Biopython
SciPy
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Biopython | SciPy | |
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31 | 50 | |
4,158 | 12,431 | |
1.8% | 1.7% | |
9.6 | 9.9 | |
9 days ago | 2 days ago | |
Python | Python | |
Freely Distributable | BSD 3-clause "New" or "Revised" License |
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.
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.
SciPy
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What Is a Schur Decomposition?
I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.
[1]: https://github.com/scipy/scipy/issues/18566
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Fortran codes are causing problems
Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html
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[D] Which BLAS library to choose for apple silicon?
There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.
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SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.
For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.
- numerically evaluating wavelets?
- Fortran in SciPy: Get rid of linalg.interpolative Fortran code
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Optimization Without Using Derivatives
Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.
PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.
There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.
- What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
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Emerging Technologies: Rust in HPC
if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)
- Python
What are some alternatives?
RDKit - The official sources for the RDKit library
SymPy - A computer algebra system written in pure Python
biotite - A comprehensive library for computational molecular biology
statsmodels - Statsmodels: statistical modeling and econometrics in Python
bioconda-recipes - Conda recipes for the bioconda channel.
NumPy - The fundamental package for scientific computing with Python.
Numba - NumPy aware dynamic Python compiler using LLVM
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
astropy - Astronomy and astrophysics core library
PyDy - Multibody dynamics tool kit.
or-tools - Google's Operations Research tools: