GBIO001-9 Bioinformatics

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GBIO001-9 Bioinformatics

• Introduction

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Instructors

• Course instructor
• Kristel Van Steen
• Office 0/15
• kristel.VanSteen_at_ulg.ac.be
• http//www.montefiore.ulg.ac.be/kvansteen/Teachin
  g20132014.html
• Practical sessions coordinator
• Kyrylo Bessonov (Kirill)
• Office B37 1/16
• kbessonov_at_ulg.ac.be

3
Overview

1. Introduction to course scope
2. Evaluation mode/schedule details
3. Online systems
4. Assignment submission system
5. HW group sign up system
6. Introduction to R language
7. Basic syntax and data types
8. Installation of key R libraries
9. Introduction to public databases
10. Homework mini assignment

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Bioinformatics

• Definition the collection, classification,
  storage, and analysis of biochemical and
  biological information using computers especially
  as applied to molecular genetics and genomics
  (Merriam-Webster dictionary)
• Definition a field that works on the problems
  involving intersection of Biology/Computer
  Science/Statistics

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Course Scope

• This course is introduction to bioinformatics
  field covering wide array of topics
• a) accessing and working with main biological DB
  (PubMed, Ensembl)
• b) sequence alignments
• c) phylogenetics
• d) statistical genetics
• f) microarray/genotype data analysis

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Course expected outcomes

• At the end of the course students are expected to
  gain a taste of various bioinformatics fields
  coupled to hands-on knowledge. Students should be
  able to perform multiple sequence alignments,
  query biological databases programmatically,
  perform GWA and microarray analysis, present
  scientific papers, have basic statistics
  knowledge (in the context of genetics)

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Course practical aspects

• Mode of delivery in class
• Activities individual and group work
• reading of scientific literature
• practical assignments (analysis of
  papers/programming in R)
• in-class group presentations
• Meeting times
• Tuesdays from 2pm-6pm (by the latest)
• Check website each week for details
• Room 1.21, Montefiore Institute (B28)

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Course practical aspects

• Course material will be posted one day before
  the next class on Prof. Kristel Van Steen
  (lectures) and/or Kyrylo Bessonovs (practicals)
  website(s).
• Assignment submission will be done online via a
  special submission website
• After the deadline, the assignment should be
  e-mailed to Kyrylo Bessonov (kbessonov_at_ulg.ac.be)

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What will we be doing?

• Well cover a selected recent topics in
  bioinformatics both trough lectures and
  assignments (including student presentations)
• that basically means that well be reading papers
  from the bioinformatics literature and
  analyzing/critiquing them
• hands-on lectures that will allow you to
  understand practical aspects of the
  bioinformatics topics
• Self-learning through assignments

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How will we do it?

• Theory classes
• All course notes are in English.
• Main instructor Kristel van Steen
• Guest lectures are to be expected on various
  bioinformatics topics
• The theory part of the course is meant to be
  interactive
• In-class discussions of papers / topics

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How will we do it?

• Practical classes
• During these classes will be looking at practical
  aspects of the topics introduced in theory
  classes. It is suggested to execute sample R
  scripts and demonstrations on your PCs.
• Optional reading assignments will be assigned
• to prepare for discussions in class based on the
  previously posted papers (no grading yet
  participation grades)
• Homework assignments are of 3 types (graded)
• Homework assignments result in a group report
  and can be handed in electronically in French or
  English
• Homework assignments constitute an important part
  of this course

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Types of HW assignments

• Three types of homework assignments are
• Literature style assignment (Type 1)
• A group of students is asked to select a paper
  from the provided ones. The group prepares
  in-class presentation and a written report
• All oral presentations of HW1,HW2, HW3 will be
  done during our last class on Dec 10th,2013
• Programming style assignment (Type 2)
• A group is asked to develop an R code to answer
  assignment questions
• Classical style assignment (Type 3)
• A group is provided with questions to be answered
  in the written report. Usually R scripts are
  provided and require execution / modification

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HW assignments details

• Every homework assignment involves writing a
  short report of no more than the equivalent of
  four single-spaced typed pages of text, excluding
  figures, tables and bibliography.
• It should contain an abstract (e.g., depending on
  the homework style description of the paper
  content, description of the problem) and a
  results/discussion part. If citations are made to
  other papers, there should be a bibliography (any
  style is OK)! Only one report per group is
  needed.
• One member of the group should submit only the
  selected type of the HW and full names of group
  participants via online system

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Selection of HW

• Total of 4 graded assignments.
• Students are asked to try all 3 types of
  assignments to gain broader exposure to course
  material
• e.g. if group 1 selected type 1 assignment for
  HW1b, it should select either type 2 or 3 for HW2
• Assignments will be posted on the practical
  website

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Assigned HW deadlines
HW ID Main topic Due Date
HW1a Databases Oct 8th
HW1b GWAS Nov 8th
HW2 Sequences alignments Dec 10th
HW3 Microarrays / Clustering Jan 8th (preliminary)
Notes 1) Type 1 Literature style HW 1 to 3
will be all three presented during Dec 10th
class 2) The written report should be submitted
as per due dates shown in the above table
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Course Grading

• Written exam 40 of final mark
• Multiple choice questions/open book
• Assignments 50 of final mark
• Reports of Homework assignments (1 per group)
  are handed in electronically in English or French
  
• Participation in group and in-class discussions
  (10)

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Course materials

• These will be both posted on Prof. Kristel van
  Steens and Kyrylo Bessonovs websites. Please
  check both sites
• There is no course book
• Course syllabus and schedule will be posted online

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Assignment Submission

• Step by Step Guide

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Assignment submission

• All assignments should be zipped into one file
  (.zip) and submitted online
• Create a submission account

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Account creation

• Any member of the group can submit assignment
• Account details will be emailed to you
  automatically
• All GBIO009-1 students should create an account

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Submit your assignment

• After account creation login into a submission
  page
• The remaining time to deadline is displayed. Good
  idea to check it from time to time in order to be
  on top of things
• File extension should be zip
• Can submit assignment as many times as you wish

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Introduction to

• A basic tutorial

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Definition

• R is a free software environment for statistical
  computing and graphics1
• R is considered to be one of the most widely used
  languges amongst statisticians, data miners,
  bioinformaticians and others.
• R is free implementation of S language
• Other commercial statistical packages are SPSS,
  SAS, MatLab

1 R Core Team, R A Language and Environment for
Statistical Computing, Vienna, Austria
(http//www.R-project.org/)
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Why to learn R?

• Since it is free and open-source, R is widely
  used by bioinformaticians and statisticians
• It is multiplatform and free
• Has wide very wide selection of additional
  libraries that allow it to use in many domains
  including bioinformatics
• Main library repositories CRAN and BioConductor

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Programming? Should I be scared?

• R is a scripting language and, as such, is much
  more easier to learn than other compiled
  languages as C
• R has reasonably well written documentation
  (vignettes)
• Syntax in R is simple and intuitive if one has
  basic statistics skills
• R scripts will be provided and explained in-class

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Topics covered in this tutorial

• Operators / Variables
• Main objects types
• Plotting and plot modification functions
• Writing and reading data to/from files

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Variables/Operators

• Variables store one element
• x lt- 25
• Here x variable is assigned value 25
• Check value assigned to the variable x
• gtx
• 1 25
• Basic mathematical operators that could be
  applied to variables (),(-),(/),()
• Use parenthesis to obtain desired sequence of
  mathematical operations

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Arithmetic operators

• What is the value of small z here?
• gtx lt- 25
• gt y lt- 15
• gt z lt- (x y)2
• gt Z lt- zz
• gt z
• 1 80

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Vectors

• Vectors have only 1 dimension and represent
  enumerated sequence of data. They can also store
  variables
• gt v1 lt- c(1, 2, 3, 4, 5)
• gt mean(v1)
• 1 3
• The elements of a vector are specified /modified
  with braces (e.g. number)
• gt v11 lt- 48
• gt v1
• 1 48 2 3 4 5

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Logical operators

• These operators mostly work on vectors, matrices
  and other data types
• Type of data is not important, the same operators
  are used for numeric and character data types

Operator Description
lt less than
lt less than or equal to
gt greater than
gt greater than or equal to
exactly equal to
! not equal to
!x Not x
x y x OR y
x y x AND y
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Logical operators

• Can be applied to vectors in the following way.
  The return value is either True or False
• gt v1
• 1 48 2 3 4 5
• gt v1 lt 3
• 1 FALSE TRUE TRUE FALSE FALSE

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R workspace

• Display all workplace objects (variables,
  vectors, etc.) via ls()
• gtls()
• 1 "Z" "v1" "x" "y" "z"
• Useful tip to save workplace and restore from
  a file use
• gtsave.image(file " workplace.rda")
• gtload(file "workplace.rda")

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How to find help info?

• Any function in R has help information
• To invoke help use ? Sign or help()
• ? function_name()
• ? mean
• help(mean, try.all.packagesT)
• To search in all packages installed in your R
  installation always use try.all.packagesT in
  help()
• To search for a key word in R documentation use
  help.search()
• help.search("mean")

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Basic data types

• Data could be of 3 basic data types
• numeric
• character
• logical
• Numeric variable type
• gt x lt- 1
• gt mode(x)
• 1 "numeric"

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Basic data types

• Logical variable type (True/False)
• gt y lt- 3lt4
• gt mode(y)
• 1 "logical"
• Character variable type
• gt z lt- "Hello class"
• gt mode(z)
• 1 "character"

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Objects/Data structures

• The main data objects in R are
• Matrices (single data type)
• Data frames (supports various data types)
• Lists (contain set of vectors)
• Other more complex objects with slots
• Matrices are 2D objects (rows/columns)
• gt m lt- matrix(0,2,3)
• gt m
• ,1 ,2 ,3
• 1, 0 0 0
• 2, 0 0 0

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Lists

• Lists contain various vectors. Each vector in the
  list can be accessed by double braces number
• gt x lt- c(1, 2, 3, 4)
• gt y lt- c(2, 3, 4)
• gt L1 lt- list(x, y)
• gt L1
• 1
• 1 1 2 3 4
• 2
• 1 2 3 4

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Data frames

• Data frames are similar to matrices but can
  contain various data types
• gt x lt- c(1,5,10)
• gt y lt- c("A", "B", "C")
• gt z lt-data.frame(x,y)
• x y
• 1 1 A
• 2 5 B
• 3 10 C
• To get/change column and row names use colnames()
  and rownames()

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Factors

• Factors are special in that they contain both
  integer and character vectors. Thus each unique
  variable has corresponding name and number
• gt letters c("A","B","C","A","C","C")
• gt letters factor(letters)
• 1 A B C A C C
• Levels A B C
• gt summary(letters)
• A B C
• 2 1 3

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Input/Output

• To read data into R from a text file use
  read.table()
• read help(read.table) to learn more
• scan() is a more flexible alternative
• raw_data lt-read.table(file"data_file.txt")
• To write data into R from a text file use
  read.table()
• gt write.table(mydata, "data_file.txt")

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Conversion between data types

• One can convert one type of data into another
  using as.xxx where xxx is a data type

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Plots generation in R

• R provides very rich set of plotting
  possibilities
• The basic command is plot()
• Each library has its own version of plot()
  function
• When R plots graphics it opens graphical device
  that could be either a window or a file

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Plotting functions

• R offers following array of plotting functions

Function Description
plot(x) plot of the values of x variable on the y axis
plot(x,y) bi-variable plot of x and y values (both axis scaled based on values of x and y variables)
pie(y) circular pie-char
boxplot(x) Plots a box plot showing variables via their quantiles
hist(x) Plots a histogram(bar plot)
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Plot modification functions

• Often R plots are not optimal and one would like
  to add colors or to correct position of the
  legend or do other appropriate modifications
• R has an array of graphical parameters that are a
  bit complex to learn at first glance. Consult
  here the full list
• Some of the graphical parameters can be specified
  inside plot() or using other graphical functions
  such as lines()

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Plot modification functions
Function Description
points(x,y) add points to the plot using coordinates specified in x and y vectors
lines(x,y) adds a line using coordinates in x and y
mtext(text,side3) adds text to a given margin specified by side number
boxplot(x) this a histogram that bins values of x into categories represented as bars
arrows(x0,y0,x1,y1, angle30, code1) adds arrow to the plot specified by the x0, y0, x1, y1 coordinates. Angle provides rotational angle and code specifies at which end arrow should be drawn
abline(hy) draws horizontal line at y coordinate
rect(x1, y1, x2, y2) draws rectangle at x1, y1, x2, y2 coordinates
legend(x,y) plots legend of the plot at the position specified by x and y vectors used to generate a given plot
title() adds title to the plot
axis(side, vect) adds axis depending on the chosen one of the 4 sides vector specifying where tick marks are drawn
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Installation of new libraries

• There are two main R repositories
• CRAN
• BioConductor
• To install package/library from CRAN
• install.packages("seqinr")
• To install packages from BioConductor
• source("http//bioconductor.org/biocLite.R")
  biocLite("GenomicRanges")

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Installation of new libraries

• Download and install latest R version on your PC.
  Go to http//cran.r-project.org/
• Install following libraries by running
• install.packages(c("seqinr", "muscle", "ape",
  "GenABEL")
• source("http//bioconductor.org/biocLite.R")
• biocLite("limma","affy","hgu133plus2.db","Biosting
  s")

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Conclusions

• We hope this course will provide you with the
  good array of analytical and practical skills
• We chose R for this course as it is very flexible
  language with large scope of applications and is
  widely used
• Our next class is October 1st
• Prof. Kristel van Steen will cover introduction
  to bioinformatics and molecular biology topics

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What are we looking for?

• Data databases

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Biologists Collect Lots of Data

• Hundreds of thousands of species to explore
• Millions of written articles in scientific
  journals
• Detailed genetic information
• gene names
• phenotype of mutants
• location of genes/mutations on chromosomes
• linkage (distances between genes)
• High Throughput lab technologies
• PCR
• Rapid inexpensive DNA sequencing (Illumina HiSeq)
• Microarrays (Affymetrix)
• Genome-wide SNP chips / SNP arrays (Illumina)
• Must store data such that
• Minimum data quality is checked
• Well annotated according to standards
• Made available to wide public to foster research

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What is database?

• Organized collection of data
• Information is stored in "records, "fields,
  tables
• Fields are categories
• Must contain data of the same type (e.g. columns
  below)
• Records contain data that is related to one
  object (e.g. protein, SNP) (e.g. rows below)

SNP ID SNPSeqID Gene primer -primer
D1Mit160_1 10.MMHAP67FLD1.seq lymphocyte antigen 84 AAGGTAAAAGGCAATCAGCACAGCC TCAACCTGGAGTCAGAGGCT
M-05554_1 12.MMHAP31FLD3.seq procollagen, type III, alpha TGCGCAGAAGCTGAAGTCTA TTTTGAGGTGTTAATGGTTCT
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Genome sequencing generates lots of data
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Biological Databases
The number of databases is contantly growing!-
OBRC Online Bioinformatics Resources Collection
currently lists over 2826 databases
(2013)
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Main databases by category

• Literature
• PubMed scientific medical abstracts/citations
• Health
• OMIM online mendelian inheritance in man
• Nucleotide Sequences
• Nucleotide DNA and RNA sequences
• Genomes
• Genome genome sequencing projects by organism
• dbSNP short genetic variations
• Genes
• Protein protein sequences
• UniProt protein sequences and related
  information
• Chemicals
• PubChem Compound chemical information with
  structures, information and links
• Pathways
• BioSystems molecular pathways with links to
  genes, proteins
• KEGG Pathway information on main biological
  pathways

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Growth of UniProtKB database

• UniProtKB contains mainly protein sequences
  (entries). The database growth is exponential
• Data management issues? (e.g. storage, search,
  indexing?)

number of entries
Source http//www.ebi.ac.uk/uniprot/TrEMBLstats
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Primary and Secondary Databases
Primary databases REAL EXPERIMENTAL DATA
(raw) Biomolecular sequences or structures and
associated annotation information (organism,
function, mutation linked to disease,
functional/structural patterns, bibliographic
etc.) Secondary databases DERIVED INFORMATION
(analyzed and annotated) Fruits of analyses of
primary data in the primary sources (patterns,
blocks, profiles etc. which represent the most
conserved features of multiple alignments)
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Primary Databases

• Sequence Information
• DNA EMBL, Genbank, DDBJ
• Protein SwissProt, TREMBL, PIR, OWL
• Genome Information
• GDB, MGD, ACeDB
• Structure Information
• PDB, NDB, CCDB/CSD

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Secondary Databases

• Sequence-related Information
• ProSite, Enzyme, REBase
• Genome-related Information
• OMIM, TransFac
• Structure-related Information
• DSSP, HSSP, FSSP, PDBFinder
• Pathway Information
• KEGG, Pathways

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GenBank database

• Contains all DNA and protein sequences described
  in the scientific literature or collected in
  publicly funded research
• One can search by protein name to get DNA/mRNA
  sequences
• The search results could be filtered by species
  and other parameters

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GenBank main fields
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NCBI Databases contain more than just DNA
protein sequences
NCBI main portal http//www.ncbi.nlm.nih.gov/
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Fasta format to store sequences

• The FASTA format is now universal for all
  databases and software that handles DNA and
  protein sequences
• Specifications
• One header line
• starts with gt with a ends with return

• Saccharomyces cerevisiae strain YC81 actin (ACT1)
  gene
• GenBank JQ288018.1
• gtgi380876362gbJQ288018.1 Saccharomyces
  cerevisiae strain YC81 actin (ACT1) gene, partial
  cds TGGCATCATACCTTCTACAACGAATTGAGAGTTGCCCCAGAAGAAC
  ACCCTGTTCTTTTGACTGAAGCTCCAATGAACCCTAAATCAAACAGAGAA
  AAGATGACTCAAATTATGTTTGAAACTTTCAACGTTCCAGCCTTCTACGT
  TTCCATCCAAGCCGTTTTGTCCTTGTACTCTTCCGGTAGAACTACTGGTA
  TTGTTTTGGATTCCGGTGATGGTGTTACTCACGTCGTTCCAATTTACGCT
  GGTTTCTCTCTACCTCACGCCATTTTGAGAATCGATTTGGCCGGTAGAGA
  TTTGACTGACTACTTGATGAAGATCTTGAGTGAACGTGGTTACTCTTTCT
  CCACCACTGCTGAAAGAGAAATTGTCCGTGACATCAAGGAAAAACTATGT
  TACGTCGCCTTGGACTTCGAGCAAGAAATGCAAACCGCTGCTCAATCTTC
  TTCAATTGAAAAATCCTACGAACTTCCAGATGGTCAAGTCATCACTATTG
  GTAAC

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OMIM database

• Online Mendelian Inheritance in Man (OMIM)
•  information on all known mendelian disorders
  linked to over 12,000 genes
• Started at 1960s by Dr. Victor A. McKusick as a
  catalog of mendelian traits and disorders
• Linked disease data
• Links disease phenotypes and causative genes
• Used by physicians and geneticists

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OMIM basic search

• Online Tutorial http//www.openhelix.com/OMIM
• Each search results entry has , , or symbol
  
• entries are the most informative as molecular
  basis of phenotype genotype association is
  known is known
• Will do search on Ankylosing spondylitis (AS)
• AS characterized by chronic inflammation of spine

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OMIM-search results

• Look for the entires that link to the genes.
  Apply filters if needed

Filter results if known SNP is associated to the
entry
Some of the interesting entries. Try to look for
the ones with sign
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OMIM-entries
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OMIM Gene ID -entries
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OMIM-Finding disease linked genes

• Read the report of given top gene linked
  phenotype
• Mapping Linkage heterogeneity section
• Go back to the original results
• Previously seen entry 607562 IL23R

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PubMed database

• PubMed is one of the best known database in the
  whole scientific community
• Most of biology related literature from all the
  related fields are being indexed by this database
• It has very powerful mechanism of constructing
  search queries
• Many search fields ? Logical operatiors (AND,
  OR)
• Provides electronic links to most journals
• Example of searching by author articles published
  within 2012-2013

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Homework 1a

• Exploring OMIM and PubMed databases

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Homework 1a

• Instructions
• Only one type of HW.
• This is Type 3 HW
• Individual work. No groups
• Total of 2 easy questions to answer
• Do not forget to take print screen snapshots to
  show your work
• Due date October 8th at midnight
• Upload your completed HW using the submission
  system

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Homework 1a

• Even though it is not critical for this HW,
  register still online for HW1a as shown below to
  gain the habit

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• Last slide! Thanks for attention!
• Next class is on Oct 1st!