Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Project description:We used RNA-seq to profile gene expression changes during flg22 activated pattern-triggered immunity in multiple Brassicaceae including Capsella rubella, Cardamine hirsuta and Eutrema salsugineum as well as in multiple Arabidopsis thaliana accessions. This allows comparative transcriptomics within and across species to investigate the evolution of stress-responsive transcrption changes in these species.
Project description:ChIP-on-Chip experiment using chromatin from wild-type 5 weeks old swiss mice ovaries (Nishi et al, 2001) and an isovolumic blend of our custom anti-Foxl2 polyclonal antibodies (Cocquet J et al, 2002). Non precipitated sheared matched deproteinized chromatin (Input) was used a control to estimate genomic enrichment peaks (and thus Foxl2 binding sites) from IPed DNA. DNA from three independent ChIP assays (Input extractions) was pooled, and 100ng of DNA was linearly amplified using the Whole Genome Amplification kit (Sigma).
Project description:Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.