Download Introductory lecture: course details, R-language

GBIO001-9 Bioinformatics
Introduction
Instructors
• Course instructor
– Kristel Van Steen
• Office: 0/15
• [email protected]
• http://www.montefiore.ulg.ac.be/~kvansteen/
Teaching20132014.html
• Practical sessions coordinator
– Kyrylo Bessonov (Kirill)
• Office: B37 1/16
• [email protected]
Overview
1. 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 language
1. 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 handson 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 ([email protected])
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 inclass 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 class
2) 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 Submission
Step 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
<
<=
>
>=
==
!=
!x
x|y
x&y
Description
less than
less than or equal to
greater than
greater than or equal to
exactly equal to
not equal to
Not x
x OR 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 alternative
raw_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
Description
plot(x)
plot of the values of x variable on the y axis
bi-variable plot of x and y values (both axis scaled based
on values of x and y variables)
circular pie-char
Plots a box plot showing variables via their quantiles
Plots a histogram(bar plot)
plot(x,y)
pie(y)
boxplot(x)
hist(x)
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)
lines(x,y)
add points to the plot using coordinates specified in x and y vectors
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
adds arrow to the plot specified by the x0, y0, x1, y1 coordinates.
arrows(x0,y0,x1,y1, Angle provides rotational angle and code specifies at which end arrow
angle=30, code=1) should be drawn
abline(h=y)
draws horizontal line at y coordinate
rect(x1, y1, x2, y2) draws rectangle at x1, y1, x2, y2 coordinates
legend(x,y)
title()
plots legend of the plot at the position specified by x and y vectors
used to generate a given plot
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 CRAN
install.packages("seqinr")
To install packages from BioConductor
source("http://bioconductor.org/biocLite.R")
biocLite("GenomicRanges")
Installation of new libraries
• Download and install latest R version
on your PC. Go to http://cran.rproject.org/
• Install following libraries by running
install.packages(c("seqinr", "muscle", "ape",
"GenABEL")
source("http://bioconductor.org/biocLite.R")
biocLite("limma","affy","hgu133plus2.db","Biosti
ngs")
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
AAGGTAAAAGGCAAT
CAGCACAGCC
TCAACCTGGAGTCAGA
GGCT
M-05554_1
12.MMHAP31FLD3.seq
procollagen, type III,
alpha
TGCGCAGAAGCTGA
AGTCTA
TTTTGAGGTGTTAATGG
TTCT
Genome sequencing generates lots of data
Biological Databases
The number of databases is contantly growing!
- OBRC: Online Bioinformatics Resources Collection
currently lists over 2826 databases (2013)
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
Growth of UniProtKB database
number of entries
• 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
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
• 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]
Saccharomyces cerevisiae strain YC81 actin (ACT1) gene
GenBank: JQ288018.1
>gi|380876362|gb|JQ288018.1| Saccharomyces cerevisiae strain YC81
actin (ACT1) gene, partial cds
TGGCATCATACCTTCTACAACGAATTGAGAGTTGCCCCAGAAGAACACCCTGTTCTTTTGACTGA
AGCTCCAATGAACCCTAAATCAAACAGAGAAAAGATGACTCAAATTATGTTTGAAACTTTCAACG
TTCCAGCCTTCTACGTTTCCATCCAAGCCGTTTTGTCCTTGTACTCTTCCGGTAGAACTACTGGT
ATTGTTTTGGATTCCGGTGATGGTGTTACTCACGTCGTTCCAATTTACGCTGGTTTCTCTCTACC
TCACGCCATTTTGAGAATCGATTTGGCCGGTAGAGATTTGACTGACTACTTGATGAAGATCTTGA
GTGAACGTGGTTACTCTTTCTCCACCACTGCTGAAAGAGAAATTGTCCGTGACATCAAGGAAAAA
CTATGTTACGTCGCCTTGGACTTCGAGCAAGAAATGCAAACCGCTGCTCAATCTTCTTCAATTGA
AAAATCCTACGAACTTCCAGATGGTCAAGTCATCACTATTGGTAAC
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!