bioawk
orange
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bioawk | orange | |
---|---|---|
8 | 27 | |
572 | 4,611 | |
- | 1.9% | |
0.0 | 9.6 | |
over 1 year ago | 3 days ago | |
C | Python | |
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
cligen - Nim library to infer/generate command-line-interfaces / option / argument parsing; Docs at
glue - Linked Data Visualizations Across Multiple Files
csvquote - Enables common unix utlities like cut, awk, wc, head to work correctly with csv data containing delimiters and newlines
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
zarp - The Zavolab Automated RNA-seq Pipeline
RDKit - The official sources for the RDKit library
MethylDackel - A (mostly) universal methylation extractor for BS-seq experiments.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
readfq - Fast multi-line FASTA/Q reader in several programming languages
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
Biopython - Official git repository for Biopython (originally converted from CVS)
NumPy - The fundamental package for scientific computing with Python.