src

src

Installation

Nano-tRNAseq is written in Python3 and should work in most UNIX systems.

Make sure you install all programs listed below, before runnning the pipeline. It's recommended to create virtual environment and use Python version 3.7.

# create & activate new virtual environment
mkdir -p ~/src/venv
python3 -m venv ~/src/venv/Nano-tRNAseq
source ~/src/venv/Nano-tRNAseq/bin/activate

# install dependencies
pip install numpy seaborn pandas mappy pysam

Note, this will install only Python dependencies. We assume you have tools such as bwa mem and samtools already installed in your system. If not, those can be easily installed with conda:

conda install bwa samtools

Alignment

Note, below we'll use only 10% of randomly selected reads from replicate 1. This will align tRNA reads from guppy/*/:

cd ~/src/Nano-tRNAseq/test

ref=~/src/Nano-tRNAseq/ref/yeast.tRNA.ext.fa
bwa index $ref

params="-W 13 -k 6 -x ont2d -T20"
mkdir -p bwamem
for f in guppy/*/; do
  s=`basename $f`
  echo `date` $f $s
  bwa mem -t6 $params $ref <(zcat $f/*.fastq.gz|sed 's/U/T/g') 2> /dev/null | samtools sort --write-index -o bwamem/$s.bam
done; date

Note, you'll need to basecall the reads before.

Estimate expression

The expression of tRNAs can be estimated as follows:

~/src/Nano-tRNAseq/src/get_counts.py -o rep1.counts.tsv \
  -i bwamem/{wt,pus4ko,h2o2}.bam > rep1.stats.tsv

This will produce an output file rep1.counts.tsv similar to this:

ref     wt      pus4ko  h2o2
Ala-AGC 1175    1103    847
Ala-TGC 472     314     419
Arg-ACG 1547    1342    1019
...
iMet-CAT        81      182     154
RDN5    8231    4936    3051
RDN58   1400    239     532
oligo3  848     474     582
oligo5  36      27      20
antisense       45      27      11
not_unique      16131   8050    7118
*       44947   32046   27221

Adittionally, it'll report a statistics table to rep1.stats.tsv like this one

sample  reads   aligned %       unique tRNA     %       antisense       %       oligo3  %       oligo5  %
wt      116139  71192   61.3    44501   62.51   45      0.1     848     1.19    36      0.05
pus4ko  78583   46537   59.22   32784   70.45   27      0.08    474     1.02    27      0.06
h2o2    65275   38054   58.3    26740   70.27   11      0.04    582     1.53    20      0.05

Finally, you can plot scatterplot of expression for all (or for selected --sample) using:

~/src/Nano-tRNAseq/src/plot_scatter.py -i rep1.counts.tsv -o rep1.scatter.pdf

Modification detection

First, you'll need to generate difference in sum of basecalling errors between WT and some other sample(s) as follows:

~/src/Nano-tRNAseq/src/get_sum_err.py -o heatmap/rep1 -f $ref \
  -i bwamem/{wt,pus4ko,h2o2}.bam

Optionally, you can add modification annotation using -b ~/src/Nano-tRNAseq/ref/yeast.tRNA.modomics.tsv.bed.

Then, the heatmaps can be plotted using:

~/src/Nano-tRNAseq/src/plot_heatmap.py -i heatmap/rep1.err_diff.tsv.gz \
  -f $ref -a ~/src/Nano-tRNAseq/ref/yeast.tRNA.modomics.aln.fa