DelSIEVE_benchmark_pipeline

This repository hosts the benchmarking framework, built upon Snakemake, for DelSIEVE.

Required packages

This framework contains scripts written in Python 3 and R. Apart from an environment containing snakemake (we recommend using Conda), the following packages should be installed before running the pipeline:

• For Python3:
  • paramiko >= 2.8.0
• For R:
  • base >= 4.0
  • stringr
  • scales
  • dplyr
  • optparse
  • ape
  • phangorn
  • tools

Configuration

A few files should be configured before running.

1. The pipeline is mainly configured in config.yaml. There are some entries to be filled by users, which are marked by a phrase # TO BE SET , followed either by [MANDATORY] (must be set) or by [OPTIONAL] (could be ignored). Users can search for the phrase to set everything up efficiently.

   • The template configuration files for SIEVE are under templates/.

   • For the key [configFiles][DelSIEVE_simulator], a configuration file for the data simulator DelSIEVE_simulator should be specified. Those simulated scenarios used in the SIEVE paper are listed in simulation_configs/. For details, check the paper.

2. In run/run.sh, users can set a few things, e.g., the name of the conda environment containing snakemake (by default, snakemake), the number of cores to use and their ranges, etc.

Advanced configuration

Since SiFit requires a large amount of memory even working on a small dataset, the framework supports running SiFit alone on another server (referred to as the remote server) with the help of git. For this to work, a few things should be noted and configured:

• The machine you plan to run the pipeline (referred to as the local machine) and the remote server should meet one of the following conditions:
  • They are in the same local network.
  • If they are not in the same local network, the local machine must have a public IP address for the remote server to clone the git repo. However, the remote server can behind a gate server with a public IP address, specified by the key [servers][jumpServerOfRemote] in config.yaml.
• In Snakefile, comment out include: "sifit_local.snake", and uncomment # include: "sifit_remote.snake".
• Set up run/run_remote_sifit_true_monovar.sh similarly to run/run.sh.

Run the pipeline

With everything set up, users can run the pipeline under the root directory of this repo simply with

$ source run/run.sh Snakefile

or manually with

$ conda activate snakemake
$ snakemake --use-conda --cores {NUM} -kp

Funding

This project received support from

• the Polish National Science Centre SONATA BIS grant No. 2020/38/E/NZ2/00305,
• European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 766030, as well as
• European Research Council (ERC-617457-PHYLOCANCER), the Spanish Ministry of Science and Innovation (PID2019-106247GB-I00), and Xunta de Galicia.