And we're now published in Nature Methods 🥳 Full article here: nature.com/articles/s41592-0…
Finally biologists can also use numpy (array programming). Handling e.g. DNA and protein sequences with convenience and speed, like physicists and machine learners for decades have worked with numerical data: nature.com/articles/s41592-0… (1/3)
Finally, a cool example showing that there are fewer mapped reads on average around genomic variants. Check out this colab if you want to try out the examples: colab.research.google.com/gi…
It’s quite easy to e.g. find the mean read pileup value around transcription start sites or other locations.
The Genome object makes it easy to read bam-files, bed-files and other data that belongs on a reference genome. Getting a pileup from intervals and slicing the pileup can be done with NumPy-like syntax.
BioNumPy has some support for gtf files, but have not yet defined a complete set of abstractions for them. Would love suggestions for useful methods!
Day 4 of small #BioNumPy examples: Sequence matching (searching for a sequence in a set of reads).
Try out the code here: colab.research.google.com/gi…
Day 3 of small BioNumPy examples: Plotting the mean base qualities across reads.
Try out the code here: colab.research.google.com/gi…
.. and remember to follow us for daily examples😋
Day 2/5 of small BioNumPy examples: Motif matching.
We download a motif from Jaspar, compute max motif score per read in a FASTQ file and plot a histogram of the scores.
Run the code here: colab.research.google.com/gi…
Every day this week, we'll share a small example of how BioNumPy can be used.
First out: FASTQ filtering
(try out the code yourself here: colab.research.google.com/gi…)
.. and remember to follow us for daily updates ☺️
