stringr
Biopython
stringr | Biopython | |
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13 | 31 | |
574 | 4,171 | |
1.2% | 1.1% | |
5.7 | 9.6 | |
22 days ago | 1 day ago | |
R | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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stringr
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Is there a better way to grep like this?
Perhaps str_extract_all() from stringr? (since you're already using dplyr)
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First time writing a script for automation
There are base R ways of finding strings grep(), as well as packages such as {stringer} (cheat sheet available here: https://stringr.tidyverse.org/)
- osdc-2023-assignment1
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how can i remove symbols from columns?
If you're unfamiliar with regex, it may help you review the stringr cheat sheet: https://stringr.tidyverse.org/
- Help Removing Double Quotes
- CLEANING DATA PROBLEM
- Recommendation for good reference materials on string/character operations in R
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Matching text strings
Without knowing more, it seems likely that some combination of stringr and fuzzyjoin will be what you need.
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HELP ME PLEASE!!!!
Search for info about the grepl-function and read the cheat sheet from the stringr-package, that should get you started: https://stringr.tidyverse.org/
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How to search a data table for multiple objects?
Looks like you have a misunderstanding about the right-most column. Don't think of the cell values as vectors of length=N elements, c("gene1", "gene2", ..."geneN"). Instead, think of each cell as a character string (vector of length=1), "gene 1, gene 2, ... geneN". Finding specific sequence of characters within a string is different from finding matching elements of vectors. Without changing the column, you can use regular expressions and base R string functions and/or stringr if you're more comfortable with tidyverse.
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.
What are some alternatives?
glue - Glue strings to data in R. Small, fast, dependency free interpreted string literals.
RDKit - The official sources for the RDKit library
cheatsheets - Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/.
biotite - A comprehensive library for computational molecular biology
ggplot2 - An implementation of the Grammar of Graphics in R
bioconda-recipes - Conda recipes for the bioconda channel.
CrispRVariants
Numba - NumPy aware dynamic Python compiler using LLVM
dplyr - dplyr: A grammar of data manipulation
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
PyDy - Multibody dynamics tool kit.
weblogo - WebLogo 3: Sequence Logos redrawn