BackgroundComparative analysis of Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq), combined with transcriptomics data (RNA-seq), can provide critical insights into gene regulatory networks controlled by transcription factors in a given biological context. While multiple programs exist, which need to be individually installed and executed to generate such information, a user-friendly tool with low system requirements which combines the various computational processes into one package, using a docker container, is lacking.ResultsIn this study, we present a user-interactive, infrastructure-independent computational pipeline, called Motifizer, which uses open source tools for processing ChIP-Seq and RNA-seq datasets. Motifizer performs extensive analysis of raw input data, performing peak calling, de-novo motif analysis and differential gene expression analysis on ChIP and RNA-seq datasets. Additionally, Motifizer can be used for analysis and quantitative comparison of transcription factor binding sites in user defined genomic regions. Motifizer also allows easy addition and/or changes of parameters, thereby adding to the versatility of the tool.ConclusionThe Motifizer tool is an easy to use tool which uses a docker container system to install and execute ChIP-seq and RNA-seq data parsing. The Analysis module of Motifizer can be employed to identify putative targets involved in gene regulatory networks. Motifizer can be accessed by a large user base and does not require programming skill by the user. Motifizer can be locally installed from the repository (https://github.com/abhikbhattacharjee/Motifizer)

BackgroundComparative analysis of Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq), combined with transcriptomics data (RNA-seq), can provide critical insights into gene regulatory networks controlled by transcription factors in a given biological context. While multiple programs exist, which need to be individually installed and executed to generate such information, a user-friendly tool with low system requirements which combines the various computational processes into one package, using a docker container, is lacking.ResultsIn this study, we present a user-interactive, infrastructure-independent computational pipeline, called Motifizer, which uses open source tools for processing ChIP-Seq and RNA-seq datasets. Motifizer performs extensive analysis of raw input data, performing peak calling, de-novo motif analysis and differential gene expression analysis on ChIP and RNA-seq datasets. Additionally, Motifizer can be used for analysis and quantitative comparison of transcription factor binding sites in user defined genomic regions. Motifizer also allows easy addition and/or changes to analysis parameters, thereby adding to the versatility of the tool.ConclusionThe Motifizer tool is an easy to use tool which uses a docker container system to install and execute ChIP-seq and RNA-seq data parsing. The Analysis module of Motifizer can be employed to identify putative targets involved in gene regulatory networks. Motifizer can be accessed by a large user base and does not require programming skill by the user. Motifizer can be locally installed from the repository (https://github.com/abhikbhattacharjee/Motifizer)