Metadata-Version: 2.1
Name: deepaclive
Version: 0.3.0
Summary: Detecting novel pathogens from NGS reads in real-time during a sequencing run.
Home-page: https://gitlab.com/dacs-hpi/deepac-live
Author: Jakub Bartoszewicz
Author-email: jakub.bartoszewicz@hpi.de
License: MIT
Description: # 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/rki_bioinformatics/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
        # 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
        # Optional: viral built-in models
        pip install deepacvir
        ```
        ## Basic usage
        ```
        # Run locally: build-in model for bacteria
        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
        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
        # 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
        ```
        
        ## 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
        ```
        
Keywords: deep learning DNA sequencing synthetic biology pathogenicity prediction
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3
Description-Content-Type: text/markdown
