Quantitative Biomedical Research Center

Dallas, TX, United States

Quantitative Biomedical Research Center

Dallas, TX, United States
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Zhong R.,Quantitative Biomedical Research Center | Kim H.S.,Yonsei University | Kim M.,Quantitative Biomedical Research Center | Kim M.,Simmons Comprehensive Cancer Center | And 4 more authors.
Nucleic Acids Research | Year: 2014

A challenge for large-scale siRNA loss-of-function studies is the biological pleiotropy resulting from multiple modes of action of siRNA reagents. A major confounding feature of these reagents is the microRNA-like translational quelling resulting from short regions of oligonucleotide complementarity to many different messenger RNAs. We developed a computational approach, deconvolution analysis of RNAi screening data, for automated quantitation of off-target effects in RNAi screening data sets. Substantial reduction of off-target rates was experimentally validated in five distinct biological screens across different genome-wide siRNA libraries. A public-access graphical-user-interface has been constructed to facilitate application of this algorithm. © 2014 The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.


Chu Y.,001 Forest Park Road | Wang T.,Quantitative Biomedical Research Center | Dodd D.,001 Forest Park Road | Xie Y.,Quantitative Biomedical Research Center | And 3 more authors.
Nucleic Acids Research | Year: 2015

RNA sequencing (RNA-Seq) is a powerful tool for analyzing the identity of cellular RNAs but is often limited by the amount of material available for analysis. In spite of extensive efforts employing existing protocols, we observed that it was not possible to obtain useful sequencing libraries from nuclear RNA derived from cultured human cells after crosslinking and immunoprecipitation (CLIP). Here, we report a method for obtaining strand-specific small RNA libraries for RNA sequencing that requires picograms of RNA. We employ an intramolecular circularization step that increases the efficiency of library preparation and avoids the need for intermolecular ligations of adaptor sequences. Other key features include random priming for full-length cDNA synthesis and gel-free library purification. Using our method, we generated CLIP-Seq libraries from nuclear RNA that had been UV-crosslinked and immunoprecipitated with anti-Argonaute 2 (Ago2) antibody. Computational protocols were developed to enable analysis of raw sequencing data and we observe substantial differences between recognition by Ago2 of RNA species in the nucleus relative to the cytoplasm. This RNA self-circularization approach to RNA sequencing (RC-Seq) allows data to be obtained using small amounts of input RNA that cannot be sequenced by standard methods. © 2015 The Author(s).


PubMed | Quantitative Biomedical Research Center and 001 Forest Park Road
Type: Journal Article | Journal: Nucleic acids research | Year: 2015

RNA sequencing (RNA-Seq) is a powerful tool for analyzing the identity of cellular RNAs but is often limited by the amount of material available for analysis. In spite of extensive efforts employing existing protocols, we observed that it was not possible to obtain useful sequencing libraries from nuclear RNA derived from cultured human cells after crosslinking and immunoprecipitation (CLIP). Here, we report a method for obtaining strand-specific small RNA libraries for RNA sequencing that requires picograms of RNA. We employ an intramolecular circularization step that increases the efficiency of library preparation and avoids the need for intermolecular ligations of adaptor sequences. Other key features include random priming for full-length cDNA synthesis and gel-free library purification. Using our method, we generated CLIP-Seq libraries from nuclear RNA that had been UV-crosslinked and immunoprecipitated with anti-Argonaute 2 (Ago2) antibody. Computational protocols were developed to enable analysis of raw sequencing data and we observe substantial differences between recognition by Ago2 of RNA species in the nucleus relative to the cytoplasm. This RNA self-circularization approach to RNA sequencing (RC-Seq) allows data to be obtained using small amounts of input RNA that cannot be sequenced by standard methods.


Zhan X.,Quantitative Biomedical Research Center | Zhan X.,University of Texas Southwestern Medical Center | Hu Y.,A9.Com Inc | Li B.,Vanderbilt University | And 2 more authors.
Bioinformatics | Year: 2016

Motivation: Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data. © The Author 2016. Published by Oxford University Press.


Allen J.D.,Southwestern Medical Center | Wang S.,Southern Methodist University | Chen M.,Quantitative Biomedical Research Center | Girard L.,Ecole Polytechnique de Montréal | And 3 more authors.
Briefings in Bioinformatics | Year: 2012

Access to gene expression data has become increasingly common in recent years; however, analysis has become more difficult as it is often desirable to integrate data from different platforms. Probe mapping across microarray platforms is the first and most crucial step for data integration. In this article, we systematically review and compare different approaches to map probes across seven platforms from different vendors: U95A, U133A and U133 Plus 2.0 from Affymetrix, Inc.; HT-12 v1, HT-12v2 and HT-12v3 from Illumina, Inc.; and 4112A from Agilent, Inc. We use a unique data set, which contains 56 lung cancer cell line samples-each of which has been measured by two different microarray platforms-to evaluate the consistency of expression measurement across platforms using different approaches. Based on the evaluation from the empirical data set, the BLAST alignment of the probe sequences to a recent revision of the Transcriptome generated better results than using annotations provided by Vendors or from Bioconductor's Annotate package. However, a combination of all three methods (deemed the 'Consensus Annotation') yielded the most consistent expression measurement across platforms. To facilitate data integration across microarray platforms for the research community, we develop a user-friendly web-based tool, an API and an R package to map data across different microarray platforms from Affymetrix, Illumina and Agilent. Information on all three can be found at. http://qbrc.swmed.edu/software/probemapper/. © The Author 2011. Published by Oxford University Press.


Kwon I.,Southwestern Medical Center | Xiang S.,Southwestern Medical Center | Kato M.,Southwestern Medical Center | Wu L.,Southwestern Medical Center | And 6 more authors.
Science | Year: 2014

Many RNA regulatory proteins controlling pre-messenger RNA splicing contain serine:arginine (SR) repeats. Here, we found that these SR domains bound hydrogel droplets composed of fibrous polymers of the low-complexity domain of heterogeneous ribonucleoprotein A2 (hnRNPA2). Hydrogel binding was reversed upon phosphorylation of the SR domain by CDC2-like kinases 1 and 2 (CLK1/2). Mutated variants of the SR domains changing serine to glycine (SR-to-GR variants) also bound to hnRNPA2 hydrogels but were not affected by CLK1/2. When expressed in mammalian cells, these variants bound nucleoli. The translation products of the sense and antisense transcripts of the expansion repeats associated with the C9orf72 gene altered in neurodegenerative disease encode GRn and PRn repeat polypeptides. Both peptides bound to hnRNPA2 hydrogels independent of CLK1/2 activity. When applied to cultured cells, both peptides entered cells, migrated to the nucleus, bound nucleoli, and poisoned RNA biogenesis, which caused cell death. © 2015, American Association for the Advancement of Science. All rights reserved.


PubMed | Quantitative Biomedical Research Center and Southwestern Medical Center
Type: Journal Article | Journal: Nature chemical biology | Year: 2016

CD437 is a retinoid-like small molecule that selectively induces apoptosis in cancer cells, but not in normal cells, through an unknown mechanism. We used a forward-genetic strategy to discover mutations in POLA1 that coincide with CD437 resistance (POLA1(R)). Introduction of one of these mutations into cancer cells by CRISPR-Cas9 genome editing conferred CD437 resistance, demonstrating causality. POLA1 encodes DNA polymerase , the enzyme responsible for initiating DNA synthesis during the S phase of the cell cycle. CD437 inhibits DNA replication in cells and recombinant POLA1 activity in vitro. Both effects are abrogated by the identified POLA1 mutations, supporting POLA1 as the direct antitumor target of CD437. In addition, we detected an increase in the total fluorescence intensity and anisotropy of CD437 in the presence of increasing concentrations of POLA1 that is consistent with a direct binding interaction. The discovery of POLA1 as the direct anticancer target for CD437 has the potential to catalyze the development of CD437 into an anticancer therapeutic.


Wang C.,Genome Institute of Singapore | Zhan X.,Quantitative Biomedical Research Center | Liang L.,Boston University | Abecasis G.R.,University of Michigan | Lin X.,Boston University
American Journal of Human Genetics | Year: 2015

Accurate estimation of individual ancestry is important in genetic association studies, especially when a large number of samples are collected from multiple sources. However, existing approaches developed for genome-wide SNP data do not work well with modest amounts of genetic data, such as in targeted sequencing or exome chip genotyping experiments. We propose a statistical framework to estimate individual ancestry in a principal component ancestry map generated by a reference set of individuals. This framework extends and improves upon our previous method for estimating ancestry using low-coverage sequence reads (LASER 1.0) to analyze either genotyping or sequencing data. In particular, we introduce a projection Procrustes analysis approach that uses high-dimensional principal components to estimate ancestry in a low-dimensional reference space. Using extensive simulations and empirical data examples, we show that our new method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantially outperform LASER 1.0 in estimating fine-scale genetic ancestry. Specifically, LASER 2.0 can accurately estimate fine-scale ancestry within Europe using either exome chip genotypes or targeted sequencing data with off-target coverage as low as 0.05×. Under the framework of LASER 2.0, we can estimate individual ancestry in a shared reference space for samples assayed at different loci or by different techniques. Therefore, our ancestry estimation method will accelerate discovery in disease association studies not only by helping model ancestry within individual studies but also by facilitating combined analysis of genetic data from multiple sources. © 2015 The American Society of Human Genetics.


PubMed | Boston University, Quantitative Biomedical Research Center, Genome Institute of Singapore and University of Michigan
Type: Evaluation Studies | Journal: American journal of human genetics | Year: 2015

Accurate estimation of individual ancestry is important in genetic association studies, especially when a large number of samples are collected from multiple sources. However, existing approaches developed for genome-wide SNP data do not work well with modest amounts of genetic data, such as in targeted sequencing or exome chip genotyping experiments. We propose a statistical framework to estimate individual ancestry in a principal component ancestry map generated by a reference set of individuals. This framework extends and improves upon our previous method for estimating ancestry using low-coverage sequence reads (LASER 1.0) to analyze either genotyping or sequencing data. In particular, we introduce a projection Procrustes analysis approach that uses high-dimensional principal components to estimate ancestry in a low-dimensional reference space. Using extensive simulations and empirical data examples, we show that our new method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantially outperform LASER 1.0 in estimating fine-scale genetic ancestry. Specifically, LASER 2.0 can accurately estimate fine-scale ancestry within Europe using either exome chip genotypes or targeted sequencing data with off-target coverage as low as 0.05. Under the framework of LASER 2.0, we can estimate individual ancestry in a shared reference space for samples assayed at different loci or by different techniques. Therefore, our ancestry estimation method will accelerate discovery in disease association studies not only by helping model ancestry within individual studies but also by facilitating combined analysis of genetic data from multiple sources.


Tang H.,Quantitative Biomedical Research Center | Xiao G.,Quantitative Biomedical Research Center | Behrens C.,University of Houston | Schiller J.,University of Texas Southwestern Medical Center | And 10 more authors.
Clinical Cancer Research | Year: 2013

Purpose: Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC. Experimental Design: An 18-hub-gene prognosis signature was developed through a systems biology approach, and its prognostic value was evaluated in six independent cohorts. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC. Results: Using a cohort of 442 stage I to III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set that robustly predicted the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis, including NKX2-1, Aurora Kinase A, PRC1, CDKN3, MBIP, and RRM2. The 12-gene predictive signature was successfully validated in two independent datasets (n = 90 and 176). The predicted benefit group showed significant improvement in survival after ACT (UT Lung SPORE data: HR = 0.34, P = 0.017; JBR.10 clinical trial data: HR = 0.36, P = 0.038), whereas the predicted nonbenefit group showed no survival benefit for 2 datasets (HR = 0.80, P = 0.70; HR = 0.91, P = 0.82). Conclusions: This is the first study to integrate genetic aberration, genome-wide RNAi data, and mRNA expression data to identify a functional gene set that predicts which resectable patients with non-small cell lung cancer will have a survival benefit with ACT. © 2013 AACR.

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