Tiling array data analysis: a multiscale approach using wavelets

<p>Abstract</p> <p>Background</p> <p>Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, <it>Coiflets</it>, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks.</p> <p>Results</p> <p>In our wavelet-transformed data, we observed that noise tends to be confined to small scales while the useful signal-of-interest spans multiple large scales. We were also able to show that wavelet coefficients due to non-specific cross-hybridization follow a log-normal distribution, and we used this fact in developing a thresholding procedure. In particular, wavelets allow one to set an unambiguous, absolute threshold, which has been hard to define in ChIP-chip experiments. One can set this threshold by requiring a similar confidence level at different length-scales of the transformed signal. We applied our algorithm to a number of representative ChIP-chip data sets, including those of Pol II and histone modifications, which have a diverse distribution of length-scales of biochemical activity, including some broad peaks.</p> <p>Conclusions</p> <p>Finally, we benchmarked our method in comparison to other approaches for scoring ChIP-chip data using spike-ins on the ENCODE Nimblegen tiling array. This comparison demonstrated excellent performance, with wavelets getting the best overall score.</p

Dual-mode of insulin action controls GLUT4 vesicle exocytosis

Insulin releases an intracellular brake and promotes fusion pore expansion to translocate GLUT4 vesicles, and switches vesicle trafficking between distinct exocytic circuits

A genomic analysis of RNA polymerase II modification and chromatin architecture related to 3′ end RNA polyadenylation

Genomic analyses have been applied extensively to analyze the process of transcription initiation in mammalian cells, but less to transcript 3′ end formation and transcription termination. We used a novel approach to prepare 3′ end fragments from polyadenylated RNA, and mapped the position of the poly(A) addition site using oligonucleotide arrays tiling 1% of the human genome. This approach revealed more 3′ ends than had been annotated. The distribution of these ends relative to RNA polymerase II (PolII) and di- and trimethylated lysine 4 and lysine 36 of histone H3 was compared. A substantial fraction of unannotated 3′ ends of RNA are intronic and antisense to the embedding gene. Poly(A) ends of annotated messages lie on average 2 kb upstream of the end of PolII binding (termination). Near the termination sites, and in some internal sites, unphosphorylated and C-terminal domain (CTD) serine 2 phosphorylated PolII (POLR2A) accumulate, suggesting pausing of the polymerase and perhaps dephosphorylation prior to release. Lysine 36 trimethylation occurs across transcribed genes, sometimes alternating with stretches of DNA in which lysine 36 dimethylation is more prominent. Lysine 36 methylation decreases at or near the site of polyadenylation, sometimes disappearing before disappearance of phosphorylated RNA PolII or release of PolII from DNA. Our results suggest that transcription termination loss of histone 3 lysine 36 methylation and later release of RNA polymerase. The latter is often associated with polymerase pausing. Overall, our study reveals extensive sites of poly(A) addition and provides insights into the events that occur during 3′ end formation

Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets

The most widely used method for detecting genome-wide protein–DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and “spike-ins” comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated