Two Families with Sibling Recurrence of the 17Q21.31 Microdeletion Syndrome Due to Low-Grade Mosaicism
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
The Endocytic Membrane Trafficking Pathway Plays a Major Role
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository RESEARCH ARTICLE The Endocytic Membrane Trafficking Pathway Plays a Major Role in the Risk of Parkinson’s Disease Sara Bandres-Ciga, PhD,1,2 Sara Saez-Atienzar, PhD,3 Luis Bonet-Ponce, PhD,4 Kimberley Billingsley, MSc,1,5,6 Dan Vitale, MSc,7 Cornelis Blauwendraat, PhD,1 Jesse Raphael Gibbs, PhD,7 Lasse Pihlstrøm, MD, PhD,8 Ziv Gan-Or, MD, PhD,9,10 The International Parkinson’s Disease Genomics Consortium (IPDGC), Mark R. Cookson, PhD,4 Mike A. Nalls, PhD,1,11 and Andrew B. Singleton, PhD1* 1Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 2Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain 3Transgenics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 4Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 5Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom 6Department of Pathophysiology, University of Tartu, Tartu, Estonia 7Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 8Department of Neurology, Oslo University Hospital, Oslo, Norway 9Department of Neurology and Neurosurgery, Department of Human Genetics, McGill University, Montréal, Quebec, Canada 10Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada 11Data Tecnica International, Glen Echo, Maryland, USA ABSTRACT studies, summary-data based Mendelian randomization Background: PD is a complex polygenic disorder. -
Transcript Variants of Genes Involved in Neurodegeneration Are Differentially Regulated by the APOE and MAPT Haplotypes
G C A T T A C G G C A T genes Article Transcript Variants of Genes Involved in Neurodegeneration Are Differentially Regulated by the APOE and MAPT Haplotypes Sulev Koks 1,2,* , Abigail L. Pfaff 1,2, Vivien J. Bubb 3 and John P. Quinn 3 1 Perron Institute for Neurological and Translational Science, Perth, WA 6009, Australia; [email protected] 2 Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA 6150, Australia 3 Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK; [email protected] (V.J.B.); [email protected] (J.P.Q.) * Correspondence: [email protected]; Tel.: +61-(0)-8-6457-0313 Abstract: Genetic variations at the Apolipoprotein E (ApoE) and microtubule-associated protein tau (MAPT) loci have been implicated in multiple neurogenerative diseases, but their exact molecular mechanisms are unclear. In this study, we performed transcript level linear modelling using the blood whole transcriptome data and genotypes of the 570 subjects in the Parkinson’s Progression Markers Initiative (PPMI) cohort. ApoE, MAPT haplotypes and two SNPs at the SNCA locus (rs356181, rs3910105) were used to detect expression quantitative trait loci eQTLs associated with the transcriptome and differential usage of transcript isoforms. As a result, we identified 151 genes associated with the genotypic variations, 29 cis and 122 trans eQTL positions. Profound effect with genome-wide significance of ApoE e4 haplotype on the expression of TOMM40 transcripts was identified. This finding potentially explains in part the frequently established genetic association Citation: Koks, S.; Pfaff, A.L.; Bubb, with the APOE e4 haplotypes in neurodegenerative diseases. -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
Transcriptome Analysis of Adipose Tissues from Two Fat-Tailed Sheep
Li et al. BMC Genomics (2018) 19:338 https://doi.org/10.1186/s12864-018-4747-1 RESEARCHARTICLE Open Access Transcriptome analysis of adipose tissues from two fat-tailed sheep breeds reveals key genes involved in fat deposition Baojun Li1, Liying Qiao1, Lixia An2, Weiwei Wang1, Jianhua Liu1, Youshe Ren1, Yangyang Pan1, Jiongjie Jing1 and Wenzhong Liu1* Abstract Background: The level of fat deposition in carcass is a crucial factor influencing meat quality. Guangling Large-Tailed (GLT) and Small-Tailed Han (STH) sheep are important local Chinese fat-tailed breeds that show distinct patterns of fat depots. To gain a better understanding of fat deposition, transcriptome profiles were determined by RNA-sequencing of perirenal, subcutaneous, and tail fat tissues from both the sheep breeds. The common highly expressed genes (co-genes) in all the six tissues, and the genes that were differentially expressed (DE genes) between these two breeds in the corresponding tissues were analyzed. Results: Approximately 47 million clean reads were obtained for each sample, and a total of 17,267 genes were annotated. Of the 47 highly expressed co-genes, FABP4, ADIPOQ, FABP5,andCD36 were the four most highly transcribed genes among all the known genes related to adipose deposition. FHC, FHC-pseudogene,and ZC3H10 were also highly expressed genes and could, thus, have roles in fat deposition. A total of 2091, 4233, and 4131 DE genes were identified in the perirenal, subcutaneous, and tail fat tissues between the GLT and STH breeds, respectively. Gene Ontology (GO) analysis showed that some DE genes were associated with adipose metabolism. -
Comparative Transcriptome Profiling of Longissimus Muscle Tissues From
www.nature.com/scientificreports OPEN Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Received: 29 April 2016 Accepted: 31 August 2016 Merino and Small Tail Han sheep Published: 20 September 2016 Limin Sun, Man Bai, Lujie Xiang, Guishan Zhang, Wei Ma & Huaizhi Jiang The Qianhua Mutton Merino (QHMM) is a new sheep (Ovis aries) variety with better meat performance compared with the traditional local variety Small Tail Han (STH) sheep. We aimed to evaluate the transcriptome regulators associated with muscle growth and development between the QHMM and STH. We used RNA-Seq to obtain the transcriptome profiles of the longissimus muscle from the QHMM and STH. The results showed that 960 genes were differentially expressed (405 were up- regulated and 555 were down-regulated). Among these, 463 differently expressed genes (DEGs) were probably associated with muscle growth and development and were involved in biological processes such as skeletal muscle tissue development and muscle cell differentiation; molecular functions such as catalytic activity and oxidoreductase activity; cellular components such as mitochondrion and sarcoplasmic reticulum; and pathways such as metabolic pathways and citrate cycle. From the potential genes, a gene-act-network and co-expression-network closely related to muscle growth and development were identified and established. Finally, the expressions of nine genes were validated by real-time PCR. The results suggested that some DEGs, including MRFs, GXP1 and STAC3, play crucial roles in muscle growth and development processes. This genome-wide transcriptome analysis of QHMM and STH muscle is reported for the first time. Sheep (Ovis aries) form a large part of the global animal husbandry industry. -
A Chromosome-Centric Human Proteome Project (C-HPP) To
computational proteomics Laboratory for Computational Proteomics www.FenyoLab.org E-mail: [email protected] Facebook: NYUMC Computational Proteomics Laboratory Twitter: @CompProteomics Perspective pubs.acs.org/jpr A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17 † ‡ § ∥ ‡ ⊥ Suli Liu, Hogune Im, Amos Bairoch, Massimo Cristofanilli, Rui Chen, Eric W. Deutsch, # ¶ △ ● § † Stephen Dalton, David Fenyo, Susan Fanayan,$ Chris Gates, , Pascale Gaudet, Marina Hincapie, ○ ■ △ ⬡ ‡ ⊥ ⬢ Samir Hanash, Hoguen Kim, Seul-Ki Jeong, Emma Lundberg, George Mias, Rajasree Menon, , ∥ □ △ # ⬡ ▲ † Zhaomei Mu, Edouard Nice, Young-Ki Paik, , Mathias Uhlen, Lance Wells, Shiaw-Lin Wu, † † † ‡ ⊥ ⬢ ⬡ Fangfei Yan, Fan Zhang, Yue Zhang, Michael Snyder, Gilbert S. Omenn, , Ronald C. Beavis, † # and William S. Hancock*, ,$, † Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States ‡ Stanford University, Palo Alto, California, United States § Swiss Institute of Bioinformatics (SIB) and University of Geneva, Geneva, Switzerland ∥ Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States ⊥ Institute for System Biology, Seattle, Washington, United States ¶ School of Medicine, New York University, New York, United States $Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia ○ MD Anderson Cancer Center, Houston, Texas, United States ■ Yonsei University College of Medicine, Yonsei University, -
Integrating Genome-Wide Association and Transcriptome Predicted Model Identify Novel Target Genes with Osteoporosis
bioRxiv preprint doi: https://doi.org/10.1101/771543; this version posted September 16, 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. Integrating genome-wide association and transcriptome predicted model identify novel target genes with osteoporosis Peng Yin1,*,†, Muchun Zhu1,†, Fan Hu1, Jiaxin Jiang1, Li Yin1, Shuqiang Wang1and Yingxiang Li2,3 1 Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, China. 2 WeGene, Shenzhen, China. 3 Department of Anthropology and Ethnology, Xiamen University, Xiamen, China. * Correspondence: [email protected] † Muchun Zhu, Peng Yin contributed equally to this work. Abstract Osteoporosis (OP) is a highly polygenetic disease which is usually characterized by low bone mineral density. Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with bone mineral density. However, the biological mechanisms of these loci remain elusive. To identify potential causal genes of the associated loci, we detected trait-gene expression associations by transcriptome-wide association study (TWAS) method. It directly imputes gene expression effects from GWAS data, using a statistical prediction model trained on GTEx reference transcriptome data, with blood and skeletal tissues data. Then we performed a colocalization analysis to evaluate the posterior probability of biological patterns: association characterized by a single shared causal variant or two distinct causal variants. The ultimate analysis identified 276 candidate genes, including 3 novel loci, 204 novel candidate genes and 69 replicated from GWAS. -
GSGS: a Computational Framework to Reconstruct Signaling Pathways
GSGS: A Computational Framework to Reconstruct Signaling Pathways from Gene Sets Lipi Acharya§, Thair Judeh∗,†, Zhansheng Duan♭, Michael Rabbat‡ and Dongxiao Zhu∗,†,¶ ∗Department of Computer Science, Wayne State University, 5057 Woodward Avenue, Detroit, MI 48202, USA. §Department of Computer Science, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. †Research Institute for Children, Children’s Hospital, New Orleans, LA 70118, USA. ♭Center for Information Engineering Science Research, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, China. ‡Department of Electrical and Computer Engineering, McGill University, 3480 University Street, Montr´eal, Qu´ebec H3A 2A7, Canada. ¶To whom correspondence should be addressed. abstract We propose a novel two-stage Gene Set Gibbs Sampling (GSGS) framework, to reverse engineer signaling pathways from gene sets inferred from molecular profiling data. We hy- pothesize that signaling pathways are structurally an ensemble of overlapping linear signal transduction events which we encode as Information Flow Gene Sets (IFGS’s). We infer pathways from gene sets corresponding to these events subjected to a random permutation of genes within each set. In Stage I, we use a source separation algorithm to derive un- ordered and overlapping IFGS’s from molecular profiling data, allowing cross talk among IFGS’s. In Stage II, we develop a Gibbs sampling like algorithm, Gene Set Gibbs Sampler, arXiv:1101.3983v3 [q-bio.QM] 13 Oct 2011 to reconstruct signaling pathways from the latent IFGS’s derived in Stage I. The novelty of this framework lies in the seamless integration of the two stages and the hypothesis of IFGS’s as the basic building blocks for signal pathways. -
17Q21.31 Microduplications
17q21.31 microduplications rarechromo.org Sources and 17q21.31 microduplications references A 17q21.31 microduplication is a rare genetic condition caused by an extra part of one of the body’s 46 chromosomes – The information in this chromosome 17. For healthy development, chromosomes guide is based on should contain just the right amount of genetic material (DNA) medical information – not too much and not too little. Even a tiny piece of extra on a number of material can influence development. individuals. Most information is drawn from the published Background on Chromosomes medical literature. Chromosomes are structures found in the nucleus of the The first-named body’s cells. Every chromosome contains thousands of genes author and publication which may be thought of as individual instruction booklets (or date are given to allow recipes) that contain all the genetic information telling the you to look for the body how to develop, grow and function. Chromosomes (and abstracts or original genes) usually come in pairs with one half of each articles on the chromosome pair being inherited from each parent. Humans internet in PubMed have 23 pairs of chromosomes giving a total of 46 individual (www.ncbi.nlm.nih.gov chromosomes. Of these 46 chromosomes, two are the sex /pubmed/). If you wish, chromosomes that determine gender. Females have two X you can obtain most chromosomes and males have one X chromosome and one Y articles from Unique. chromosome. The remaining 44 chromosomes are grouped in In addition, this leaflet 22 pairs, numbered 1 to 22 approximately from the largest to draws on information the smallest. -
Biopython Tutorial and Cookbook
Biopython Tutorial and Cookbook Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock Last Update – September 2008 Contents 1 Introduction 5 1.1 What is Biopython? ......................................... 5 1.1.1 What can I find in the Biopython package ......................... 5 1.2 Installing Biopython ......................................... 6 1.3 FAQ .................................................. 6 2 Quick Start – What can you do with Biopython? 8 2.1 General overview of what Biopython provides ........................... 8 2.2 Working with sequences ....................................... 8 2.3 A usage example ........................................... 9 2.4 Parsing sequence file formats .................................... 10 2.4.1 Simple FASTA parsing example ............................... 10 2.4.2 Simple GenBank parsing example ............................. 11 2.4.3 I love parsing – please don’t stop talking about it! .................... 11 2.5 Connecting with biological databases ................................ 11 2.6 What to do next ........................................... 12 3 Sequence objects 13 3.1 Sequences and Alphabets ...................................... 13 3.2 Sequences act like strings ...................................... 14 3.3 Slicing a sequence .......................................... 15 3.4 Turning Seq objects into strings ................................... 15 3.5 Concatenating or adding sequences ................................. 16 3.6 Nucleotide sequences and -
Jputs;Sth;Gy;Fiyf;Fofk; Common Admission Test for M.Phil/Ph.D
jpUts;Sth; gy;fiyf;fofk; THIRUVALLUVAR UNIVERSITY Vellore – 632 115 Tamil Nadu, India Common Admission Test for M.Phil/Ph.D. BIOTECHNOLOGY Syllabus UNIT 1: BIOCHEMISTRY:Units of measurements of solutesin solution. Biomolecules: Definitions,nomenclature, classification, structure, chemistry and properties of carbohydrates, amino acids,proteins, lipids and Nucleic acids. Metabolism: Metabolism of Carbohydrates - EMP, TCA, HMP. Amino Acids, Lipids and Nucleic Acids-Their Biosynthesis. Mechanism of Oxidative Phosphorylation and its Inhibitors, Photophosphorylation.Enzymology: Enzyme – classificationandstructure,allosteric mechanism, regulatory and active sites,activationenergy,iso- enzymes, enzyme kinetics(MM, LB plot, Km) and hormones. Clinical biochemistry: Blood sugar level-factors controlling blood sugar level – hypo, hyper glycemia, diabetes mellitus & its types. GTT, Metabolism of bilirubin, jaundicetypes& differential diagnosis and liver function tests. Renal functional test and gastric function test. CELL AND MOLECULAR BIOLOGY:Structure and function of cells in prokaryotes and eukaryotes, Structure and organization of Membrane - Membrane Model, active and passive, transport channels and pumps, Structure & Biogenesis of Mitochondria and Chloroplast. Structure of Endoplasmic reticulum, Golgi complex, lysosomes.Celldivision:Mitosis, Meiosis, regulation of cell cycle; factors regulating cell cycle. Nucleic acid structure,Genome Organization. DNA replication: Enzymes and mechanisms of DNA replication in prokaryotes and eukaryotes, Telomeres,