Genome2OR | Annotate Olfactory receptor CDS from genome | Data Labeling library

by ToHanwei Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Genome2OR Summary

Genome2OR is a Python library typically used in Artificial Intelligence, Data Labeling applications. Genome2OR has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However Genome2OR build file is not available. You can download it from GitHub.

Annotate Olfactory receptor CDS from genome

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Quality

Security

License

Reuse

Support

Genome2OR has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 226 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Genome2OR is current.

Quality

Genome2OR has no bugs reported.

Security

Genome2OR has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Genome2OR is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Genome2OR releases are not available. You will need to build from source code and install.

Genome2OR has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

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Genome2OR Key Features

No Key Features are available at this moment for Genome2OR.

Genome2OR Examples and Code Snippets

No Code Snippets are available at this moment for Genome2OR.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

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ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Genome2OR

STEP 1: Execute nhmmer.py ` cd YouDir/Genome2OR/scripts python nhmmer.py ../template/Mammalia.hmm genome.fasta nhmmer_out.tblout -v `. STEP 2: Execute FindOR.py ` python FindOR.py nhmmer_out.tblout genome.fasta -o ../output -v `. STEP 3: Execute IdentityFunc.py ` python IdentifyFunc.py ../output/ORannotation_Pre-ORs_pro.fa ../output/ORannotation_Pre-ORs_dna.fa -o ../output -p Identity -v `.
STEP 1: Execute nhmmer.py ` cd YouDir/Genome2OR/scripts python nhmmer.py ../template/Mammalia.hmm genome.fasta nhmmer_out.tblout -v ` The "Mammalia.hmm" file is the "HMM profile" for mammalia. This file provided by us. Of course, you can also use the NHMMER tool to construct it yourself according to your needs. Generally speaking, we do not recommend doing this unless you konw why you need to construct this file anew. You can find more species of "HMM profile" in "the directory YouDir/Genome2OR/template" if needed. To ensure the quality of annotation, please select the appropriate "HMM profile" for the species you need to annotate. The "genome.fasta" file is the genome file that you need to annotate. This file is in the FASTA file format. The "nhmmer_out.tblout" file is the output file. You will find it in the current working directory.
STEP 2: Execute FindOR.py ` python FindOR.py nhmmer_out.tblout genome.fasta -o ../output -v ` The "nhmmer_out.tblout" file here is the output file of the "STEP 1". The "genome.fasta" file is the genome file that you need to annotate. This file is the same as the file with the same name in the "STEP 1". The "../output" is the directory where the output files are saved. After the program is finished running, you will find five output files with the prefix "ORannotation" in the "../output" directory.
STEP 3: Execute IdentityFunc.py ` python IdentifyFunc.py ../output/ORannotation_Pre-ORs_pro.fa ../output/ORannotation_Pre-ORs_dna.fa -o ../output -p Identity -v ` The "../output/ORannotation_Pre-ORs_pro.fa" file here is the output file of the "STEP 2". The "../output/ORannotation_Pre-ORs_dna.fa" file here is the output file of the "STEP 2". The "../output" is the directory where the output files are saved. After the program is finished running, you will find seven output files with the prefix "Identity" in the "../output" directory. In the output directory, the file "Identity_redundant_func_ORs.fasta" contains annotated functional olfactory receptor protein sequences, the file "Identity_redundant_pseu_ORs.fasta" contatins annotated pseudogene sequences, and the file "ORannotation_truncated.txt" records truncated genes.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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