Curriculum Vitae
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Teaching Bioinformatics and Neuroinformatics Using Free Web-Based Tools
UCLA Behavioral Neuroscience Title Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools Permalink https://escholarship.org/uc/item/2689x2hk Author Grisham, William Publication Date 2009 Supplemental Material https://escholarship.org/uc/item/2689x2hk#supplemental Peer reviewed eScholarship.org Powered by the California Digital Library University of California – SUBMITTED – [Article; 34,852 Characters] Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools William Grisham 1, Natalie A. Schottler 1, Joanne Valli-Marill 2, Lisa Beck 3, Jackson Beatty 1 1Department of Psychology, UCLA; 2 Office of Instructional Development, UCLA; 3Department of Psychology, Bryn Mawr College Keywords: quantitative trait locus, digital teaching tools, web-based learning, genetic analysis, in silico tools DRAFT: Copyright William Grisham, 2009 Address correspondence to: William Grisham Department of Psychology, UCLA PO Box 951563 Los Angeles, CA 90095-1563 [email protected] Grisham Teaching Bioinformatics Using Web-Based Tools ABSTRACT This completely computer-based module’s purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. This module integrates information gathered from websites dealing with anatomy (Mouse Brain Library), Quantitative Trait Locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, NCBI Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). This module provides for teaching genetics from the phenotypic level to the molecular level, some neuroanatomy, some aspects of histology, statistics, Quantitaive Trait Locus analysis, molecular biology including in situ hybridization and microarray analysis in addition to introducing bioinformatic resources. -
Speeding up Eqtl Scans in the BXD Population Using Gpus
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.22.153742; this version posted June 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Speeding up eQTL scans in the BXD population using GPUs Chelsea Trotter1, Hyeonju Kim1, Gregory Farage1, Pjotr Prins2, Robert W. Williams2, Karl W. Broman3, and Saunak´ Sen1, 1Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 2Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 3Department of Biostatistics, University of Wisconsin-Madison, Madison, WI The BXD recombinant inbred strains of mice are an important using standard algorithms for QTL analysis, such as those reference population for systems biology and genetics that have employed by R/qtl (4). In practice, this is too slow. For exam- been full sequenced and deeply phenotyped. To facilitate inter- ple, by using the Haley-Knott algorithms (5) using genotype active use of genotype-phenotype relations using many massive probabilities on batches of phenotypes with the same miss- omics data sets for this and other segregating populations, we ing data pattern, instead of using the EM algorithm (6) on have developed new algorithms and code that enables near-real each phenotype individually, substantial speedups are possi- time whole genome QTL scans for up to 1 million traits. By ble. This is a well-known trick and is used by R/qtl. -
The Communication Dimension of the Reproducibility Crisis in Experimental Psychology and Neuroscience
European Journal for Philosophy of Science (2020) 10:44 https://doi.org/10.1007/s13194-020-00317-6 PAPER IN PHILOSOPHY OF SCIENCE IN PRACTICE Open Access Double trouble? The communication dimension of the reproducibility crisis in experimental psychology and neuroscience Witold M. Hensel1 Received: 31 December 2019 /Accepted: 18 September 2020/ # The Author(s) 2020 Abstract Most discussions of the reproducibility crisis focus on its epistemic aspect: the fact that the scientific community fails to follow some norms of scientific investigation, which leads to high rates of irreproducibility via a high rate of false positive findings. The purpose of this paper is to argue that there is a heretofore underappreciated and understudied dimension to the reproducibility crisis in experimental psychology and neuroscience that may prove to be at least as important as the epistemic dimension. This is the communication dimension. The link between communication and reproducibility is immediate: independent investi- gators would not be able to recreate an experiment whose design or implementation were inadequately described. I exploit evidence of a replicability and reproducibility crisis in computational science, as well as research into quality of reporting to support the claim that a widespread failure to adhere to reporting standards, especially the norm of descrip- tive completeness, is an important contributing factor in the current reproducibility crisis in experimental psychology and neuroscience. Keywords Reproducibility crisis in experimental psychology and neuroscience . Replicability crisis in computational modeling . Replication . Reproduction . Norms of scientific reporting . Descriptive completeness 1 Introduction The reproducibility crisis is usually depicted as resulting from a failure to follow some norms of scientific investigation (but see, e.g., Bird 2018). -
Biological Interpretation of Genome-Wide Association Studies Using Predicted Gene Functions
Edinburgh Research Explorer Biological interpretation of genome-wide association studies using predicted gene functions Citation for published version: Pers, TH, Karjalainen, JM, Chan, Y, Westra, H-J, Wood, AR, Yang, J, Lui, JC, Vedantam, S, Gustafsson, S, Esko, T, Frayling, T, Speliotes, EK, Boehnke, M, Raychaudhuri, S, Fehrmann, RSN, Hirschhorn, JN, Franke, L, Genetic Invest ANthropometric Trai, McLachlan, S, Campbell, H, Price, J, Rudan, I & Wilson, J 2015, 'Biological interpretation of genome-wide association studies using predicted gene functions', Nature Communications, vol. 6, 5890. https://doi.org/10.1038/ncomms6890 Digital Object Identifier (DOI): 10.1038/ncomms6890 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Nature Communications Publisher Rights Statement: This is the author's accepted manuscript. General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 28. Sep. 2021 HHS Public Access Author manuscript Author Manuscript Author ManuscriptNat Commun Author Manuscript. Author manuscript; Author Manuscript available in PMC 2015 May 05. Published in final edited form as: Nat Commun. -
To Access the Data
autoHGPEC: Automated prediction of novel disease-gene and disease- disease associations and evidence collection based on a random walk on heterogeneous network Duc-Hau Le1,*, Trang T.H. Tran1 1School of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam. *To whom correspondence should be addressed. User Manual 1 Table of Contents I. Setup ............................................................................................................................................ 3 II. Overview of autoHGPEC ............................................................................................................ 4 III. Case study: Prediction of novel breast cancer-associated genes and diseases ............................. 5 1. Run autoHGPEC in Cytoscape ................................................................................................ 5 Step 1: Construct a heterogeneous network ................................................................................. 5 Step 2: Select a disease of interest ............................................................................................... 5 Step 3: Select candidate sets ........................................................................................................ 6 Step 4: Prioritize .......................................................................................................................... 6 Step 5: Examine ranked genes and diseases ............................................................................... -
Downloadable List of Links to Resources
Parkville Bioinformatics Training Group 2020 Resource list March 2020 Selected Bioinformatics Resources 2020 Top recommended resources: OMIM: http://www.omim.org University of Santa Cruz Genome Browser: http://genome.ucsc.edu/ Allen Brain atlas: http://www.brain-map.org Linnarson lab Mouse brain Atlas: http://mousebrain.org/ Comparative Toxicogenomics Database: http://ctdbase.org/ Gtex: http://www.gtexportal.org/home/ Gemma: https://gemma.msl.ubc.ca/home.html GREIN: http://www.ilincs.org/apps/grein/?gse= Omics DI: https://www.omicsdi.org/ University licenced resources Ingenuity Pathway Analysis (IPA) Geneious: https://www.geneious.com/ To request a Geneious license, log a request via the Staff Portal https://unimelb.service-now.com/sp -> Information Technology -> Request Something -> Computers Email and Printers -> Software Installation - > choose ‘Geneious’ For students your supervisor will need to request the software on your behalf. Cortellus (PKA Clarivate): https://clarivate.com/cortellis/ Available through the library http://cat.lib.unimelb.edu.au/record=e1001620~S30 Selected additional resources Data and tool Repositories Omic tools https://omictools.com Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/ Omics Discovery Index: https://www.omicsdi.org/ SciCrunch: https://scicrunch.org/ GREIN (processed sequencing data): http://www.ilincs.org/apps/grein/?gse= Gemma: http://www.chibi.ubc.ca/Gemma/home.html Gene Nomenclature Human Gene Nomenclature committee: http://www.genenames.org/ Mouse Genome Informatics: http://www.informatics.jax.org -
An Atlas of Gene Expression and Gene Co-Regulation in the Human Retina
Nucleic Acids Research Advance Access published May 27, 2016 Nucleic Acids Research, 2016 1 doi: 10.1093/nar/gkw486 An atlas of gene expression and gene co-regulation in the human retina Michele Pinelli1,†, Annamaria Carissimo1,†, Luisa Cutillo1,2,†, Ching-Hung Lai1,†, Margherita Mutarelli1,†, Maria Nicoletta Moretti1,†, Marwah Veer Singh1, Marianthi Karali1, Diego Carrella1, Mariateresa Pizzo1, Francesco Russo3, Stefano Ferrari4, Diego Ponzin4, Claudia Angelini3, Sandro Banfi1,5,* and Diego di Bernardo1,6,* 1Telethon Institute of Genetics and Medicine (TIGEM), Via Campi Flegrei 34, 80078 Pozzuoli, Italy, 2Dipartimento Studi Aziendali e Quantitativi (DISAQ), Universita` degli studi di Napoli ‘Parthenope’, Via Generale Parisi, 80132 Downloaded from Napoli, Italy, 3Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerca, Via Pietro Castellino 111, 80131 Napoli, Italy, 4Fondazione Banca degli Occhi del Veneto, Via Paccagnella 11, 30174 Zelarino (Venice), Italy, 5Medical Genetics, Department of Biochemistry, Biophysics and General Pathology, Second University of Naples, via Luigi De Crecchio 7, 80138 Naples (NA), Italy and 6Dept. Of Chemical, Materials and Industrial Production Engineering, University of Naples ‘Federico II’, Piazzale Tecchio 80, 80125 Naples, Italy http://nar.oxfordjournals.org/ Received February 28, 2016; Revised May 19, 2016; Accepted May 20, 2016 ABSTRACT not been previously reported. RNA-seq data and the gene co-expression network are available online The human retina is a specialized tissue involved in (http://retina.tigem.it). light stimulus transduction. Despite its unique bi- ology, an accurate reference transcriptome is still at Univ of Tenn Health Science Library on August 15, 2016 missing. Here, we performed gene expression anal- ysis (RNA-seq) of 50 retinal samples from non- INTRODUCTION visually impaired post-mortem donors. -
CODECHECK: an Open Science Initiative For
F1000Research 2021, 10:253 Last updated: 30 MAR 2021 METHOD ARTICLE CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility [version 1; peer review: awaiting peer review] Daniel Nüst 1, Stephen J. Eglen 2 1Institute for Geoinformatics, University of Münster, Münster, Germany 2Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK v1 First published: 30 Mar 2021, 10:253 Open Peer Review https://doi.org/10.12688/f1000research.51738.1 Latest published: 30 Mar 2021, 10:253 https://doi.org/10.12688/f1000research.51738.1 Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the Abstract article can be found at the end of the article. The traditional scientific paper falls short of effectively communicating computational research. To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non- textual research artefacts. The CODECHECK website can be accessed here: https://codecheck.org.uk/. Keywords reproducible research, Open Science, peer review, reproducibility, code sharing, data sharing, quality control, scholarly publishing This article is included in the Science Policy Research gateway. -
Downloaded From
medRxiv preprint doi: https://doi.org/10.1101/2021.03.10.21253054; this version posted March 17, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease Floranne Boulogne1#, Laura R. Claus2#, Henry Wiersma1##, Roy Oelen1##, Floor Schukking1, Niek de Klein1, Shuang Li1,3, Harm-Jan Westra1, Bert van der Zwaag2, Franka van Reekum4, Dana Sierks5, Ria Schönauer5, Jan Halbritter5, Nine V.A.M. Knoers1, Genomics England Research Consortium, Patrick 1,2### 1### 2### Deelen , Lude Franke , Albertien M. van Eerde 1 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands 2 Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands 3 Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands 4 Department of Nephrology, University Medical Center Utrecht, Utrecht, the Netherlands. 5 Medical Department III - Endocrinology, Nephrology, Rheumatology Department of Internal Medicine, Division of Nephrology, University of Leipzig Medical Center, Leipzig, Germany # equal contribution ## equal contribution ### equal contribution Corresponding author: Albertien M. van Eerde Email address: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2021.03.10.21253054; this version posted March 17, 2021. -
A Multi-Omics Digital Research Object for the Genetics of Sleep Regulation. Abstract
bioRxiv preprint doi: https://doi.org/10.1101/586206; this version posted March 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 A multi-omics digital research object for the genetics of sleep 2 regulation. 3 1 1, 2 1 1 3,4 4 Maxime Jan* , Nastassia Gobet* , Shanaz Diessler , Paul Franken , Ioannis Xenarios 5 1Centre for Integrative Genomics, University of Lausanne, Switzerland 6 2Vital-IT, Swiss Institute of Bioinformatics, Switzerland 7 3Ludwig Cancer Research/CHUV-UNIL, Lausanne, Switzerland 8 4Health 2030 Genome Center, Geneva, Switzerland 9 * These authors contributed equally to this work. 10 11 12 13 Abstract 14 More and more researchers make use of multi-omics approaches to tackle complex cellular 15 and organismal systems. It has become apparent that the potential for re-use and integrate data 16 generated by different labs can enhance knowledge. However, a meaningful and efficient re- 17 use of data generated by others is difficult to achieve without in depth understanding of how 18 these datasets were assembled. We therefore designed and describe in detail a digital research 19 object embedding data, documentation and analytics on mouse sleep regulation. The aim of 20 this study was to bring together electrophysiological recordings, sleep-wake behavior, 21 metabolomics, genetics, and gene regulatory data in a systems genetics model to investigate 22 sleep regulation in the BXD panel of recombinant inbred lines. -
Report from the 2016 Society for Neuroscience Teaching Workshop William Grisham University of California, Los Angeles
Masthead Logo Smith ScholarWorks Neuroscience: Faculty Publications Neuroscience Fall 2017 Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop William Grisham University of California, Los Angeles Joshua C. Brumberg The Graduate Center, City University of New York Terri Gilbert Allen Institute for Brain Science Linda Lanyon International Neuroinformatics Coordinating Facility Robert W. Williams University of Tennessee See next page for additional authors Follow this and additional works at: https://scholarworks.smith.edu/nsc_facpubs Part of the Neuroscience and Neurobiology Commons Recommended Citation Grisham, William; Brumberg, Joshua C.; Gilbert, Terri; Lanyon, Linda; Williams, Robert W.; and Olivo, Richard F., "Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop" (2017). Neuroscience: Faculty Publications, Smith College, Northampton, MA. https://scholarworks.smith.edu/nsc_facpubs/6 This Article has been accepted for inclusion in Neuroscience: Faculty Publications by an authorized administrator of Smith ScholarWorks. For more information, please contact [email protected] Authors William Grisham, Joshua C. Brumberg, Terri Gilbert, Linda Lanyon, Robert W. Williams, and Richard F. Olivo This article is available at Smith ScholarWorks: https://scholarworks.smith.edu/nsc_facpubs/6 The Journal of Undergraduate Neuroscience Education (JUNE), Fall 2017, 16(1):A68-A76 ARTICLE Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop William -
Highlights from the Era of Open Source Web-Based Tools
The Journal of Neuroscience, 0, 2020 • 00(00):000 • 1 Symposium Highlights from the Era of Open Source Web-Based Tools Kristin R. Anderson,1–5 Julie A. Harris,6 Lydia Ng,6 Pjotr Prins,7 Sara Memar,8 Bengt Ljungquist,9 Daniel Fürth,10 Robert W. Williams,7 Giorgio A. Ascoli,9 and Dani Dumitriu1–5 1Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032, 2Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032, 3The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032, 4Columbia Population Research Center, Columbia University, New York, New York 10027, 5Zuckerman Institute, Columbia University, New York, New York 10027, 6Allen Institute for Brain Science, Seattle, Washington 98109, 7Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163, 8Robarts Research Institute, BrainsCAN, Schulich School of Medicine & Dentistry, Western University, London, Ontario N6A 3K7, Canada, 9Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030, and 10Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724 High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span sub- disciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.