Exploring the Genetic Basis for Behavior. Instructor s Notes
|
|
- Austen Brown
- 6 years ago
- Views:
Transcription
1 Exploring the Genetic Basis for Behavior Instructor s Notes Introduction This lab was designed for our 300-level Advanced Genetics course taken by juniors and seniors majoring in Biology or Biochemistry. It is a two part exercise; either part could be conducted separately and the two labs do not have to be taught concurrently. Lab 1: Mating Frequency Prelab Preparation for Instructors Consult the Bean Beetle Handbook for detailed information on bean beetle culture, handling techniques, and tips for how to identify the two sexes. A large supply of virgin Callosobruchus maculatus adults are needed to conduct this exercise. Several cultures with actively laying females need to be available three to four weeks prior to the start of the experiment. Beans with a single egg were removed and transferred to a well of a tissue culture plate and allowed to develop to adults. Not all individuals developed into adults and it is a good idea to pick more eggs than you anticipate needing. Prelab Assignment for Students Drosophila melanogaster is used as our starting point. Students have worked with fruit flies in a past pre-requisite course and are familiar with their role as a model genetic system. To explore the question of the cost of sexual reproduction, students are assigned a reading, The Sinister Side of Sex (M. Brookes Fly: The unsung hero of 20 th Century Science. HarperCollins Publishers Inc.) to prepare for the first lab. This chapter introduces students to some of the costs of sex in fruit flies and some of the genes that have been characterized to play a role in mating. It provides a starting point for students to consider what questions to ask about the bean beetle by extrapolating from the fruit fly. Several videos are available for introducing mating and courtship in D. melanogaster. Courtship Courtship set to Barry White Courtship song Abnormal courtship
2 Experimental Design A common theme was for students to vary the number of mating partners for one of the sexes and measure the lifespan after a period of mating. Some groups also suggested counting the number of eggs laid to measure fecundity. For example, individual females could be mated to no, one or five males over the course of a week in a small Petri plate with mung beans. After one week, males were removed, and the female was monitored daily to measure her lifespan. Additionally, the number of eggs the female lays was determined on a daily basis by removing beans with visible eggs. Conversely, the experiment could be conducted with individual males and varying numbers of female partners. Other courtship behaviors may be suggested for study. One group wanted to test to determine if the bean beetles had a courtship song. This area proved to be difficult to study because we do not have the equipment to record insect song. The following research articles on effects of mating in bean beetles might lead to other related questions and experimental designs: Berg, E. and Maklakov, A. (2012) Sexes suffer from suboptimal lifespan because of genetic conflict in a seed beetle. Proceedings of the Royal Society B. 279: Brown et al. (2009) Negative phenotypic and genetic associations between copulation duration and longevity in male seed beetles. Heredity 103: Crudgington, H.S. and Siva-Jothy, M.T. (2000) Genital damage, kicking and early death. Nature 407: Paukku, S and Kotiaho, J. (2005) Cost of reproduction in Callosobruchus maculatus: effects of mating on male longevity and the effect of male mating status on female longevity. Journal of Insect Physiology. 51: Rönn, J., Katvala, M., Arnqvist, G. (2006) The costs of mating and egg production in Callosobruchus seed beetles. Animal Behaviour 72: Rönn, J., Katvala, M., Arnqvist, G. (2007) Coevolution between harmful male genitalia and female resistance in seed beetles. Proceedings of the National Academy of Sciences of the United States of America. 104: Yamane, T. and Miyatake, T. (2012) Evolutionary correlation between male substances and female remating frequency in a seed beetle. Behavioral Ecology 23: Yanagi, S. and Miyatake, T.(2003) Costs of mating and egg production in female Callosobruchus chinensis. Journal of Insect Physiology 49: Data Collection After the mating period, students monitor beetles on a daily basis to determine the lifespan. If fecundity is included in the study, students count and remove beans with eggs. This part of the data collection could occur at less frequent intervals (every few days). New beans should be added to replace the beans that were removed.
3 Data Analysis A t-test was used to determine whether there was any difference in lifespan that correlated with the number of mating partners. Equipment and Supplies For a class of 30 students: Bean beetles cultures (Callosobruchus maculatus) Mung beans (Vigna radiata) 24-well flat bottom tissue culture plates for culturing virgin beetles (Corning Life Sciences, cat. # ) Plastic Petri dishes, 60 mm x 15 mm (Fisherbrand Media-Miser, cat. # A) small paint brushes for moving insects (1 for each lab group of 4 students) Dissecting scopes (1 for each lab group of 4 students) Lab 2: Comparative Genetics Prelab Preparation for Instructors The second lab utilizes bioinformatics tools available freely on the internet. The webpages for these tools are updated and will change. It is a good idea to preview sites prior to presenting the lab and update the protocol to reflect recent changes. Updates often provide more resources that could expand the scope of the exercise in the future. Prelab Assignment for Students Again, using D. melanogaster as our jumping off point, students are assigned a review on the genetics of fruit fly mating (Hall, J.C The Mating of a Fly. Science 264: ). The paper provides a table with a list of genes that have been characterized in fruit flies as well as an overview of the field. Students are asked to select genes that might be expected to have homologs in the bean beetle based on the behaviors they observed in Lab 1. Experimental Design This lab allows for the opportunity to use primary literature in conjunction with bioinformatics tools to make decisions about which sequences to use in the analysis. Points to consider are: a. DNA sequence or Protein sequence: The protein sequence is more useful than the DNA sequence when searching for similarity between species. Similar functions would imply conserved amino acid sequences, while the DNA sequence could vary greatly. b. Which sequence or isoform to use? Sequencing projects have dumped a lot of sequence data into Genbank but there may be no experimental data to support the function of these genes. There may be multiple versions of the gene sequence in the database to choose. The GenBank flat file in conjunction with the primary literature can help in deciding the best choice for which sequence to use. After reading the review, students speculate on which fruit fly genes might be expected in the bean beetle genome, based on their observations of the beetles behavior. Some genes have multiple phenotypic effects while others are specific to courtship and mating. There is some freedom to narrow or widen the gene choice, depending on the instructor s preference.
4 Data collection Unless you have used some of these tools previously in class, students will need some guidance working with websites. A guideline has been provided in the appendix. The D. melanogaster genes can be found NCBI ( Students search GenBank for the protein sequence (FASTA format) of their D. melanogaster gene of interest. They use that protein sequence to search the Bean Beetle genome ( using tblastn. They evaluate their bean beetle sequence matches by the quality scores and alignment to determine if the match is a good candidate. If it is, the scaffold sequence for the top hit can be downloaded (scaffold sequence contains more sequence than the match but represents a contiguous region of genomic DNA). This DNA sequence can be used to perform a blastn against the bean beetle genome to try to extend the sequence and annotate the gene. It can also be used to perform a blastx against GenBank to confirm that it is matching similar genes to the original D. melanogaster sequence. Data Analysis Students evaluate the quality of their blast analyses to determine whether or not they have identified a similar gene in the bean beetle. Not all D. melanogaster genes may be good probes for the bean beetle genome, so some choices may lead to negative results. Equipment and Supplies For a class of 30 students: Laptop computers with internet access (many students bring their own) Appendix Bioinformatics Tools To locate protein sequence for your gene of interest: a. Go to b. There is a search bar at the top of the page. Change the default (All Databases) to Protein. Type in the name of the gene of interest followed by Drosophila. c. The search will yield several results (multiple isoforms) and can open a discussion on which sequence to pick. First, be sure the sequence is from Drosophila melanogaster, then look for Full Protein. If Full Protein does not exist, pick the best choice (first isoform or largest size). Select the entry by clicking on the title. You will be brought to the flat file or submission entry for that sequence. d. Flat files contain a lot of useful information but not in the most accessible format. Some translation for the students is necessary. You want to scroll down to the first literature reference associated with this sequence. If it is a primary article specific for the gene of study, it is a good choice. However, if the first reference is for a whole genome project, it is not specific for your gene and you may have a gene prediction. For example, I searched for couch potato Drosophila and received 364 entries with multiple isoforms. When I amended the search to couch potato Drosophila full, I only received one entry. In that entry, the first reference to the primary literature in the flat file was:
5 AUTHORS TITLE Bellen,H.J., Kooyer,S., D'Evelyn,D. and Pearlman,J. The Drosophila couch potato protein is expressed in nuclei of peripheral neuronal precursors and shows homology to RNA-binding proteins JOURNAL Genes Dev. 6 (11), (1992) The title to this article provides some meaningful information. The gene name is mentioned and the title indicates that expression studies were performed. The date indicates that it is pregenome sequencing projects (before 2000) and it has less than 10 authors. This entry indicates that this is a good sequence to use because it is based on the characterization of an individual gene. Entries to avoid are the following: AUTHORS Adams,M.D., et al.(almost 100 authors), TITLE The genome sequence of Drosophila melanogaster JOURNAL Science 287 (5461), (2000) Such an entry indicates no experimental work was performed on the individual gene, but that it is part of a bulk download of genomic sequence. If this reference is the only one associated with the sequence, then the sequence is not the best choice and may be a prediction or a variant. Not every gene in GenBank has the same level of experimental data to support a predicted role. e. Now that the most meaningful and best-supported sequence is selected, go to the top of the flat file, and select FASTA (under the gene name in the title). Hopefully, you see: RecName: Full=Protein couch potato UniProtKB/Swiss-Prot: Q GenPept Graphics >gi sp Q CPO_DROME RecName: Full=Protein couch potato MVKIANYQDLLGSHHQLLIAATAAAAAAAAAEPQLQLQHLLPAAPTTPAVISNPINSIGPINQISSSSHP SNNNQQAVFEKAITISSIAIKRRPTLPQTPASAPQVLSPSPKRQCAAAVSVLPVTVPVPVPVSVPLPVSV PVPVSVKGHPISHTHQIAHTHQISHSHPISHPHHHQLSFAHPTQFAAAVAAHHQQQQQQQAQQQQQAVQQ QQQQAVQQQQVAYAVAASPQLQQQQQQQQHRLAQFNQAAAAALLNQHLQQQHQAQQQQHQAQQQSLAHYG GYQLHRYAPQQQQQHILLSSGSSSSKHNSNNNSNTSAGAASAAVPIATSVAAVPTTGGSLPDSPAHESHS HESNSATASAPTTPSPAGSVTSAAPTATATAAAAGSAAATAAATGTPATSAVSDSNNNLNSSSSSNSNSN AIMENQMALAPLGLSQSMDSVNTASNEEEVRTLFVSGLPMDAKPRELYLLFRAYEGYEGSLLKVTSKNGK TASPVGFVTFHTRAGAEAAKQDLQGVRFDPDMPQTIRLEFAKSNTKVSKPKPQPNTATTASHPALMHPLT GHLGGPFFPGGPELWHHPLAYSAAAAAELPGAAALQHATLVHPALHPQVPTQMTMPPHHQTTAIHPGAAM AHMAAAAAAAAAGGGGGAATAAAAPQSAAATAAAAAAASHHHYLSSPALASPAGSTNNASHPGNPQIAAN
6 APCSTLFVANLGQFVSEHELKEVFSSHGNSNWLKLLHQ The protein is in the FASTA format appropriate for conducting BLAST analysis. The sequence can be copied into a simple text program and saved. f. Go to Paste the FASTA file into the search box. Select program tblastn (to use your protein sequence to search the translated nucleotide database) and select bean beetle database. Then select basic search. Output should look like: TBLASTN Reference: Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25: Database:./db/longContigs.fasta 85,859 sequences; 315,317,553 total letters Query= gi sp Q CPO_DROME RecName: Full=Protein couch potato Length=738 Sequences producing significant alignments: Score E (Bits) Value scaffold e-08 scaffold e-07 scaffold scaffold > gi sp Q CPO_DROME on scaffold Length=2108 Score = 60.8 bits (146), Expect = 2e-08, Method: Compositional matrix adjust. Identities = 31/41 (76%), Positives = 32/41 (78%), Gaps = 1/41 (2%) Frame = -1 Query 522 MPQTIRLEFAKSNTKVSKPKPQPNTATTASHPALMHPLTGH 562 MPQTIRLEFAKSNTKVSKPK Q A +HP LMHPLTG Sbjct 1049 MPQTIRLEFAKSNTKVSKPKQQATNAAN-THPTLMHPLTGR 930 > gi sp Q CPO_DROME on scaffold Length=14563 Score = 57.8 bits (138), Expect = 2e-07, Method: Compositional matrix adjust. Identities = 28/53 (53%), Positives = 36/53 (68%), Gaps = 0/53 (0%) Frame = +3 Query 684 GSTNNASHPGNPQIAANAPCSTLFVANLGQFVSEHELKEVFSSHGNSNWLKLL 736 GS+++ G +N PCSTLFVANLGQFVSEHELKE+F+ + + L L Sbjct 6555 GSSSSQPGVGGGMGVSNHPCSTLFVANLGQFVSEHELKEIFARYESRTVLMFL 6713
7 > gi sp Q CPO_DROME on scaffold Length=5685 Score = 39.3 bits (90), Expect = 0.087, Method: Compositional matrix adjust. Identities = 18/19 (95%), Positives = 19/19 (100%), Gaps = 0/19 (0%) Frame = +1 Query 475 EGYEGSLLKVTSKNGKTAS 493 +GYEGSLLKVTSKNGKTAS Sbjct 4486 QGYEGSLLKVTSKNGKTAS 4542 > gi sp Q CPO_DROME on scaffold50631 Length=9815 Score = 33.5 bits (75), Expect = 6.6, Method: Compositional matrix adjust. Identities = 14/35 (40%), Positives = 23/35 (66%), Gaps = 0/35 (0%) Frame = -1 Query 423 MENQMALAPLGLSQSMDSVNTASNEEEVRTLFVSG 457 +E Q L LG+ + +S+ T SNE+ ++ LF+SG Sbjct 1406 LEKQFILLSLGIPREQESLCTLSNEQYLQVLFISG 1302 Lambda K H a alpha Gapped Lambda K H a alpha sigma Effective search space used: Database:./db/longContigs.fasta Posted date: Mar 26, :46 PM Number of letters in database: 315,317,553 Number of sequences in database: 85,859 Matrix: BLOSUM62 Gap Penalties: Existence: 11, Extension: 1 Neighboring words threshold: 13 Window for multiple hits: 40 g. Students will need to evaluate the quality of their hits based on sequence similarity, length and quality. (For example, four hits are found with couch potato but only two have expected values low enough for further consideration. A good cut off range is an e-values smaller than 10-6 ). Click the sequences in the subject column and click submit to download complete scaffolds. These sequences include data beyond just the area of the hit. Students may want to annotate the sequence region identified in the blast analysis, especially if the scaffold is large. h. Use scaffold sequence to perform a blastn against the bean beetle genome. Can any regions of overlap be identified to extend the sequence?
8 i. Use scaffold sequence to perform a blastx against GenBank. This analysis can be used to confirm that the quality of the bean beetle sequence. If the sequence is a good candidate for a similar gene, the hits retrieved should list similar functions to the original fruit fly sequence. However, if the sequence was a weak hit, unrelated or unfamiliar function will be seen. j. The sequence quality of the Callosobruchus maculatus genome is variable and there are gaps in the sequence. You may see tracks of Ns (bases that could not be determined). Individual sequence reads are small and it may not be possible to annotate the whole gene. This study was written by M. Ramesh, 2013 (
Genomic Annotation Lab Exercise By Jacob Jipp and Marian Kaehler Luther College, Department of Biology Genomics Education Partnership 2010
Genomic Annotation Lab Exercise By Jacob Jipp and Marian Kaehler Luther College, Department of Biology Genomics Education Partnership 2010 Genomics is a new and expanding field with an increasing impact
More informationData Retrieval from GenBank
Data Retrieval from GenBank Peter J. Myler Bioinformatics of Intracellular Pathogens JNU, Feb 7-0, 2009 http://www.ncbi.nlm.nih.gov (January, 2007) http://ncbi.nlm.nih.gov/sitemap/resourceguide.html Accessing
More informationWhy learn sequence database searching? Searching Molecular Databases with BLAST
Why learn sequence database searching? Searching Molecular Databases with BLAST What have I cloned? Is this really!my gene"? Basic Local Alignment Search Tool How BLAST works Interpreting search results
More informationSequence Analysis. BBSI 2006: Lecture #(χ+3) Takis Benos (2006) BBSI MAY P. Benos 1
Sequence Analysis (part III) BBSI 2006: Lecture #(χ+3) Takis Benos (2006) BBSI 2006 31-MAY-2006 2006 P. Benos 1 Outline Sequence variation Distance measures Scoring matrices Pairwise alignments (global,
More informationBIOINFORMATICS IN BIOCHEMISTRY
BIOINFORMATICS IN BIOCHEMISTRY Bioinformatics a field at the interface of molecular biology, computer science, and mathematics Bioinformatics focuses on the analysis of molecular sequences (DNA, RNA, and
More informationEvolutionary Genetics. LV Lecture with exercises 6KP
Evolutionary Genetics LV 25600-01 Lecture with exercises 6KP HS2017 >What_is_it? AATGATACGGCGACCACCGAGATCTACACNNNTC GTCGGCAGCGTC 2 NCBI MegaBlast search (09/14) 3 NCBI MegaBlast search (09/14) 4 Submitted
More informationTwo Mark question and Answers
1. Define Bioinformatics Two Mark question and Answers Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. There are three
More informationLast Update: 12/31/2017. Recommended Background Tutorial: An Introduction to NCBI BLAST
BLAST Exercise: Detecting and Interpreting Genetic Homology Adapted by T. Cordonnier, C. Shaffer, W. Leung and SCR Elgin from Detecting and Interpreting Genetic Homology by Dr. J. Buhler Recommended Background
More informationAnnotation Practice Activity [Based on materials from the GEP Summer 2010 Workshop] Special thanks to Chris Shaffer for document review Parts A-G
Annotation Practice Activity [Based on materials from the GEP Summer 2010 Workshop] Special thanks to Chris Shaffer for document review Parts A-G Introduction: A genome is the total genetic content of
More informationAnnotation Walkthrough Workshop BIO 173/273 Genomics and Bioinformatics Spring 2013 Developed by Justin R. DiAngelo at Hofstra University
Annotation Walkthrough Workshop NAME: BIO 173/273 Genomics and Bioinformatics Spring 2013 Developed by Justin R. DiAngelo at Hofstra University A Simple Annotation Exercise Adapted from: Alexis Nagengast,
More informationSequence Based Function Annotation
Sequence Based Function Annotation Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University Sequence Based Function Annotation 1. Given a sequence, how to predict its biological
More informationOutline. Evolution. Adaptive convergence. Common similarity problems. Chapter 7: Similarity searches on sequence databases
Chapter 7: Similarity searches on sequence databases All science is either physics or stamp collection. Ernest Rutherford Outline Why is similarity important BLAST Protein and DNA Interpreting BLAST Individualizing
More informationBiotechnology Explorer
Biotechnology Explorer C. elegans Behavior Kit Bioinformatics Supplement explorer.bio-rad.com Catalog #166-5120EDU This kit contains temperature-sensitive reagents. Open immediately and see individual
More informationCOMPUTER RESOURCES II:
COMPUTER RESOURCES II: Using the computer to analyze data, using the internet, and accessing online databases Bio 210, Fall 2006 Linda S. Huang, Ph.D. University of Massachusetts Boston In the first computer
More informationThe String Alignment Problem. Comparative Sequence Sizes. The String Alignment Problem. The String Alignment Problem.
Dec-82 Oct-84 Aug-86 Jun-88 Apr-90 Feb-92 Nov-93 Sep-95 Jul-97 May-99 Mar-01 Jan-03 Nov-04 Sep-06 Jul-08 May-10 Mar-12 Growth of GenBank 160,000,000,000 180,000,000 Introduction to Bioinformatics Iosif
More informationFiles for this Tutorial: All files needed for this tutorial are compressed into a single archive: [BLAST_Intro.tar.gz]
BLAST Exercise: Detecting and Interpreting Genetic Homology Adapted by W. Leung and SCR Elgin from Detecting and Interpreting Genetic Homology by Dr. J. Buhler Prequisites: None Resources: The BLAST web
More informationBasic Bioinformatics: Homology, Sequence Alignment,
Basic Bioinformatics: Homology, Sequence Alignment, and BLAST William S. Sanders Institute for Genomics, Biocomputing, and Biotechnology (IGBB) High Performance Computing Collaboratory (HPC 2 ) Mississippi
More informationBIO4342 Lab Exercise: Detecting and Interpreting Genetic Homology
BIO4342 Lab Exercise: Detecting and Interpreting Genetic Homology Jeremy Buhler March 15, 2004 In this lab, we ll annotate an interesting piece of the D. melanogaster genome. Along the way, you ll get
More informationELE4120 Bioinformatics. Tutorial 5
ELE4120 Bioinformatics Tutorial 5 1 1. Database Content GenBank RefSeq TPA UniProt 2. Database Searches 2 Databases A common situation for alignment is to search through a database to retrieve the similar
More informationTutorial for Stop codon reassignment in the wild
Tutorial for Stop codon reassignment in the wild Learning Objectives This tutorial has two learning objectives: 1. Finding evidence of stop codon reassignment on DNA fragments. 2. Detecting and confirming
More informationCAP 5510/CGS 5166: Bioinformatics & Bioinformatic Tools GIRI NARASIMHAN, SCIS, FIU
CAP 5510/CGS 5166: Bioinformatics & Bioinformatic Tools GIRI NARASIMHAN, SCIS, FIU !2 Sequence Alignment! Global: Needleman-Wunsch-Sellers (1970).! Local: Smith-Waterman (1981) Useful when commonality
More informationImaging informatics computer assisted mammogram reading Clinical aka medical informatics CDSS combining bioinformatics for diagnosis, personalized
1 2 3 Imaging informatics computer assisted mammogram reading Clinical aka medical informatics CDSS combining bioinformatics for diagnosis, personalized medicine, risk assessment etc Public Health Bio
More informationDownload the Lectin sequence output from
Computer Analysis of DNA and Protein Sequences Over the Internet Part I. IN CLASS Download the Lectin sequence output from http://stan.cropsci.uiuc.edu/courses/cpsc265/ Open these in BioEdit (free software).
More informationWhy study sequence similarity?
Sequence Similarity Why study sequence similarity? Possible indication of common ancestry Similarity of structure implies similar biological function even among apparently distant organisms Example context:
More informationFACULTY OF BIOCHEMISTRY AND MOLECULAR MEDICINE
FACULTY OF BIOCHEMISTRY AND MOLECULAR MEDICINE BIOMOLECULES COURSE: COMPUTER PRACTICAL 1 Author of the exercise: Prof. Lloyd Ruddock Edited by Dr. Leila Tajedin 2017-2018 Assistant: Leila Tajedin (leila.tajedin@oulu.fi)
More informationLab Week 9 - A Sample Annotation Problem (adapted by Chris Shaffer from a worksheet by Varun Sundaram, WU-STL, Class of 2009)
Lab Week 9 - A Sample Annotation Problem (adapted by Chris Shaffer from a worksheet by Varun Sundaram, WU-STL, Class of 2009) Prerequisites: BLAST Exercise: An In-Depth Introduction to NCBI BLAST Familiarity
More informationAP BIOLOGY. Investigation #3 Comparing DNA Sequences to Understand Evolutionary Relationships with BLAST. Slide 1 / 32. Slide 2 / 32.
New Jersey Center for Teaching and Learning Slide 1 / 32 Progressive Science Initiative This material is made freely available at www.njctl.org and is intended for the non-commercial use of students and
More informationProtein Bioinformatics Part I: Access to information
Protein Bioinformatics Part I: Access to information 260.655 April 6, 2006 Jonathan Pevsner, Ph.D. pevsner@kennedykrieger.org Outline [1] Proteins at NCBI RefSeq accession numbers Cn3D to visualize structures
More informationCollect, analyze and synthesize. Annotation. Annotation for D. virilis. GEP goals: Evidence Based Annotation. Evidence for Gene Models 12/26/2018
Annotation Annotation for D. virilis Chris Shaffer July 2012 l Big Picture of annotation and then one practical example l This technique may not be the best with other projects (e.g. corn, bacteria) l
More informationSequence Based Function Annotation. Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University
Sequence Based Function Annotation Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University Usage scenarios for sequence based function annotation Function prediction of newly cloned
More informationIntroduction to Bioinformatics CPSC 265. What is bioinformatics? Textbooks
Introduction to Bioinformatics CPSC 265 Thanks to Jonathan Pevsner, Ph.D. Textbooks Johnathan Pevsner, who I stole most of these slides from (thanks!) has written a textbook, Bioinformatics and Functional
More informationCollect, analyze and synthesize. Annotation. Annotation for D. virilis. Evidence Based Annotation. GEP goals: Evidence for Gene Models 08/22/2017
Annotation Annotation for D. virilis Chris Shaffer July 2012 l Big Picture of annotation and then one practical example l This technique may not be the best with other projects (e.g. corn, bacteria) l
More informationBIOINF525: INTRODUCTION TO BIOINFORMATICS LAB SESSION 1
BIOINF525: INTRODUCTION TO BIOINFORMATICS LAB SESSION 1 Bioinformatics Databases http://bioboot.github.io/bioinf525_w17/module1/#1.1 Dr. Barry Grant Jan 2017 Overview: The purpose of this lab session is
More informationSequence Databases and database scanning
Sequence Databases and database scanning Marjolein Thunnissen Lund, 2012 Types of databases: Primary sequence databases (proteins and nucleic acids). Composite protein sequence databases. Secondary databases.
More informationTextbook Reading Guidelines
Understanding Bioinformatics by Marketa Zvelebil and Jeremy Baum Last updated: May 1, 2009 Textbook Reading Guidelines Preface: Read the whole preface, and especially: For the students with Life Science
More informationBIMM 143: Introduction to Bioinformatics (Winter 2018)
BIMM 143: Introduction to Bioinformatics (Winter 2018) Course Instructor: Dr. Barry J. Grant ( bjgrant@ucsd.edu ) Course Website: https://bioboot.github.io/bimm143_w18/ DRAFT: 2017-12-02 (20:48:10 PST
More informationBioinformatics for Proteomics. Ann Loraine
Bioinformatics for Proteomics Ann Loraine aloraine@uab.edu What is bioinformatics? The science of collecting, processing, organizing, storing, analyzing, and mining biological information, especially data
More informationBiology 4100 Minor Assignment 1 January 19, 2007
Biology 4100 Minor Assignment 1 January 19, 2007 This assignment is due in class on February 6, 2007. It is worth 7.5% of your final mark for this course. Your assignment must be typed double-spaced on
More informationHC70AL SUMMER 2014 PROFESSOR BOB GOLDBERG Gene Annotation Worksheet
HC70AL SUMMER 2014 PROFESSOR BOB GOLDBERG Gene Annotation Worksheet NAME: DATE: QUESTION ONE Using primers given to you by your TA, you carried out sequencing reactions to determine the identity of the
More informationFUNCTIONAL BIOINFORMATICS
Molecular Biology-2018 1 FUNCTIONAL BIOINFORMATICS PREDICTING THE FUNCTION OF AN UNKNOWN PROTEIN Suppose you have found the amino acid sequence of an unknown protein and wish to find its potential function.
More informationProtein Architecture: Conserved Functional Domains
PROTOCOL Protein Motif Analysis compiled by John R. Finnerty Protein Architecture: Conserved Functional Domains Proteins are like machines in that different parts of the protein perform different sub-functions,
More informationProtein Sequence Analysis. BME 110: CompBio Tools Todd Lowe April 19, 2007 (Slide Presentation: Carol Rohl)
Protein Sequence Analysis BME 110: CompBio Tools Todd Lowe April 19, 2007 (Slide Presentation: Carol Rohl) Linear Sequence Analysis What can you learn from a (single) protein sequence? Calculate it s physical
More informationBLASTing through the kingdom of life
Information for teachers Description: In this activity, students copy unknown DNA sequences and use them to search GenBank, the main database of nucleotide sequences at the National Center for Biotechnology
More informationCS313 Exercise 1 Cover Page Fall 2017
CS313 Exercise 1 Cover Page Fall 2017 Due by the start of class on Monday, September 18, 2017. Name(s): In the TIME column, please estimate the time you spent on the parts of this exercise. Please try
More informationExercise I, Sequence Analysis
Exercise I, Sequence Analysis atgcacttgagcagggaagaaatccacaaggactcaccagtctcctggtctgcagagaagacagaatcaacatgagcacagcaggaaaa gtaatcaaatgcaaagcagctgtgctatgggagttaaagaaacccttttccattgaggaggtggaggttgcacctcctaaggcccatgaagt
More informationBLAST. compared with database sequences Sequences with many matches to high- scoring words are used for final alignments
BLAST 100 times faster than dynamic programming. Good for database searches. Derive a list of words of length w from query (e.g., 3 for protein, 11 for DNA) High-scoring words are compared with database
More informationIdentifying Regulatory Regions using Multiple Sequence Alignments
Identifying Regulatory Regions using Multiple Sequence Alignments Prerequisites: BLAST Exercise: Detecting and Interpreting Genetic Homology. Resources: ClustalW is available at http://www.ebi.ac.uk/tools/clustalw2/index.html
More informationBioinformatics Tools. Stuart M. Brown, Ph.D Dept of Cell Biology NYU School of Medicine
Bioinformatics Tools Stuart M. Brown, Ph.D Dept of Cell Biology NYU School of Medicine Bioinformatics Tools Stuart M. Brown, Ph.D Dept of Cell Biology NYU School of Medicine Overview This lecture will
More informationG4120: Introduction to Computational Biology
G4120: Introduction to Computational Biology Oliver Jovanovic, Ph.D. Columbia University Department of Microbiology Lecture 3 February 13, 2003 Copyright 2003 Oliver Jovanovic, All Rights Reserved. Bioinformatics
More informationMatch the Hash Scores
Sort the hash scores of the database sequence February 22, 2001 1 Match the Hash Scores February 22, 2001 2 Lookup method for finding an alignment position 1 2 3 4 5 6 7 8 9 10 11 protein 1 n c s p t a.....
More informationWorksheet for Bioinformatics
Worksheet for Bioinformatics ACTIVITY: Learn to use biological databases and sequence analysis tools Exercise 1 Biological Databases Objective: To use public biological databases to search for latest research
More informationINVESTIGATION 3 COMPARING DNA SEQUENCES TO UNDERSTAND EVOLUTIONARY RELATIONSHIPS WITH BLAST
Big Idea 1 Evolution INVESTIGATION 3 COMPARING DNA SEQUENCES TO UNDERSTAND EVOLUTIONARY RELATIONSHIPS WITH BLAST How can bioinformatics be used as a tool to determine evolutionary relationships and to
More informationGenomics and Database Mining (HCS 604.3) April 2005
Genomics and Database Mining (HCS 604.3) April 2005 David M. Francis OARDC 1680 Madison Ave Wooster, OH 44691 e-mail: francis.77@osu.edu Introduction: Computers have changed the way biologists go about
More informationA Prac'cal Guide to NCBI BLAST
A Prac'cal Guide to NCBI BLAST Leonardo Mariño-Ramírez NCBI, NIH Bethesda, USA June 2018 1 NCBI Search Services and Tools Entrez integrated literature and molecular databases Viewers BLink protein similarities
More informationQuestion 2: There are 5 retroelements (2 LINEs and 3 LTRs), 6 unclassified elements (XDMR and XDMR_DM), and 7 satellite sequences.
Bio4342 Exercise 1 Answers: Detecting and Interpreting Genetic Homology (Answers prepared by Wilson Leung) Question 1: Low complexity DNA can be described as sequences that consist primarily of one or
More informationBioinformatics Course AA 2017/2018 Tutorial 2
UNIVERSITÀ DEGLI STUDI DI PAVIA - FACOLTÀ DI SCIENZE MM.FF.NN. - LM MOLECULAR BIOLOGY AND GENETICS Bioinformatics Course AA 2017/2018 Tutorial 2 Anna Maria Floriano annamaria.floriano01@universitadipavia.it
More informationWhy Use BLAST? David Form - August 15,
Wolbachia Workshop 2017 Bioinformatics BLAST Basic Local Alignment Search Tool Finding Model Organisms for Study of Disease Can yeast be used as a model organism to study cystic fibrosis? BLAST Why Use
More informationMaking Sense of DNA and Protein Sequences. Lily Wang, PhD Department of Biostatistics Vanderbilt University
Making Sense of DNA and Protein Sequences Lily Wang, PhD Department of Biostatistics Vanderbilt University 1 Outline Biological background Major biological sequence databanks Basic concepts in sequence
More informationMichelle Wang Department of Biology, Queen s University, Kingston, Ontario Biology 206 (2008)
An investigation of the fitness and strength of selection on the white-eye mutation of Drosophila melanogaster in two population sizes under light and dark treatments over three generations Image Source:
More informationExercises (Multiple sequence alignment, profile search)
Exercises (Multiple sequence alignment, profile search) 8. Using Clustal Omega program, available among the tools at the EBI website (http://www.ebi.ac.uk/tools/msa/clustalo/), calculate a multiple alignment
More informationFrom AP investigative Laboratory Manual 1
Comparing DNA Sequences to Understand Evolutionary Relationships. How can bioinformatics be used as a tool to determine evolutionary relationships and to better understand genetic diseases? BACKGROUND
More informationHot Topics. What s New with BLAST?
Hot Topics What s New with BLAST? Slides based on NCBI talk at American Society of Human Genetics October 2005 Hot Topics Outline I. New BLAST Algorithm: Discontiguous MegaBLAST II. New Databases III.
More informationBLAST. Basic Local Alignment Search Tool. Optimized for finding local alignments between two sequences.
BLAST Basic Local Alignment Search Tool. Optimized for finding local alignments between two sequences. An example could be aligning an mrna sequence to genomic DNA. Proteins are frequently composed of
More informationBioinformatics of the Green Fluorescent Proteins
Tested Studies for Laboratory Teaching Proceedings of the Association for Biology Laboratory Education Volume 39, Article 66, 2018 Bioinformatics of the Green Fluorescent Proteins Alma E. Rodriguez Estrada
More informationWeek 1 BCHM 6280 Tutorial: Gene specific information using NCBI, Ensembl and genome viewers
Week 1 BCHM 6280 Tutorial: Gene specific information using NCBI, Ensembl and genome viewers Web resources: NCBI database: http://www.ncbi.nlm.nih.gov/ Ensembl database: http://useast.ensembl.org/index.html
More informationBasic Local Alignment Search Tool
14.06.2010 Table of contents 1 History History 2 global local 3 Score functions Score matrices 4 5 Comparison to FASTA References of BLAST History the program was designed by Stephen W. Altschul, Warren
More informationLecture 8: Transgenic Model Systems and RNAi
Lecture 8: Transgenic Model Systems and RNAi I. Model systems 1. Caenorhabditis elegans Caenorhabditis elegans is a microscopic (~1 mm) nematode (roundworm) that normally lives in soil. It has become one
More informationAgenda. Web Databases for Drosophila. Gene annotation workflow. GEP Drosophila annotation projects 01/01/2018. Annotation adding labels to a sequence
Agenda GEP annotation project overview Web Databases for Drosophila An introduction to web tools, databases and NCBI BLAST Web databases for Drosophila annotation UCSC Genome Browser NCBI / BLAST FlyBase
More informationHands-On Four Investigating Inherited Diseases
Hands-On Four Investigating Inherited Diseases The purpose of these exercises is to introduce bioinformatics databases and tools. We investigate an important human gene and see how mutations give rise
More informationOutline. Annotation of Drosophila Primer. Gene structure nomenclature. Muller element nomenclature. GEP Drosophila annotation projects 01/04/2018
Outline Overview of the GEP annotation projects Annotation of Drosophila Primer January 2018 GEP annotation workflow Practice applying the GEP annotation strategy Wilson Leung and Chris Shaffer AAACAACAATCATAAATAGAGGAAGTTTTCGGAATATACGATAAGTGAAATATCGTTCT
More informationFollowing text taken from Suresh Kumar. Bioinformatics Web - Comprehensive educational resource on Bioinformatics. 6th May.2005
Bioinformatics is the recording, annotation, storage, analysis, and searching/retrieval of nucleic acid sequence (genes and RNAs), protein sequence and structural information. This includes databases of
More informationFINDING GENES AND EXPLORING THE GENE PAGE AND RUNNING A BLAST (Exercise 1)
FINDING GENES AND EXPLORING THE GENE PAGE AND RUNNING A BLAST (Exercise 1) 1.1 Finding a gene using text search. Note: For this exercise use http://www.plasmodb.org a. Find all possible kinases in Plasmodium.
More informationWho, When, and Where. Section Days & Times
1 GENERAL INFORMATION Who, When, and Where Section 01 02 Days & Times Professors Teaching Assistants Laboratory Coordinator M 12:20 4:25 pm W 1:25 4:25 pm Sam Hazen Assistant Professor, Biology 409A Morrill
More informationFinding Genes, Building Search Strategies and Visiting a Gene Page
Finding Genes, Building Search Strategies and Visiting a Gene Page 1. Finding a gene using text search. For this exercise use http://www.plasmodb.org a. Find all possible kinases in Plasmodium. Hint: use
More informationBioinformatic analysis of similarity to allergens. Mgr. Jan Pačes, Ph.D. Institute of Molecular Genetics, Academy of Sciences, CR
Bioinformatic analysis of similarity to allergens Mgr. Jan Pačes, Ph.D. Institute of Molecular Genetics, Academy of Sciences, CR Scope of the work Method for allergenicity search used by FAO/WHO Analysis
More informationWSSP-10 Chapter 9 Determine ORF and BLASTP
WSSP-10 Chapter 9 Determine ORF and BLASTP Steps and terms used in protein expression 1 st ATG in mrna p 9-1 Cloning the cdna library p 9-1 Possible reading frames p 9-2 Possible types of clones in the
More informationAnnotation and the analysis of annotation terms. Brian J. Knaus USDA Forest Service Pacific Northwest Research Station
Annotation and the analysis of annotation terms. Brian J. Knaus USDA Forest Service Pacific Northwest Research Station 1 Library preparation Sequencing Hypothesis testing Bioinformatics 2 Why annotate?
More informationBCHM 6280 Tutorial: Gene specific information using NCBI, Ensembl and genome viewers
BCHM 6280 Tutorial: Gene specific information using NCBI, Ensembl and genome viewers Web resources: NCBI database: http://www.ncbi.nlm.nih.gov/ Ensembl database: http://useast.ensembl.org/index.html UCSC
More informationSmall Exon Finder User Guide
Small Exon Finder User Guide Author Wilson Leung wleung@wustl.edu Document History Initial Draft 01/09/2011 First Revision 08/03/2014 Current Version 12/29/2015 Table of Contents Author... 1 Document History...
More informationAnnotation of contig27 in the Muller F Element of D. elegans. Contig27 is a 60,000 bp region located in the Muller F element of the D. elegans.
David Wang Bio 434W 4/27/15 Annotation of contig27 in the Muller F Element of D. elegans Abstract Contig27 is a 60,000 bp region located in the Muller F element of the D. elegans. Genscan predicted six
More informationBME 110 Midterm Examination
BME 110 Midterm Examination May 10, 2011 Name: (please print) Directions: Please circle one answer for each question, unless the question specifies "circle all correct answers". You can use any resource
More informationUNIVERSITY OF KWAZULU-NATAL EXAMINATIONS: MAIN, SUBJECT, COURSE AND CODE: GENE 320: Bioinformatics
UNIVERSITY OF KWAZULU-NATAL EXAMINATIONS: MAIN, 2010 SUBJECT, COURSE AND CODE: GENE 320: Bioinformatics DURATION: 3 HOURS TOTAL MARKS: 125 Internal Examiner: Dr. Ché Pillay External Examiner: Prof. Nicola
More informationNCBI web resources I: databases and Entrez
NCBI web resources I: databases and Entrez Yanbin Yin Most materials are downloaded from ftp://ftp.ncbi.nih.gov/pub/education/ 1 Homework assignment 1 Two parts: Extract the gene IDs reported in table
More informationGene-centered resources at NCBI
COURSE OF BIOINFORMATICS a.a. 2014-2015 Gene-centered resources at NCBI We searched Accession Number: M60495 AT NCBI Nucleotide Gene has been implemented at NCBI to organize information about genes, serving
More informationAdaptive Molecular Evolution. Reading for today. Neutral theory. Predictions of neutral theory. The neutral theory of molecular evolution
Adaptive Molecular Evolution Nonsynonymous vs Synonymous Reading for today Li and Graur chapter (PDF on website) Evolutionary EST paper (PDF on website) Neutral theory The majority of substitutions are
More informationTeaching Principles of Enzyme Structure, Evolution, and Catalysis Using Bioinformatics
KBM Journal of Science Education (2010) 1 (1): 7-12 doi: 10.5147/kbmjse/2010/0013 Teaching Principles of Enzyme Structure, Evolution, and Catalysis Using Bioinformatics Pablo Sobrado Department of Biochemistry,
More informationComparative Bioinformatics. BSCI348S Fall 2003 Midterm 1
BSCI348S Fall 2003 Midterm 1 Multiple Choice: select the single best answer to the question or completion of the phrase. (5 points each) 1. The field of bioinformatics a. uses biomimetic algorithms to
More informationRetrieval of gene information at NCBI
Retrieval of gene information at NCBI Some notes 1. http://www.cs.ucf.edu/~xiaoman/fall/ 2. Slides are for presenting the main paper, should minimize the copy and paste from the paper, should write in
More informationSAMPLE LITERATURE Please refer to included weblink for correct version.
Edvo-Kit #340 DNA Informatics Experiment Objective: In this experiment, students will explore the popular bioninformatics tool BLAST. First they will read sequences from autoradiographs of automated gel
More informationDiscover the Microbes Within: The Wolbachia Project. Bioinformatics Lab
Bioinformatics Lab ACTIVITY AT A GLANCE "Understanding nature's mute but elegant language of living cells is the quest of modern molecular biology. From an alphabet of only four letters representing the
More informationSequence Analysis Lab Protocol
Sequence Analysis Lab Protocol You will need this handout of instructions The sequence of your plasmid from the ABI The Accession number for Lambda DNA J02459 The Accession number for puc 18 is L09136
More informationBrowser Exercises - I. Alignments and Comparative genomics
Browser Exercises - I Alignments and Comparative genomics 1. Navigating to the Genome Browser (GBrowse) Note: For this exercise use http://www.tritrypdb.org a. Navigate to the Genome Browser (GBrowse)
More informationPre-Lab Questions. 1. Use the following data to construct a cladogram of the major plant groups.
Pre-Lab Questions Name: 1. Use the following data to construct a cladogram of the major plant groups. Table 1: Characteristics of Major Plant Groups Organism Vascular Flowers Seeds Tissue Mosses 0 0 0
More informationMODULE TSS2: SEQUENCE ALIGNMENTS (ADVANCED)
MODULE TSS2: SEQUENCE ALIGNMENTS (ADVANCED) Lesson Plan: Title MEG LAAKSO AND JAMIE SIDERS Identifying the TSS for a gene in D. eugracilis using sequence alignment with the D. melanogaster ortholog Objectives
More informationArray-Ready Oligo Set for the Rat Genome Version 3.0
Array-Ready Oligo Set for the Rat Genome Version 3.0 We are pleased to announce Version 3.0 of the Rat Genome Oligo Set containing 26,962 longmer probes representing 22,012 genes and 27,044 gene transcripts.
More informationuser s guide Question 3
Question 3 During a positional cloning project aimed at finding a human disease gene, linkage data have been obtained suggesting that the gene of interest lies between two sequence-tagged site markers.
More informationLesson: Bioinformatics: How can analysis of protein and / or nucleotide sequences help us to better understand living organisms?
GENA Project Summer Workshop 2008 Partnership: Anita Klein and Linda Albright Lesson: Bioinformatics: How can analysis of protein and / or nucleotide sequences help us to better understand living organisms?
More informationPRESENTING SEQUENCES 5 GAATGCGGCTTAGACTGGTACGATGGAAC 3 3 CTTACGCCGAATCTGACCATGCTACCTTG 5
Molecular Biology-2017 1 PRESENTING SEQUENCES As you know, sequences may either be double stranded or single stranded and have a polarity described as 5 and 3. The 5 end always contains a free phosphate
More informationProkaryotic Annotation Pipeline SOP HGSC, Baylor College of Medicine
1 Abstract A prokaryotic annotation pipeline was developed to automatically annotate draft and complete bacterial genomes. The protein coding genes in the genomes are predicted by the combination of Glimmer
More informationProduct Applications for the Sequence Analysis Collection
Product Applications for the Sequence Analysis Collection Pipeline Pilot Contents Introduction... 1 Pipeline Pilot and Bioinformatics... 2 Sequence Searching with Profile HMM...2 Integrating Data in a
More information