ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data

<p>Abstract</p> <p>Background</p> <p>ChIP-Seq is a powerful tool for identifying the interaction between genomic regulators and their bound DNAs, especially for locating transcription factor binding sites. However, high cost and high rate of false discovery of transcription factor binding sites identified from ChIP-Seq data significantly limit its application.</p> <p>Results</p> <p>Here we report a new algorithm, ChIP-PaM, for identifying transcription factor target regions in ChIP-Seq datasets. This algorithm makes full use of a protein-DNA binding pattern by capitalizing on three lines of evidence: 1) the tag count modelling at the peak position, 2) pattern matching of a specific tag count distribution, and 3) motif searching along the genome. A novel data-based two-step eFDR procedure is proposed to integrate the three lines of evidence to determine significantly enriched regions. Our algorithm requires no technical controls and efficiently discriminates falsely enriched regions from regions enriched by true transcription factor (TF) binding on the basis of ChIP-Seq data only. An analysis of real genomic data is presented to demonstrate our method.</p> <p>Conclusions</p> <p>In a comparison with other existing methods, we found that our algorithm provides more accurate binding site discovery while maintaining comparable statistical power.</p

Metformin and the gastrointestinal tract

Metformin is an effective agent with a good safety profile that is widely used as a first-line treatment for type 2 diabetes, yet its mechanisms of action and variability in terms of efficacy and side effects remain poorly understood. Although the liver is recognised as a major site of metformin pharmacodynamics, recent evidence also implicates the gut as an important site of action. Metformin has a number of actions within the gut. It increases intestinal glucose uptake and lactate production, increases GLP-1 concentrations and the bile acid pool within the intestine, and alters the microbiome. A novel delayed-release preparation of metformin has recently been shown to improve glycaemic control to a similar extent to immediate-release metformin, but with less systemic exposure. We believe that metformin response and tolerance is intrinsically linked with the gut. This review examines the passage of metformin through the gut, and how this can affect the efficacy of metformin treatment in the individual, and contribute to the side effects associated with metformin intolerance

Discovering Transcription Factor Binding Sites in Highly Repetitive Regions of Genomes with Multi-Read Analysis of ChIP-Seq Data

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis (ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome (uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome (multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Autistic behavior in boys with fragile X syndrome: social approach and HPA-axis dysfunction

The primary goal of this study was to examine environmental and neuroendocrine factors that convey increased risk for elevated autistic behavior in boys with Fragile X syndrome (FXS). This study involves three related analyses: (1) examination of multiple dimensions of social approach behaviors and how they vary over time, (2) investigation of mean levels and modulation of salivary cortisol levels in response to social interaction, and (3) examination of the relationship of social approach and autistic behaviors to salivary cortisol. Poor social approach and elevated baseline and regulation cortisol are discernible traits that distinguish boys with FXS and ASD from boys with FXS only and from typically developing boys. In addition, blunted cortisol change is associated with increased severity of autistic behaviors only within the FXS and ASD group. Boys with FXS and ASD have distinct behavioral and neuroendocrine profiles that differentiate them from those with FXS alone and typically developing boys

Integrating Multiple Biomarkers of Fish Health: A Case Study of Fish Health in Ports

Biomarkers of fish health are recognised as valuable biomonitoring tools that inform on the impact of pollution on biota. The integration of a suite of biomarkers in a statistical analysis that better illustrates the effects of exposure to xenobiotics on living organisms is most informative; however, most published ecotoxicological studies base the interpretation of results on individual biomarkers rather than on the information they carry as a set. To compare the interpretation of results from individual biomarkers with an interpretation based on multivariate analysis, a case study was selected where fish health was examined in two species of fish captured in two ports located in Western Australia. The suite of variables selected included chemical analysis of white muscle, body condition index, liver somatic index (LSI), hepatic ethoxyresorufin-O-deethylase activity, serum sorbitol dehydrogenase activity, biliary polycyclic aromatic hydrocarbon metabolites, oxidative DNA damage as measured by serum 8-oxo-dG, and stress protein HSP70 measured on gill tissue. Statistical analysis of individual biomarkers suggested little consistent evidence of the effects of contaminants on fish health. However, when biomarkers were integrated as a set by principal component analysis, there was evidence that the health status of fish in Fremantle port was compromised mainly due to increased LSI and greater oxidative DNA damage in fish captured within the port area relative to fish captured at a remote site. The conclusions achieved using the integrated set of biomarkers show the importance of viewing biomarkers of fish health as a set of variables rather than as isolated biomarkers of fish health

Evidence for the h_b(1P) meson in the decay Upsilon(3S) --> pi0 h_b(1P)

Using a sample of 122 million Upsilon(3S) events recorded with the BaBar detector at the PEP-II asymmetric-energy e+e- collider at SLAC, we search for the $h_b(1P)$ spin-singlet partner of the P-wave chi_{bJ}(1P) states in the sequential decay Upsilon(3S) --> pi0 h_b(1P), h_b(1P) --> gamma eta_b(1S). We observe an excess of events above background in the distribution of the recoil mass against the pi0 at mass 9902 +/- 4(stat.) +/- 2(syst.) MeV/c^2. The width of the observed signal is consistent with experimental resolution, and its significance is 3.1sigma, including systematic uncertainties. We obtain the value (4.3 +/- 1.1(stat.) +/- 0.9(syst.)) x 10^{-4} for the product branching fraction BF(Upsilon(3S)-->pi0 h_b) x BF(h_b-->gamma eta_b).Comment: 8 pages, 4 postscript figures, submitted to Phys. Rev. D (Rapid Communications

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge