A generalized linear model for peak calling in ChIP-Seq data

Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) has become a routine for detecting genome-wide protein-DNA interaction. The success of ChIP-Seq data analysis highly depends on the quality of peak calling (i.e., to detect peaks of tag counts at a genomic location and evaluate if the peak corresponds to a real protein-DNA interaction event). The challenges in peak calling include (1) how to combine the forward and the reverse strand tag data to improve the power of peak calling and (2) how to account for the variation of tag data observed across different genomic locations. We introduce a new peak calling method based on the generalized linear model (GLMNB) that utilizes negative binomial distribution to model the tag count data and account for the variation of background tags that may randomly bind to the DNA sequence at varying levels due to local genomic structures and sequence contents. We allow local shifting of peaks observed on the forward and the reverse stands, such that at each potential binding site, a binding profile representing the pattern of a real peak signal is fitted to best explain the observed tag data with maximum likelihood. Our method can also detect multiple peaks within a local region if there are multiple binding sites in the region.     

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.     

Small-animal imaging using clinical positron emission tomography/computed tomography and super-resolution

Considering the high cost of dedicated small-animal positron emission tomography/computed tomography (PET/CT), an acceptable alternative in many situations might be clinical PET/CT. However, spatial resolution and image quality are of concern. The utility of clinical PET/CT for small-animal research and image quality improvements from super-resolution (spatial subsampling) were investigated. National Electrical Manufacturers Association (NEMA) NU 4 phantom and mouse data were acquired with a clinical PET/CT scanner, as both conventional static and stepped scans. Static scans were reconstructed with and without point spread function (PSF) modeling. Stepped images were postprocessed with iterative deconvolution to produce super-resolution images. Image quality was markedly improved using the super-resolution technique, avoiding certain artifacts produced by PSF modeling. The 2 mm rod of the NU 4 phantom was visualized with high contrast, and the major structures of the mouse were well resolved. Although not a perfect substitute for a state-of-the-art small-animal PET/CT scanner, a clinical PET/CT scanner with super-resolution produces acceptable small-animal image quality for many preclinical research studies.     

Model-based Analysis of ChIP-Seq (MACS)

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.     

ChIP技术及其在基因组水平上分析DNA与蛋白质相互作用

染色质免疫沉淀(Chromatin immunoprecipitaion, ChIP)技术是分析细胞内生理状态下DNA结合蛋白与基因组DNA相互作用的技术。ChIP与高密度芯片(ChIP-chip)或高通量测序(ChIP-Seq)相结合能产生大量的研究数据, 在细胞的基因表达调控网络研究中发挥重要作用。文章主要介绍ChIP、ChIP-chip和ChIP-Seq的技术特点以及发展趋势, 重点讨论了ChIP-Seq数据分析方法及相关的应用实例。     

Fast, automated implementation of temporally precise blind deconvolution of multiphasic excitatory postsynaptic currents

Records of excitatory postsynaptic currents (EPSCs) are often complex, with overlapping signals that display a large range of amplitudes. Statistical analysis of the kinetics and amplitudes of such complex EPSCs is nonetheless essential to the understanding of transmitter release. We therefore developed a maximum-likelihood blind deconvolution algorithm to detect exocytotic events in complex EPSC records. The algorithm is capable of characterizing the kinetics of the prototypical EPSC as well as delineating individual release events at higher temporal resolution than other extant methods. The approach also accommodates data with low signal-to-noise ratios and those with substantial overlaps between events. We demonstrated the algorithm's efficacy on paired whole-cell electrode recordings and synthetic data of high complexity. Using the algorithm to align EPSCs, we characterized their kinetics in a parameter-free way. Combining this approach with maximum-entropy deconvolution, we were able to identify independent release events in complex records at a temporal resolution of less than 250 μs. We determined that the increase in total postsynaptic current associated with depolarization of the presynaptic cell stems primarily from an increase in the rate of EPSCs rather than an increase in their amplitude. Finally, we found that fluctuations owing to postsynaptic receptor kinetics and experimental noise, as well as the model dependence of the deconvolution process, explain our inability to observe quantized peaks in histograms of EPSC amplitudes from physiological recordings.     

Design and evaluation of PCR primers which differentiate Escherichia coli O157:H7 and related serotypes

Aims:  To develop methods to differentiate Escherichia coli O157:H7 and related serotypes by the use of amplicon length polymorphism (ALP) analysis based on identifying DNA sequence deletions within highly homologous regions of three sequenced E. coli strains.
Methods and Results:  Potential primer locations along the ancestral genomic backbone were identified and evaluated against three sequenced genomes and then applied to a reference set of pathogenic E. coli strains. All 16 primer combinations generated the expected diagnostic fragments as predicted for the E. coli K12 MG1655, O157:H7 EDL933, and O157:H7B Sakai genomes.
Conclusions:  This study defines a collection of primers distributed along the length of the E. coli genome that were applied to ALP analysis methods to successfully differentiate between serotypes of E. coli O157:H7 and other E. coli serotypes.
Significance and Impact of the Study:  ALP-PCR analysis method was validated as an independent method of classification when compared with that of rep-PCR. The principles underlying ALP analysis can be readily applied for the detection and differentiation of other closely related microbial species because of the abundance of complete DNA sequence data for a large number of microbial genomes.