deepaclive 0.3.2

Detecting novel pathogens from NGS reads in real-time during a sequencing run.

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 [e-mail]:~/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