Bioinformatics Methods and Applications Dr. Hongyu Zhang Ceres Inc.
Dec 25, 2015
Bioinformatics
Methods and Applications
Dr. Hongyu Zhang
Ceres Inc.
Goals of the talk
• The major battle fields in Bioinformatics research
• The most popular weapons used in the battle
History
• Human genome project
• Overlapping with other branches– Computational Biology– Biocomputing– Biostatistics– Cheminfomatics
The Central Dogma ofMolecular Biology
DNA RNA ProteinTranscription Translation
Major battle fields in bioinformatics
• DNA– Genome sequencing– Gene discovery
• mRNA– Micro-array analysis– Sequencing
• Protein– Structure modeling and prediction– Proteomics
• …
Major weapons• Computational algorithm
– Hash method– Dynamic algorithm– String and Tree (binary, suffix)– Clustering
• Probability and Statistical theory and methods– Bayesian theorem, Markov chain (HMM), Principle component– Monte Carlo simulation– Neural Network
• Physical chemistry– Functions to describe the physical chemistry interactions in bio-molecules– Molecular mechanics, Molecular dynamics algorithm
• Data storage and access– Database: Oracle, MySQL etc.– Web interface
• Large-scale computing platform– Hardware– Software
Genome sequencing: Celera shotgun assemblyVenter et al. 2001
Gene discoverybased on sequence comparison
• Finding new genes based on their sequence similarity and evolution relationship with known genes
• Methods– Hash-based database search method, like BLAST
(PSI-BLAST), FASTA, BLAT etc.– Sequence alignment using Dynamic Programming
algorithm
BLAST database search (http://www.ncbi.nih.gov/BLAST/)
Query sequence
Database sequences
Querydatabase
Sequence alignment
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• Example
• Programs
• CLUSTALW • DIALIGN
Dynamics algorithm
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Ab initio gene prediction methods
• Statistics based gene prediction– Nucleotides distribution frequencies in the cod
ing regions – Exon/Intron boundary signal
• Examples– GenScan, Burge and Karlin 1997 – Fgenesh, Solovyev and Salamov 1994
Hybrid gene prediction method
• Example: Celera Otto program– BLAST against Refseq database– BLAST against EST database, other genomic
sequences etc.– Genscan, Fgenesh
Problems in Gene discovery
• Example: Given a cDNA sequence, find its true location in the genome map among lots of alternatives
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Genomic component
Query transcript/protein
Two-step solution
1. BLAST search of the cDNA sequence against the whole genome map
2. Using an LIS algorithm to find the correct genomic component hit
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Phylogenetic analysis
• Goal: study the function and evolution relationship among a group of genes– Divide homologous genes into function families – Find the evolution relationship between the ortholog g
enes belonging to different species (e.g., the theory of Out of Africa)
• Methods– Hierarchical Clustering– Neighbore-joining etc.
• PHYLIP program, Univ. of Washington
Micro-array analysis
• Expression-genomics
• Primary goals
– Look for the genes with different expression levels between experiments, which are candidates of functional genes
– Look for the group of genes that have correlated gene expression levels, which could suggest that they are in the same biological pathway
• Methods– General probability and statistics methods– Dimension reduction
• Principle components• Lowess
– Clustering
• Tools– S-plus, R
Example
• Herbicide– Plants was treated with herbicide to observe
the gene expression profiles in a series of time steps.
– The genes that appeared right before plant dies (12 hours) are the possible “death” genes
– If we knock down the “death” genes in the normal plants, they could last longer time than the herbs.
Protein structure prediction
• Why is protein structure important?– The functions of a gene depend on its translat
ed protein structure • Protein binding with its ligands• Protein-protein interactions
– A protein molecule usually keeps one stable structure under normal physiological conditions (Anfinson, 1960es)
– Drug design• Docking and high throughput drug screening.
Sequence
Protein structure
Function
Bioinformatics
Protein structure prediction methods
Protein sequence
Database search
Sequence alignment
Select template structure
Build conserved regions first
Loop modeling
Build side-chains
Optimizing
Homology modeling procedure
Homology modeling programs
• Academic software– MODELER, Sali A.– COMPOSER, Blundell T.– SWISS-MODEL – Rasmol (graphics)
• Commercial software– QUANTA, MSI inc.– SYBYL, TRIPOS inc.
Threading• Find the best fold candidates among a limited number of
choices• Add 3D information to the score function of dynamic prog
ramming
Ab initio protein structure principle
• Threading programs– Topits, Eisenberg D.– Threader, Jones D.– ProSup, Sipple M– 123D, Alexandra N.
• Ab initio programs– Rosetta, David Baker
Current status in the protein structure prediction field
• Moult J., CASP (Critical Assessment of Techniques for Protein Structure Prediction).
• Homology modeling is very mature already
• Threading and Ab initio method have been used in industry
• Structure genomics
Large scale computing platform
• Hardware– Super-computers
• Cray/SGI• DEC/Compaq• Intel
– Linux clusters– Blade
• Software– Parallel computing (MPP, P
VM etc.)– Linux – Grid computing: the Globus
Project
Linux clusters
Data storage and access
• Bioinformatics is producing huge amount of data each day– How to organize and store data – How to access data
• Database software– Commercial
• Oracle, DB2, Sybase
– Freeware• MySQL, PostgreSQL
Data store and access• Bioinformatics is producing huge amount of data each day
– How to organize and store data – How to access data
• Database software– Commercial
• Oracle, DB2, Sybase– Freeware
• MySQL, PostgreSQL
• Current popular database– DNA, protein sequence, like Genbank, SwisProt, PIR etc.– Protein structure, like PDB, Scop– DNA, mRNA, protein function, like GO, PFAM
Database example: Gene Ontology (GO)
Molecular function
Biologicalprocess
Cellularcomponent
Data access
• Web interface– Protocol
• CGI, JSP, ASP
– Computer languages• Perl, Java, C/C++, Visual Basic, Visual C++
Forth looking
• Where are the markets– Develop new programs– Assemble current programs to build more efficient data mining
pipelines– Data storage and access– Integrate the current database to use them more effectively– Computing platform, including hardware, software support,
consulting etc.
• What we can offer– Multi-talents– Team work– Networking
http://www.hongyu.org/paper/bioinformatics.ppt