Unix-Pledge
bioawk
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almost 5 years ago | over 1 year ago | |
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Unix-Pledge
bioawk
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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.
What are some alternatives?
cligen - Nim library to infer/generate command-line-interfaces / option / argument parsing; Docs at
csvquote - Enables common unix utlities like cut, awk, wc, head to work correctly with csv data containing delimiters and newlines
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
zarp - The Zavolab Automated RNA-seq Pipeline
MethylDackel - A (mostly) universal methylation extractor for BS-seq experiments.
Biopython - Official git repository for Biopython (originally converted from CVS)
readfq - Fast multi-line FASTA/Q reader in several programming languages
tiny_python_projects - Code for Tiny Python Projects (Manning, 2020, ISBN 1617297518). Learning Python through test-driven development of games and puzzles.
dsutils - Command-line tools for doing data science
vcftools - A set of tools written in Perl and C++ for working with VCF files, such as those generated by the 1000 Genomes Project.
OpenRefine - OpenRefine is a free, open source power tool for working with messy data and improving it