semvec/pypi-packages huggingface dataset:
liftover-bam 0.1.0
A simple module to liftover bam alignments
License: MIT
Requires Python: >=3.9,<4.0

# Lifting over bam #

[![poetry CI](https://github.com/wckdouglas/liftover_bam/actions/workflows/ci.yml/badge.svg)](https://github.com/wckdouglas/liftover_bam/actions/workflows/ci.yml)

Sometimes for amplicon sequencings, we would like to map reads to the amplicon sequence only but bringing them back to genomic coordinates for easy variant calling and viewing.

Let's say we have a gene in `chr1:100-1000`, and we would first extract this locus from the genome fasta file to make a new fasta record with name `>chr1:100-1000`, this can be done with:
```
echo "chr1:100-1000" | samtools faidx -r - genome.fa > gene.fa 
```
and map the reads to this single gene fasta file with `bwa` or `bowtie2` to make a bam alignment file:
```
bwa mem gene.fa query.fq | samtools view -b > gene.bam
```

So what if you want to put these alignments back to the genomic coordinates after that?

The `liftover_bam.liftover` function is trying to solve this problem in pure python!

```
gene_bam="gene.bam"
genome_bam="any.bam_file_that_maps_to_the_genome"
out_bam="where_you_want_your_output_bam_file_to_be"
liftover(gene_bam, genome_bam, out_bam)
```
tmu2 0.6.1
Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
License: MIT

Implements the Tsetlin Machine (https://arxiv.org/abs/1804.01508), Coalesced Tsetlin Machine (https://arxiv.org/abs/2108.07594), Convolutional Tsetlin Machine (https://arxiv.org/abs/1905.09688), Regression Tsetlin Machine (https://royalsocietypublishing.org/doi/full/10.1098/rsta.2019.0165), and Weighted Tsetlin Machine (https://ieeexplore.ieee.org/document/9316190), with support for continuous features (https://arxiv.org/abs/1905.04199), drop clause (https://arxiv.org/abs/2105.14506), Type III Feedback (to be published), focused negative sampling (to be published), multi-task classifier (to be published), autoencoder (to be published), literal budget (to be published), and one-vs-one multi-class classifier (to be published). TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
korrektor-py 1.0.0
Python wrapper for the https://korrektor.uz/api
License: MIT
Requires Python: >=3.8

# :gb: **Korrektor-Py**

<p align="center">
    <img src="assets/logo.png"></img> <br>
     <img src="https://github.com/AbduazizZiyodov/korrektor-py/actions/workflows/ci.yml/badge.svg?branch=master"></img> <br>
    <b>Korrektor-Py</b> - python library & wrapper for API of the https://korrektor.uz
</p>

## **Installation**

Using `pip`:

```bash
pip3 install --upgrade korrektor-py
```

Build & install from source code:

```
git clone https://github.com/AbduazizZiyodov/korrektor-py.git
cd korrektor-py/

python3 setup.py sdist bdist_wheel && cd dist/
pip install Korrektor_Py-*.whl

```

<hr>

- Owner: [Manuchehr Usmonov](https://github.com/con9799)
- Author of this library: [Abduaziz Ziyodov](https://github.com/AbduazizZiyodov)

# :uzbekistan: **Korrektor-Py**

<p align="center">
    <img src="assets/logo.png"></img> <br>
     <img src="https://github.com/AbduazizZiyodov/korrektor-py/actions/workflows/ci.yml/badge.svg?branch=master"></img> <br>
    <b>Korrektor-Py</b> - python library & wrapper for API of the https://korrektor.uz
</p>

## **O'rnatish**

`pip` yordamida:

```bash
pip3 install --upgrade korrektor-py
```

Loyihani asl manba kodidan `build` qilib, so'ng o'rnatish:

```
git clone https://github.com/AbduazizZiyodov/korrektor-py.git
cd korrektor-py/

python3 setup.py sdist bdist_wheel && cd dist/
pip install Korrektor_Py-*.whl

```

<hr>

- Loyiha asoschisi: [Manuchehr Usmonov](https://github.com/con9799)
- Ushbu kutubxona muallifi: [Abduaziz Ziyodov](https://github.com/AbduazizZiyodov)