semvec/pypi-packages huggingface dataset:
proxy-service 0.0.1
An empty CDK Python app
Requires Python: >=3.6

# Welcome to your CDK Python project!

This is a blank project for Python development with CDK.

The `cdk.json` file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project.  The initialization
process also creates a virtualenv within this project, stored under the `.venv`
directory.  To create the virtualenv it assumes that there is a `python3`
(or `python` for Windows) executable in your path with access to the `venv`
package. If for any reason the automatic creation of the virtualenv fails,
you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

```
$ python3 -m venv .venv
```

After the init process completes and the virtualenv is created, you can use the following
step to activate your virtualenv.

```
$ source .venv/bin/activate
```

If you are a Windows platform, you would activate the virtualenv like this:

```
% .venv\Scripts\activate.bat
```

Once the virtualenv is activated, you can install the required dependencies.

```
$ pip install -r requirements.txt
```

At this point you can now synthesize the CloudFormation template for this code.

```
$ cdk synth
```

To add additional dependencies, for example other CDK libraries, just add
them to your `setup.py` file and rerun the `pip install -r requirements.txt`
command.

## Useful commands

 * `cdk ls`          list all stacks in the app
 * `cdk synth`       emits the synthesized CloudFormation template
 * `cdk deploy`      deploy this stack to your default AWS account/region
 * `cdk diff`        compare deployed stack with current state
 * `cdk docs`        open CDK documentation

Enjoy!
rivindel 0.0.1
RivIndel: Complex indel detection from short read sequencing
Keywords: complex indel detection

# RivIndel
IN CONSTRUCTION!

## Motivation
Complex indels (cxIndels) often introduce multiple deletions, insertions and mismatches during the alignment phase.
Complex indels are prone to be found on highly unstable genomes such as Lung Adenocarcinoma (LUAD).
A correct interpretation in terms of net base change is crucial: e.g. exon 19 EGFR inframe deletions are actionable, but not frameshift deletions.

These variants are typically called as multiple individual variants by some popular somatic callers (Mutect2, Lancet, etc).
Since these individual variants are closely located, there is a high chance that they come from a single compound variant.

RivIndel detects complex indels from targeted sequencing (Illumina).
RivIndel scans every targeted region (supplied as a BED file) in a similar fashion than indelSeek.
However, RivIndel validates candidate cxIdels after a successful assembly of candidate reads (plus neighbouring soft-clipped reads).
This, in turn, leads to a better allele frequency estimation.

## Method overview
![Slide](img/rivindel_schema2.png)

## Install RivIndel locally

1. Clone RivIndel repository:
```
git clone https://github.com/bdolmo/RivIndel.git
cd RivIndel/
```

2. Install via pip
```
pip3 install -e .
```

## Usage
```
python3 RivIndel.py --bam <input.bam> --bed <test.bed> --vcf <out.vcf>
```