A couple of small utilities to quickly peek at results from nanopore-based sequencing runs.
# create a virtual environment (e.g. using conda)
# cd into a directory where you store source code
git clone https://github.com/phiweger/peek
cd peek
pip install -e .For plotting in R there are 3 dependencies:
# R
install.packages('ggplot2')
install.packages('R.devices')
install.packages('readr')List subcommands:
peekLook at the API of subcommands:
peek stats --help
Usage: peek stats [OPTIONS]
Return basic stats from basecalled fastq.
stats: read name, length, median quality, label
Usage:
peek stats -f bar.fq -o bar.txt -l bad
# 3964e59a-f07d-46ff-bff5-1e2c03e4254a 981 10.0 bad
# c0dd59b3-bd29-488f-a78f-fa75d6fc6a3d 987 12.0 bad
# 3f6a3cd0-c788-40c2-900a-4ea3a6eb424c 869 13.0 bad
# only summary
peek stats --summary -f bar.fq > /dev/null
# or read from stdin
seqtk sample -s42 bar.fq 10000 | peek stats -f - -o bar.txt -l bad
cat workspace/pass/barcode01/* | peek stats --summary -f - > /dev/null
# concatenate multiple fastq stats and plot using R
peek stats --label bc01 -f BC01.fastq --summary > read_len_dist.tsv
peek stats --label bc02 -f BC02.fastq --summary >> read_len_dist.tsv
# vis w/ R
library(ggplot2)
library(readr)
df <- read_tsv(
'read_len_dist.tsv', col_names=c('name', 'length', 'quality', 'label'))
ggplot(df, aes(x=length, colour=label)) +
geom_histogram(bins=100) +
scale_x_log10(labels=scales::comma)
Options:
--summary Write summary to stderr
--header Include header
-l, --label TEXT Label
-n INTEGER How many reads?
-o TEXT Where to write output?
-f TEXT Raw fastq file from e.g. Albacore basecaller [required]
--help Show this message and exit.
The idea here is to not wrap command line tools w/ arbitrary python code, but to use Snakemake behind the command line interface of a Python package, as e.g. illustrated here and described in more detail here.
Initially, only host depletion is implemented. This means that we provide a reference genome and only care about reads that are not (or are) in this genome. For example in the following example data set, we only care for viral sequences but are not interested in the human reads present:
- Ebola data set -- a mixture of human cells and virus
- Ebola reference genome
cd peek/workflow/
peek offshore \
--paramsfile params-deplete.json \
--workflowfile workflow-deplete.json \
--snakefile Snakefile \
-o ~/tmp/results
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