From 2dd0566b491110f2781ed47531303c17c22b721a Mon Sep 17 00:00:00 2001 From: Kenzo-Hugo Hillion <kenzo-hugo.hillion1@pasteur.fr> Date: Mon, 22 Jul 2019 15:36:58 +0200 Subject: [PATCH] start changing simulation tool --- simulation/README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/simulation/README.md b/simulation/README.md index 1959af1..bf1e918 100644 --- a/simulation/README.md +++ b/simulation/README.md @@ -1,17 +1,18 @@ # Simulation -[CAMISIM](https://github.com/CAMI-challenge/CAMISIM) can model different microbial abundance -profiles (from multi-sample time series to differential abundance studies) and was used to -generate the benchmark data sets of the first CAMI challenge. +[InSilicoSeq](https://github.com/HadrienG/InSilicoSeq) is a sequencing simulator producing +realistic Illumina reads. Primarily intended for simulating metagenomic samples, it can also +be used to produce sequencing data from a single genome. -We describe here only the usage of de novo metagenomes simulation. For a more exhaustive -documentation, please refer to [CASISIM wiki](https://github.com/CAMI-challenge/CAMISIM/wiki). +We describe here only the usage for a simple simulation. For a more exhaustive documentation, +please refer to [InSilicoSeq documentation](https://insilicoseq.readthedocs.io/en/latest/?badge=latest). ## Install -CAMISIM contains a lot of dependencies and the list can be found [Here](https://github.com/CAMI-challenge/CAMISIM/wiki/User-manual#installation). +You have several ways of installing InSilico seq that can be found [Here](https://insilicoseq.readthedocs.io/en/latest/iss/install.html). -However, we recommand the use of the docker image `cami/camisim:latest` or the singularity one ([here](https://gitlab.pasteur.fr/metagenomics/singularity/tree/master/tools/camisim), only from docker for the moment). +Here, we will use of the docker image `hadrieng/insilicoseq:1.4.2` or the singularity one +([here]) built from the docker image. ## Run de novo metagenomes simulation -- GitLab