DescriptionThe iPool-Seq (Uhse et al. 2018, Uhse et al.2019) protocol enables large-scale insertional mutagenesis screens of pathogens such as U. maydis (maize smut). It uses a combination of tagmentation, affinity purification and unique molecular identifiers (UMIs) to overcome the problem of the genetic material of the pathogen being severely underrepresented within the host, and allows mutant abundances to be quantified via next-generation sequencing (NGS) accurately enough to detect differences in infection efficiacy between mutants and wildtypes.
Apart from the wet-lab protocol, achieving this level of accuracy requires carefull analysis of the sequencing data to remove artifacs and to deal with differences of mutant abundances in the pre-infection mutant pool, with mutant-specific PCR biases, and different sequencing depths and detection efficiencies between different libraries.
Our iPool-Seq analysis pipeline is based on the
(Pflug et al. 2018) for the
quantitative analysis of UMI data, and takes are of all steps of the analysis of iPool-Seq
data. From from raw sequencing reads it computes the differential virulences of the mutants
in the pre-infection pool compared to a set of reference mutants.
Using the Pipeline
latest release of the iPool-Seq analysis pipeline, and unzip it. On a Linux
terminal, this is achieved with
curl -L -O $URL/$VER.tar.gz
tar xzf $VER.tar.gz
environment containing all necessary dependencies
The file environment.yaml defines a
provides all programs necessary for running the iPool-Seq analysis pipeline. To
ensure reproducibility of that environment even if Conda packages are replaced
and removed, our source code repository also contains environment.tar.gz,
a conda-pack archive of that
environent. To unpack that environment into ./environment and make it
Remember that (as all conda environments), this environment must, before it
can be used, be activated for the current terminal session by doing
Testing the installation
The iPool-Seq protocol was introduced by
Uhse et al. To download and analyse their experiment A1 with the iPool-Seq
The pipeline will generate the table
data/Uhse_et_al.2018/expA.r1.dv.tab containing the results of the differential
virulence analysis for the mutants screened by Uhse et al, and produces an accompanying report
that can be viewed with a web browser.
Analyzing your own data
See our publication (Uhse et
al., 2019) in Current Protocols in Plant Biology that describes both the web-lab
and the data-analysis parts of iPool-Seq in detail, and includes a step-by-step description of
how to use this pipeline.
For a brief overview of the necessary input files, run
PublicationsSimon Uhse, Florian G. Pflug, Arndt von Haeseler, Armin Djamei (2019). Insertion pool sequencing for insertional mutant analysis in complex host-microbe interactions. Current Protocols in Plant Biology 4: e20097. DOI: 10.1002/cppb.20097
Simon Uhse, Florian G. Pflug, Stirnberg Alexandra, Ehrlinger Klaus, Arndt von Haeseler, Armin Djamei (2018). In vivo insertion pool sequencing identifies virulence factors in a complex fungal–host interaction. PLoS Biology 16(4): e2005129. DOI: 10.1371/journal.pbio.2005129
Florian G. Pflug, Arndt von Haeseler (2018). TRUmiCount: correctly counting absolute numbers of molecules using unique molecular identifiers. Bioinformatics Volume 34, Issue 18, 15 September 2018, Pages 3137–3144. DOI: 10.1093/bioinformatics/bty283
LicenseThe iPool-Seq pipeline is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
The iPool-Seq pipeline is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.