UPIMAPI
Biopython
UPIMAPI | Biopython | |
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3 | 31 | |
26 | 4,188 | |
- | 1.6% | |
7.5 | 9.6 | |
5 months ago | 7 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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UPIMAPI
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Any programs/packages that will allow me to compare cluster annotations obtained from metagenomic data?
You may run MOSCA (https://github.com/iquasere/MOSCA), it performs all major steps of metagenomics analysis. It includes that functional classification you are looking for, since with UPIMAPI (https://github.com/iquasere/UPIMAPI) it annotates with UniProt DB as reference, and obtains information including taxonomy, EC numbers, and even those GOs, and reCOGnizer (https://github.com/iquasere/reCOGnizer), which annotates with CDD DB as reference, and obtains orthologous groups information (COG, Pfam, etc).
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Metatranscriptomics Workflow Questions?
Prediction of coding sequences takes as input the contigs you obtained, and gives you the translated genes. Besides annotating with the KEGG database, you may also want to annotate with more general purpose databases (e.g. UniProt), as these provide more taxonomies and functional information. MOSCA includes UPIMAPI (https://github.com/iquasere/UPIMAPI) and reCOGnizer (https://github.com/iquasere/reCOGnizer), which annotate genes with reference to UniProt and CDD databases using two different methods, providing complementary information. This is the same methodology used by widely popular tools such as eggNOG-mapper and Prokka, but these use other databases.
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Problems using Prokka
Install with mamba instead of conda, no more problems. Or use UPIMAPI (https://github.com/iquasere/UPIMAPI) together with reCOGnizer (https://github.com/iquasere/reCOGnizer), since these tools obtain better results when annotating proteins
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?
ncbi-genome-download - Scripts to download genomes from the NCBI FTP servers
RDKit - The official sources for the RDKit library
biotite - A comprehensive library for computational molecular biology
bioconda-recipes - Conda recipes for the bioconda channel.
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
PyDy - Multibody dynamics tool kit.
weblogo - WebLogo 3: Sequence Logos redrawn
statsmodels - Statsmodels: statistical modeling and econometrics in Python
bccb - Incubator for useful bioinformatics code, primarily in Python and R
bcbio-nextgen - Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
SciPy - SciPy library main repository