CR Cistrome: a Chip-Seq Database for Chromatin Regulators and Histone Modification Linkages in Human and Mouse
Total Page:16
File Type:pdf, Size:1020Kb
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. -
Genome-Wide Analysis of Transcriptional Bursting-Induced Noise in Mammalian Cells
bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Title: Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells Authors: Hiroshi Ochiai1*, Tetsutaro Hayashi2, Mana Umeda2, Mika Yoshimura2, Akihito Harada3, Yukiko Shimizu4, Kenta Nakano4, Noriko Saitoh5, Hiroshi Kimura6, Zhe Liu7, Takashi Yamamoto1, Tadashi Okamura4,8, Yasuyuki Ohkawa3, Itoshi Nikaido2,9* Affiliations: 1Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-0046, Japan 2Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama, 351-0198, Japan 3Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-0054, Japan 4Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 5Division of Cancer Biology, The Cancer Institute of JFCR, Tokyo, 135-8550, Japan 6Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8503, Japan 7Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA 8Section of Animal Models, Department of Infectious Diseases, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 9Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako, 351-0198, Japan *Corresponding authors Corresponding authors e-mail addresses Hiroshi Ochiai, [email protected] Itoshi Nikaido, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. -
UNIVERSITY of CALIFORNIA RIVERSIDE Investigations Into The
UNIVERSITY OF CALIFORNIA RIVERSIDE Investigations into the Role of TAF1-mediated Phosphorylation in Gene Regulation A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Cell, Molecular and Developmental Biology by Brian James Gadd December 2012 Dissertation Committee: Dr. Xuan Liu, Chairperson Dr. Frank Sauer Dr. Frances M. Sladek Copyright by Brian James Gadd 2012 The Dissertation of Brian James Gadd is approved Committee Chairperson University of California, Riverside Acknowledgments I am thankful to Dr. Liu for her patience and support over the last eight years. I am deeply indebted to my committee members, Dr. Frank Sauer and Dr. Frances Sladek for the insightful comments on my research and this dissertation. Thanks goes out to CMDB, especially Dr. Bachant, Dr. Springer and Kathy Redd for their support. Thanks to all the members of the Liu lab both past and present. A very special thanks to the members of the Sauer lab, including Silvia, Stephane, David, Matt, Stephen, Ninuo, Toby, Josh, Alice, Alex and Flora. You have made all the years here fly by and made them so enjoyable. From the Sladek lab I want to thank Eugene, John, Linh and Karthi. Special thanks go out to all the friends I’ve made over the years here. Chris, Amber, Stephane and David, thank you so much for feeding me, encouraging me and keeping me sane. Thanks to the brothers for all your encouragement and prayers. To any I haven’t mentioned by name, I promise I haven’t forgotten all you’ve done for me during my graduate years. -
Structure and Mechanism of the RNA Polymerase II Transcription Machinery
Downloaded from genesdev.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Structure and mechanism of the RNA polymerase II transcription machinery Allison C. Schier and Dylan J. Taatjes Department of Biochemistry, University of Colorado, Boulder, Colorado 80303, USA RNA polymerase II (Pol II) transcribes all protein-coding ingly high resolution, which has rapidly advanced under- genes and many noncoding RNAs in eukaryotic genomes. standing of the molecular basis of Pol II transcription. Although Pol II is a complex, 12-subunit enzyme, it lacks Structural biology continues to transform our under- the ability to initiate transcription and cannot consistent- standing of complex biological processes because it allows ly transcribe through long DNA sequences. To execute visualization of proteins and protein complexes at or near these essential functions, an array of proteins and protein atomic-level resolution. Combined with mutagenesis and complexes interact with Pol II to regulate its activity. In functional assays, structural data can at once establish this review, we detail the structure and mechanism of how enzymes function, justify genetic links to human dis- over a dozen factors that govern Pol II initiation (e.g., ease, and drive drug discovery. In the past few decades, TFIID, TFIIH, and Mediator), pausing, and elongation workhorse techniques such as NMR and X-ray crystallog- (e.g., DSIF, NELF, PAF, and P-TEFb). The structural basis raphy have been complemented by cryoEM, cross-linking for Pol II transcription regulation has advanced rapidly mass spectrometry (CXMS), and other methods. Recent in the past decade, largely due to technological innova- improvements in data collection and imaging technolo- tions in cryoelectron microscopy. -
Cpg Islands and the Regulation of Transcription
Downloaded from genesdev.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW CpG islands and the regulation of transcription Aime´e M. Deaton and Adrian Bird1 The Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3JR, United Kingdom Vertebrate CpG islands (CGIs) are short interspersed DNA In spite of their link with transcription, the functional sequences that deviate significantly from the average significance of CGIs is only just beginning to emerge. CGI genomic pattern by being GC-rich, CpG-rich, and pre- promoters turn out to have distinctive patterns of tran- dominantly nonmethylated. Most, perhaps all, CGIs are scription initiation and chromatin configuration. Their sites of transcription initiation, including thousands that regulation involves proteins (some of which specifically are remote from currently annotated promoters. Shared bind nonmethylated CpG) that influence the modifica- DNA sequence features adapt CGIs for promoter function tion status of CGI chromatin. In addition, the CpG moieties by destabilizing nucleosomes and attracting proteins that themselves are sometimes subject to cytosine methylation, create a transcriptionally permissive chromatin state. which correlates with stable shutdown of the associated Silencing of CGI promoters is achieved through dense promoter. Here we examine the properties shared by ver- CpG methylation or polycomb recruitment, again using tebrate CGIs and how transcription is regulated at these their distinctive DNA sequence composition. CGIs are sites. Recent related reviews include Illingworth and Bird therefore generically equipped to influence local chroma- (2009), Mohn and Schubeler (2009), and Blackledge and tin structure and simplify regulation of gene activity. Klose (2011). Vertebrate genomes are methylated predominantly at the Evolutionary conservation of CGIs dinucleotide CpG, and consequently are CpG-deficient owing to the mutagenic properties of methylcytosine CGIs are distinct in vertebrates due to their lack of DNA (Coulondre et al. -
Histone H3Q5 Serotonylation Stabilizes H3K4 Methylation and Potentiates Its Readout
Histone H3Q5 serotonylation stabilizes H3K4 methylation and potentiates its readout Shuai Zhaoa,1, Kelly N. Chuhb,1, Baichao Zhanga,1, Barbara E. Dulb, Robert E. Thompsonb, Lorna A. Farrellyc,d, Xiaohui Liue, Ning Xue, Yi Xuee, Robert G. Roederf, Ian Mazec,d,2, Tom W. Muirb,2, and Haitao Lia,g,2 aMinistry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China; bDepartment of Chemistry, Princeton University, Princeton, NJ 08540; cNash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029; dDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029; eNational Protein Science Technology Center, School of Life Sciences, Tsinghua University, Beijing 100084, China; fLaboratory of Biochemistry and Molecular Biology, The Rockefeller University, New York, NY 10065; and gTsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing 100084, China Edited by Peter Cheung, York University, Toronto, ON, Canada, and accepted by Editorial Board Member Karolin Luger November 12, 2020 (received for review August 7, 2020) Serotonylation of glutamine 5 on histone H3 (H3Q5ser) was recently established that H3K4me3 regulates gene expression through re- identified as a permissive posttranslational modification that coexists cruitment of nuclear factors that contain reader domains specific with adjacent lysine 4 trimethylation (H3K4me3). While the resulting for the mark. These include ATP-dependent chromatin remod- dual modification, H3K4me3Q5ser, is enriched at regions of active eling enzymes, as well as several transcription factors (13). -
Non-Canonical TAF Complexes Regulate Active Promoters in Human Embryonic Stem Cells
University of Massachusetts Medical School eScholarship@UMMS Program in Gene Function and Expression Publications and Presentations Molecular, Cell and Cancer Biology 2012-11-13 Non-canonical TAF complexes regulate active promoters in human embryonic stem cells Glenn A. Maston University of Massachusetts Medical School Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/pgfe_pp Part of the Genetics and Genomics Commons Repository Citation Maston GA, Zhu LJ, Chamberlain L, Lin L, Fang M, Green MR. (2012). Non-canonical TAF complexes regulate active promoters in human embryonic stem cells. Program in Gene Function and Expression Publications and Presentations. https://doi.org/10.7554/eLife.00068. Retrieved from https://escholarship.umassmed.edu/pgfe_pp/211 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Program in Gene Function and Expression Publications and Presentations by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. RESEARCH ARTICLE elife.elifesciences.org Non-canonical TAF complexes regulate active promoters in human embryonic stem cells Glenn A Maston1,2, Lihua Julie Zhu1,3, Lynn Chamberlain1,2, Ling Lin1,2, Minggang Fang1,2, Michael R Green1,2* 1Programs in Gene Function and Expression and Molecular Medicine, University of Massachusetts Medical School, Worcester, United States; 2Howard Hughes Medical Institute, Chevy Chase, United States; 3Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, United States Abstract The general transcription factor TFIID comprises the TATA-box-binding protein (TBP) and approximately 14 TBP-associated factors (TAFs). -
Genome-Wide Mapping of Histone H3 Lysine 4 Trimethylation in Eucalyptus
Hussey et al. BMC Plant Biology (2015) 15:117 DOI 10.1186/s12870-015-0499-0 RESEARCH ARTICLE Open Access Genome-wide mapping of histone H3 lysine 4 trimethylation in Eucalyptus grandis developing xylem Steven G Hussey1, Eshchar Mizrachi1, Andrew Groover2,3, Dave K Berger4 and Alexander A Myburg1* Abstract Background: Histone modifications play an integral role in plant development, but have been poorly studied in woody plants. Investigating chromatin organization in wood-forming tissue and its role in regulating gene expression allows us to understand the mechanisms underlying cellular differentiation during xylogenesis (wood formation) and identify novel functional regions in plant genomes. However, woody tissue poses unique challenges for using high-throughput chromatin immunoprecipitation (ChIP) techniques for studying genome-wide histone modifications in vivo. We investigated the role of the modified histone H3K4me3 (trimethylated lysine 4 of histone H3) in gene expression during the early stages of wood formation using ChIP-seq in Eucalyptus grandis, a woody biomass model. Results: Plant chromatin fixation and isolation protocols were optimized for developing xylem tissue collected from field-grown E. grandis trees. A “nano-ChIP-seq” procedure was employed for ChIP DNA amplification. Over 9 million H3K4me3 ChIP-seq and 18 million control paired-end reads were mapped to the E. grandis reference genome for peak-calling using Model-based Analysis of ChIP-Seq. The 12,177 significant H3K4me3 peaks identified covered ~1.5% of the genome and overlapped some 9,623 protein-coding genes and 38 noncoding RNAs. H3K4me3 library coverage, peaking ~600 - 700 bp downstream of the transcription start site, was highly correlated with gene expression levels measured with RNA-seq. -
Evolution of Gene Expression Between Closely Related Taxa of Mus
Evolution of gene expression between closely related taxa of Mus I n a u g u r a l – D i s s e r t a t i o n zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Universität zu Köln vorgelegt von Christian Voolstra aus Offenbach am Main Köln, 2006 Berichterstatter: Prof. Dr. Diethard Tautz Prof. Dr. Thomas Wiehe Tag der letzten mündlichen Prüfung: 29.05.2006 Table of Contents Table of Contents Table of Contents ........................................................................................................................i Danksagung................................................................................................................................ v Zusammenfassung.....................................................................................................................vi Abstract ...................................................................................................................................viii 1 Introduction ........................................................................................................................ 1 1.1 Microarrays as a tool to study evolution of gene expression ..................................... 1 1.2 Intra-specific transcriptome variation ........................................................................ 2 1.3 Inter-specific transcriptome variation - tempo and mode of transcriptome evolution3 1.4 Transcriptome divergence and speciation.................................................................. 4 1.5 The genetic -
Integrative Framework for Identification of Key Cell Identity Genes Uncovers
Integrative framework for identification of key cell PNAS PLUS identity genes uncovers determinants of ES cell identity and homeostasis Senthilkumar Cinghua,1, Sailu Yellaboinaa,b,c,1, Johannes M. Freudenberga,b, Swati Ghosha, Xiaofeng Zhengd, Andrew J. Oldfielda, Brad L. Lackfordd, Dmitri V. Zaykinb, Guang Hud,2, and Raja Jothia,b,2 aSystems Biology Section and dStem Cell Biology Section, Laboratory of Molecular Carcinogenesis, and bBiostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709; and cCR Rao Advanced Institute of Mathematics, Statistics, and Computer Science, Hyderabad, Andhra Pradesh 500 046, India Edited by Norbert Perrimon, Harvard Medical School and Howard Hughes Medical Institute, Boston, MA, and approved March 17, 2014 (received for review October 2, 2013) Identification of genes associated with specific biological pheno- (mESCs) for genes essential for the maintenance of ESC identity types is a fundamental step toward understanding the molecular resulted in only ∼8% overlap (8, 9), although many of the unique basis underlying development and pathogenesis. Although RNAi- hits in each screen were known or later validated to be real. The based high-throughput screens are routinely used for this task, lack of concordance suggest that these screens have not reached false discovery and sensitivity remain a challenge. Here we describe saturation (14) and that additional genes of importance remain a computational framework for systematic integration of published to be discovered. gene expression data to identify genes defining a phenotype of Motivated by the need for an alternative approach for iden- interest. We applied our approach to rank-order all genes based on tification of key cell identity genes, we developed a computa- their likelihood of determining ES cell (ESC) identity. -
393LN V 393P 344SQ V 393P Probe Set Entrez Gene
393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393 -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19