t 8 ${transcripts} ${file} > ${output}/${filename}.remapped.sam
  fi
  
  # convert to sorted.bam file
  samtools view -bS ${output}/${filename}.remapped.sam -o ${output}/${filename}.remapped.bam
  samtools sort ${output}/${filename}.remapped.bam -o ${output}/${filename}.remapped.sorted.bam
  samtools index ${output}/${filename}.remapped.sorted.bam
  fi
done

Detection of transcript boundaries

The determination of enriched 5´and 3´ends was carried out in the same way, but independently of each other, and is briefly explained in the following: First, strand-specific read ends in bedgraph format were created from BAM files using bedtools genomecov (-5 or -3 option, -bga). Next, the previously published Termseq_peaks script was used to call peaks for each sample individually without including replicates (https://github.com/NICHD-BSPC/termseq-peaks). This script is based on scipy.signal.find_peaks, which is running in the background of Termseq_peaks with lenient parameters (prominence=(None,None), width=(1,None), rel_height=0.75). However, we deliberately used Termseq_peaks since its ability to include replicates by applying an Irreproducible Discovery Rate method which can be applied to future studies. For end detection, only the leniently called peaks in the narrowPeak file were used after adding the number of counts for each position using bedtools intersect.

5´end detection

5´end peak calling was performed in the following way:

input=microbepore/data/mapped

# perform tss detection for pychopper auto > cutadapt_polyA > SSP-cutadapt > clipped  or for raw mapped reads
for file in ${input}/trimmed/*/*/*clipped.sorted.bam # ||  for file in ${input}/raw/*/*/*.sorted.bam
do 
  # file and folder names
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
  
  # make directories
  mkdir microbepore/data/tss/trimmed
  mkdir microbepore/data/tss/trimmed/${foldername}
  output=microbepore/data/tss/trimmed/${foldername}/${filename}
  mkdir ${output}

  # step 1: calculate 5´positions for plus and minus strand
  bedtools genomecov \
    -ibam ${file} \
    -bga \
    -5 \
    -strand + > ${output}/${filename}.plus.bedgraph
  
  bedtools genomecov \
    -ibam ${file} \
    -bga \
    -5 \
    -strand - > ${output}/${filename}.minus.bedgraph
    
  # step 2: termseq peaks
  termseq_peaks ${output}/${filename}.plus.bedgraph ${output}/${filename}.plus.bedgraph --peaks ${output}/${filename}.plus.peaks --strand +
  termseq_peaks ${output}/${filename}.minus.bedgraph ${output}/${filename}.minus.bedgraph --peaks ${output}/${filename}.minus.peaks --strand -
    
  # step 3: add coverage information
  bedtools intersect \
    -wao \
    -a ${output}/${filename}.plus.peaks.oracle.narrowPeak \
    -b ${output}/${filename}.plus.bedgraph \
    > ${output}/${filename}.plus.peaks.oracle.narrowPeak.counts
    
  bedtools intersect \
    -wao \
    -a ${output}/${filename}.minus.peaks.oracle.narrowPeak \
    -b ${output}/${filename}.minus.bedgraph \
    > ${output}/${filename}.minus.peaks.oracle.narrowPeak.counts

done

3´end detection

3´end peak calling was performed in the following way:

input=microbepore/data/mapped

# perform tts detection for pychopper auto > cutadapt_polyA > SSP-cutadapt > clipped  or for raw mapped reads
for file in ${input}/trimmed/*/*/*clipped.sorted.bam # ||  for file in ${input}/raw/*/*/*.sorted.bam
do 
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}
    
  echo ${filename}

  mkdir microbepore/data/tts/trimmed
  mkdir microbepore/data/tts/trimmed
  mkdir microbepore/data/tts/trimmed/${foldername}
  output=microbepore/data/tts/trimmed/${foldername}/${filename}
  mkdir ${output}

  # step 1: calculate 3´positions for plus and minus strand
  bedtools genomecov \
    -ibam ${file} \
    -bga \
    -3 \
    -strand + > ${output}/${filename}.plus.bedgraph
  
  bedtools genomecov \
    -ibam ${file} \
    -bga \
    -3 \
    -strand - > ${output}/${filename}.minus.bedgraph
    
  # step 2: termseq peaks
  termseq_peaks ${output}/${filename}.plus.bedgraph ${output}/${filename}.plus.bedgraph --peaks ${output}/${filename}.plus.peaks --strand +
    termseq_peaks ${output}/${filename}.minus.bedgraph ${output}/${filename}.minus.bedgraph --peaks ${output}/${filename}.minus.peaks --strand -
    
  # step 3: add coverage information
  bedtools intersect \
    -wao \
    -a ${output}/${filename}.plus.peaks.oracle.narrowPeak \
    -b ${output}/${filename}.plus.bedgraph \
    > ${output}/${filename}.plus.peaks.oracle.narrowPeak.counts
    
  bedtools intersect \
    -wao \
    -a ${output}/${filename}.minus.peaks.oracle.narrowPeak \
    -b ${output}/${filename}.minus.bedgraph \
    > ${output}/${filename}.minus.peaks.oracle.narrowPeak.counts

done

Gene body coverage analysis

To assess the impact of trimmings on gene body coverage, a coverage meta-analysis was performed. First, a transcript file was created for all genes with an ONT-annotated primary 5´ and 3´ end (see previous section). Based on this, strand-specific coverage files were created from the bam files and coverage analysis performed using a custom R script.

input=microbepore/data/mapped

# calculate coverage over transcripts with TSS and TTS | for pychopper auto > cutadapt > clipped or RAW 
for file in ${input}/trimmed/*/*/*clipped.sorted.bam # ||  for file in ${input}/raw/*/*/*.sorted.bam
do 
  filename_extended=${file##*/}
  keyword=$(echo $filename_extended | cut -d"." -f 2)
  foldername=$(echo $filename_extended | cut -d"_" -f 1,2,3)
  filename=${filename_extended%%.*}

  # mk dirs
  mkdir microbepore/data/coverage/trimmed
  mkdir microbepore/data/coverage/trimmed/${foldername}
  output=microbepore/data/coverage/trimmed/${foldername}/${filename}
  mkdir ${output}

  # calc coverage
  samtools view -F 16 -o temp.sorted.bam ${file} 
  bedtools coverage \
  -d \
  -a ${dir}/data/bed/transcripts.plus.bedgraph \ # bed file of genes with annotated 5´and 3´end
  -b temp.sorted.bam \
  > ${output}/${filename}.plus.coverage
  
  samtools view -f 16 -o temp.sorted.bam ${file} 
  bedtools coverage \
  -d \
  -a ${dir}/data/bed/transcripts.minus.bedgraph \ # bed file of genes with annotated 5´and 3´end
  -b temp.sorted.bam \
  > ${output}/${filename}.minus.coverage
done

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README.md

README.md

Table of Contents

Library preparation

Sequencing

Data analysis

Data management

Basecalling of raw reads using `guppy_basecaller`

Demultiplexing of basecalled reads using `guppy_barcoder`

Mapping of reads to the genome using `minimap2`

Gene abundance estimation using `salmon` in alignment-based mode

Trimming of reads using `pychopper`, `cutadapt` & `samclip`

Identification of full-length reads using `pychopper`

Remove polyA-tails using `cutadapt`

Remove remaining SSP adapter using `cutadapt`

Mapping of trimmed reads, removing clips using `samclip`

Detection of transcript boundaries

5´end detection

3´end detection

Gene body coverage analysis

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README.md

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Table of Contents

Library preparation

Sequencing

Data analysis

Data management

Basecalling of raw reads using guppy_basecaller

Demultiplexing of basecalled reads using guppy_barcoder

Mapping of reads to the genome using minimap2

Gene abundance estimation using salmon in alignment-based mode

Trimming of reads using pychopper, cutadapt & samclip

Identification of full-length reads using pychopper

Remove polyA-tails using cutadapt

Remove remaining SSP adapter using cutadapt

Mapping of trimmed reads, removing clips using samclip

Detection of transcript boundaries

5´end detection

3´end detection

Gene body coverage analysis

Basecalling of raw reads using `guppy_basecaller`

Demultiplexing of basecalled reads using `guppy_barcoder`

Mapping of reads to the genome using `minimap2`

Gene abundance estimation using `salmon` in alignment-based mode

Trimming of reads using `pychopper`, `cutadapt` & `samclip`

Identification of full-length reads using `pychopper`

Remove polyA-tails using `cutadapt`

Remove remaining SSP adapter using `cutadapt`

Mapping of trimmed reads, removing clips using `samclip`