Starr: Simple Tiling Array Analysis of Affymetrix ChIP-chip data

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay for DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires a thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is the reliable identification and localization of genomic regions that bind a specific protein. The second step comprises comparison of binding profiles of functionally related proteins, or of binding profiles of the same protein in different genetic backgrounds or environmental conditions. Ultimately, one would like to gain a mechanistic understanding of the effects of DNA binding events on gene expression. We present a free, open-source R package Starr that, in combination with the package Ringo, facilitates the comparative analysis of ChIP-chip data across experiments and across different microarray platforms. Core features are data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions. The use of common Bioconductor classes ensures the compatibility with other R packages. Most importantly, Starr provides methods for integration of complementary genomics data, e.g., it enables systematic investigation of the relation between gene expression and dna binding

Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression.</p> <p>Results</p> <p>We present a free, open-source <b>R</b>/Bioconductor package <it>Starr </it>that facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. <it>Starr </it>includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, <it>Starr </it>automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome.</p> <p>Conclusion</p> <p><it>Starr </it>is an <b>R </b>package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e.g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. <it>Starr </it>enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.</p

An Integrated Pipeline for the Genome-Wide Analysis of Transcription Factor Binding Sites from ChIP-Seq

ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data

ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of <it>Drosophila melanogaster</it>.</p> <p>Results</p> <p>Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.</p> <p>Conclusions</p> <p>Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.</p

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients

Control of Neuroendocrine Cell Physiology by a Single Transcription Factor, Drosophila Basic Helix Loop Helix Regulator DIMMED

Neuroendocrine cells feature a large capacity for the processing, accumulation and regulated release of bioactive peptides and peptide hormones. The ultrastructural correlate of this regulated secretory pathway is a specialized organelle, called a dense core vesicle: DCV). DCVs are typically larger than conventional, small synaptic vesicles. Past work has identified intrinsic DCV proteins: non-cargo proteins, like the processing enzyme, carboxypeptidase) or ancillary ones that play a role in DCV trafficking and exocytosis: like CAPS, the Ca2+-dependent activator protein for secretion). Currently, there is a lack of understanding of the developmental and physiological mechanisms that permit neurosecretory cells to coordinate and scale the regulated secretory pathway. In this context, the basic helix-loop-helix transcription factor dimmed: dimm) is especially important in the fruit fly Drosophila, but it is not involved in neuroendocrine cell fate determination. Neuroendocrine cells require DIMM to accumulate, and process large amounts of secretory peptides, but DIMM does not target individual neuropeptide-encoding genes. Instead, we show that DIMM supports the complete resolution of NE-specific cellular properties by organizing the cellular machinery required to support a highly active RSP. The mouse orthologue Mist1 likewise plays a role in supporting the RSP of serous exocrine cells. This thesis has three goals. First, I evaluated a set of putative DIMM targets obtained by another scientist in the lab, and ask whether or not these are direct targets of this transcription factor. To accomplish this, I use in vivo chromatin immunoprecipitation: ChIP) followed by measuring DIMM binding to putative DIMM enhancers by quantitative Polymerase Chain Reaction: qPCR). This work is described in Chapter 2. Secondly, I extend DIMM ChIP analysis to identify direct DIMM transcriptional targets on a genome-wide level in vivo in adult neurons. This was done by DIMM chromatin immunoprecipitation coupled to tiling microarrays: ChIP-chip), and also applying Fluorescence Activated Cell Sorting: FACS) and deep sequencing: RNA-seq) to define the transcriptome of DIMM neuroendocrine cells, as described in Chapter 3. I then integrate the ChIP-chip and RNA-seq datasets to provide new viewpoints on how DIMM is used to coordinate and appropriately scale the RSP in NE cells. The intersection of the RNA-Seq and ChIP-chip data presents a list of genes that is likely to mediate the bulk of the transcriptional output of DIMM - i.e., its molecular mechanism . In order to conduct a functional assay and thus validate the list of intersected genes, I conducted a behavioral genetic screen. DIMM-expressing cells have previously been shown to regulate sleep amount in flies. I conducted an RNA interference-based screen, in which expression of individual DIMM target genes was knocked down in DIMM neurons and the effects of this manipulation on sleep were quantified. This in vivo validation provides an important filter with which to ascribe single gene functions and gives further insights into the general mechanisms by which DIMM operates

Differential genome-wide DNA methylation in prostate tumours from South African men

Background: DNA methylation is an epigenetic mechanism known to aid the progression of cancer, including prostate cancer. It is part of a cluster of molecular processes that initiate tumorigenesis and drive its early evolution by altering other molecular processes. While studies have looked at DNA methylation in prostate cancer, most have been limited by targeted gene analysis, with further bias towards non-African cohorts. Considering the enhanced coverage of more recent genome-wide arrays, such as the Illumina Infinium HumanMethylationEPIC BeadChip, which measures DNA methylation over more than 850,000 CpG sites genome-wide, many studies that have employed a more global approach to DNA methylation analysis are further limited by frequently utilising lower-coverage arrays. Due to the bias against African cohorts, African-relevant bioinformatic tools for the processing of African DNA methylation data, particularly generated by the EPIC array, are scarce. As a result, the genomic mechanisms that underlie African prostate cancer as well as the contribution of DNA methylation alterations to African prostate cancer are poorly understood. Results: Working with EPIC DNA methylation data, I present a novel established African-relevant genome-wide bioinformatic pipeline for the processing and normalisation of African tumour-derived genome-wide DNA methylation data. Pilot application of this pipeline on prostate tissue identified differentially methylated CpG dinucleotides that may contribute to aggressive prostate cancer in a small cohort of men of South African ancestry. Additionally, I identified top genes in South African prostate cancer that are significantly enriched for differentially methylated CpG sites. Finally, patient-matched genomic-epigenomic data integration revealed preliminary evidence for interplay between these two systems in African prostate cancer, although the identification of DNA methylation signatures would prove more insightful. Conclusions: Ultimately, this work highlights the marginalization of Africans in scientific research. As a preliminary solution to this underrepresentation, this dissertation provides a novel toolset to appropriately handle African DNA methylation data with the ultimate goal of generating a deeper understanding of the genomic mechanisms harboured within African prostate cancer, a field with limited knowledge. Potential improvements to this tool, complications encountered when interpreting epigenome-wide results as well as the near future of cancer genomics is discussed.Dissertation (MSc (Human Genetics))--University of Pretoria, 2021.Australian National Health and Medical Research CouncilGeneticsMSc (Human Genetics)Unrestricte