Biopython
bioawk
Biopython | bioawk | |
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31 | 8 | |
4,171 | 572 | |
1.1% | - | |
9.6 | 0.0 | |
1 day ago | over 1 year ago | |
Python | C | |
GNU General Public License v3.0 or later | - |
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Biopython
- Invitación a proyecto - Biopython en Español
- Biopython – Python Tools for Computational Molecular Biology
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comparing the similarity between a set of protein sequences
Usearch will do all-against-all comparisons, cluster sequences, and produce alignments for each cluster. You can set the clustering threshold (proportion of residues identical). The alignments are in fasta format, which is pretty standard. If all you want is basic similarity it might be easiest to just write something that calculates normalized Hamming distances (typically called p-distances in the molecular evolution literature) between pairs of sequences. I suspect the biopython fasta reader (you can install biopython from https://biopython.org/) will be good enough.
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u/Responsible-Gas3852 comments on "Why is Cancer so Hard to Cure?"
Yes, the computing tool for biological computation.
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My boss is considering letting me take a programming course if I have some good reasons why.
Beside that their core lectures to non-computer scientists are public (survey), workshops by software carpentry move around the globe. Maybe your intent to seed hands-on knowledge is in similar tune before heading for biopython, bioperl, bioawk. It doesn't hurt to tap into resources initially written for non-labrats either, e.g. about regular expressions by programming historian.
- Can you run ScanProsite locally?
- How to iterate over the whole GRCh38 genome with python?
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Help they’re turning me into a programmer
Well, what language do you want to learn? What is your background so far? Assuming it is more on the side of biology, software carpentry's Python may eventually lead to biopython? Though there equally is a chance for AWK (Hack the planet's text! and bioawk...
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Biology related exercices and "challenges" to train by myself
I think you mind find something of a community around BioPython, which might be helpful. Just looking at the capabilities will probably be instructive as well.
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Joining the Open Source Development Course
Python is the main programming language I use nowadays. In particular numpy and pandas are of course extremely useful. I also use biopython package - a collection of software tools for biological computation written in Python by an international group of researchers and developers.
bioawk
- Bioawk: Awk Modified for Biological Data
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Any links to R-scripts for common NGS pipelines?
Data wrangling is actually what awk excels at, and it's generally much more concise than R for that sort of thing. I'm aware that a lot of awk one liners look like gibberish to the uninitiated, but it actually makes a lot of sense when you understand the pattern-action structure of awk programs. It is also installed on any *nix system, there's no need to worry about installing dependencies or setting up virtual environments. And it's several times faster than R. Also Bioawk is glorious.
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Is BioAwk frequently used, or even useful?
A few months ago, I learned about this utility known as bioawk, written by Heng Li of samtools fame. Apparently, it is essentially a tweaked version of awk, with some extra goodies added for parsing and processing of bioinformatics file formats. While the functionality seems cool, I was wondering whether it is worth installing on my server, and incorporating into our workflows, because it seems so niche. I have not seen many references to it. Or is it better if we stick to Python scripts for this sort of work? Are there any computational speed advantages, etc. that bioawk offers over regular Python scripts for processing of, let's say, BED files or VCF files?
- What are the most useful cutting edge tools I should learn for bioinformatics?
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My boss is considering letting me take a programming course if I have some good reasons why.
Beside that their core lectures to non-computer scientists are public (survey), workshops by software carpentry move around the globe. Maybe your intent to seed hands-on knowledge is in similar tune before heading for biopython, bioperl, bioawk. It doesn't hurt to tap into resources initially written for non-labrats either, e.g. about regular expressions by programming historian.
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What are strictly data analysis jobs?
On the other hand, some of the techniques to set the ground for data analysis are equally valuable in other situations. The two installments about regular expressions on programming historian Understanding Regular Expressions and Cleaning OCR’d text with Regular Expressions, for example. They have no relevance to handling chemicals in the lab, yet since then, I find myself working with data files more efficiently, than earlier because of grep, an utility in Linux to crawl across data files. Or AWK, actually picking up theses "regexes", which I find generally useful since Benjamin Porter's "Hack the planet's text" (presentation video, and exercise video) with its link back to chem/bio e.g., to bioawk (btw, there equally is biopython, too).
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Help they’re turning me into a programmer
Well, what language do you want to learn? What is your background so far? Assuming it is more on the side of biology, software carpentry's Python may eventually lead to biopython? Though there equally is a chance for AWK (Hack the planet's text! and bioawk...
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Awk: The Power and Promise of a 40-Year-Old Language
There's even a version of awk specifically designed for bioinformatics that natively knows how to handle fasta, fastq, and bam files, among other formats.
https://github.com/lh3/bioawk
What are some alternatives?
RDKit - The official sources for the RDKit library
cligen - Nim library to infer/generate command-line-interfaces / option / argument parsing; Docs at
biotite - A comprehensive library for computational molecular biology
csvquote - Enables common unix utlities like cut, awk, wc, head to work correctly with csv data containing delimiters and newlines
bioconda-recipes - Conda recipes for the bioconda channel.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Numba - NumPy aware dynamic Python compiler using LLVM
zarp - The Zavolab Automated RNA-seq Pipeline
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
MethylDackel - A (mostly) universal methylation extractor for BS-seq experiments.
PyDy - Multibody dynamics tool kit.
readfq - Fast multi-line FASTA/Q reader in several programming languages