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…

.. or around transcription factor peak summits. Here we plot the pileup for reads on the positive and negative strand, and clearly see the pileups we expect around the summit.

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 5/5 of short BioNumPy examples: Finding the most common kmers in a FASTQ-file. Try out the code here: colab.research.google.com/gi… Check out our documentation for more cool examples: bionumpy.github.io/bionumpy🤠

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 ☺️