GBIO001-9 BioinformaticsIntroduction

Instructors

• Course instructor– Kristel Van Steen

• Office: 0/15• kristel.VanSteen@ulg.ac.be• http://www.montefiore.ulg.ac.be/~kvansteen/

Teaching20132014.html

• Practical sessions coordinator– Kyrylo Bessonov (Kirill)

• Office: B37 1/16• kbessonov@ulg.ac.be

Overview1. Introduction to course scope

2. Evaluation mode/schedule details

3. Online systems:1. Assignment submission system

2. HW group sign up system

4. Introduction to R language1. Basic syntax and data types

2. Installation of key R libraries

5. Introduction to public databases

6. Homework mini assignment

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

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

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)

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)

Course practical aspects

• Course material: will be posted one day before the next class on Prof. Kristel Van Steen (lectures) and/or Kyrylo Bessonov’s (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@ulg.ac.be)

What will we be doing?• We’ll cover a selected recent topics

in bioinformatics both trough lectures and assignments (including student presentations)– that basically means that we’ll 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

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

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

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

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

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

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 class2) The written report should be submitted as per due dates shown in the above table

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%)

Course materials

• These will be both posted on Prof. Kristel van Steen’s and Kyrylo Bessonov’s websites. Please check both sites

• There is no course book

• Course syllabus and schedule will be posted online

Assignment SubmissionStep by Step Guide

Assignment submission

• All assignments should be zipped into one file (*.zip) and submitted online

• Create a submission account

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

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

Introduction to A basic tutorial

Definition

• “R is a free software environment for statistical computing and graphics”1

• 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/)

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

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

Topics covered in this tutorial

• Operators / Variables

• Main objects types

• Plotting and plot modification functions

• Writing and reading data to/from files

Variables/Operators

• Variables store one element x <- 25

Here x variable is assigned value 25

• Check value assigned to the variable x

>x

[1] 25• Basic mathematical operators that could be applied

to variables: (+),(-),(/),(*)• Use parenthesis to obtain desired sequence of

mathematical operations

Arithmetic operators

• What is the value of small z here?>x <- 25

> y <- 15

> z <- (x + y)*2

> Z <- z*z

> z

[1] 80

Vectors

• Vectors have only 1 dimension and represent enumerated sequence of data. They can also store variables

> v1 <- c(1, 2, 3, 4, 5)

> mean(v1)

[1] 3

The elements of a vector are specified /modified with braces (e.g. [number])

> v1[1] <- 48

> v1

[1] 48 2 3 4 5

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< less than<= less than or equal to> greater than>= greater than or equal to== exactly equal to!= not equal to!x Not xx | y x OR yx & y x AND y

Logical operators

• Can be applied to vectors in the following way. The return value is either True or False

> v1

[1] 48 2 3 4 5

> v1 <= 3

[1] FALSE TRUE TRUE FALSE FALSE

R workspace

• Display all workplace objects (variables, vectors, etc.) via ls():

>ls()

[1] "Z" "v1" "x" "y" "z"

• Useful tip: to save “workplace” and restore from a file use:

>save.image(file = " workplace.rda")

>load(file = "workplace.rda")

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.packages=T)

• To search in all packages installed in your R installation always use try.all.packages=T in help()

• To search for a key word in R documentation use help.search():

help.search("mean")

Basic data types

• Data could be of 3 basic data types:– numeric– character– logical

• Numeric variable type:> x <- 1

> mode(x)

[1] "numeric"

Basic data types

• Logical variable type (True/False):> y <- 3<4

> mode(y)

[1] "logical"

• Character variable type:> z <- "Hello class"

> mode(z)

[1] "character"

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) > m <- matrix(0,2,3)

> m

[,1] [,2] [,3]

[1,] 0 0 0

[2,] 0 0 0

Lists

• Lists contain various vectors. Each vector in the list can be accessed by double braces [[number]]

> x <- c(1, 2, 3, 4)

> y <- c(2, 3, 4)

> L1 <- list(x, y)

> L1

[[1]]

[1] 1 2 3 4

[[2]]

[1] 2 3 4

Data frames• Data frames are similar to matrices but

can contain various data types> x <- c(1,5,10)

> y <- c("A", "B", "C")

> z <-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()

Factors

• Factors are special in that they contain both integer and character vectors. Thus each unique variable has corresponding name and number

> letters = c("A","B","C","A","C","C")

> letters = factor(letters)

[1] A B C A C C

Levels: A B C

> summary(letters)

A B C

2 1 3

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 alternativeraw_data <-read.table(file="data_file.txt")

• To write data into R from a text file use read.table()

> write.table(mydata, "data_file.txt")

Conversion between data types

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

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

Plotting functions

• R offers following array of plotting functions

Function Descriptionplot(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-charboxplot(x) Plots a box plot showing variables via their quantileshist(x) Plots a histogram(bar plot)

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()

Plot modification functions

Function Description

points(x,y) add points to the plot using coordinates specified in x and y vectorslines(x,y) adds a line using coordinates in x and y

mtext(text,side=3) 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, angle=30, code=1)

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(h=y) draws horizontal line at y coordinaterect(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

Installation of new libraries

• There are two main R repositories– CRAN– BioConductor

• To install package/library from CRANinstall.packages("seqinr")

To install packages from BioConductorsource("http://bioconductor.org/biocLite.R")

biocLite("GenomicRanges")

Installation of new libraries

• Download and install latest R version on your PC. Go to http://cran.r-project.org/

• Install following libraries by runninginstall.packages(c("seqinr", "muscle", "ape",

"GenABEL")

source("http://bioconductor.org/biocLite.R")

biocLite("limma","affy","hgu133plus2.db","Biostings")

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

What are we looking for?

Data & databases

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

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

Genome sequencing generates lots of data

Biological DatabasesThe number of databases is contantly growing!- OBRC: Online Bioinformatics Resources Collection currently lists over 2826 databases (2013)

Main databases by categoryLiterature• PubMed: scientific & medical abstracts/citations Health• OMIM: online mendelian inheritance in manNucleotide SequencesNucleotide: DNA and RNA sequencesGenomes• Genome: genome sequencing projects by organism• dbSNP: short genetic variationsGenes• Protein: protein sequences• UniProt: protein sequences and related informationChemicals• PubChem Compound: chemical information with structures,

information and linksPathways• BioSystems: molecular pathways with links to genes, proteins• KEGG Pathway: information on main biological pathways

Growth of UniProtKB database

• UniProtKB contains mainly protein sequences (entries). The database growth is exponential

• Data management issues? (e.g. storage, search, indexing?)

Source: http://www.ebi.ac.uk/uniprot/TrEMBLstats

num

ber

of e

ntrie

s

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)

Primary Databases

Sequence Information– DNA: EMBL, Genbank, DDBJ– Protein: SwissProt, TREMBL, PIR, OWL

Genome Information– GDB, MGD, ACeDB

Structure Information– PDB, NDB, CCDB/CSD

Secondary Databases

Sequence-related Information– ProSite, Enzyme, REBase

Genome-related Information– OMIM, TransFac

Structure-related Information– DSSP, HSSP, FSSP, PDBFinder

Pathway Information– KEGG, Pathways

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

GenBank main fields

NCBI Databases contain more than just DNA & protein sequences

NCBI main portal: http://www.ncbi.nlm.nih.gov/

Fasta format to store sequences

Saccharomyces cerevisiae strain YC81 actin (ACT1) geneGenBank: JQ288018.1>gi|380876362|gb|JQ288018.1| Saccharomyces cerevisiae strain YC81 actin

(ACT1) gene, partial cds TGGCATCATACCTTCTACAACGAATTGAGAGTTGCCCCAGAAGAACACCCTGTTCTTTTGACTGAAGCTCCAATGAACCCTAAATCAAACAGAGAAAAGATGACTCAAATTATGTTTGAAACTTTCAACGTTCCAGCCTTCTACGTTTCCATCCAAGCCGTTTTGTCCTTGTACTCTTCCGGTAGAACTACTGGTATTGTTTTGGATTCCGGTGATGGTGTTACTCACGTCGTTCCAATTTACGCTGGTTTCTCTCTACCTCACGCCATTTTGAGAATCGATTTGGCCGGTAGAGATTTGACTGACTACTTGATGAAGATCTTGAGTGAACGTGGTTACTCTTTCTCCACCACTGCTGAAAGAGAAATTGTCCGTGACATCAAGGAAAAACTATGTTACGTCGCCTTGGACTTCGAGCAAGAAATGCAAACCGCTGCTCAATCTTCTTCAATTGAAAAATCCTACGAACTTCCAGATGGTCAAGTCATCACTATTGGTAAC

• The FASTA format is now universal for all databases and software that handles DNA and protein sequences

• Specifications:• One header line• starts with > with a ends with [return]

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

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

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

OMIM-entries

OMIM Gene ID -entries

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

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

Homework 1a

Exploring OMIM and PubMed databases

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

Homework 1a• Even though it is not critical for this

HW, register still online for HW1a as shown below to gain the habit

Last slide! Thanks for attention!

Next class is on Oct 1st!