DOCSLIB.ORG

A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast By Jessica M. Vera B.S. University of Wisconsin-Madison, 2007 A thesis submitted to the Faculty of the Graduate School in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Molecular, Cellular, and Developmental Biology 2015 This thesis entitled: A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast written by Jessica M. Vera has been approved for the Department of Molecular, Cellular, and Developmental Biology Tom Blumenthal Robin Dowell Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline iii Vera, Jessica M. (Ph.D., Molecular, Cellular and Developmental Biology) A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast Thesis Directed by Robin Dowell Cryptic unstable transcripts (CUTs) are a largely unexplored class of nuclear exosome degraded, non-coding RNAs in budding yeast. It is highly debated whether CUT transcription has a functional role in the cell or whether CUTs represent noise in the yeast transcriptome. I sought to ascertain the extent of conserved CUT expression across a variety of Saccharomyces yeast strains to further understand and characterize the nature of CUT expression. To this end I designed a Hidden Markov Model (HMM) to analyze strand-specific RNA sequencing data from nuclear exosome rrp6Δ mutants to identify and compare CUTs in four different yeast strains: S288c, Σ1278b, JAY291 (S.cerevisiae) and N17 (S.paradoxus). My RNA-seq based method has greatly expanded upon previous CUT annotations in S.cerevisiae, underscoring the extensive and pervasive nature of unstable transcription. Utilizing a four-way genomic alignment I identified a large population of CUTs with conserved syntenic expression across all four strains. Furthermore I observed that certain configurations of gene-CUT pairs, where CUT expression originates from a gene 5’ or 3’ nucleosome free region, correlate with distinct expression trends for the associated gene. Bidirectional gene-CUT pairs correlate with higher expression of genes, and antisense gene-CUT pairs correlate with reduced gene expression. Interestingly these effects on gene expression are most prevalent in the presence of conserved CUT expression. Additionally I have shown that CUTs lack a well-defined 3’ nucleosome free region that is commonly observed at protein-coding genes, and suggests that 3’ NFRs are not characteristic of Sen1-dependent terminated transcripts. iv Table of Contents Chapter I - Introduction ........................................................................................................... 1 Yeast ncRNAs ........................................................................................................................................... 2 The Basics of Transcription ....................................................................................................................... 3 Alternative RNAP II Transcription Termination Pathways......................................................................... 4 TRAMP (Trf4/Air2/Mtr4 polymerase complex) .......................................................................................... 7 Nuclear Exosome ...................................................................................................................................... 8 Evolutionary Context of Sen1-dependent Termination ........................................................................... 10 Cryptic Unstable Transcripts (CUTs) ...................................................................................................... 11 Conclusion ............................................................................................................................................... 12 Figures .................................................................................................................................................... 14 Chapter II - Survey of Cryptic Unstable Transcripts in Yeast ..............................................16 Authors' Contributions ............................................................................................................................. 16 Introduction .............................................................................................................................................. 16 Results and Discussion ........................................................................................................................... 18 Explicit duration HMM identifies CUTs de novo from RNA-seq data .................................................. 18 CUTs lack a defined 3’ nucleosome free region ................................................................................. 20 A large set of CUTs show conserved expression between S.cerevisiae and S.paradoxus ............... 22 Distinct trends of gene expression correlate with CUT expression in specific architectures with genes ............................................................................................................................................................ 24 Antisense CUT expression shows evidence of transcriptional interference on sense strand ............ 25 Divergent CUT expression correlates with higher gene expression ................................................... 27 Conclusion ............................................................................................................................................... 28 Methods ................................................................................................................................................... 29 Strain construction .............................................................................................................................. 29 Genome sequences and annotations ................................................................................................. 29 RNA-sequencing libraries ................................................................................................................... 30 Explicit duration hidden Markov model ............................................................................................... 30 CUT identification ................................................................................................................................ 31 Annotation overlap and significance test ............................................................................................ 31 Nucleosome occupancy and metagene analysis ................................................................................ 32 CUT transcription start site comparisons ............................................................................................ 32 Pecan whole genome alignment ......................................................................................................... 32 Conserved CUT expression ................................................................................................................ 33 CUT expression validation by RT-qPCR ............................................................................................. 33 NFR sharing between CUTs and protein-coding genes ..................................................................... 34 Figures .................................................................................................................................................... 35 Tables ...................................................................................................................................................... 49 v Chapter III - What Mechanisms Govern CUT Expression? ................................................................... 52 Introduction .............................................................................................................................................. 52 Results .................................................................................................................................................... 53 Discussion ............................................................................................................................................... 56 Methods ................................................................................................................................................... 57 Unique CUT Expression ...................................................................................................................... 57 Nucleosome Occupancy and Metagene Analysis............................................................................... 57 Promoter Nucleosome Occupancy Profile Clustering ......................................................................... 57 Figures .................................................................................................................................................... 58 Chapter IV - Assessment of Nascent CUT Expression by NET-qPCR ................................................. 64 Introduction .............................................................................................................................................. 64 Results ...................................................................................................................................................
Load more

Recommended publications
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • Modulation of Mrna and Lncrna Expression Dynamics by the Set2–Rpd3s Pathway
    ARTICLE Received 20 Apr 2016 | Accepted 7 Oct 2016 | Published 28 Nov 2016 DOI: 10.1038/ncomms13534 OPEN Modulation of mRNA and lncRNA expression dynamics by the Set2–Rpd3S pathway Ji Hyun Kim1,2,*, Bo Bae Lee1,2,*, Young Mi Oh1,2, Chenchen Zhu3,4,5, Lars M. Steinmetz3,4,5, Yookyeong Lee1, Wan Kyu Kim1, Sung Bae Lee6, Stephen Buratowski7 & TaeSoo Kim1,2 H3K36 methylation by Set2 targets Rpd3S histone deacetylase to transcribed regions of mRNA genes, repressing internal cryptic promoters and slowing elongation. Here we explore the function of this pathway by analysing transcription in yeast undergoing a series of carbon source shifts. Approximately 80 mRNA genes show increased induction upon SET2 deletion. A majority of these promoters have overlapping lncRNA transcription that targets H3K36me3 and deacetylation by Rpd3S to the mRNA promoter. We previously reported a similar mechanism for H3K4me2-mediated repression via recruitment of the Set3C histone deacetylase. Here we show that the distance between an mRNA and overlapping lncRNA promoter determines whether Set2–Rpd3S or Set3C represses. This analysis also reveals many previously unreported cryptic ncRNAs induced by specific carbon sources, showing that cryptic promoters can be environmentally regulated. Therefore, in addition to repression of cryptic transcription and modulation of elongation, H3K36 methylation maintains optimal expression dynamics of many mRNAs and ncRNAs. 1 Department of Life Science, Ewha Womans University, Seoul 03760, Korea. 2 The Research Center for Cellular Homeostasis, Ewha Womans University, Seoul 03760, Korea. 3 Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. 4 Stanford Genome Technology Center, Stanford University School of Medicine, Stanford, California 94305, USA.
    [Show full text]
  • PAR-CLIP Data Indicate That Nrd1-Nab3
    Webb et al. Genome Biology 2014, 15:R8 http://genomebiology.com/2014/15/1/R8 RESEARCH Open Access PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast Shaun Webb2, Ralph D Hector1, Grzegorz Kudla3 and Sander Granneman1,2* Abstract Background: Nrd1 and Nab3 are essential sequence-specific yeast RNA binding proteins that function as a heterodimer in the processing and degradation of diverse classes of RNAs. These proteins also regulate several mRNA coding genes; however, it remains unclear exactly what percentage of the mRNA component of the transcriptome these proteins control. To address this question, we used the pyCRAC software package developed in our laboratory to analyze CRAC and PAR-CLIP data for Nrd1-Nab3-RNA interactions. Results: We generated high-resolution maps of Nrd1-Nab3-RNA interactions, from which we have uncovered hundreds of new Nrd1-Nab3 mRNA targets, representing between 20 and 30% of protein-coding transcripts. Although Nrd1 and Nab3 showed a preference for binding near 5′ ends of relatively short transcripts, they bound transcripts throughout coding sequences and 3′ UTRs. Moreover, our data for Nrd1-Nab3 binding to 3′ UTRs was consistent with a role for these proteins in the termination of transcription. Our data also support a tight integration of Nrd1-Nab3 with the nutrient response pathway. Finally, we provide experimental evidence for some of our predictions, using northern blot and RT-PCR assays. Conclusions: Collectively, our data support the notion that Nrd1 and Nab3 function is tightly integrated with the nutrient response and indicate a role for these proteins in the regulation of many mRNA coding genes.
    [Show full text]
  • RNA Polymerase II CTD Phosphatase Rtr1 Fine-Tunes Transcription Termination
    PLOS GENETICS RESEARCH ARTICLE RNA Polymerase II CTD phosphatase Rtr1 fine-tunes transcription termination 1☯ 1☯ 1 Jose F. VictorinoID , Melanie J. FoxID , Whitney R. Smith-KinnamanID , Sarah A. Peck 1 1 1 1 1 JusticeID , Katlyn H. BurrissID , Asha K. Boyd , Megan A. Zimmerly , Rachel R. Chan , 1 2,3 1,3¤ Gerald O. Hunter , Yunlong LiuID , Amber L. MosleyID * 1 Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 2 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 3 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America a1111111111 a1111111111 ☯ These authors contributed equally to this work. a1111111111 ¤ Current address: Amber L. Mosley, Department of Biochemistry and Molecular Biology, Indiana University a1111111111 School of Medicine, Indianapolis, Indiana, United States of America a1111111111 * [email protected] Abstract OPEN ACCESS RNA Polymerase II (RNAPII) transcription termination is regulated by the phosphorylation Citation: Victorino JF, Fox MJ, Smith-Kinnaman status of the C-terminal domain (CTD). The phosphatase Rtr1 has been shown to regulate WR, Peck Justice SA, Burriss KH, Boyd AK, et al. serine 5 phosphorylation on the CTD; however, its role in the regulation of RNAPII termina- (2020) RNA Polymerase II CTD phosphatase Rtr1 tion has not been explored. As a consequence of RTR1 deletion, interactions within the ter- fine-tunes transcription termination. PLoS Genet mination machinery and between the termination machinery and RNAPII were altered as 16(3): e1008317. https://doi.org/10.1371/journal.
    [Show full text]
  • Genechip® Mouse Tiling 2.0R Array Set Is a Seven-Array Set Designed ORDERING INFORMATION for Chromatin Immunoprecipitation (Chip) Experiments
    ARRAYS Critical Specifi cations Feature Size 5 µm Tiling Resolution 35 base pair Hybridization Controls bioB, bioC, bioD, cre Tiling mRNA controls B. subtilis: dap, lys, phe, thr A.thaliana: CAB, RCA, RBCL, LTP4, LTP6, XCP2, RCP1, NAC1, TIM, PRKASE Array Format 49 Fluidics Protocol Fluidics Station 450: FS450_0001 Fluidics Station 400: EukGE-WS2v5, and manually add Array Holding Buffer to the cartridge prior to scan- ning Hybridization Volume 200 µL. The total fi ll volume of the cartridge is 250 µL. Library Files Mm35b_P01R_v01, Mm35b_P02R_v01, ® Mm35b_P03R_v01, Mm35b_P04R_v01, GeneChip Mouse Tiling 2.0R Mm35b_P05R_v01, Mm35b_P06R_v01, Array Set Mm35b_P07R_v01 For studying genome-wide protein/DNA interactions in chromatin ACCESSORY FILES immunoprecipitation experiments. Fluidics The fl uidics scripts can be downloaded from the Affymetrix web site. Addi- tional information, including lists of steps in the fl uidics protocol, can be found in the Affymetrix® Chromatin Immunoprecipitation Assay Protocol. Library Files Library fi les contain information about the probe array design layout and other characteristics, probe use and content, and scanning and analysis pa- rameters. Tiling arrays are accompanied by .bpmap fi les which associate each probe on the array with the genomic location. These fi les are unique for each probe array type. The library fi les can be downloaded from the following URL: www.affymetrix.com/support/technical/libraryfi lesmain.affx. INTENDED USE The GeneChip® Mouse Tiling 2.0R Array Set is a seven-array set designed ORDERING INFORMATION for chromatin immunoprecipitation (ChIP) experiments. Sequences used in the P/N Product Name Description design of the Mouse Tiling 2.0R Array Set were selected from NCBI mouse ARRAYS genome assembly (Build 33).
    [Show full text]
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.
    [Show full text]
  • Systematic Evaluation of Variability in Chip-Chip Experiments Using Predefined DNA Targets
    Downloaded from genome.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets David S. Johnson,1,24 Wei Li,2,24,25 D. Benjamin Gordon,3 Arindam Bhattacharjee,3 Bo Curry,3 Jayati Ghosh,3 Leonardo Brizuela,3 Jason S. Carroll,4 Myles Brown,5 Paul Flicek,6 Christoph M. Koch,7 Ian Dunham,7 Mark Bieda,8 Xiaoqin Xu,8 Peggy J. Farnham,8 Philipp Kapranov,9 David A. Nix,10 Thomas R. Gingeras,9 Xinmin Zhang,11 Heather Holster,11 Nan Jiang,11 Roland D. Green,11 Jun S. Song,2 Scott A. McCuine,12 Elizabeth Anton,1 Loan Nguyen,1 Nathan D. Trinklein,13 Zhen Ye,14 Keith Ching,14 David Hawkins,14 Bing Ren,14 Peter C. Scacheri,15 Joel Rozowsky,16 Alexander Karpikov,16 Ghia Euskirchen,17 Sherman Weissman,18 Mark Gerstein,16 Michael Snyder,16,17 Annie Yang,19 Zarmik Moqtaderi,20 Heather Hirsch,20 Hennady P. Shulha,21 Yutao Fu,22 Zhiping Weng,21,22 Kevin Struhl,20,26 Richard M. Myers,1,26 Jason D. Lieb,23,26 and X. Shirley Liu2,26 1–23[See full list of author affiliations at the end of the paper, just before the Acknowledgments section.] 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.
    [Show full text]
  • EMBO Encounters Issue43.Pdf
    WINTER 2019/2020 ISSUE 43 Nine group leaders selected Meet the first EMBO Global Investigators PAGE 6 Accelerating scientific publishing EMBO publishing costs Review Commons Making our journals’ platform announced finances public PAGE 3 PAGES 10 – 11 Welcome, Young Investigators! Contract replaces stipend Marking ten years 27 group leaders join the programme EMBO Postdoctoral Fellowships EMBO Molecular Medicine receive an update celebrates anniversary PAGES 4 – 5 PAGE 7 PAGE 13 www.embo.org TABLE OF CONTENTS EMBO NEWS EMBO news Review Commons: accelerating publishing Page 3 EMBO Molecular Medicine turns ten © Marietta Schupp, EMBL Photolab Marietta Schupp, © Page 13 Editorial MBO was founded by scientists for Introducing 27 new Young Investigators scientists. This philosophy remains at Pages 4-5 Ethe heart of our organization until today. EMBO Members are vital in the running of our Meet the first EMBO Global programmes and activities: they screen appli- Accelerating scientific publishing 17 journals on board Investigators cations, interview candidates, decide on fund- Review Commons will manage the transfer of ing, and provide strategic direction. On pages EMBO and ASAPbio announced pre-journal portable review platform the manuscript, reviews, and responses to affili- Page 6 8-9 four members describe why they chose to ate journals. A consortium of seventeen journals New members meet in Heidelberg dedicate their time to an EMBO Committee across six publishers (see box) have joined the Fellowships: from stipends to contracts Pages 14 – 15 and what they took away from the experience. n December 2019, EMBO, in partnership with decide to submit their work to a journal, it will project by committing to use the Review Commons Page 7 When EMBO was created, the focus lay ASAPbio, launched Review Commons, a multi- allow editors to make efficient editorial decisions referee reports for their independent editorial deci- specifically on fostering cross-border inter- Ipublisher partnership which aims to stream- based on existing referee comments.
    [Show full text]
  • A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
    Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context.
    [Show full text]
  • Modeling Dna Methylation Tiling Array Data
    Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 2010 - 22nd Annual Conference Proceedings MODELING DNA METHYLATION TILING ARRAY DATA Gayla Olbricht [email protected] Bruce A. Craig R. W. Doerge Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Olbricht, Gayla; Craig, Bruce A.; and Doerge, R. W. (2010). "MODELING DNA METHYLATION TILING ARRAY DATA," Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1061 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University MODELING DNA METHYLATION TILING ARRAY DATA Gayla R. Olbricht1, Bruce A. Craig1, and R. W. Doerge1;2 1Department of Statistics, Purdue University, West Lafayette, IN 47907, U.S.A. 2Department of Agronomy, Purdue University, West Lafayette, IN 47907, U.S.A. Abstract: Epigenetics is the study of heritable changes in gene function that occur without a change in DNA sequence. It has quickly emerged as an essential area for understanding inheri- tance and variation that cannot be explained by the DNA sequence alone. Epigenetic modifications have the potential to regulate gene expression and may play a role in diseases such as cancer.
    [Show full text]
  • Measuring Gene Expression Part 3 Key Steps in Microarray Analysis
    Measuring Gene Expression Part 3 David Wishart Bioinformatics 301 [email protected] Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (peak picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Comet Tailing • Often caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution. Uneven Spotting/Blotting • Problems with print tips or with overly viscous solution • Problems with humidity in spottiing chamber High Background • Insufficient Blocking • Precipitation of labelled probe Gridding Errors Spotting errors Uneven hybridization Gridding errors Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (spot picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Microarray Scanning PMT Pinhole Detector lens Laser Beam-splitter Objective Lens Dye Glass Slide Microarray Principles Laser 1 Laser 2 Green channel Red channel Scan and detect with overlay images Image process confocal laser system and normalize and analyze Microarray Images • Resolution – standard 10µm [currently, max 5µm] – 100µm spot on chip = 10 pixels in diameter • Image format – TIFF (tagged image file format) 16 bit (64K grey levels) – 1cm x 1cm image at 16 bit = 2Mb (uncompressed) – other formats exist i.e. SCN (Stanford University) • Separate image for each fluorescent sample – channel 1, channel 2, etc. Image
    [Show full text]
  • UCLA UCLA Electronic Theses and Dissertations
    UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities.
    [Show full text]