Overview of Research at Bioinformatics Lablliao/signewgrad09f/faculty...Overview of Research at...

Overview of Research at Bioinformatics Lab

Li Liao

Develop new algorithms and (statistical) learning methods that help solve biological problems

> Capable of incorporating domain knowledge

> Effective, Expressive, Interpretable

1Li Liao, SIG NewGrad, 09/28/2009

Motivations

• Understanding correlations between genotype

and phenotype

• Predicting genotype <=> phenotype

• Some Phenotype examples:

– Protein function

– Drug/therapy response

– Drug-drug interactions for expression

– Drug mechanism

– Interacting pathways of metabolism


Bioinformatics in a … cell


Credit:Kellis & Indyk4Li Liao, SIG NewGrad, 09/28/2009

Projects– Genome sequencing and assembly (funded by NSF)

– Homology detection, protein family classification (funded by a DuPont S&E award)

Support Vector Machines

Hidden Markov models

Graph theoretic methods

– Probabilistic modeling for BioSequence (funded by NIH)

HMMs, and beyond

Motifs finding

Secondary structure

– Systems BioinformaticsPrediction of Protein-Protein Interactions

Inference of Gene Regulatory Networks

Prediction of other regulatory elements

Pattern analysis for RNAi (funded by UDRF)

– Comparative Genomics Identify genome features for diagnostic and therapeutic purposes

(funded by an Army grant)


PeopleCurrent members:

- Roger Craig (PhD student)

- Alvaro Gonzalez (PhD student)

- Kevin McCormick (PhD student)

- Colin Kern (PhD student)

Past members:

- Dr. Wen-Zhong Wang (Postdoc Fellow)

- Robel Kahsay (Ph.D. currently at DuPont Central Research & Development)

- Kishore Narra (M.S. currently at VistaPrint, Inc.)

- Arpita Gandhi (M.S. currently at Colgate-Palmolive Company)

- Gaurav Jain (M.S. currently at Institute of Genomics, Univ. of Maryland)

- Shivakundan Singh Tej (M.S.)

- Tapan Patel (B.S. currently in MD/PhD program at U Penn)

- Laura Shankman (B.S., currently in PhD program at U Virginia)


Hybrid Hierarchical Assembly

• Three types of reads: Sanger (~1000bp), 454 (~100bp), and SBS (~30bp).

• Assembly of individual types using the best suited assemblers.– Phrap, TIGR, etc. for Sanger reads

– Euler assembler and Newbler for 454 reads

– Euler short, Shorty for SBS reads

• Hybrid and hierarchical – Use longer reads as scaffolding to resolve repeat regions

that are difficult for shorter reads

– Use contigs from shorter reads (pyrosequencing) as pseudoreads to bridge gaps (nonclonable and hard stops) with Sanger reads.


Major Findings

• Hybrid hierarchical assembly is proved to be an effective

way for assembling short reads

• Incremental approach to selecting ABI reads is more

effective than random approach in generating high

coverage contigs

• Staged assembly using Phrap is an effective alternative to

the proprietary Newbler assembler.

Publications:Gonzalez & Liao, BMC Bioinformatics 2008, 9:102.


Blue lines are contigs generated from hybrid assembly


Detect remote homologues

Attributes:

- Sequence similarity, Aggregate statistics (e.g., protein families), Pattern/motif, and more attributes (presence at phylogenetic tree).

How to incorporate domain specific knowledge into the model so a classifier can be more accurate?

Results:

- Quasi-consensus based comparison of profile HMM for protein sequences

(Kahsay et al, Bioinformatics 2005)

- Using extended phylogenetic profiles and support vector machines for protein

family classification (Narra & Liao, SNPD04, Craig & Liao, ICMLA’05, Craig &

Liao SAC’06, Craig & Liao, Int’l J. Bioinfo & DM 2007)

- Combining Pairwise Sequence Similarity and Support Vector Machines for

Detecting Remote Protein Evolutionary and Structural Relationships (JCB 2003)


Non-linear mapping to a feature space

Φ( )

xi Φ(xi)Φ(xj)

xj

L() = i ½ i j yi yj Φ (xi )·Φ (xj )


1 1 1 1 0

1 1 1 1 1= 3 0.5

0 1 1 1 1 = 3 0.1x =

y =

z =

Ham

min

g d

ista

nce

Tre

e-bas

ed d

ista

nce

Data: phylogenetic profiles

- How to account for correlations among profile components?

profile extension (Narra & Liao, SNPD 04)

Transductive learning (Craig & Liao, ICMLA’05, SAC’06, IJBDM, 2007)


1 1 0 1 0 0 0 1 1

1

0.33

0.67

0.34

0.5

0.75

0.55

1 0.33 0.67 0.34 0.5 0.75 0.55

Post-order traversal


Sequence Models (HMMs and beyond)

Motivations: What is responsible for the function?

– Patterns/motifs

– Secondary structure

To capture long range correlations of bio sequences

– Transporter proteins

– RNA secondary structure

Methods: generative versus discriminative

– Linear dependent processes

– Stochastic grammars

– Model equivalence


TMMOD: An improved hidden Markov model for predicting

transmembrane topology

(Kahsay, Gao & Liao. Bioinformatics 2005)


Mod. Reg.Data

set

Correct

topology

Correct

location

Sens-

itivity

Speci-

ficity

TMMOD 1

(a)

(b)

(c)

S-83

65 (78.3%)

51 (61.4%)

64 (77.1%)

67 (80.7%)

52 (62.7%)

65 (78.3%)

97.4%

71.3%

97.1%

97.4%

71.3%

97.1%

TMMOD 2

(a)

(b)

(c)

S-83

61 (73.5%)

54 (65.1%)

54 (65.1%)

65 (78.3%)

61 (73.5%)

66 (79.5%)

99.4%

93.8%

99.7%

97.4%

71.3%

97.1%

TMMOD 3

(a)

(b)

(c)

S-83

70 (84.3%)

64 (77.1%)

74 (89.2%)

71 (85.5%)

65 (78.3%)

74 (89.2%)

98.2%

95.3%

99.1%

97.4%

71.3%

97.1%

TMHMM S-83

64 (77.1%) 69 (83.1%) 96.2% 96.2%

PHDtm S-83

(85.5%) (88.0%) 98.8% 95.2%

TMMOD 1

(a)

(b)

(c)

S-160

117 (73.1%)

92 (57.5%)

117 (73.1%)

128 (80.0%)

103 (64.4%)

126 (78.8%)

97.4%

77.4%

96.1%

97.0%

80.8%

96.7%

TMMOD 2

(a)

(b)

(c)

S-160

120 (75.0%)

97 (60.6%)

118 (73.8%)

132 (82.5%)

121 (75.6%)

135 (84.4%)

98.4%

97.7%

98.4%

97.2%

95.6%

97.2%

TMMOD 3

(a)

(b)

(c)

S-160

120 (75.0%)

110 (68.8%)

135 (84.4%)

133 (83.1%)

124 (77.5%)

143 (89.4%)

97.8%

94.5%

98.3%

97.6%

98.1%

98.1%

TMHMM S-160 123 (76.9%) 134 (83.8%) 97.1% 97.7%


Li Liao, SIG NewGrad, 09/28/2009 22

Inferring Regulatory Networks from Time Course Expression Data(Gandhi, Cogburn & Liao, 2008)

Expression Profile Clustering

Binary heat map

Boolean

network

algorithm

K-mean


GENOMIC COMPARISON OF BACTERIAL SPECIES BASED

ON METABOLIC CHARACTERISTICS (Jain, Wang, Boyd, Liao, ISIBM 2009)

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