deepaclive 0.3.2
Detecting novel pathogens from NGS reads in real-time during a sequencing run.
License: MIT
Requires Python: >=3
Keywords: deep learning DNA sequencing synthetic biology pathogenicity prediction

# DeePaC-Live
A DeePaC plugin for real-time analysis of Illumina sequencing runs. Captures HiLive2 output and uses deep neural nets to
 detect novel pathogens directly from NGS reads. 
 
We recommend having a look at:

* DeePaC main repo: https://gitlab.com/dacs-hpi/deepac
    * tutorial
    * trained built-in models
    * datasets used for both original and deepac-live models
    * code and documentation

* HiLive2 repo: https://gitlab.com/rki_bioinformatics/HiLive2.
    * extensive tutorial
    * code and documentation
  
## Installation
```
# Optional, but recommended: for GPU users
conda install tensorflow-gpu
# Install deepac-live
conda install -c bioconda deepac-live
# Recommended: download and compile deepac-live custom models
deepac getmodels --fetch
# Optional: viral built-in models
conda install -c bioconda deepacvir
```
Alternatively, you can also use pip:
```
# Optional, but recommended: for GPU users
pip install tensorflow-gpu
# Install deepac-live
pip install deepac-live
# Recommended: download and compile deepac-live custom models
deepac getmodels --fetch

# Optional: viral built-in models (not necessary)
pip install deepacvir
```

## DeePaC-Live models
DeePaC-Live ships new, updated models for bacterial pathogenic potential and viral infectious potential prediction.
The Illumina models are trained on 25-250bp subreads to ensure high performance over the whole sequencing run. 
The Nanopore models are trained on 250bp subreads corresponding to just around 0.5s of sequencing.
To fetch the models, use `deepac getmodels --fetch`. In the created directory, you will find the following models ready for inference:

* illu-bac-res18.h5 : an Illumina bacterial model
* illu-vir-res18.h5 : an Illumina viral model
* nano-bac-res18.h5 : a Nanopore bacterial model
* illu-vir-res18.h5 : a Nanopore viral model

## Basic usage
```
# Run locally: deepac-live Illumina models
deepac-live local -C -m illu-bac-res18.h5 -s 25,50,75,100,133,158,183,208 -l 100 -i hilive-out -o temp -I temp -O output -B ACAG-TCGA,undetermined
# Run locally: custom model
deepac-live local -C -m custom_model.h5 -s 25,50,75,100,133,158,183,208 -l 100 -i hilive-out -o temp -I temp -O output -B ACAG-TCGA,undetermined

# Run locally: build-in model for bacteria (not recommended)
deepac-live local -c deepac -m rapid -s 25,50,75,100,133,158,183,208 -l 100 -i hilive-out -o temp -I temp -O output -B ACAG-TCGA,undetermined
# Run locally: build-in model for viruses (not recommended)
deepac-live local -c deepacvir -m rapid -s 25,50,75,100,133,158,183,208 -l 100 -i hilive-out -o temp -I temp -O output -B ACAG-TCGA,undetermined
```

## Advanced usage
### Setting up a remote receiver
```
# Setup sender on the source machine
deepac-live sender -s 25,50,75,100,133,158,183,208 -l 100 -A -i hilive-out -o temp -r user@remote.host:~/rem-temp -k privatekey -B ACAG-TCGA,undetermined
# Setup receiver on the target machine
deepac-live receiver -c deepacvir -m rapid -s 25,50,75,100,133,158,183,208 -l 100 -I rem-temp -O output -B ACAG-TCGA,undetermined
```

### Refilter: ensembles and alternative thresholds
```
# Setup an ensemble on the target machine
deepac-live refilter -s 25,50,75,100,133,158,183,208 -l 100 -i rem-temp -I output_1,output_2 -O final_output -B ACAG-TCGA,undetermined
# Use another threshold
deepac-live refilter -s 25,50,75,100,133,158,183,208 -l 100 -i rem-temp -I output_1 -O final_output -t 0.75 -B ACAG-TCGA,undetermined
```