Overview of Research at Bioinformatics Lab

Transcription

1 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 1

2 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 2

3 Bioinformatics in a cell 3

4 Credit:Kellis & Indyk 4

5 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 Bioinformatics Prediction 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) 5

6 People Current 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) 6

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9 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. 9

10 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:

11 Blue lines are contigs generated from hybrid assembly 11

12 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) 12

13 Non-linear mapping to a feature space Φ( ) x j x i Φ(x i ) Φ(x j ) L( ) = i ½ i j y i y j Φ (x i ) Φ (x j ) 13

14 Hamming distance Tree-based distance 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) x = y = z = = =

15 Post-order traversal

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17 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 17

18 TMMOD: An improved hidden Markov model for predicting transmembrane topology (Kahsay, Gao & Liao. Bioinformatics 2005) 18

19 Mod. Reg. Data set Correct topology Correct location Sensitivity Specificity TMMOD 1 (a) (b) (c) S (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 (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 (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 (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 (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 (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 (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 (76.9%) 134 (83.8%) 97.1% 97.7% 19

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22 Inferring Regulatory Networks from Time Course Expression Data (Gandhi, Cogburn & Liao, 2008) Expression Profile Clustering K-mean Binary heat map Boolean network algorithm 22

23 GENOMIC COMPARISON OF BACTERIAL SPECIES BASED ON METABOLIC CHARACTERISTICS (Jain, Wang, Boyd, Liao, ISIBM 2009) 23

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