semvec/pypi-packages huggingface dataset:
inferelator-prior 0.3.8
Inferelator-Prior Network Generation Tool

# inferelator-prior
[![PyPI version](https://badge.fury.io/py/inferelator-prior.svg)](https://badge.fury.io/py/inferelator-prior)
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[![codecov](https://codecov.io/gh/flatironinstitute/inferelator-prior/branch/release/graph/badge.svg)](https://codecov.io/gh/flatironinstitute/inferelator-prior)


This is a set of pipelines to create expression, dynamic response, and prior matrices for network inference. 
They are designed to create data that is compatible with the [inferelator](https://github.com/flatironinstitute/inferelator) package. 

### Usage

    python -m inferelator_prior.network_from_motifs
    usage: network_from_motifs.py --motif motif_PWM_file.meme
                                  -f genome_fasta_file.fasta
                                  -g genome_annotation_file.gtf
                                  -o ~/output/path/prefix
                                  --species {yeast,fly,mouse,human}
                                  -b constraning_bed_file.bed
                                  --cpu num_cores
                                  --genes gene_list.txt
                                  --tfs tf_list.txt
                                  
This requires a motif PWM database (`-m PATH`), 
a genome to search (both sequence as a FASTA `-f PATH` and annotations `-g PATH`),
and an output prefix for several files (`-o PATH`).
In addition, default settings for a specific species can be set with (`--species`).
A BED file can be provided (`-b PATH`) based on some constraining experiment to restrict searching to 
specific genomic areas.
This will use multiple cores to search for motifs and process the resulting data.
By default, all available processors will be used, but this can be overridden with `--cpu N`.
A list of genes (on e per line in a text file) can be provided to `--genes` 
and a list of tfs (one per line in a text file) can be provided to `--tfs`.
A network will be built for only these genes or TFs.

The output from this script is an unsigned connectivity matrix (0, 1) connecting genes on rows to regulators on columns.
In addition, this produces an unfiltered score matrix (0, ) connecting genes on rows to regulators on columns.

### Requirements

In addition to
python dependencies, this package also requires 
[STAR](https://github.com/alexdobin/STAR),
[sra-tools](http://ncbi.github.io/sra-tools/), 
[bedtools](https://bedtools.readthedocs.io/en/latest/),
[samtools](http://www.htslib.org/), and
[fimo](http://meme-suite.org/doc/fimo.html).
apachecn-csharp-zh-pt2 2022.3.19.0
ApacheCN C# 译文集（二）
Requires Python: >=3.6

# ApacheCN C# 译文集（二）

> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 要打多久，就打多久，一直打到完全胜利！——教员

* [在线阅读](https://csharp2.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 贡献指南

本项目需要校对，欢迎大家提交 Pull Request。

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/apachecn-csharp-zh-pt2](https://github.com/apachecn/apachecn-csharp-zh-pt2) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](https://www.apachecn.org/#/docs/join) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/apachecn-csharp-zh-pt2
docker run -tid -p <port>:80 apachecn0/apachecn-csharp-zh-pt2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install apachecn-csharp-zh-pt2
apachecn-csharp-zh-pt2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g apachecn-csharp-zh-pt2
apachecn-csharp-zh-pt2 <port>
# 访问 http://localhost:{port} 查看文档
```

## 其它协议

霞鹜文楷采用 [SIL 开放字体协议 1.1](https://github.com/lxgw/LxgwWenKai/blob/main/SIL_Open_Font_License_1.1.txt)。

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)
Fileprank 1.0.1
prank.create()
Requires Python: >=3.6

# Просто модуль для приколов.
**Установка через [PyPi](https://pypi.org/project/fileprank/)**:
```python
pip install fileprank
```
___

**Использования:**
```python
prank.create()
```
Создает 100 txt файлов на 1 гигобайт
___
```python
prank.clear()
```
Удаляет все txt файлы из директории