Morten Nielsen, CBS, BioSys, DTU

Slides:



Advertisements
Similar presentations
CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Project in Immunological Bioinformatics Morten Nielsen, CBS, BioCentrum, DTU.
Advertisements

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequence information, logos and Hidden Markov Models Morten Nielsen, CBS, BioCentrum,
Supplemental Fig. S1 Icelogo plots of HLA-A2, HLA-B7 and HLA-B44 binding peptides of 8-14 mer length and Icelogo plots of HLA-C binding peptides of 8-11.
The fast way Morten Nielsen BioSys, DTU. The fast algorithm (O2) Database (m) Query (n) Open a gapExtending a gap P Q Affine gap penalties.
Gibbs sampling Morten Nielsen, CBS, BioSys, DTU. Class II MHC binding MHC class II binds peptides in the class II antigen presentation pathway Binds peptides.
Biological Databases Morten Nielsen BioSys, DTU. Different kinds of data DNA –NCBI GenBankNCBI GenBank –Organism specific databases Protein –UniProt SwissProt.
CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU T cell Epitope predictions using bioinformatics (Neural Networks and hidden.
Stabilization matrix method (Rigde regression) Morten Nielsen Department of Systems Biology, DTU.
Gibbs Clustering Massimo Andreatta, Morten Nielsen CBS, Department of Systems biology DTU, Denmark.
Bioinformatics Motif Detection Revised 27/10/06. Overview Introduction Multiple Alignments Multiple alignment based on HMM Motif Finding –Motif representation.
Artificial Neural Networks 2 Morten Nielsen BioSys, DTU.
Optimization methods Morten Nielsen Department of Systems Biology, DTU.
Optimization methods Morten Nielsen Department of Systems biology, DTU.
Bioinformatics Finding signals and motifs in DNA and proteins Expectation Maximization Algorithm MEME The Gibbs sampler Lecture 10.
Algorithms in Bioinformatics Morten Nielsen BioSys, DTU.
Gibbs sampling for motif finding in biological sequences Christopher Sheldahl.
Artificial Neural Networks 2 Morten Nielsen Depertment of Systems Biology, DTU.
Immunological bioinformatics Ole Lund, Center for Biological Sequence Analysis (CBS) Denmark.
Biological sequence analysis and information processing by artificial neural networks Morten Nielsen CBS.
Performance measures Morten Nielsen, CBS, BioCentrum, DTU.
Class I pathway Prediction of proteasomal cleavage and TAP binidng Morten Nielsen, CBS, BioCentrum, DTU.
A Very Basic Gibbs Sampler for Motif Detection Frances Tong July 28, 2004 Southern California Bioinformatics Summer Institute.
Introduction to BioInformatics GCB/CIS535
Inside of cell Interior of rough endoplasmic reticulum 5' Receptor protein Signal recognition particle mRNA Ribosome Signal sequence Protein synthesis.
An analysis of “Alignments anchored on genomic landmarks can aid in the identification of regulatory elements” by Kannan Tharakaraman et al. Sarah Aerni.
CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequence motifs, information content, logos, and HMM’s Morten Nielsen, CBS,
In silico cis-analysis promoter analysis - Promoters and cis-elements - Searching for patterns - Searching redundant patterns.
Biological Sequence Pattern Analysis Liangjiang (LJ) Wang March 8, 2005 PLPTH 890 Introduction to Genomic Bioinformatics Lecture 16.
In silico cis-analysis promoter analysis - Promoters and cis-elements - Searching for patterns - Searching redundant patterns.
Project list 1.Peptide MHC binding predictions using position specific scoring matrices including pseudo counts and sequences weighting clustering (Hobohm)
Immunological Bioinformatics Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark
Algorithms in Bioinformatics Morten Nielsen Department of Systems Biology, DTU.
발표자 석사 2 년 김태형 Vol. 11, Issue 3, , March 2001 Comparative DNA Sequence Analysis of Mouse and Human Protocadherin Gene Clusters 인간과 마우스의 PCDH 유전자.
Protein Secondary Structure Prediction: A New Improved Knowledge-Based Method Wen-Lian Hsu Institute of Information Science Academia Sinica, Taiwan.
Motif finding with Gibbs sampling CS 466 Saurabh Sinha.
Sequence encoding, Cross Validation Morten Nielsen BioSys, DTU
Project list 1.Peptide MHC binding predictions using position specific scoring matrices including pseudo counts and sequences weighting clustering (Hobohm)
What is a Project Purpose –Use a method introduced in the course to describe some biological problem How –Construct a data set describing the problem –Define.
The Blosum scoring matrices Morten Nielsen BioSys, DTU.
Biological Signal Detection for Protein Function Prediction Investigators: Yang Dai Prime Grant Support: NSF Problem Statement and Motivation Technical.
Algorithms in Bioinformatics: A Practical Introduction
Protein Structure Prediction: Homology Modeling & Threading/Fold Recognition D. Mohanty NII, New Delhi.
Alternative Splicing (a review by Liliana Florea, 2005) CS 498 SS Saurabh Sinha 11/30/06.
Dealing with Sequence redundancy Morten Nielsen Department of Systems Biology, DTU.
7. Metropolis Algorithm. Markov Chain and Monte Carlo Markov chain theory describes a particularly simple type of stochastic processes. Given a transition.
Blosum matrices What are they? Morten Nielsen BioSys, DTU
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features 王荣 14S
Special Topics in Genomics Motif Analysis. Sequence motif – a pattern of nucleotide or amino acid sequences GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGA TAACATGTGACTCCTATAACCTCTTTGGGTGGTACATGAA.
Psi-Blast Morten Nielsen, Department of systems biology, DTU.
Optimization methods Morten Nielsen Department of Systems biology, DTU IIB-INTECH, UNSAM, Argentina.
Stabilization matrix method (Ridge regression) Morten Nielsen Department of Systems Biology, DTU.
Transcription factor binding motifs (part II) 10/22/07.
Motif identification with Gibbs Sampler Xuhua Xia
Prediction of T cell epitopes using artificial neural networks Morten Nielsen, CBS, BioCentrum, DTU.
Dr. Abdelkrim Rachedi. 1. General introduction to bioinformatics. 2. Databases in biology: -> 2.1. Databases for the primary structure of Proteins and.
Protein Structure Prediction. Protein Sequence Analysis Molecular properties (pH, mol. wt. isoelectric point, hydrophobicity) Secondary Structure Super-secondary.
T cell receptor & MHC complexes-Antigen presentation
Control of Gene Expression
T Cell Receptor (TCR) & MHC Complexes-Antigen Presentation
A Very Basic Gibbs Sampler for Motif Detection
MHC Class II Antigen Processing
Recognition of Antigen By T cells: The TCR
Molecular Docking Profacgen. The interactions between proteins and other molecules play important roles in various biological processes, including gene.
Ligand Docking to MHC Class I Molecules
Finding regulatory modules
Sequence Alignment Algorithms Morten Nielsen BioSys, DTU
Nora Pierstorff Dept. of Genetics University of Cologne
A Protein Interface.
T cells and T-cell receptors in acute renal failure
Introduction to Bioinformatics Tuesday, 19 March
Presentation transcript:

Morten Nielsen, CBS, BioSys, DTU Gibbs sampling Morten Nielsen, CBS, BioSys, DTU

Class II MHC binding MHC class II binds peptides in the class II antigen presentation pathway Binds peptides of length 9-18 (even whole proteins can bind!) Binding cleft is open Binding core is 9 aa

Gibbs sampler www.cbs.dtu.dk/biotools/EasyGibbs 100 10mer peptides 2100~1030 combinations Monte Carlo simulations can do it

Gibbs sampler. Monte Carlo simulations RFFGGDRGAPKRG YLDPLIRGLLARPAKLQV KPGQPPRLLIYDASNRATGIPA GSLFVYNITTNKYKAFLDKQ SALLSSDITASVNCAK GFKGEQGPKGEP DVFKELKVHHANENI SRYWAIRTRSGGI TYSTNEIDLQLSQEDGQTIE RFFGGDRGAPKRG YLDPLIRGLLARPAKLQV KPGQPPRLLIYDASNRATGIPA GSLFVYNITTNKYKAFLDKQ SALLSSDITASVNCAK GFKGEQGPKGEP DVFKELKVHHANENI SRYWAIRTRSGGI TYSTNEIDLQLSQEDGQTIE E1 = 5.4 E2 = 5.7 Paccept =1 E2 = 5.2 0 < Paccept < 1

Monte Carlo Temperature What is the Monte Carlo temperature, T? Say dE=-0.2, T=1 T=0.001

Gibbs sampler. Monte Carlo simulations Getting stucked in local minima Shift alignment window

It works High Temperature Low Temperature

Gibbs sampler. Prediction accuracy

More than 1,000 papers in PubMed using Gibbs sampling methods Use of Gibbs sampling More than 1,000 papers in PubMed using Gibbs sampling methods Transcription start-sites Receptor binding sites Acceptor:Donor sites ...

Summary Weight matrices can accurately describe a sequence motif like MHC class I Use sequence weighting to remove data redundancy Use pseudo count to compensate for few data points Gibbs sampling can detect MHC class II binding motif (and other gap-free motif with weak sequence signal)