semvec/pypi-packages huggingface dataset:
srslyrun 0.2.1
Not your grandma's NGS analysis - software for analyzing FASTQs from SRSLY libraries
Requires Python: >=3.7

# ClaretBio's SRSLY Library processing software 
This software is for the basic informatic processing of sequencing data 
generated using ClaretBio's SRSLY library prep kit with or without using UMIs.

# Installation

This sofware can be installed as a python package using the command
`pip install srslyrun`

# Usage

The basic analysis can be run with `srsly runsamples` for standard libraries
or `srsly runsamples --umi` for libraries with unique molecular identifiers (UMIs).
This software takes in raw fastqs and trims adapters, aligns to a user-specified
reference genome, and marks duplicates. For UMI aware demultiplexing of SRSLY libraries
please use our SRSLYumi python package (more info at https://github.com/claretbio/SRSLYumi). 

In order to run, this software requires an installation of conda- installation instructions
 can be found [here](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links).We recommend using mamba, which can be installed with `conda install -n base -c conda-forge mamba` once conda is installed. It can also be installed directly from

Required Arguments
    
    `--reference` : a path to the reference genome you wish to align to (no default, this must be provided, and must be a file ending in `.fasta` or `.fa`)
    
    `--libraries` or `--libfile`: the library IDs you would like analyzed in comma separated format and in a file with one id per line, respectively
    
Optional Arguments


    `--fastqdir` : the directory containing the raw fastqs you wish to process (if not specified, defaults to current working directory)
    
    `--resultsdir` : the directory you would like the output to be in (if not specified, defaults to current working directory)

The library IDs provided should match the beginning of the fastq files, for example the library ID for fastq files named `lib1_R1.fastq.gz` and `lib1_R2.fastq.gz` would be `lib1`. This can be provided directly on the command line with a comma separated list like `--libraries lib1,lib2` or as a file that lists one library ID per line with `--libfile libfile.txt`.

an example command:

`srsly runsamples --fastqdir /home/user/fastqfiles --resultsdir /home/user/srslyanalysis --reference /home/user/genomes/hg19.fa --libraries lib1,lib2,lib3`

For reproducibility's sake and to ensure appropriate versions we use snakemake wrappers for many of the tools in this pipeline, which are often slow to create the first time they are used. As a result, your first time running the software may take a long time - don't worry, this is totally normal!
ug-salsa 0.1.8
System Analytics Library for Substrate Analysis
Requires Python: >=3.8,<4.0

# SALSA - System Analytics Library for Substrate Analysis

SALSA is a Python package designed to allow for modular, customizable analyses of different substrates/optics data to be outputted as plots and reports. It includes functionalities for data objects, visualization, and metric aggregation with substrates. This document provides instructions for installing SALSA along with its dependencies.

## Installation

Before you proceed with the installation of SALSA, ensure that you have Python 3.8 or later installed on your system.

### Step 1: Install SALSA

Open your terminal or command prompt and execute the following command to install SALSA using pip:

```bash
pip install -i https://test.pypi.org/simple/ salsa
```

### Dependencies
SALSA's dependencies are listed in the pyproject.toml file under the [tool.poetry.dependencies] section, and are automatically installed upon installation of SALSA. For reference, here are SALSA's utilized packages:

- numpy
- pandas
- scipy
- plotly
- requests
- tqdm
- matplotlib
- fastparquet
- boto3

### Usage
After installing SALSA, you can start using the library in your substrate analyses.
```python
import salsa
```

#### Example 1: Bead Density WaferPlot
In this example, we will create a bead density waferplot to be outputted in a report.
1. Make the routine
```python
routine = salsa.Routine()
```

2. Load the data
```python
runID = “123456_1”
data = salsa.RefBeadData(runID)
data.load()
```
3. Add the analysis to the routine, then run

```python
routine.append(salsa.PlotBeadDensity(data))
routine.run()
```

### Contact

For any inquiries or support related to SALSA, you can reach out to:

- William Huang: william.huang@ultimagen.com
- Jerry Quan: jerry.quan@ultimagen.com