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5th International Conference on bioinformatics and Computational Biology 2013, BICoB 2013

作者： Ramadan, Emad Perincheri, Sudhir Sprecher, Emmett Harris, Lyndsay Tuck, David Information and Computer Science Department King Fahd University Dhahran Saudi Arabia Department of Pathology Yale University New Haven United States Computational Biology and Bioinformatics Program Yale University New Haven United States Division of Hematology and Oncology Case Western Reserve University Cleveland United States

ISBN: (纸本)9781622769711

Background: Understanding crosstalk and feedback among oncogenic pathways is a critical challenge to overcoming resistance to targeted anticancer therapy. The topology of biological networks has increasingly been used to complement studies based on individual genes or gene collections. We apply network analysis to understand interactions among critical biological signaling pathways in breast cancer. Results: An overlapping clustering of the networks showed highly distinct patterns in patients with high IGF versus low IGF axis. We demonstrate through cluster comparison metrics and permutation studies that these differences are unlikely to occur by chance. IRS-1, an IGF adaptor protein, is shown to have a highly central place in the IGF high as compared to the IGF low networks. We further demonstrate that network connections reveal information about interactions of genes in TGF-beta, MAPK, and other pathways known to interact with IGF. Conclusions: Network analyses can provide novel insights and hypotheses about signaling pathways involved in feedback and crosstalk in breast cancer.

关键词： Crosstalk

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Using Folding Ensemble and Stem Probability Maximization Methods to Predict RNA H-Type Pseudoknots

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Tsinghua science and Technology 2012年第6期17卷 691-700页

作者： Junilda Spirollari Shawn Xiong Wang Jason T.L. Wang Bioinformatics Program Department of Computer Science New Jersey Institute of Technology University Heights Department of Computer Science California State University

We present in this paper an ab initio method, named KnotFold, for RNA H-type pseudoknot prediction. Our method employs an ensemble of RNA folding tools and a filtering heuristic to generate a set of pseudoknot-free stems, and then predicts pseudoknots by utilizing a search technique with a pseudo-probability scoring scheme. Experimental results show that KnotFold achieves higher sensitivity than existing methods. The KnotFold package with documentation is freely available at http：//***/KnotFold.

关键词： RNA structure pseudoknots tool ensemble

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Proceedings of the 8th Annual Conference on the science of Dissemination and Implementation : Washington, DC, USA. 14-15 December 2015

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Implementation science : IS 2016年第2期11 Suppl 2卷 100页

作者： Chambers, David Simpson, Lisa Hill-Briggs, Felicia Neta, Gila Vinson, Cynthia Beidas, Rinad Marcus, Steven Aarons, Gregory Hoagwood, Kimberly Schoenwald, Sonja Evans, Arthur Hurford, Matthew Rubin, Ronnie Hadley, Trevor Barg, Frances Walsh, Lucia Adams, Danielle Mandell, David Martin, Lindsey Mignogna, Joseph Mott, Juliette Hundt, Natalie Kauth, Michael Kunik, Mark Naik, Aanand Cully, Jeffrey McGuire, Alan White, Dominique Bartholomew, Tom McGrew, John Luther, Lauren Rollins, Angie Salyers, Michelle Cooper, Brittany Funaiole, Angie Richards, Julie Lee, Amy Lapham, Gwen Caldeiro, Ryan Lozano, Paula Gildred, Tory Achtmeyer, Carol Ludman, Evette Addis, Megan Marx, Larry Bradley, Katharine VanDeinse, Tonya Wilson, Amy Blank Stacey, Burgin Powell, Byron Bunger, Alicia Cuddeback, Gary Barnett, Miya Stadnick, Nicole Brookman-Frazee, Lauren Lau, Anna Dorsey, Shannon Pullmann, Michael Mitchell, Shannon Schwartz, Robert Kirk, Arethusa Dusek, Kristi Oros, Marla Hosler, Colleen Gryczynski, Jan Barbosa, Carolina Dunlap, Laura Lounsbury, David O’Grady, Kevin Brown, Barry Damschroder, Laura Waltz, Thomas Ritchie, Mona Atkins, David Imel, Zac E. Xiao, Bo Can, Doğan Georgiou, Panayiotis Narayanan, Shrikanth Berkel, Cady Gallo, Carlos Sandler, Irwin Brown, C. Hendricks Wolchik, Sharlene Mauricio, Anne Marie Mehrotra, Sanjay Chandurkar, Dharmendra Bora, Siddhartha Das, Arup Tripathi, Anand Saggurti, Niranjan Raj, Anita Hughes, Eric Jacobs, Brian Kirkendall, Eric Loeb, Danielle Trinkley, Katy Yang, Michael Sprowell, Andrew Nease, Donald Lyon, Aaron Lewis, Cara Boyd, Meredith Melvin, Abigail Nicodimos, Semret Liu, Freda Jungbluth, Nathanial Flynn, Allen Landis-Lewis, Zach Sales, Anne Baloh, Jure Ward, Marcia Zhu, Xi Bennett, Ian Unutzer, Jurgen Mao, Johnny Proctor, Enola Vredevoogd, Mindy Chan, Ya-Fen Williams, Nathaniel Green, Phillip Bernstein, Steven Rosner, June-Marie DeWitt, Michelle Tetrault, Jeanette Dziura, James Hsiao, Allen Sussman, Scott O’Connor, Patrick Toll, Benjamin Jones, Michael Gassaway, Julie Tobin, Jonathan Zatzic Division of Cancer Control and Population Sciences National Cancer Institute National Institutes of Health Rockville USA AcademyHealth Washington USA Department of Medicine and Welch Center for Prevention Epidemiology and Clinical Research Johns Hopkins Medical Institutions Baltimore USA Psychiatry University of Pennsylvania Philadelphia USA Family Medicine and Community Health University of Pennsylvania Philadelphia USA School of Social Policy and Practice University of Pennsylvania Philadelphia USA Psychiatry UC San Diego La Jolla USA Child & Adolescent Psychiatry The Child Study Center at NYU Langone Medical Center New York NY New York USA Psychiatry and Behavioral Sciences MUSC Charleston USA DBHIDS City of Philadelphia Philadelphia USA CBH City of Philadelphia Philadelphia USA Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia USA Health Services Research & Development Department of Veterans Affairs & Baylor College of Medicine Houston USA Treatment Core Department of Veterans Affairs Waco USA National Center for PTSD Executive Division Department of Veterans Affairs White River Junction USA HSR&D HSR&D Center for Health Information and Communication Roudebush VA Medical Center Indianapolis USA Psychology Indiana University Purdue University Indianapolis Indianapolis USA Department of Psychiatric Rehabilitation and Counseling Rutgers University Newark USA Health Services Research & Development Richard L. Roudebush VAMC Indianapolis USA IUPUI Department of Psychology ACT Center of Indiana Indianapolis USA Human Development Washington State University Pullman USA Prevention Science Washington State University Pullman USA Group Health Research Institute Group Health Seattle USA Behavioral Health Services Group Health Seattle USA Preventive Care Group Health Seattle USA Health Services Research & Development Primary and Specialty Medical Care Service VA Puget Sound Seattle USA School of Social Work Un

A1 Introduction to the 8th Annual Conference on the science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing

关键词： Veteran Affair Motivational Interview Qualitative Comparative Analysis Community That Care Supplemental Nutrition Assistance program

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1006-LBP

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Human Immunology 2014年第6期75卷 481-481页

作者： Mack, Steven J. Yamaguchi, Ken D. Salamon, Hugh Pena, Anthony G. Horton, Robert M. Francis-Lyon, Patricia Maiers, Martin Hollenbach, Jill A. Center for Genetics Children’s Hospital Oakland Research Institute Oakland CA 94609 United States Knowledge Synthesis Inc. Berkeley CA 94710 United States Department of Computer Science University of San Francisco San Francisco CA 94117 United States Department of Microbiology and Immunology University of California San Francisco San Francisco CA 94143 United States Division of Bioinformatics National Marrow Donor Program Minneapolis MN 55413 United States

Aim Analyses of highly polymorphic HLA and KIR locus data require specialized tools and methods, but most modern analytical programs are tailored for SNPs. A typical analytical workflow for HLA and KIR data requires trafficking data between several programs, which is time intensive, error prone and limits reproducibility. The IDAWG has been developing an integrated data-management and analysis sy stem tailored to these important genetic systems. Methods We have developed the Toolkit for Immunogenomic Data Exchange and Storage (TIDES) and Bridging ImmunoGenomic Data-Analysis Workflow Gaps (BIGDAWG) systems to address the unmet need for consistent HLA and KIR data management and analysis. TIDES is a free, open-source system that converts HLA and KIR genotypes derived from widely used genotyping platforms into Genotype List (GL) Strings, which are registered with the NMDP’s GL Service (***). BIGDAWG is an automated pipeline, scripted in R, that performs common data analyses of multi-locus highly polymorphic genetic data characteristic of HLA and KIR genes. Results TIDES stores GL String-encoded data with project-related data, and exports these to PED and POP data-analysis formats. TIDES can be deployed on an AWS EC2 or equivalent Linux environment, on a local network or on a standalone machine, per user preference. A prototype TIDES implementation is deployed at ***. Starting with unambiguous multi-locus genotype data for case-control groups, BIGDAWG estimates user-specified haplotypes, bins low-frequency haplotypes to enable chi-squared testing, calculates odds-ratios, confidence intervals and p-values for each haplotype, and generates figures and tables for each comparison. Conclusions The integration of TIDES and BIGDAWG will create a workflow that accepts ambiguous genotype data and returns case-control analysis results. This approach streamlines data analysis and allows the consistent storage and exchange of HLA and KIR genot

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Genome-wide Search for Coaxial Helical Stacking Motifs

Genome-wide Search for Coaxial Helical Stacking Motifs

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BIBE 2012

作者： Kevin Byron Jason T. L. Wang Dongrong Wen Bioinformatics Program and Department of Computer Science New Jersey Institute of Technology Newark

ISBN: (纸本)9781467343572

Motif finding in DNA, RNA and proteins plays an important role in life science research. In this paper, we present a computational approach to searching for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking motif occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

关键词： RNA Junctions Stacking Genomics bioinformatics Thermodynamics Proteins

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Modeling the breakage-fusion-bridge mechanism: Combinatorics and cancer genomics

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16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012

作者： Kinsella, Marcus Bafna, Vineet Bioinformatics and Systems Biology Program University of California San Diego San Diego CA 92093 United States Department of Computer Science and Engineering University of California San Diego San Diego CA 92093 United States

ISBN: (纸本)9783642296260

The breakage-fusion-bridge (BFB) mechanism was proposed over seven decades ago and is a source of genomic variability and gene amplification in cancer. Here we formally model and analyze the BFB mechanism, to our knowledge this first time this has been undertaken. We show that BFB can be modeled as successive inverted prefix duplications of a string. Using this model, we show that BFB can achieve a surprisingly broad range of amplification patterns. We find that a sequence of BFB operations can be found that nearly fits most patterns of copy number increases along a chromosome. We conclude that this limits the usefulness of methods like array CGH for detecting BFB and discuss other implications for understanding mechanisms of genomic instability. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Pattern matching

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71 Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 45-46页

作者： Raluca Gordan[a][b][*] Ning Shen[c] Iris Dror[d] Tianyin Zhou[d] John Horton[b] Remo Rohs[d] & Martha L. Bulyk[e][f] [a] Departments of Biostatistics and Bioinformatics Computer Science and Molecular Genetics and Microbiology Duke University Durham NC USA [b] Institute for Genome Sciences and Policy Duke University Durham NC 27708 USA Phone: Phone: +1 (919) 667-8673 Fax: Phone: +1 (919) 667-8673 [c] Department of Pharmacology and Cancer Biology Duke University Durham NC 27708 USA [d] Molecular and Computational Biology Program Departments of Biological Sciences Chemistry and Physics and Astronomy University of Southern California 1050 Childs Way Los Angeles CA 90089 USA [e] Division of Genetics Departments of Medicine and Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA 02115 USA [f] Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School Boston MA 02115 USA

Transcriptional regulation of gene expression is enacted mainly through binding of transcription factors (TFs) to specific, short DNA sites in cis-regulatory regions of genes. Most TFs are members of protein families that share a common DNA-binding domain and thus recognize similar DNA-binding sequences. It is not well understood why paralogous TFs often bind different genomic target sitesin vivoto effect different regulatory programs, despite apparently recognizing the same sequence motifs. Here, we designed custom protein-binding microarrays (PBMs) to analyze the DNA-binding specificities of twoSaccharomyces cerevisiaebasic helix-loop-helix (bHLH) proteins, Tye7 and Cbf1, as a model system. Our data reveal that E-box DNA-binding sequences (CAnnTG), when tested in the context of their native genomic flanking sequences, are bound differently by Cbf1 and Tye7. Computational models of the PBM data indicate that DNA sequence features located in the genomic sequences outside the E-box contribute to DNA-binding specificityin vitro. Our analyses suggest that these flanking regions affect DNA-binding specificity indirectly by influencing the three-dimensional structure of the E-box binding sites. Finally, we show that these subtle differences in intrinsic sequence preferences of Cbf1 and Tye7in vitrohelp to explain their differential DNA-binding preferencesin vivo. Our results provide further evidence that the local shape of DNA-binding sites may be an important feature in distinguishing the DNA-binding preferences among paralogous TFs and thus may play a widespread role in determining how transcriptional regulatory specificity within TF families is achieved.

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Secondary structure predictions for long RNA sequences based on inversion excursions - Preliminary results 12

Secondary structure predictions for long RNA sequences based...

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2012 ACM Conference on bioinformatics, Computational Biology and Biomedicine, BCB 2012

作者： Yehdego, Daniel T. Zhang, Boyu Taufer, Michela Kodimala, Vikram Kumar Reddy Vegesna, Rahulsimham Viswakula, Sameera Johnson, Kyle L. Leung, Ming-Ying Computational Science Program University of Texas El Paso United States Department of Computer and Information Sciences University of Delaware United States Bioinformatics Program Border Biomedical Research Center University of Texas El Paso United States Department of Mathematical Sciences University of Texas El Paso United States Department of Biological Sciences University of Texas El Paso United States Department of Mathematics and Statistics Old Dominion University Norfolk VA United States University of Texas M. D. Anderson Cancer Center Houston TX United States

ISBN: (纸本)9781450316705

The tremendous demand on computer memory and computing time for prediction of complex secondary structures limits the applicability of most RNA secondary structure prediction programs available to short RNA sequences. We propose to approach this problem by segmenting a long RNA sequence into shorter non-overlapping chunks, predicting the secondary structures of each chunk individually, and then assembling the prediction results to give the structure of the original sequence. The selection of cutting points is a crucial component of the approach. Noting that stem-loops and pseudoknots always contain an inversion, we developed two cutting methods, the centered and optimized methods, for segmenting long RNA sequences based on inversion excursions. For the majority of the sequences in a dataset of 50 RNAs from the RFAM database, the prediction algorithm PKnotsRG used with these cutting methods produces more accurate secondary structures than those predicted for the whole sequence without segmentation. Both the centered and optimized cutting methods outperform the naïve regular segmentation. These results support our claim that cutting is a promising approach for the prediction of long RNA sequences, and choosing the cutting points intelligently by considering sequence features such as inversion excursions can further enhance prediction accuracy.

关键词： Forecasting

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Corrigendum to “Weight loss intervention for young adults using mobile technology: Design and rationale of a randomized controlled trial — Cell phone Intervention for You (CITY)” [Contemp Clin Trials 37/2 (2014) 333–341]

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Contemporary Clinical Trials 2014年第2期39卷 351-351页

作者： Bryan C. Batch Crystal Tyson Jacqueline Bagwell Leonor Corsino Stephen Intille Pao-Hwa Lin Tony Lazenka Gary Bennett Hayden B. Bosworth Corrine Voils Steven Grambow Aziza Sutton Rachel Bordogna Matthew Pangborn Jenifer Schwager Kate Pilewski Carla Caccia Jasmine Burroughs Laura P. Svetkey Department of Medicine Division of Endocrinology Duke University Medical Center DUMC Box 3921 Durham NC 27710 USA Sarah W. Stedman Nutrition and Metabolism Center 3475 Erwin Road Duke University Medical Center Durham NC 27710 USA Department of Medicine Division of Nephrology Duke University Medical Center DUMC Box 103105 Durham NC 27710 USA College of Computer and Information Science Northeastern University 202 West Village H Office 450 360 Huntington Avenue Boston MA 02115 USA Bouvé College of Health Sciences Northeastern University 202 West Village H Office 450 360 Huntington Avenue Boston MA 02115 USA Department of Psychology & Neuroscience Duke University Medical Center Box 90086 417 Chapel Drive Duke University Durham NC 27708-0086 USA Duke Obesity Prevention Program Duke University Medical Center Durham NC USA Duke Global Health Institute Duke University Medical Center 310 Trent Drive Durham NC 27710 USA Center for Health Services Research in Primary Care Durham Veterans Affairs Medical Center 508 Fulton Street Durham NC 27705 USA Department of Medicine Division of General Internal Medicine Duke University Medical Center Box 3240 Durham NC 27710 USA Department of Psychiatry Duke University Medical Center 2301 Erwin Road Durham NC 27710 USA Duke University School of Nursing 307 Trent Drive DUMC 3322 Durham NC 27710 USA Department of Biostatistics and Bioinformatics Duke University Medical Center DUMC Box 2721 Durham NC 27710 USA

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