Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Analysis Pipeline

摘要: The rapidly advancing high-throughput and next generation sequencing technologies facilitate deeper insights into the molecular mechanisms underlying expression of phenotypes in living organisms. Experimental data scientific publications following this technological advancement have accumulated public databases. Meaningful analysis currently available genomic databases requires sophisticated computational tools algorithms, presents considerable challenges to biologists without specialized training bioinformatics. To study their phenotype interest must prioritize large lists poorly characterized genes generated experiments. date, prioritization primarily been designed work with human diseases as defined by known be associated those diseases. There is therefore a need for more which are not related generally or no yet particular. Chromatin immunoprecipitation followed (ChIP-Seq) method choice gene regulation processes responsible cellular phenotypes. Among publicly pipelines processing ChIP-Seq data, there lack downstream composite motifs preferred binding distances DNA proteins. This thesis aimed address gap existing process provide rapid interpretation genes. Additionally, programs transcription factors were integrated pipeline expedited results. A algorithm linking non-disease described meaningful keywords was developed. can used candidate genetic targets factor produced analysis.