BioNumPy
@BioNumPy
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Python library for array programming on biological datasets. Documentation available at: https://t.co/R622AQLPzs
Joined January 2023
And we're now published in Nature Methods 🥳 Full article here:
nature.com
Nature Methods - BioNumPy: array programming for biology
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: (1/3).
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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:
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. 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.
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It’s quite easy to e.g. find the mean read pileup value around transcription start sites or other locations.
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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.
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BioNumPy has been updated with changes that make it a lot easier to work with genomic intervals and data on a reference genome 😀 . Here are a few cool examples to illustrate the new stuff:.
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Day 5/5 of short BioNumPy examples: Finding the most common kmers in a FASTQ-file. Try out the code here: Check out our documentation for more cool examples: �
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Day 3 of small BioNumPy examples: Plotting the mean base qualities across reads. Try out the code here: and remember to follow us for daily examples😋
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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:
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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: . and remember to follow us for daily updates ☺️
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