High-resolution computational models of genome binding events

Abstract

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

Toggle involving cis -interfering noncoding RNAs controls variegated gene expression in yeast

“…The data were transformed under the assumption that Cy3 ϭ Cy5 is a good fit. JBD algorithm identified binding events (64).…”

ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes

“…To compute a combined statistic representing the probability of a binding event within the region spanned by multiple probes, we adapt a commonly used strategy (19) of using a fixed-size sliding window and integrating the values of probes falling within this window. Based on published measurements of fragmentation lengths (7), in this work we used a 500-bp window and a step size of 150 bp. Assuming that measurements from adjacent probes are independent in the null hypothesis, Fisher's method can again be applied to integrate the values from nearby probes.…”

“…Recently, several elegant ChIP 2 analysis methods have been proposed to tackle problems such as integrating measurements from adjacent probes (3)(4)(5)(6) or inferring binding site locations at subprobe resolution (7). However, the lower-level problem of developing an accurate error model to define meaningful statistical thresholds has received comparably little attention [see SI and Fig.…”

Genome-wide identification of DNA–protein interactions using chromatin immunoprecipitation coupled with flow cell sequencing

“…This can make the identification of the actual binding site within the probe difficult, especially for transcription factors with poorly characterized binding site preferences or that bind highly degenerate sequences. Even state-of-the art commercial arrays (Qi et al 2006) cannot offer the spatial resolution possible with ChIP-seq. In fact, using ChIP-seq, the actual binding site of a factor can often be identified within 10-30 bp of the peak maximum (Kharchenko et al 2008, Zhang et al 2008a (Fig.…”