Limma: Linear Models for Microarray and RNA-Seq Data User’S Guide
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Bioconductor: Open Software Development for Computational Biology and Bioinformatics Robert C
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Collection Of Biostatistics Research Archive Bioconductor Project Bioconductor Project Working Papers Year 2004 Paper 1 Bioconductor: Open software development for computational biology and bioinformatics Robert C. Gentleman, Department of Biostatistical Sciences, Dana Farber Can- cer Institute Vincent J. Carey, Channing Laboratory, Brigham and Women’s Hospital Douglas J. Bates, Department of Statistics, University of Wisconsin, Madison Benjamin M. Bolstad, Division of Biostatistics, University of California, Berkeley Marcel Dettling, Seminar for Statistics, ETH, Zurich, CH Sandrine Dudoit, Division of Biostatistics, University of California, Berkeley Byron Ellis, Department of Statistics, Harvard University Laurent Gautier, Center for Biological Sequence Analysis, Technical University of Denmark, DK Yongchao Ge, Department of Biomathematical Sciences, Mount Sinai School of Medicine Jeff Gentry, Department of Biostatistical Sciences, Dana Farber Cancer Institute Kurt Hornik, Computational Statistics Group, Department of Statistics and Math- ematics, Wirtschaftsuniversitat¨ Wien, AT Torsten Hothorn, Institut fuer Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, DE Wolfgang Huber, Department for Molecular Genome Analysis (B050), German Cancer Research Center, Heidelberg, DE Stefano Iacus, Department of Economics, University of Milan, IT Rafael Irizarry, Department of Biostatistics, Johns Hopkins University Friedrich Leisch, Institut fur¨ Statistik und Wahrscheinlichkeitstheorie, Technische Universitat¨ Wien, AT Cheng Li, Department of Biostatistical Sciences, Dana Farber Cancer Institute Martin Maechler, Seminar for Statistics, ETH, Zurich, CH Anthony J. Rossini, Department of Medical Education and Biomedical Informat- ics, University of Washington Guenther Sawitzki, Statistisches Labor, Institut fuer Angewandte Mathematik, DE Colin Smith, Department of Molecular Biology, The Scripps Research Institute, San Diego Gordon K. -
Statistical Computing with Pathway Tools Using Rcyc
Statistical Computing with Pathway Tools using RCyc Statistical Computing with Pathway Tools using RCyc Tomer Altman [email protected] Biomedical Informatics, Stanford University Statistical Computing with Pathway Tools using RCyc R & BioConductor S: software community over 30 years of statistical computing, data mining, machine learning, and data visualization knowledge R: open-source S with a lazy Scheme interpreter at its heart (including closures, symbols, and even macros!) RCyc: an R package to allow the interaction between Pathway / Genome Databases and the wealth of biostatistics software in the R community Statistical Computing with Pathway Tools using RCyc BioConductor Figure: BioConductor: Thousands of peer-reviewed biostatistics packages. Statistical Computing with Pathway Tools using RCyc Software`R'-chitecture C code extension to R to allow Unix socket access Common Lisp code to hack in XML-based communication Make the life of *Cyc API developers easier. Currently supports exchange of numbers, strings, and lists R code and documentation Provides utilities for starting PTools and marshaling data types Assumes user is familiar with the PTools API: http://bioinformatics.ai.sri.com/ptools/api/ All wrapped up in R package Easily installs via standard command-line R interface Statistical Computing with Pathway Tools using RCyc Simple Example callPToolsFn("so",list("'meta")) callPToolsFn("get-slot-value",list("'proton", "'common-name")) callPToolsFn("get-class-all-instances",list("'|Reactions|")) Statistical Computing with Pathway Tools using RCyc Availability http://github.com/taltman/RCyc Linked from PTools website Statistical Computing with Pathway Tools using RCyc Next Steps Dynamic instantiation of API functions in R Coming next release (coordination with BRG) Make development of *Cyc APIs easier, less boilerplate code Frame to Object import/export Provide \RCelot" functionality to slurp Ocelot frames directly into R S4 reference objects for direct data access Support for more exotic data types Symbols, hash tables, arrays, structures, etc. -
A New Genetic Method for Isolating Functionally Interacting Genes
Copyright 2000 by the Genetics Society of America A New Genetic Method for Isolating Functionally Interacting Genes: High plo1؉-Dependent Mutants and Their Suppressors De®ne Genes in Mitotic and Septation Pathways in Fission Yeast C. Fiona Cullen,*,² Karen M. May,* Iain M. Hagan,³ David M. Glover²,§ and Hiroyuki Ohkura*,² *Institute of Cell and Molecular Biology, The University of Edinburgh, Edinburgh EH9 3JR, United Kingdom, ²Department of Anatomy and Physiology, Medical Sciences Institute, The University of Dundee, Dundee DD1 4HN, United Kingdom, ³School of Biological Sciences, The University of Manchester, Manchester M13 9PT, United Kingdom and §Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom Manuscript received February 2, 2000 Accepted for publication April 10, 2000 ABSTRACT We describe a general genetic method to identify genes encoding proteins that functionally interact with and/or are good candidates for downstream targets of a particular gene product. The screen identi®es mutants whose growth depends on high levels of expression of that gene. We apply this to the plo1ϩ gene that encodes a ®ssion yeast homologue of the polo-like kinases. plo1ϩ regulates both spindle formation and septation. We have isolated 17 high plo1ϩ-dependent (pld) mutants that show defects in mitosis or septation. Three mutants show a mitotic arrest phenotype. Among the 14 pld mutants with septation defects, 12 mapped to known loci: cdc7, cdc15, cdc11 spg1, and sid2. One of the pld mutants, cdc7-PD1, was selected for suppressor analysis. As multicopy suppressors, we isolated four known genes involved in septation in ®ssion yeast: spg1ϩ, sce3ϩ, cdc8ϩ, and rho1ϩ, and two previously uncharacterized genes, mpd1ϩ and mpd2ϩ. -
Dynamics of Gene Silencing During X Inactivation Using Allele-Specific RNA-Seq Hendrik Marks1*, Hindrik H
Marks et al. Genome Biology (2015) 16:149 DOI 10.1186/s13059-015-0698-x RESEARCH Open Access Dynamics of gene silencing during X inactivation using allele-specific RNA-seq Hendrik Marks1*, Hindrik H. D. Kerstens1, Tahsin Stefan Barakat3, Erik Splinter4, René A. M. Dirks1, Guido van Mierlo1, Onkar Joshi1, Shuang-Yin Wang1, Tomas Babak5, Cornelis A. Albers2, Tüzer Kalkan6, Austin Smith6, Alice Jouneau7, Wouter de Laat4, Joost Gribnau3 and Hendrik G. Stunnenberg1* Abstract Background: During early embryonic development, one of the two X chromosomes in mammalian female cells is inactivated to compensate for a potential imbalance in transcript levels with male cells, which contain a single X chromosome. Here, we use mouse female embryonic stem cells (ESCs) with non-random X chromosome inactivation (XCI) and polymorphic X chromosomes to study the dynamics of gene silencing over the inactive X chromosome by high-resolution allele-specific RNA-seq. Results: Induction of XCI by differentiation of female ESCs shows that genes proximal to the X-inactivation center are silenced earlier than distal genes, while lowly expressed genes show faster XCI dynamics than highly expressed genes. The active X chromosome shows a minor but significant increase in gene activity during differentiation, resulting in complete dosage compensation in differentiated cell types. Genes escaping XCI show little or no silencing during early propagation of XCI. Allele-specific RNA-seq of neural progenitor cells generated from the female ESCs identifies three regions distal to the X-inactivation center that escape XCI. These regions, which stably escape during propagation and maintenance of XCI, coincide with topologically associating domains (TADs) as present in the female ESCs. -
Identification of 10 Important Genes with Poor Prognosis in Non-Small Cell Lung Cancer Through Bioinformatical Analysis
Journal of ISSN: 2581-7388 Inno Biomedical Research and Reviews Volume 3: 2 J Biomed Res Rev 2020 Identification of 10 Important Genes with Poor Prognosis in Non-Small Cell Lung Cancer through Bioinformatical Analysis Wenbin Wu†1 1Experiment Animal Center, Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, PR China †2,3 Cheng Fang 2Center for Traditional Chinese Medicine and Immunology Research, Shanghai University of Traditional Chinese Medicine, PR China Chaochao Zhang1 3Department of Immunology and Pathogenic Biology, School of Basic Medical Nanhua Hu*4 Sciences, Shanghai University of Traditional Chinese Medicine, PR China 4Department of Hepatopancreatobiliary and Traditional Chinese Medicine, Minhang *2,3 Lixin Wang Branch, Fudan University Shanghai Cancer Center, PR China † These authors contributed equally to this work Abstract Article Information Objective: The lung cancer has become the most lethal cause of Article Type: Research Article cancer-related death in China and is responsible for more than 1 million Article Number: JBRR-143 deaths all over the world every year, especially non-small cell lung cancer Received Date: 16 October, 2020 (NSCLC). Although great advance in pharmaceutical therapies for lung Accepted Date: 11 November, 2020 Published Date: 18 November, 2020 the effective biomarkers in order to improve and predict the prognosis ofcancer lung patients, cancer patients.the overall The survival integrated is still bioinformaticalpoor. It is necessary analysis, to find as out a *Corresponding author: Lixin Wang, Center for useful tool to dig up the valuable clues, can be applied to search new Traditional Chinese Medicine and Immunology Research; effective therapeutic targets. -
Bioconductor: Open Software Development for Computational Biology and Bioinformatics Robert C
Bioconductor Project Bioconductor Project Working Papers Year 2004 Paper 1 Bioconductor: Open software development for computational biology and bioinformatics Robert C. Gentleman, Department of Biostatistical Sciences, Dana Farber Can- cer Institute Vincent J. Carey, Channing Laboratory, Brigham and Women’s Hospital Douglas J. Bates, Department of Statistics, University of Wisconsin, Madison Benjamin M. Bolstad, Division of Biostatistics, University of California, Berkeley Marcel Dettling, Seminar for Statistics, ETH, Zurich, CH Sandrine Dudoit, Division of Biostatistics, University of California, Berkeley Byron Ellis, Department of Statistics, Harvard University Laurent Gautier, Center for Biological Sequence Analysis, Technical University of Denmark, DK Yongchao Ge, Department of Biomathematical Sciences, Mount Sinai School of Medicine Jeff Gentry, Department of Biostatistical Sciences, Dana Farber Cancer Institute Kurt Hornik, Computational Statistics Group, Department of Statistics and Math- ematics, Wirtschaftsuniversitat¨ Wien, AT Torsten Hothorn, Institut fuer Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, DE Wolfgang Huber, Department for Molecular Genome Analysis (B050), German Cancer Research Center, Heidelberg, DE Stefano Iacus, Department of Economics, University of Milan, IT Rafael Irizarry, Department of Biostatistics, Johns Hopkins University Friedrich Leisch, Institut fur¨ Statistik und Wahrscheinlichkeitstheorie, Technische Universitat¨ Wien, AT Cheng Li, Department -
Decode™ Bioinformatic Analysis
PROTOCOL Decode™ Bioinformatic Analysis An introductory guide for the use of high-throughput sequencing to determine primary hits from an shRNA pooled screen. Table of contents 1. Legal disclaimers 1. Legal disclaimers 1 SOFTWARE LICENSE TERMS. With respect to any software products 2. Summary 2 incorporated in or forming a part of the software provided or described 3. Intended audience 2 hereunder (“Software”), Horizon Discovery (“Seller”) and you, the purchaser, 4. Software requirements 2 licensee and/or end-user of the software (“Buyer”) intend and agree that 5. FASTQ files from high-throughput sequencing 2 such software products are being licensed and not sold, and that the words 6. Align the FASTQ files to reference shRNA FASTA files 3 “purchase”, “sell” or similar or derivative words are understood and agreed to A. Create a Bowtie index 3 mean “license”, and that the word “Buyer” or similar or derivative words are B. Align using Bowtie 3 understood and agreed to mean “licensee”. Notwithstanding anything to the C. Batch process 4 contrary contained herein, Seller or its licensor, as the case may be, retains all D. Alignment summaries 4 rights and interest in software products provided hereunder. 7. Differential expression analysis 5 A. Convert Bowtie files into DESeq input 5 Seller hereby grants to Buyer a royalty-free, non-exclusive, nontransferable B. Run DESeq on .rtable files 7 license, without power to sublicense, to use software provided hereunder 8. Conclusions 9 solely for Buyer’s own internal business purposes and to use the related documentation solely for Buyer’s own internal business purposes. -
R / Bioconductor for High-Throughput Sequence Analysis
R / Bioconductor for High-Throughput Sequence Analysis Nicolas Delhomme1 21 October - 26 October, 2013 [email protected] Contents 1 Day2 of the workshop2 1.1 Introduction............................................2 1.2 Main Bioconductor packages of interest for the day......................2 1.3 A word on High-throughput sequence analysis.........................2 1.4 A word on Integrated Development Environment (IDE)...................2 1.5 Today's schedule.........................................2 2 Prelude 4 2.1 Purpose..............................................4 2.2 Creating GAlignment objects from BAM files.........................4 2.3 Processing the files in parallel..................................4 2.4 Processing the files one chunk at a time............................5 2.5 Pros and cons of the current solution..............................6 2.5.1 Pros............................................6 2.5.2 Cons............................................6 3 Sequences and Short Reads7 3.1 Alignments and Bioconductor packages............................7 3.1.1 The pasilla data set...................................7 3.1.2 Alignments and the ShortRead package........................8 3.1.3 Alignments and the Rsamtools package........................9 3.1.4 Alignments and other Bioconductor packages..................... 13 3.1.5 Resources......................................... 17 4 Interlude 18 5 Estimating Expression over Genes and Exons 20 5.1 Counting reads over known genes and exons.......................... 20 5.1.1 -
HERV-K(HML7) Integrations in the Human Genome: Comprehensive Characterization and Comparative Analysis in Non-Human Primates
biology Article HERV-K(HML7) Integrations in the Human Genome: Comprehensive Characterization and Comparative Analysis in Non-Human Primates Nicole Grandi 1,* , Maria Paola Pisano 1 , Eleonora Pessiu 1, Sante Scognamiglio 1 and Enzo Tramontano 1,2 1 Laboratory of Molecular Virology, Department of Life and Environmental Sciences, University of Cagliari, 09042 Monserrato, Cagliari, Italy; [email protected] (M.P.P.); [email protected] (E.P.); [email protected] (S.S.); [email protected] (E.T.) 2 Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Cagliari, Italy * Correspondence: [email protected] Simple Summary: The human genome is not human at all, but it includes a multitude of sequences inherited from ancient viral infections that affected primates’ germ line. These elements can be seen as the fossils of now-extinct retroviruses, and are called Human Endogenous Retroviruses (HERVs). View as “junk DNA” for a long time, HERVs constitute 4 times the amount of DNA needed to produce all cellular proteins, and growing evidence indicates their crucial role in primate brain evolution, placenta development, and innate immunity shaping. HERVs are also intensively studied for a pathological role, even if the incomplete knowledge about their exact number and genomic position has thus far prevented any causal association. Among possible relevant HERVs, the HERV-K Citation: Grandi, N.; Pisano, M.P.; supergroup is of particular interest, including some of the oldest (HML5) as well as youngest (HML2) Pessiu, E.; Scognamiglio, S.; integrations. Among HERV-Ks, the HML7 group still lack a detailed description, and the present Tramontano, E. -
Lncrna Jpx Induces Xist Expression in Mice Using Both Trans and Cis Mechanisms
RESEARCH ARTICLE LncRNA Jpx induces Xist expression in mice using both trans and cis mechanisms Sarah Carmona, Benjamin Lin, Tristan Chou, Katti Arroyo, Sha Sun* Department of Developmental and Cell Biology, Ayala School of Biological Sciences, University of California, Irvine, Irvine, CA, United States of America * [email protected] a1111111111 a1111111111 Abstract a1111111111 a1111111111 Mammalian X chromosome dosage compensation balances X-linked gene products a1111111111 between sexes and is coordinated by the long noncoding RNA (lncRNA) Xist. Multiple cis and trans-acting factors modulate Xist expression; however, the primary competence factor responsible for activating Xist remains a subject of dispute. The lncRNA Jpx is a proposed competence factor, yet it remains unknown if Jpx is sufficient to activate Xist expression in OPEN ACCESS mice. Here, we utilize a novel transgenic mouse system to demonstrate a dose-dependent relationship between Jpx copy number and ensuing Jpx and Xist expression. By localizing Citation: Carmona S, Lin B, Chou T, Arroyo K, Sun S (2018) LncRNA Jpx induces Xist expression in transcripts of Jpx and Xist using RNA Fluorescence in situ Hybridization (FISH) in mouse mice using both trans and cis mechanisms. PLoS embryonic cells, we provide evidence of Jpx acting in both trans and cis to activate Xist. Our Genet 14(5): e1007378. https://doi.org/10.1371/ data contribute functional and mechanistic insight for lncRNA activity in mice, and argue that journal.pgen.1007378 Jpx is a competence factor for Xist activation in vivo. Editor: Gregory S. Barsh, Stanford University School of Medicine, UNITED STATES Received: November 20, 2017 Accepted: April 24, 2018 Author summary Published: May 7, 2018 Long noncoding RNA (lncRNA) have been identified in all eukaryotes but mechanisms Copyright: © 2018 Carmona et al. -
Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle
animals Article Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle Masoumeh Naserkheil 1 , Abolfazl Bahrami 1 , Deukhwan Lee 2,* and Hossein Mehrban 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; [email protected] (M.N.); [email protected] (A.B.) 2 Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do 17579, Korea 3 Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran; [email protected] * Correspondence: [email protected]; Tel.: +82-31-670-5091 Received: 25 August 2020; Accepted: 6 October 2020; Published: 9 October 2020 Simple Summary: Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. -
Multiple Hits for the Association of Uterine Fibroids on Human Chromosome 1Q43
Multiple Hits for the Association of Uterine Fibroids on Human Chromosome 1q43 Brahim Aissani1*, Howard Wiener1, Kui Zhang2 1 Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America, 2 Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America Abstract Uterine leiomyomas (or fibroids) are the most common tumors in women of reproductive age. Early studies of two familial cancer syndromes, the multiple cutaneous and uterine leiomyomatosis (MCUL1), and the hereditary leiomyomatosis and renal cell cancer (HLRCC), implicated FH, a gene on chromosome 1q43 encoding the tricarboxylic acid cycle fumarate hydratase enzyme. The role of this metabolic housekeeping gene in tumorigenesis is still a matter of debate and pseudo- hypoxia has been suggested as a pathological mechanism. Inactivating FH mutations have rarely been observed in the nonsyndromic and common form of fibroids; however, loss of heterozygosity across FH appeared as a significant event in the pathogenesis of a subset of these tumors. To assess the role of FH and the linked genes in nonsyndromic uterine fibroids, we explored a two-megabase interval spanning FH in the NIEHS Uterine fibroid study, a cross-sectional study of fibroids in 1152 premenopausal women. Association mapping with a dense set of single nucleotide polymorphisms revealed several peaks of association (p = 1022–8.1025) with the risk and/or growth of fibroids. In particular, genes encoding factors suspected (cytosolic FH) or known (EXO1 - exonuclease 1) to be involved in DNA mismatch repair emerged as candidate susceptibility genes whereas those acting in the autophagy/apoptosis (MAP1LC3C - microtubule-associated protein) or signal transduction (RGS7 - Regulator of G-protein and PLD5– Phospoholipase D) appeared to affect tumor growth.