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Search Results: 1 - 10 of 93405 matches for " X. Shirley Liu "
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Getting Started in Tiling Microarray Analysis
X. Shirley Liu
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030183
Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays
W. Evan Johnson,X. Shirley Liu,Jun S. Liu
Statistics , 2009, DOI: 10.1214/09-AOAS248
Abstract: Microarrays have been developed that tile the entire nonrepetitive genomes of many different organisms, allowing for the unbiased mapping of active transcription regions or protein binding sites across the entire genome. These tiling array experiments produce massive correlated data sets that have many experimental artifacts, presenting many challenges to researchers that require innovative analysis methods and efficient computational algorithms. This paper presents a doubly stochastic latent variable analysis method for transcript discovery and protein binding region localization using tiling array data. This model is unique in that it considers actual genomic distance between probes. Additionally, the model is designed to be robust to cross-hybridized and nonresponsive probes, which can often lead to false-positive results in microarray experiments. We apply our model to a transcript finding data set to illustrate the consistency of our method. Additionally, we apply our method to a spike-in experiment that can be used as a benchmark data set for researchers interested in developing and comparing future tiling array methods. The results indicate that our method is very powerful, accurate and can be used on a single sample and without control experiments, thus defraying some of the overhead cost of conducting experiments on tiling arrays.
Identifying Positioned Nucleosomes with Epigenetic Marks in Human from ChIP-Seq
Yong Zhang, Hyunjin Shin, Jun S Song, Ying Lei, X Shirley Liu
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-537
Abstract: This paper describes a novel computational framework to efficiently identify positioned nucleosomes and their histone modification profiles from nucleosome-resolution histone modification ChIP-Seq data. We applied the algorithm to histone methylation ChIP-Seq data in human CD4+ T cells and identified over 438,000 positioned nucleosomes, which appear predominantly at functionally important regions such as genes, promoters, DNase I hypersensitive regions, and transcription factor binding sites. Our analysis shows the identified nucleosomes play a key role in epigenetic gene regulation within those functionally important regions via their positioning and histone modifications.Our method provides an effective framework for studying nucleosome positioning and epigenetic marks in mammalian genomes. The algorithm is open source and available at http://liulab.dfci.harvard.edu/NPS/ webcite.Chromatin structure widely manifests itself in various aspects of mammalian development and disease. The key structural element of chromatin is the nucleosome, which consists of an octameric histone core wrapped by 146 bps of DNA [1]. Nucleosomes play two major roles in epigenetic regulation of gene expression. The first is to limit DNA accessibility to cellular machinery [2-5] through specific positioning of nucleosome core particles, which can be remodeled in an ATP-dependent manner. The second is to regulate transcriptional activities through covalent modifications (e.g. methylation, acetylation and phosphorylation) of the tails of four core histone types H2A, H2B, H3 and H4 [6-9]. Therefore, characterizing the global locations and modification marks of positioned nucleosomes is a crucial step towards unraveling the mechanism of epigenetic regulation in eukaryotes.High-throughput mapping of positioned nucleosomes has been conducted in yeast [10,11] and selected human promoters [12] using high resolution tiling microarrays. Several studies have also profiled genome-scale histone modifica
Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model
Naiqian Zhang?,Haiyun Wang?,Yun Fang?,Jun Wang?,Xiaoqi Zheng?,X. Shirley Liu
PLOS Computational Biology , 2015, DOI: 10.1371/journal.pcbi.1004498
Abstract: The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
Model-based analysis of two-color arrays (MA2C)
Jun S Song, W Evan Johnson, Xiaopeng Zhu, Xinmin Zhang, Wei Li, Arjun K Manrai, Jun S Liu, Runsheng Chen, X Shirley Liu
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-8-r178
Abstract: High-density oligonucleotide tiling-microarrays currently provide the most powerful method of investigating genome-wide protein-DNA interactions and chromatin structure in vivo. As illustrated in Figure 1, the technology allows tiling regions of interest on DNA with probes separated by short chromosome distances. A typical NimbleGen array has about 400,000 probes that are 40-60 nucleotides long and separated by 10-100 base-pairs (bp) in the genome. Both NimbleGen and Agilent provide two-color microarrays with flexible designs where one can choose probes that are partially overlapping for high resolution studies of chromatin structure. The experimental protocol requires labeling the treatment and control samples with fluorescent dyes, usually green and red, and then hybridizing them on a microarray. Each probe's intensity of fluorescence upon scanning the microarray will give an approximate measure of the abundance of DNA that hybridized to the probe. Because each probe has an associated genomic coordinate, one can plot the intensities as a function of chromosome locations and then reconstruct the enrichment of particular DNA or RNA fragments compared to the genomic background. As in Figure 1, the enriched regions appear as peaks, which can represent protein-bound DNA fragments.The technology is continuing to develop rapidly, but certainly not without difficulties that are imposed by the inherent complexity of biological systems and, as such, must be addressed by computational means for the foreseeable future. The main computational challenge lies in properly normalizing the data and distinguishing true peaks from the noisy background. Many problems that confound this type of microarray data actually arise from probe-specific biases, such as differential sequence copy numbers in the genome or variable melting temperature dependent upon the GC content. For Affymetrix tiling arrays, several good model-based methods already exist to account for probe biases and, thus, t
Genomics in 2011: challenges and opportunities
David J Adams, Bonnie Berger, Olivier Harismendy, Curtis Huttenhower, X Shirley Liu, Chad L Myers, Alicia Oshlack, John L Rinn, Albertha J M Walhout
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-12-137
Abstract: DA: I have a relatively broad area of scientific interest encompassing cancer genetics, mouse genetics and genome sequencing. With my cancer hat on, the paper that impressed me most was by Sodir et al. [1], which showed that inhibition of Myc can cause cancer regression even in advanced tumors. This work illustrates the critical role that Myc plays in tumorigenesis, and clearly defines it as a therapeutic target. In terms of genome sequencing, I'm very impressed by some of the new de novo assembly algorithms, such as Li et al. [2], and I think our contribution of sequencing mouse genomes, Keane et al. [3], is also important and will be of great use to the mouse genetics community. These sequences now make it possible to take a systems biology approach to mouse genetics and to link variants to phenotypes like never before.BB: I would say the Foldit paper by David Baker's group [4] because it shows a new model of research is possible. Some large-scale problems such as protein folding remain challenging to solve with just computational approaches, and the problem is too difficult for even experts to solve manually. However, by designing tools that can break such problems into puzzles that people, even non-experts, can play with in their spare time can provide cutting-edge solutions [5].OH: In cancer genetics, two studies struck me this year. The first is the discovery and analysis of chromothripsis by Peter Campbell at the Sanger Institute [6]. This idea that some of the complex chromosomal rearrangements observed in cancer cells can come from a single catastrophic event, where an entire chromosome gets shattered and stuck back together at random, is astounding. This has changed our understanding of cancer genome instability and repair: a truly novel finding and groundbreaking idea [7]. The other cancer genetic study is the work of Inder Verma [8], Rusty Gage and colleagues at the Salk Institute [9] demonstrating a new function for BRCA1 involved in heterochromatin chr
Model-based Analysis of ChIP-Seq (MACS)
Yong Zhang, Tao Liu, Clifford A Meyer, Jér?me Eeckhoute, David S Johnson, Bradley E Bernstein, Chad Nusbaum, Richard M Myers, Myles Brown, Wei Li, X Shirley Liu
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-9-r137
Abstract: The determination of the 'cistrome', the genome-wide set of in vivo cis-elements bound by trans-factors [1], is necessary to determine the genes that are directly regulated by those trans-factors. Chromatin immunoprecipitation (ChIP) [2] coupled with genome tiling microarrays (ChIP-chip) [3,4] and sequencing (ChIP-Seq) [5-8] have become popular techniques to identify cistromes. Although early ChIP-Seq efforts were limited by sequencing throughput and cost [2,9], tremendous progress has been achieved in the past year in the development of next generation massively parallel sequencing. Tens of millions of short tags (25-50 bases) can now be simultaneously sequenced at less than 1% the cost of traditional Sanger sequencing methods. Technologies such as Illumina's Solexa or Applied Biosystems' SOLiD? have made ChIP-Seq a practical and potentially superior alternative to ChIP-chip [5,8].While providing several advantages over ChIP-chip, such as less starting material, lower cost, and higher peak resolution, ChIP-Seq also poses challenges (or opportunities) in the analysis of data. First, ChIP-Seq tags represent only the ends of the ChIP fragments, instead of precise protein-DNA binding sites. Although tag strand information and the approximate distance to the precise binding site could help improve peak resolution, a good tag to site distance estimate is often unknown to the user. Second, ChIP-Seq data exhibit regional biases along the genome due to sequencing and mapping biases, chromatin structure and genome copy number variations [10]. These biases could be modeled if matching control samples are sequenced deeply enough. However, among the four recently published ChIP-Seq studies [5-8], one did not have a control sample [5] and only one of the three with control samples systematically used them to guide peak finding [8]. That method requires peaks to contain significantly enriched tags in the ChIP sample relative to the control, although a small ChIP peak region often
Cistrome: an integrative platform for transcriptional regulation studies
Tao Liu, Jorge A Ortiz, Len Taing, Clifford A Meyer, Bernett Lee, Yong Zhang, Hyunjin Shin, Swee S Wong, Jian Ma, Ying Lei, Utz J Pape, Michael Poidinger, Yiwen Chen, Kevin Yeung, Myles Brown, Yaron Turpaz, X Shirley Liu
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-8-r83
Abstract: The term 'cistrome' refers to the set of cis-acting targets of a trans-acting factor on a genome-wide scale, also known as the in vivo genome-wide location of transcription factors or histone modifications. Cistromes were initially identified using chromatin immunoprecipitation (ChIP) combined with microarrays (ChIP-chip) [1]. However, with the recent advent of next generation sequencing (NGS) technologies, ChIP combined with NGS (ChIP-seq) [2] has become the more popular technique due to its higher sensitivity and resolution.Computational analyses of cistrome data have become increasingly complex and integrative. Investigators often examine the data from many different angles by combining cistrome, epigenome, genomic sequence, and transcriptome analyses. Many algorithms and tools have been published over the years to facilitate such analyses. However, these tools require investigators to have both the hardware resources and computational expertise to install, configure, and run these different algorithms effectively. Integrated platforms such as CisGenome [3] and seqMINER [4] have been developed to streamline data analyses; however, the maintenance of these platforms demands suitable hardware resources and computational skills. In addition, these tools lack useful features such as the integration of cistrome data with gene expression analysis, data sharing between researchers, and reusable analysis workflows.To address the above challenges, we developed the Cistrome platform to provide a flexible bioinformatics workbench with an analysis platform for ChIP-chip/seq and gene expression microarray analysis. Cistrome was built on top of Galaxy [5], an open-source web based computational framework that allows the easy integration of different tools. Cistrome integrates useful functions specific for ChIP-chip/seq and gene expression analyses. These functions were implemented in a modular fashion to allow easy incorporation of new tools in the future. Cistrome was deploye
Family structure and wellbeing of out-of-wedlock children: The significance of the biological parents' relationship
Frank Heiland,Shirley H. Liu
Demographic Research , 2006,
Abstract: This study examines the role of the relationship between the biological parents in determining child wellbeing using longitudinal data from the Fragile Families and Child Wellbeing Study (FFCWS). We extend prior research by considering children born to unmarried parents in an investigation of the effect of the relationship structure between the biological parents on infant health and behavior. The main findings are that children born to cohabiting biological parents (i) realize better outcomes, on average, than those born to mothers who are less involved with the child's biological father, and (ii) whose parents marry within a year after childbirth do not display significantly better outcomes than children of parents who continue to cohabit. Furthermore, children born to cohabiting or visiting biological parents who end their relationship within the first year of the child's life are up to 9 percent more likely to have asthma compared to children whose biological parents remain (romantically) involved. The results are robust to a rich set of controls for socioeconomic status, health endowments, home investments, and relationship characteristics.
X. Liu
Abstract: . In this paper, we characterize the complete space-like submanifolds with parallel mean curvature vector satisfying $H^2=\frac{4(n-1)c}{n^2}$ in the de Sitter space completely.
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