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Our group develops computational tools for the design and analysis of high-throughput screening experiments. A particular focus is on the design of reagents for CRISPR/Cas9 and RNAi experiments, as well as on phenotype databases for large-scale functional genomic experiments.

Software packages and supplemental data files developed by the lab can be downloaded from our Github repository.

E-CRISP webservice

E-CRISP is a computational tool to design and evaluate guide RNAs for use with CRISPR/Cas9. The web application uses fast algorithms to identify target sequences for use with mediated genome editing. E-CRISP analyzes target specificity of the putative designs and assesses their genomic context (e.g. exons, transcripts, CpG islands). The design process incorporates different parameters of how CRISPR constructs can be used in experimental applications, such as knock-out and tagging experiments. E-CRISP can design guide RNAs for genomes of more than 30 organisms.

Heigwer, F., Kerr, G., Boutros, M. (2014). E-CRISP: fast CRISPR target site identification. Nature Methods 11:122-123.

CRISPR Library Designer (CLD) software

CRISPR library designer (CLD) is an integrated bioinformatics application for the design of custom single guide RNA (sgRNA) libraries for all organisms with annotated genomes. CLD is suitable for the design of libraries using modified CRISPR enzymes and targeting non-coding regions. It predicts a high fraction of functional sgRNAs.

Heigwer F, Zhan T, Breinig M, Winter J, Brügemann D, Leible S, Boutros M. (2016). CRISPR library designer (CLD): software for multispecies design of single guide RNA libraries. Genome Biology 17:55.

GenomeCRISPR database

GenomeCRISPR is a database for high-throughput screening experiments performed by using the CRISPR/Cas9 system. A dynamic web interface guides users through the process of finding information about published CRISPR screens. The database contains detailed data about observed hits and phenotypes. Moreover, it provides knowledge about performance of individual single guide RNAs (sgRNAs) used under various experimental conditions.

Rauscher, B., Heigwer, F., Breinig, M., Winter, J., Boutros, M. (2017). GenomeCRISPR - a database for high-throughput CRISPR/Cas9 screens. Nucleic Acids Res 45:D679-D86

CRISPRAnalyzeR software

CRISPRAnalyzeR has been developed to provide an interactive and exploratory analysis platform for pooled CRISPR screens. Screens can be analyzed using eight different hit calling  methods. The web application can be used to annotate guide RNAs, identified hits and perform gene set enrichment analysis.

caRpools R/Bioconductor package

caRpools provides an analysis pipeline of high-throughput CRISPR screening data. As an output, it generates standardized PDF and HTML reports including an in-depth analysis of screening quality, candidate hit calling using multiple, independent algorithms, in-depth analysis of sgRNA phenotypes and annotation of candidate genes using biomaRt. After completion of a configuration file, caRpools runs the complete analysis workflow, including NGS data extraction and mapping.

Winter, J., Breinig, M., Heigwer, F., Brueggemann, D., Leible, S., Pelz, O., Zhan, T., Boutros, M. (2015). caRpools: An R package for exploratory data analysis and documentation of pooled CRISPR/Cas9 screens. Bioinformatics 32:632-4.

E-RNAi webservice

E-RNAi is a web application that automates all tasks required for the de-novo design of RNAi probes used in small-scale and high-throughput experiments. The software predicts specific and efficient target sites for RNAi probes and suggests primer sequences for the amplification of appropriate templates.

Horn, T., Boutros, M. (2010). E-RNAi: a web application for the multi-species design of RNAi reagents - 2010 update. Nucleic Acids Research 38:W332-339.

NEXT-RNAi software

NEXT-RNAi is a software for the design and evaluation of genome-wide RNAi libraries. It performs all steps from the prediction of specific and efficient RNAi target sites to the visualization of designed reagents in their genomic context.

Horn, T., Sandmann, T., Boutros., M. (2010). Design and evaluation of genome-wide libraries for RNAi screens. Genome Biology 11:R61

GenomeRNAi database

The GenomeRNAi database (http://www.genomernai.org) is a repository for RNAi phenotype and reagent data, aiming to provide a platform for data mining and comparisons. Data is extracted from the literature by manual curation, or directly submitted by data producers. Currently, the database contains 217 experiments in human, and 201 experiments in Drosophila, totaling to more than 1,2 million individual gene-phenotype associations. It also holds information on more than 400,000 RNAi reagents, along with quality assessment data like efficiency and specificity. The website provides functionalities for searching, browsing and downloading, and features a “frequent hitter” function as well as a functionality for the overlay of genes sharing the same phenotype onto known gene networks provided by the String database.

Schmidt, EE., Pelz, O., Buhlmann, S., Kerr, G., Horn, T., Boutros, M. (2013). GenomeRNAi: a database for cell-based and in vivo RNAi phenotypes, 2013 update. Nucleic Acids Research 41:D1021-6.

HTSvis R/Bioconductor package

HTSvis is a web application for exploratory data analysis and visualization of arrayed high-throughput screens. It can be used to interactively visualize raw data, perform quality control and assess screening results from single to multi-channel measurements such as image-based screens.

Scheeder, C., Heigwer, F., Boutros, M. (2017). HTSvis: a web app for exploratory data analysis and visualization of arrayed high-throughput screens. Bioinformatics 33:2960-2962

cellHTS R/Bioconductor package

External links

cellHTS is a software package implemented in Bioconductor/R to analyze cell-based high-throughput RNAi screens. The cellHTS2 package is an updated version of the cellHTS package, offering improved functionality for the analysis and integration of multi-channel screens and multiple screens.

Boutros, M., Bras, L., Huber, W. (2006). Analysis of cell-based RNAi screens. Genome Biology, 7:R66.

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