CS5263 Bioinformatics

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Oct 2, 2013 (4 years and 3 months ago)

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CS5263 Bioinformatics

Lecture 20

Practical issues in motif finding

Final project

Homework problem 3.1


Count separately the number of character
comparisons and the number of steps
needed to find the next matching character
using the bad character rule


Question: can you give an example?

Extended bad character rule

char

Position in P

a

6,
3

b

7, 4

p

2

t

1

x

5

T: xpbctbxabpqq
a
abpqz

P: tpabxab


*^^

P: tp
a
bxab

Find T(k) in P that is
immediately left to i,
shift P to align T(k)
with that position

k

In this iteration:

# of comparison = 3

Table lookup: 2

Results for some real genes

llr = 394


E
-
value = 2.0e
-
023


llr = 347


E
-
value = 9.8e
-
002


llr = 110


E
-
value = 1.4e+004


Strategies to improve results


Combine results from different algorithms
usually helpful


Ones that appeared multiple times are
probably more interesting


Except simple repeats like AAAAA or ATATATATA


Cluster motifs into groups according to their
similarities

Strategies to improve results


Compare with known motifs in database


TRANSFAC


JASPAR


Issues:


How to determine similarity between motifs?


Alignment between matrices


How similar is similar?


Empirically determine some threshold

Strategies to improve results


Statistical test of significance


Enrichment in target sequences vs
background sequences

Target set

T

Background set

B

Assumed to contain a
common motif, P

Assumed to not contain P,
or with very low frequency

Ideal case: every sequence in T has P, no sequence in B has P

Statistical test for significance


Intuitively: if n / N >> m / M


P is enriched (over
-
represented) in T


Statistical significance?

Target set

T

Background set + target set

B + T

Size = N

Size = M

P

Appear in
n

sequences

Appear in
m

sequences

Hypergeometric distribution


A box with M balls, of which N are
red, and the rest are blue.


Red

ball: target sequences


Blue

ball: background sequences


If we
randomly

draw
m

balls from
the box,


what’s the probability we’ll see
n

red

balls?


If probability very small, we are
probably not drawing randomly



Total # of choices:
(M choose m)


# of choices to have
n

red

balls:
(N choose n) x (M
-
N choose m
-
n)

Cumulative hypergeometric test for
motif significance


We are interested in: if we
randomly pick m balls, how likely
that we’ll see
at least

n

red

balls?

This can be interpreted as the p
-
value for the null
hypothesis that we are randomly picking.

Alternative hypothesis: our selection favors
red

balls.

Equivalent: the target set T is enriched with motif P.

Or: P is over
-
represented in T.

Examples


Yeast genome has 6000 genes


Select 50 genes believed to be co
-
regulated by a common TF


Found a motif for these 50 genes


It appeared in 20 out of these 50 genes


In the whole genome, 100 genes have this motif


M = 6000, N = 50, m = 100+20 = 120, n = 20


Intuition:


m/M = 120/6000. In Genome, 1 out 50 genes have the motif


N = 50, would expect only 1 gene in the target set to have the motif


20
-
fold enrichment


P
-
value = 6 x 10
-
22


n = 5. 5
-
fold enrichment. P
-
value = 0.003


Normally a very low p
-
value is needed, e.g. 10
-
10

ROC curve for motif significance


Motif is usually a PWM


Any word will have a score


Typical scoring function: Log P(W | M) / P(W | B)


W: a word.


M: a PWM.


B: background model


To determine whether a sequence contains a
motif, a cutoff has to be decided


With different cutoffs, you get different number of
genes with the motif


Hyper
-
geometric test first assumes a cutoff


It may be better to look at a range of cutoffs

ROC curve for motif significance


With different score cutoff, will have different
m

and
n


Assume you want to use P to classify T and B


Sensitivity:
n / N


Specificity: (M
-
N
-
m+n) / (M
-
N)


False Positive Rate = 1


specificity: (m


n) / (M
-
N)


With decreasing cutoff, sensitivity

, FPR


Target set

T

Background set + target set

B + T

Size = N

Size = M

P

Appeared in
n

sequences

Appeared in
m

sequences

Given a score cutoff

ROC curve for motif significance

ROC
-
AUC: area under curve.


1: perfect separation.


0.5: random.


Motif 1 is better than motif 2.

1
-
specificity

sensitivity

Motif 1

Motif 2

Random

A good cutoff

Highest cutoff. No motif can pass the cutoff. Sensitivity = 0. specificity = 1.

Lowest cutoff. Every sequence
has the motif. Sensitivity = 1.
specificity = 0.

0

1

1

0

Other strategies


Cross
-
validation


Randomly divide sequences into 10 sets, hold 1 set
for test.


Do motif finding on 9 sets. Does the motif also appear
in the testing set?


Phylogenetic conservation information


Does a motif also appears in the homologous genes
of another species?


Strongest evidence


However, will not be able to find species
-
specific ones

Other strategies


Finding motif modules


Will two motifs always appear in the same gene?


Location preference


Some motifs appear to be in certain location


E.g., within 50
-
150bp upstream to transcription start


If a detect motif has strong positional bias, may be a sign of its
function


Evidence from other types of data sources


Do the genes having the motif always have similar activities
(gene expression levels) across different conditions?


Interact with the same set of proteins?


Similar functions?


etc.

To search for new instances


Usually many false positives


Score cutoff is critical


Can estimate a score cutoff from the “true”
binding sites


Motif finding

Scoring function

A set of scores for the “true” sites. Take mean
-

std as a cutoff.

(or a cutoff such that the majority of “true” sites can be predicted).

To search for new instances


Use other information, such as positional
biases of motifs to restrict the regions that
a motif may appear


Use gene expression data to help: the
genes having the true motif should have
similar activities


Phylogenetic conservation is the key

Final project


Write a review paper on a topic that we didn’t cover in
lectures

Or


Implement an algorithm and do some experiments


Compare several algorithms (existing implementation ok)


Combine several algorithms to form a pipeline (e.g. gene
expression + motif analysis)



Final:


5
-
10 pages report (single space, single column, 12pt) + 15
minutes presentation

Possible topics for term paper


Possible topics:


Haplotype inferencing


Computational challenges associated with new
microarray technologies


Phylogenetic footprinting


Small RNA gene / target prediction (siRNA, mRNA,
…)


Biomedical text mining


Protein structure prediction


Topology of biological networks

An example project


Given a gene expression data (say cell cycle)


Cluster genes using k
-
means


Find motifs using several algorithms


(Cluster and combine similar motifs)


Rank motifs according to their specificity to the
target sequences comparing to the other
clusters


Get their logos


Use the sequences to search the whole genome
for more genes with the motif


Do they have any functional significance?