Analyzing Local Properties• Many local properties are important for the function of

your protein– Hydrophobic regions are potential transmembrane domains

– Coiled-coiled regions are potential protein-interaction domains

– Hydrophilic stretches are potential loops

• You can discover these regions– Using sliding-widow techniques (easy)

– Using prediction methods such as hidden Markov Models (more sophisticated)

Sliding-window Techniques• Ideal for identifying strong

signals• Very simple methods

– Few artifacts– Not very sensitive

• Use ProtScale on www.expasy.org

• Make the window the same size as the feature you’re looking for

www.expasy.org/cgi-bin/protscale.pl

www.expasy.org/cgi-bin/protscale.pl

Hphob. / Eisenberg

Using TMHMM

• TMHMM is the best method for predicting transmembrane domains

• TMHMM uses an HMM

• Its principle is very different from that of ProtScale

• TMHMM output is a prediction

Searching for PROSITE Patterns

• Search your protein against PROSITE on ExPAsy– www.expasy.org/tools/scanprosite

• PROSITE motifs are written as patterns– Short patterns are not very informative by themselves

– They only indicate a possibility

– Combine them with other information to draw a conclusion

• Remember: Not everything is in PROSITE !

www.expasy.org/tools/scanprosite

P12259

www.expasy.org/tools/scanprosite

Protein Domains

• Proteins are usually made of domains

• A domain is an autonomous folding unit

• Domains are more than 50 amino acids long

• It’s common to find these together:– A regulatory domain

– A binding domain

– A catalytic domain

www.ebi.ac.uk/InterProScan

www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

Secondary Structures

• Helix– Amino acid that twists like a spring

• Beta strand or extended– Amino acid forms a line without

twisting

• Random coils– Amino acid with a structure neither

helical nor extended

– Amino-acid loops are usually coils

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

Servers

• www.predictprotein.org

• cubic.bioc.columbia.edu/predictprotein

• www.sdsc.edu/predicprotein

• www.cbi.pku.edu.cn/predictprotein

www.rcsb.org

www.rcsb.org

ncbi.nlm.nih.gov/BLAST

zhanglab.ccmb.med.umich.edu/I-TASSER/

zhanglab.ccmb.med.umich.edu/I-TASSER/

http://zhanglab.ccmb.med.umich.edu/I-TASSER/output/S102840/

R-programming

Introduction•R is:

– a suite of operators for calculations on arrays, in particular matrices,

– a large, coherent, integrated collection of intermediate tools for interactive data analysis,

– graphical facilities for data analysis and display either directly at the computer or on hardcopy

– a well developed programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.

•The core of R is an interpreted computer language.– It allows branching and looping as well as modular programming using

functions. – Most of the user-visible functions in R are written in R, calling upon a smaller

set of internal primitives. – It is possible for the user to interface to procedures written in C, C++ or

FORTRAN languages for efficiency, and also to write additional primitives.

R and statisticso Packaging: a crucial infrastructure to efficiently produce, load

and keep consistent software libraries from (many) different sources / authors

o Statistics: most packages deal with statistics and data analysis

o State of the art: many statistical researchers provide their methods as R packages

Data Analysis and Presentation

• The R distribution contains functionality for large number of statistical procedures. – linear and generalized linear models– nonlinear regression models– time series analysis– classical parametric and nonparametric tests– clustering – smoothing

• R also has a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.

R as a calculator

> log2(32)

[1] 5

> sqrt(2)

[1] 1.414214

> seq(0, 5, length=6)

[1] 0 1 2 3 4 5

> plot(sin(seq(0, 2*pi, length=100)))

0 20 40 60 80 100

-1.0

-0.5

0.0

0.5

1.0

Index

sin

(se

q(0

, 2 *

pi,

len

gth

= 1

00

))

Object orientation

primitive (or: atomic) data types in R are:

• numeric (integer, double, complex)• character• logical• function

out of these, vectors, arrays, lists can be built.

Object orientation

• Object: a collection of atomic variables and/or other objects that belong together

• Example: a microarray experiment• probe intensities• patient data (tissue location, diagnosis, follow-up)• gene data (sequence, IDs, annotation)

Parlance:• class: the “abstract” definition of it• object: a concrete instance• method: other word for ‘function’• slot: a component of an object

Object orientation

Advantages:

Encapsulation (can use the objects and methods someone else has written without having to care about the internals)

Generic functions (e.g. plot, print)

Inheritance (hierarchical organization of complexity)

Caveat:Overcomplicated, baroque program architecture…

variables

> a = 24

> b<-25> sqrt(a+b)[1] 7

> a = "The dog ate my homework"> sub("dog","cat",a)[1] "The cat ate my homework"

> a = (1+1==3)> a[1] FALSE

numeric

character string

logical

variables> paste("X", "Y")

> paste("X", "Y", sep = " + ")

> paste("Fig", 1:4)

> paste(c("X", "Y"), 1:4, sep = "", collapse = " + ")

x<-2.17y<-as.character(x)z<-as.numeric(y)

Help(as)

vectors, matrices and arrays• vector: an ordered collection of data of the same type> a = c(1,2,3)> a*2[1] 2 4 6

• Example: the mean spot intensities of all 15488 spots on a chip: a vector of 15488 numbers

• In R, a single number is the special case of a vector with 1 element.

• Other vector types: character strings, logical

vectors, matrices and arrays

• matrix: a rectangular table of data of the same typeexample: the expression values for 10000 genes for 30

tissue biopsies: a matrix with 10000 rows and 30 columns.

• array: 3-,4-,..dimensional matrixexample: the red and green foreground and background

values for 20000 spots on 120 chips: a 4 x 20000 x 120 (3D) array.

Lists• vector: an ordered collection of data of the same type. > a = c(7,5,1)> a[2][1] 5

• list: an ordered collection of data of arbitrary types. > doe = list(name="john",age=28,married=F)> doe$name[1] "john "> doe$age[1] 28• Typically, vector elements are accessed by their index (an integer),

list elements by their name (a character string). But both types support both access methods.

Data frames

data frame: is like a spreadsheet.

It is a rectangular table with rows and columns; data within each column has the same type (e.g. number, text, logical), but different columns may have different types.

Example:> a localisation tumorsize progressXX348 proximal 6.3 FALSEXX234 distal 8.0 TRUEXX987 proximal 10.0 FALSE

id<-c("xx348", "xx234", "xx987")locallization<-c("proximal", "distal", "proximal")progress<-c(F, T, F)tumorsize<-c(6.3, 8.0, 10.0)results<-data.frame(id, locallization , tumorsize, progress)

> results id locallization tumorsize progress1 xx348 proximal 6.3 FALSE2 xx234 distal 8.0 TRUE3 xx987 proximal 10.0 FALSE

results<-edit(results)

> summary(results) id locallization tumorsize progress xx234:1 distal :1 Min. : 6.30 Mode :logical xx348:1 proximal:2 1st Qu.: 7.15 FALSE:1 xx987:1 Median : 8.00 TRUE :2 Mean : 8.10 NA's :0 3rd Qu.: 9.00 Max. :10.00

>x<-summary(results)>x id locallization tumorsize progress xx234:1 distal :1 Min. : 6.30 Mode :logical xx348:1 proximal:2 1st Qu.: 7.15 FALSE:1 xx987:1 Median : 8.00 TRUE :2 Mean : 8.10 NA's :0 3rd Qu.: 9.00 Max. :10.00

SubsettingIndividual elements of a vector, matrix, array or data frame are accessed with “[ ]” by specifying their index, or their name> results localisation tumorsize progressXX348 proximal 6.3 0XX234 distal 8.0 1XX987 proximal 10.0 0> results[3, 2][1] 10> a["XX987", "tumorsize"][1] 10> results["XX987",] localisation tumorsize progressXX987 proximal 10 0

SubsettingSubsetting> results localisation tumorsize progressXX348 proximal 6.3 0XX234 distal 8.0 1XX987 proximal 10.0 0> results[c(1,3),] localisation tumorsize progressXX348 proximal 6.3 0XX987 proximal 10.0 0> results[c(T,F,T),] localisation tumorsize progressXX348 proximal 6.3 0XX987 proximal 10.0 0> results$localisation[1] "proximal" "distal" "proximal"> results $localisation=="proximal"[1] TRUE FALSE TRUE> results[ results$localisation=="proximal", ] localisation tumorsize progressXX348 proximal 6.3 0XX987 proximal 10.0 0

subset rows by a vector of indices

subset rows by a logical vector

subset a column

comparison resulting in logical vector

subset the selected rows

results[2,]

results[2,2]

results[1:3,]

results[c(1,3),]

results[c(T,F,T),]

x<-summary(results)xX[2,2]

x = c(1, 1, 2, 3, 5, 8)

x[c(TRUE, TRUE, FALSE, FALSE, TRUE, TRUE)]

x[c(TRUE, FALSE)]

x == 1

x[x == 1]

x[x%%2 == 0]

y = c(1, 2, 3)

y[]=3

y

Matrix

• a matrix is a vector with an additional attribute (dim) that defines the number of columns and rows

• only one mode (numeric, character, complex, or logical) allowed

• can be created using matrix()x<-matrix(data=0,nr=2,nc=2) orx<-matrix(0,2,2)

Data Frame

• several modes allowed within a single data frame

• can be created using data.frame()L<-LETTERS[1:4] #A B C Dx<-1:4 #1 2 3 4data.frame(x,L) #create data frame

• attach() and detach()– the database is attached to the R search path so that the database is searched by R

when it is evaluating a variable.– objects in the database can be accessed by simply giving their names

a=matrix(1:9, ncol = 3, nrow = 3)a

b=matrix(c(TRUE, FALSE, TRUE), ncol = 3, nrow = 3)b

x=1:10y=11:20z=matrix(c(x,y))z

z=matrix(c(x,y),nrow=2)z

z=matrix(c(x,y),nrow=4)z

R code

max(z) min(z) length(z) mean(z) sd(z) sum(z)

index=c(15,27,34,10,9)welcome=c(13,26,30,10,7)paper=c(2,1,3,0,1)days=c("mon", "tues", "wed", "thurs", "fri")filenames=c("index.html", "welcom.png", "paper.pdf")downloads=matrix(c(index,welcome,paper), nrow=5, dimnames=list(days,filenames))downloads

filesizes = c(1624, 23172, 1234065)downloads%*%filesizes

image(as.matrix(downloads))

Factors

A character string can contain arbitrary text. Sometimes it is useful to use a limited vocabulary, with a small number of allowed words. A factor is a variable that can only take such a limited number of values, which are called levels.

expression<-factor(c("over","under","over","unchanged","under","under"))

levels(expression)

protein<-list("glucose oxidas", "1CF3", 63355)protein

protein<-list(name="glucose oxidas", accession="1CF3", weight=63355)

x<-c(16614, 50660, 6066, 6118)protein$GOIDs<-x

protein

class(protein)

length(protein)

attributes(protein)

Working directorysetwd("D:/data")

x<-read.table("profiles.csv", sep=",", header=TRUE)

x<-read.table("http://www.bixsolutions.net/profiles.csv", sep=",", header=TRUE)

matplot(x, type="l")

matplot(x, type="l", xlab="fraction", ylab="quantity", col=1:6, lty=1:5, lwd=2)

lty: line stylelwd: line width

xmax<-apply(x, 2, max)xmax

ymax<-apply(x, 1, max)ymax

Apply the max function on columns (2) or rows (1) of matrix x

cummean = function(x){n = length(x)y = numeric(n)z = c(1:n)y = cumsum(x)y = y/zreturn(y)

}

n = 10000z = rnorm(n)x = seq(1,n,1)y = cummean(z)X11()plot(x,y,type= 'l',main= 'Convergence Plot')

Apply the max function on columns (2) or rows (1) of matrix x