Diapositiva 1 - Montefiore Institute

Diapositiva 1 - Montefiore Institute

GBIO0002-1 Genetics and Bioinformatics Introduction to DB and R Instructors Practical sessions Kyrylo Bessonov (Kirill) Office: B37 1/16 [email protected] Office hours: by appointment Overview

1. Intro to basic R 2. Introduction to public databases 3. The submission system Bioinformatics Definition 1: 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 2: a field that works on the

problems involving intersection of Biology/Computer Science/Statistics Practical classes. Why? 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. Homework assignments are of 2 types (graded) Homework assignments result in a group report and

can be handed in electronically in French or English Introduction to A basic tutorial Statistical languages GUIs SAS SPSS

R GUI Less fancy and no frills, but free! Definition R is a free software environment for statistical computing and graphics1 R is considered to be one of the most widely used languages 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" 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 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() 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 (data coerce) 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. Here is 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 angle=30, code=1) arrow 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", "ape", "GenABEL")) source("http://bioconductor.org/biocLite.R") biocLite(c("limma","affy","hgu133plus2.db","Bios trings", "muscle")) 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


alpha TGCGCAGAAGCTGA AGTCTA TTTTGAGGTGTTAATG GTTCT Genome sequencing generates lots of data Biological Databases

The number of databases is constantly 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 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 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


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 operators (AND, OR) Provides electronic links to most journals Example of searching by author articles published within 2012-2013 References

[1] Durinck, Steffen, et al. "BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis." Bioinformatics 21.16 (2005): 3439-3440. [2] Hamosh, Ada, et al. "Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders." Nucleic acids research30.1 (2002): 52-55. [3] Ihaka, Ross, and Robert Gentleman. "R: a language for data analysis and graphics." Journal of computational and graphical statistics 5.3 (1996): 299-314. 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 See you the next class: Tue, on Sept 22nd ! Next class form groups of 2-3 persons to work on HW

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