A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling
|
|
|
- Linette Morris
- 5 years ago
- Views:
Transcription
1 A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling Ari Ugarte, Riccardo Vicedomini, Juliana Silva Bernardes, Alessandra Carbone 9 September, 2018 Laboratory of Computational and Quantitative Biology (LCQB) - Sorbonne University
2 Introduction
3 Metagenomic analysis workflow Functional profiling Protein domain identification of MG/MT data allows to study and analyze which are the main functions expressed in a specific environment. 1
4 Protein domains Size of individual structural domains: from 36 aa to 692 aa most of them has < 200 residues average of 100 residues 2
5 Domain identification in short MG/MT reads Very short fragments ( bp) Two possible approaches: Assembly-based (e.g., HMM-GRASPx) Direct read annotation (e.g., MetaCLADE, UProC) 3
6 Y I W A QA E K R D LK RAFWS A A D N TT T S EK S S C I K L LMI V I I WL VF I V L S DAN A TRS AQ E KT AH YR HK S D I H L Q M T AGR LI G HNI QF SLRSC AV M Q L V LRN HMN AMAV L I SCL S II SVTY ATVM S L VGAG T C A MCSV YFV FALA L Q T Y G Y R H F M I C MCLQ KQ WER KAR QHLT L K E K H Q A LQ TI Y MA DF ERV VHYWYLFH Y LSR DKC PGG T L Q N T W S FLA L YWW W V A YCG SAC T LI NASS SM VP I H Q S T V WF P HNP E CD IG FLWE LNY SAV L Q T N MQS I MS QNGA V C M S I LEV T V M P G H ENDQ S TPAK R S I L FAYS V D S G EW DG A TSPV N WebLogo How can we represent a domain? Multiple sequence alignment (MSA) of homologous domain sequences F VL C KQ QE RR R LE N T F L Q L T I S D F V A C F L S S AGM V IFY KQRTGLV PM CG AQ L MLT QI WRN KACE SE T G VFYW K E V MTP SNHY HRK G HD GN P SDP LIN WL H TQV E MA E WEAM QD RR N A I VDWF NL HYAG L P F EDI AG AN Y L F MWC GS CS G V AA CST N P CV I V LGN C K CAT F A VQ YF Probabilistic representations: - position-specific scoring matrices (PSSMs) - profile hidden Markov models (phmms) 4
7 The Pfam database A large collection of protein domain families Each family is represented by two MSAs (Full and Seed) and a profile HMM 5
8 CLADE (CLoser sequences for Annotations Directed by Evolution) 6
9 MetaCLADE
10 MetaCLADE - Main features Extends CLADE to handle MG/MT data Puts all domain hits in a two-dimensional space Uses two-dimensional thresholds (defined with a Naive Bayes classifier) to assign confidence values to predictions Provides data visualization of functional annotation 7
11 MetaCLADE - General overview Predicted CDS/ORFs in MG/MT reads Conservation profiles Hit identification Domain prediction D 2 D 8 1 MAKLKVANDKA... Input sequence D 2 D 2 D 2 D 8 D 6 1 D 2 D 3 1. Removal of overlapping hits 2. Selection of hits with prob Selection of hits with best bit-score and % identity Domain hits on the input sequence D 2 D 2 D 6 1 D 2 D 3 D 6 1 D 2 D 3 1 D 2 D 8 D 2 D 8 D 2 D 8 D 3 CCMs Domain D i in CLADE CLADE model library D i.. Global-consensus D N. Sets of positive and negative sequences Identification of domain-specific separating parameters Domain-dependent probability space pre-computation 8
12 MetaCLADE - Model construction Pfam domains D i D M Seed i phmm i Inherited from CLADE Several models are built in order to represent each known Pfam family Pfam27: domains CCMs (PSSMs) and phmm 9
13 MetaCLADE - Model construction Pfam domains D i D M Seed i Full i phmm i S i 1... S i ni n i 350 Inherited from CLADE Several models are built in order to represent each known Pfam family Pfam27: domains CCMs (PSSMs) and phmm 9
14 MetaCLADE - Model construction Pfam domains Seed i phmm i D i D M Full i S i 1 PSI-BLAST CCM i 1... NR... S i ni n i 350 Inherited from CLADE Several models are built in order to represent each known Pfam family Pfam27: domains CCMs (PSSMs) and phmm 9
15 MetaCLADE - Model construction Pfam domains Seed i phmm i D i D M Full i S i 1 PSI-BLAST CCM i 1... NR... S i ni PSI-BLAST CCM i ni n i 350 Inherited from CLADE Several models are built in order to represent each known Pfam family Pfam27: domains CCMs (PSSMs) and phmm 9
16 MetaCLADE - Model construction CLADE Library Pfam domains Seed i phmm i Global consensus models phmm 1... phmm i... phmm M D i Clade-centered models D M Full i S i 1... PSI-BLAST NR CCM i 1... CCM CCM i CCM 1 n CCM i ni S i ni n i 350 PSI-BLAST CCM i ni CCM M 1... CCM M nm Inherited from CLADE Several models are built in order to represent each known Pfam family Pfam27: domains CCMs (PSSMs) and phmm 9
17 MetaCLADE - General idea D 2 D 6 1 D 2 D 2 D 8 D 2 D 3 10
18 MetaCLADE - Training set The set of positive sequences for a domain D i is based on suffixes, prefixes and random fragments from Seed i. The set of negative sequences for a domain D i is based on three different methods: 1. 2-mer shuffling 2. sequence reversal 3. Markov model (probabilities based on 4-mers of Seed i ) 11
19 MetaCLADE - Training set Negative sequences are generated until negative sequences 1 positive sequences 2 Generation of a 2-dimension space by considering the bit score and the mean bit score of domain hits 12
20 MetaCLADE - Hit classification A discrete version of a naive Bayes classifier is used in order to partition the hit space in regions with an associated probability 13
21 MetaCLADE - Hit filtering and domain prediction 1. Removal of overlapping hits D 2 D 6 1 D 2 D 2 D 8 D 2 D 3 2. Selection of hits with prob 0.9 D 2 D 8 D 6 1 D 2 D 3 3. Selection of hits with best bit-score and % identity D 2 D 8 1 D 3 Final prediction D 2 D
22 Results
23 MetaCLADE on metatranscriptomics Marine eukaryotic phytoplankton metatranscriptoms 1.5M high quality cdna sequences, average length of 242bp 15
24 MetaCLADE - Functional Annotation 16
25 MetaCLADE - MetaCLADE vs HMMER (Ion transport) 17
26 MetaCLADE - Higher resolution 18
27 MetaCLADE - Comparison with other methods Guerrero Negro Hypersaline Microbial Mats (GNHM): 100/200 bp Precision Recall curve Tool TP FP FN TPR PPV F-score 100 bp UProC MetaCLADE HMMGRASP MetaCLADE+UProC UProC+MetaCLADE bp UProC MetaCLADE HMMGRASP MetaCLADE+UProC UProC+MetaCLADE Precision UProC (AUC=0.529) MetaCLADE (AUC=0.699) MetaCLADE+UProC (AUC=0.727) Recall 19
28 Future improvements More domains and new models for an improved annotation Constructing a library of conserved small motifs Annotation of longer sequences Reduction of the number of redundant models New criteria to filter overlapping hits 20
29 Conclusions Learning about the functional activity of the community and its sub-communities is a crucial step to understand species interactions and large-scale environmental impact Functional annotation methods need to be as precise as possible in identifying remote homology MetaCLADE allows for the discovery of patterns in highly divergent sequences Unknown sequences will augment in number, hence probabilistic models are expected to play a major role in the annotation of sequences spanning unrepresented sequence spaces 21
30 Thank you for your attention! Acknowledgments Ari Ugarte Juliana Silva Bernardes Alessandra Carbone References A. Ugarte, R. Vicedomini, J.S. Bernardes, A. Carbone, A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling, Microbiome, 2018, 6:149. J.S. Bernardes, C. Vaquero, G. Zaverucha, A. Carbone, Improvement in protein domain identification is reached by breaking consensus, with the agreement of many profiles and domain co-occurrence, PLoS Computational Biology, (7):e
Week 10: Homology Modelling (II) - HHpred
Week 10: Homology Modelling (II) - HHpred Course: Tools for Structural Biology Fabian Glaser BKU - Technion 1 2 Identify and align related structures by sequence methods is not an easy task All comparative
Protein Bioinformatics. Rickard Sandberg Dept. of Cell and Molecular Biology Karolinska Institutet sandberg.cmb.ki.
Protein Bioinformatics Rickard Sandberg Dept. of Cell and Molecular Biology Karolinska Institutet rickard.sandberg@ki.se sandberg.cmb.ki.se Outline Protein features motifs patterns profiles signals 2 Protein
Sequence Analysis and Databases 2: Sequences and Multiple Alignments
1 Sequence Analysis and Databases 2: Sequences and Multiple Alignments Jose María González-Izarzugaza Martínez CNIO Spanish National Cancer Research Centre (jmgonzalez@cnio.es) 2 Sequence Comparisons:
Bioinformatics. Proteins II. - Pattern, Profile, & Structure Database Searching. Robert Latek, Ph.D. Bioinformatics, Biocomputing
Bioinformatics Proteins II. - Pattern, Profile, & Structure Database Searching Robert Latek, Ph.D. Bioinformatics, Biocomputing WIBR Bioinformatics Course, Whitehead Institute, 2002 1 Proteins I.-III.
An Introduction to Bioinformatics Algorithms Hidden Markov Models
Hidden Markov Models Outline 1. CG-Islands 2. The Fair Bet Casino 3. Hidden Markov Model 4. Decoding Algorithm 5. Forward-Backward Algorithm 6. Profile HMMs 7. HMM Parameter Estimation 8. Viterbi Training
Hidden Markov Models
Hidden Markov Models Outline 1. CG-Islands 2. The Fair Bet Casino 3. Hidden Markov Model 4. Decoding Algorithm 5. Forward-Backward Algorithm 6. Profile HMMs 7. HMM Parameter Estimation 8. Viterbi Training
CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan
CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 254; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs15.html Describing & Modeling Patterns
SUPPLEMENTARY INFORMATION
Supplementary information S1 (box). Supplementary Methods description. Prokaryotic Genome Database Archaeal and bacterial genome sequences were downloaded from the NCBI FTP site (ftp://ftp.ncbi.nlm.nih.gov/genomes/all/)
Taxonomical Classification using:
Taxonomical Classification using: Extracting ecological signal from noise: introduction to tools for the analysis of NGS data from microbial communities Bergen, April 19-20 2012 INTRODUCTION Taxonomical
Using Ensembles of Hidden Markov Models for Grand Challenges in Bioinformatics
Using Ensembles of Hidden Markov Models for Grand Challenges in Bioinformatics Tandy Warnow Founder Professor of Engineering The University of Illinois at Urbana-Champaign http://tandy.cs.illinois.edu
HMM applications. Applications of HMMs. Gene finding with HMMs. Using the gene finder
HMM applications Applications of HMMs Gene finding Pairwise alignment (pair HMMs) Characterizing protein families (profile HMMs) Predicting membrane proteins, and membrane protein topology Gene finding
Similarity searching summary (2)
Similarity searching / sequence alignment summary Biol4230 Thurs, February 22, 2016 Bill Pearson wrp@virginia.edu 4-2818 Pinn 6-057 What have we covered? Homology excess similiarity but no excess similarity
EECS730: Introduction to Bioinformatics
EECS730: Introduction to Bioinformatics Lecture 07: profile Hidden Markov Model http://bibiserv.techfak.uni-bielefeld.de/sadr2/databasesearch/hmmer/profilehmm.gif Slides adapted from Dr. Shaojie Zhang
Performance Evaluation and Comparison
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Introduction 2 Cross Validation and Resampling 3 Interval Estimation
CISC 636 Computational Biology & Bioinformatics (Fall 2016)
CISC 636 Computational Biology & Bioinformatics (Fall 2016) Predicting Protein-Protein Interactions CISC636, F16, Lec22, Liao 1 Background Proteins do not function as isolated entities. Protein-Protein
Intro Protein structure Motifs Motif databases End. Last time. Probability based methods How find a good root? Reliability Reconciliation analysis
Last time Probability based methods How find a good root? Reliability Reconciliation analysis Today Intro to proteinstructure Motifs and domains First dogma of Bioinformatics Sequence structure function
Large-Scale Genomic Surveys
Bioinformatics Subtopics Fold Recognition Secondary Structure Prediction Docking & Drug Design Protein Geometry Protein Flexibility Homology Modeling Sequence Alignment Structure Classification Gene Prediction
Model Accuracy Measures
Model Accuracy Measures Master in Bioinformatics UPF 2017-2018 Eduardo Eyras Computational Genomics Pompeu Fabra University - ICREA Barcelona, Spain Variables What we can measure (attributes) Hypotheses
Tools and Algorithms in Bioinformatics
Tools and Algorithms in Bioinformatics GCBA815, Fall 2015 Week-4 BLAST Algorithm Continued Multiple Sequence Alignment Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and
Sequence analysis and comparison
The aim with sequence identification: Sequence analysis and comparison Marjolein Thunnissen Lund September 2012 Is there any known protein sequence that is homologous to mine? Are there any other species
Computational Genomics and Molecular Biology, Fall
Computational Genomics and Molecular Biology, Fall 2014 1 HMM Lecture Notes Dannie Durand and Rose Hoberman November 6th Introduction In the last few lectures, we have focused on three problems related
Overview of IslandPick pipeline and the generation of GI datasets
Overview of IslandPick pipeline and the generation of GI datasets Predicting GIs using comparative genomics By using whole genome alignments we can identify regions that are present in one genome but not
Tools and Algorithms in Bioinformatics
Tools and Algorithms in Bioinformatics GCBA815, Fall 2013 Week3: Blast Algorithm, theory and practice Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and Systems Biology
Neural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha
Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha Outline Goal is to predict secondary structure of a protein from its sequence Artificial Neural Network used for this
Motifs, Profiles and Domains. Michael Tress Protein Design Group Centro Nacional de Biotecnología, CSIC
Motifs, Profiles and Domains Michael Tress Protein Design Group Centro Nacional de Biotecnología, CSIC Comparing Two Proteins Sequence Alignment Determining the pattern of evolution and identifying conserved
Genome Annotation. Qi Sun Bioinformatics Facility Cornell University
Genome Annotation Qi Sun Bioinformatics Facility Cornell University Some basic bioinformatics tools BLAST PSI-BLAST - Position-Specific Scoring Matrix HMM - Hidden Markov Model NCBI BLAST How does BLAST
Hidden Markov Models in computational biology. Ron Elber Computer Science Cornell
Hidden Markov Models in computational biology Ron Elber Computer Science Cornell 1 Or: how to fish homolog sequences from a database Many sequences in database RPOBESEQ Partitioned data base 2 An accessible
Hidden Markov Models
Hidden Markov Models Outline CG-islands The Fair Bet Casino Hidden Markov Model Decoding Algorithm Forward-Backward Algorithm Profile HMMs HMM Parameter Estimation Viterbi training Baum-Welch algorithm
CSE182-L7. Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding CSE182
CSE182-L7 Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding 10-07 CSE182 Bell Labs Honors Pattern matching 10-07 CSE182 Just the Facts Consider the set of all substrings
Hidden Markov Models (HMMs) and Profiles
Hidden Markov Models (HMMs) and Profiles Swiss Institute of Bioinformatics (SIB) 26-30 November 2001 Markov Chain Models A Markov Chain Model is a succession of states S i (i = 0, 1,...) connected by transitions.
RNA Search and! Motif Discovery" Genome 541! Intro to Computational! Molecular Biology"
RNA Search and! Motif Discovery" Genome 541! Intro to Computational! Molecular Biology" Day 1" Many biologically interesting roles for RNA" RNA secondary structure prediction" 3 4 Approaches to Structure
SUPPLEMENTARY INFORMATION
Supplementary information S3 (box) Methods Methods Genome weighting The currently available collection of archaeal and bacterial genomes has a highly biased distribution of isolates across taxa. For example,
MEME - Motif discovery tool REFERENCE TRAINING SET COMMAND LINE SUMMARY
Command line Training Set First Motif Summary of Motifs Termination Explanation MEME - Motif discovery tool MEME version 3.0 (Release date: 2002/04/02 00:11:59) For further information on how to interpret
Christian Sigrist. November 14 Protein Bioinformatics: Sequence-Structure-Function 2018 Basel
Christian Sigrist General Definition on Conserved Regions Conserved regions in proteins can be classified into 5 different groups: Domains: specific combination of secondary structures organized into a
Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences
Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD Department of Computer Science University of Missouri 2008 Free for Academic
PROTEIN FUNCTION PREDICTION WITH AMINO ACID SEQUENCE AND SECONDARY STRUCTURE ALIGNMENT SCORES
PROTEIN FUNCTION PREDICTION WITH AMINO ACID SEQUENCE AND SECONDARY STRUCTURE ALIGNMENT SCORES Eser Aygün 1, Caner Kömürlü 2, Zafer Aydin 3 and Zehra Çataltepe 1 1 Computer Engineering Department and 2
-max_target_seqs: maximum number of targets to report
Review of exercise 1 tblastn -num_threads 2 -db contig -query DH10B.fasta -out blastout.xls -evalue 1e-10 -outfmt "6 qseqid sseqid qstart qend sstart send length nident pident evalue" Other options: -max_target_seqs:
Prediction and Classif ication of Human G-protein Coupled Receptors Based on Support Vector Machines
Article Prediction and Classif ication of Human G-protein Coupled Receptors Based on Support Vector Machines Yun-Fei Wang, Huan Chen, and Yan-Hong Zhou* Hubei Bioinformatics and Molecular Imaging Key Laboratory,
CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I)
CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I) Contents Alignment algorithms Needleman-Wunsch (global alignment) Smith-Waterman (local alignment) Heuristic algorithms FASTA BLAST
Database update 3PFDB+: improved search protocol and update for the identification of representatives of protein sequence domain families
Database update 3PFDB+: improved search protocol and update for the identification of representatives of protein sequence domain families Agnel P. Joseph 1, Prashant Shingate 1,2, Atul K. Upadhyay 1 and
Hidden Markov Models
Hidden Markov Models A selection of slides taken from the following: Chris Bystroff Protein Folding Initiation Site Motifs Iosif Vaisman Bioinformatics and Gene Discovery Colin Cherry Hidden Markov Models
Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment
Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment Substitution score matrices, PAM, BLOSUM Needleman-Wunsch algorithm (Global) Smith-Waterman algorithm (Local) BLAST (local, heuristic) E-value
Some Problems from Enzyme Families
Some Problems from Enzyme Families Greg Butler Department of Computer Science Concordia University, Montreal www.cs.concordia.ca/~faculty/gregb gregb@cs.concordia.ca Abstract I will discuss some problems
2MHR. Protein structure classification is important because it organizes the protein structure universe that is independent of sequence similarity.
Protein structure classification is important because it organizes the protein structure universe that is independent of sequence similarity. A global picture of the protein universe will help us to understand
Smart Home Health Analytics Information Systems University of Maryland Baltimore County
Smart Home Health Analytics Information Systems University of Maryland Baltimore County 1 IEEE Expert, October 1996 2 Given sample S from all possible examples D Learner L learns hypothesis h based on
Stephen Scott.
1 / 35 (Adapted from Ethem Alpaydin and Tom Mitchell) sscott@cse.unl.edu In Homework 1, you are (supposedly) 1 Choosing a data set 2 Extracting a test set of size > 30 3 Building a tree on the training
Exercise 5. Sequence Profiles & BLAST
Exercise 5 Sequence Profiles & BLAST 1 Substitution Matrix (BLOSUM62) Likelihood to substitute one amino acid with another Figure taken from https://en.wikipedia.org/wiki/blosum 2 Substitution Matrix (BLOSUM62)
HMMs and biological sequence analysis
HMMs and biological sequence analysis Hidden Markov Model A Markov chain is a sequence of random variables X 1, X 2, X 3,... That has the property that the value of the current state depends only on the
Supplementary Information
Supplementary Information Supplementary Figure 1. Schematic pipeline for single-cell genome assembly, cleaning and annotation. a. The assembly process was optimized to account for multiple cells putatively
Mathangi Thiagarajan Rice Genome Annotation Workshop May 23rd, 2007
-2 Transcript Alignment Assembly and Automated Gene Structure Improvements Using PASA-2 Mathangi Thiagarajan mathangi@jcvi.org Rice Genome Annotation Workshop May 23rd, 2007 About PASA PASA is an open
Hidden Markov Models and Their Applications in Biological Sequence Analysis
Hidden Markov Models and Their Applications in Biological Sequence Analysis Byung-Jun Yoon Dept. of Electrical & Computer Engineering Texas A&M University, College Station, TX 77843-3128, USA Abstract
Sequence Alignments. Dynamic programming approaches, scoring, and significance. Lucy Skrabanek ICB, WMC January 31, 2013
Sequence Alignments Dynamic programming approaches, scoring, and significance Lucy Skrabanek ICB, WMC January 31, 213 Sequence alignment Compare two (or more) sequences to: Find regions of conservation
Alignment principles and homology searching using (PSI-)BLAST. Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU)
Alignment principles and homology searching using (PSI-)BLAST Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU) http://ibivu.cs.vu.nl Bioinformatics Nothing in Biology makes sense except in
Nature Biotechnology: doi: /nbt Supplementary Figure 1. Detailed overview of the primer-free full-length SSU rrna library preparation.
Supplementary Figure 1 Detailed overview of the primer-free full-length SSU rrna library preparation. Detailed overview of the primer-free full-length SSU rrna library preparation. Supplementary Figure
Protein Structure Prediction, Engineering & Design CHEM 430
Protein Structure Prediction, Engineering & Design CHEM 430 Eero Saarinen The free energy surface of a protein Protein Structure Prediction & Design Full Protein Structure from Sequence - High Alignment
Statistical Machine Learning Methods for Bioinformatics IV. Neural Network & Deep Learning Applications in Bioinformatics
Statistical Machine Learning Methods for Bioinformatics IV. Neural Network & Deep Learning Applications in Bioinformatics Jianlin Cheng, PhD Department of Computer Science University of Missouri, Columbia
Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences
Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD William and Nancy Thompson Missouri Distinguished Professor Department
NRProF: Neural response based protein function prediction algorithm
Title NRProF: Neural response based protein function prediction algorithm Author(s) Yalamanchili, HK; Wang, J; Xiao, QW Citation The 2011 IEEE International Conference on Systems Biology (ISB), Zhuhai,
Pattern Matching (Exact Matching) Overview
CSI/BINF 5330 Pattern Matching (Exact Matching) Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Pattern Matching Exhaustive Search DFA Algorithm KMP Algorithm
An Introduction to Sequence Similarity ( Homology ) Searching
An Introduction to Sequence Similarity ( Homology ) Searching Gary D. Stormo 1 UNIT 3.1 1 Washington University, School of Medicine, St. Louis, Missouri ABSTRACT Homologous sequences usually have the same,
VOGUE: A Novel Variable Order-Gap State Machine for Modeling Sequences
VOGUE: A Novel Variable Order-Gap State Machine for Modeling Sequences Bouchra Bouqata, Christopher D. Carothers, Boleslaw K. Szymanski, and Mohammed J. Zaki CS Department, Rensselaer Polytechnic Institute,
Jeremy Chang Identifying protein protein interactions with statistical coupling analysis
Jeremy Chang Identifying protein protein interactions with statistical coupling analysis Abstract: We used an algorithm known as statistical coupling analysis (SCA) 1 to create a set of features for building
Final Examination CS 540-2: Introduction to Artificial Intelligence
Final Examination CS 540-2: Introduction to Artificial Intelligence May 7, 2017 LAST NAME: SOLUTIONS FIRST NAME: Problem Score Max Score 1 14 2 10 3 6 4 10 5 11 6 9 7 8 9 10 8 12 12 8 Total 100 1 of 11
Machine Learning in Action
Machine Learning in Action Tatyana Goldberg (goldberg@rostlab.org) August 16, 2016 @ Machine Learning in Biology Beijing Genomics Institute in Shenzhen, China June 2014 GenBank 1 173,353,076 DNA sequences
6.047 / Computational Biology: Genomes, Networks, Evolution Fall 2008
MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, etworks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
Probabilistic Arithmetic Automata
Probabilistic Arithmetic Automata Applications of a Stochastic Computational Framework in Biological Sequence Analysis Inke Herms PhD thesis defense Overview 1 Probabilistic Arithmetic Automata 2 Application
Efficient Remote Homology Detection with Secondary Structure
Efficient Remote Homology Detection with Secondary Structure 2 Yuna Hou 1, Wynne Hsu 1, Mong Li Lee 1, and Christopher Bystroff 2 1 School of Computing,National University of Singapore,Singapore 117543
Hidden Markov Models. Main source: Durbin et al., Biological Sequence Alignment (Cambridge, 98)
Hidden Markov Models Main source: Durbin et al., Biological Sequence Alignment (Cambridge, 98) 1 The occasionally dishonest casino A P A (1) = P A (2) = = 1/6 P A->B = P B->A = 1/10 B P B (1)=0.1... P
Lecture 7 Sequence analysis. Hidden Markov Models
Lecture 7 Sequence analysis. Hidden Markov Models Nicolas Lartillot may 2012 Nicolas Lartillot (Universite de Montréal) BIN6009 may 2012 1 / 60 1 Motivation 2 Examples of Hidden Markov models 3 Hidden
L3: Blast: Keyword match basics
L3: Blast: Keyword match basics Fa05 CSE 182 Silly Quiz TRUE or FALSE: In New York City at any moment, there are 2 people (not bald) with exactly the same number of hairs! Assignment 1 is online Due 10/6
Comprehensive genome analysis of 203 genomes provides structural genomics with new insights into protein family space
Published online February 15, 26 166 18 Nucleic Acids Research, 26, Vol. 34, No. 3 doi:1.193/nar/gkj494 Comprehensive genome analysis of 23 genomes provides structural genomics with new insights into protein
STRUCTURAL BIOINFORMATICS II. Spring 2018
STRUCTURAL BIOINFORMATICS II Spring 2018 Syllabus Course Number - Classification: Chemistry 5412 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Ronald Levy, SERC 718 (ronlevy@temple.edu)
Matrix-based pattern discovery algorithms
Regulatory Sequence Analysis Matrix-based pattern discovery algorithms Jacques.van.Helden@ulb.ac.be Université Libre de Bruxelles, Belgique Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe)
Regularization. CSCE 970 Lecture 3: Regularization. Stephen Scott and Vinod Variyam. Introduction. Outline
Other Measures 1 / 52 sscott@cse.unl.edu learning can generally be distilled to an optimization problem Choose a classifier (function, hypothesis) from a set of functions that minimizes an objective function
Methods and Criteria for Model Selection. CS57300 Data Mining Fall Instructor: Bruno Ribeiro
Methods and Criteria for Model Selection CS57300 Data Mining Fall 2016 Instructor: Bruno Ribeiro Goal } Introduce classifier evaluation criteria } Introduce Bias x Variance duality } Model Assessment }
Proteome-wide label-free quantification with MaxQuant. Jürgen Cox Max Planck Institute of Biochemistry July 2011
Proteome-wide label-free quantification with MaxQuant Jürgen Cox Max Planck Institute of Biochemistry July 2011 MaxQuant MaxQuant Feature detection Data acquisition Initial Andromeda search Statistics
Assigning Taxonomy to Marker Genes. Susan Huse Brown University August 7, 2014
Assigning Taxonomy to Marker Genes Susan Huse Brown University August 7, 2014 In a nutshell Taxonomy is assigned by comparing your DNA sequences against a database of DNA sequences from known taxa Marker
Computational Methods for Mass Spectrometry Proteomics
Computational Methods for Mass Spectrometry Proteomics Eidhammer, Ingvar ISBN-13: 9780470512975 Table of Contents Preface. Acknowledgements. 1 Protein, Proteome, and Proteomics. 1.1 Primary goals for studying
Lecture 8 Learning Sequence Motif Models Using Expectation Maximization (EM) Colin Dewey February 14, 2008
Lecture 8 Learning Sequence Motif Models Using Expectation Maximization (EM) Colin Dewey February 14, 2008 1 Sequence Motifs what is a sequence motif? a sequence pattern of biological significance typically
Gibbs Sampling Methods for Multiple Sequence Alignment
Gibbs Sampling Methods for Multiple Sequence Alignment Scott C. Schmidler 1 Jun S. Liu 2 1 Section on Medical Informatics and 2 Department of Statistics Stanford University 11/17/99 1 Outline Statistical
Mitochondrial Genome Annotation
Protein Genes 1,2 1 Institute of Bioinformatics University of Leipzig 2 Department of Bioinformatics Lebanese University TBI Bled 2015 Outline Introduction Mitochondrial DNA Problem Tools Training Annotation
Genomics and bioinformatics summary. Finding genes -- computer searches
Genomics and bioinformatics summary 1. Gene finding: computer searches, cdnas, ESTs, 2. Microarrays 3. Use BLAST to find homologous sequences 4. Multiple sequence alignments (MSAs) 5. Trees quantify sequence
Comparative Gene Finding. BMI/CS 776 Spring 2015 Colin Dewey
Comparative Gene Finding BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 2015 Colin Dewey cdewey@biostat.wisc.edu Goals for Lecture the key concepts to understand are the following: using related genomes
Intelligent Systems (AI-2)
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 19 Oct, 23, 2015 Slide Sources Raymond J. Mooney University of Texas at Austin D. Koller, Stanford CS - Probabilistic Graphical Models D. Page,
Discovering Binding Motif Pairs from Interacting Protein Groups
Discovering Binding Motif Pairs from Interacting Protein Groups Limsoon Wong Institute for Infocomm Research Singapore Copyright 2005 by Limsoon Wong Plan Motivation from biology & problem statement Recasting
Introduction to Bioinformatics
Introduction to Bioinformatics Jianlin Cheng, PhD Department of Computer Science Informatics Institute 2011 Topics Introduction Biological Sequence Alignment and Database Search Analysis of gene expression
Multiple sequence alignment
Multiple sequence alignment Multiple sequence alignment: today s goals to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs to introduce databases of multiple
Protein Structure Prediction using String Kernels. Technical Report
Protein Structure Prediction using String Kernels Technical Report Department of Computer Science and Engineering University of Minnesota 4-192 EECS Building 200 Union Street SE Minneapolis, MN 55455-0159
Intelligent Systems (AI-2)
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 19 Oct, 24, 2016 Slide Sources Raymond J. Mooney University of Texas at Austin D. Koller, Stanford CS - Probabilistic Graphical Models D. Page,
Sequence Analysis, '18 -- lecture 9. Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene.
Sequence Analysis, '18 -- lecture 9 Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene. How can I represent thousands of homolog sequences in a compact
STRUCTURAL BIOINFORMATICS I. Fall 2015
STRUCTURAL BIOINFORMATICS I Fall 2015 Info Course Number - Classification: Biology 5411 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Vincenzo Carnevale - SERC, Room 704C;
Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 8. Chapter 8. Classification: Basic Concepts
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 8 1 Chapter 8. Classification: Basic Concepts Classification: Basic Concepts Decision Tree Induction Bayes Classification Methods Rule-Based Classification
CSCE555 Bioinformatics. Protein Function Annotation
CSCE555 Bioinformatics Protein Function Annotation Why we need to do function annotation? Fig from: Network-based prediction of protein function. Molecular Systems Biology 3:88. 2007 What s function? The
Sequence Alignment: A General Overview. COMP Fall 2010 Luay Nakhleh, Rice University
Sequence Alignment: A General Overview COMP 571 - Fall 2010 Luay Nakhleh, Rice University Life through Evolution All living organisms are related to each other through evolution This means: any pair of
The Pennsylvania State University. The Graduate School. College of Engineering A COMPUTATIONAL FRAMEWORK FOR INFERRING STRUCTURE, FUNCTION,
The Pennsylvania State University The Graduate School College of Engineering A COMPUTATIONAL FRAMEWORK FOR INFERRING STRUCTURE, FUNCTION, AND EVOLUTION OF PROTEINS A Dissertation in Computer Science and
Mining and classification of repeat protein structures
Mining and classification of repeat protein structures Ian Walsh Ph.D. BioComputing UP, Department of Biology, University of Padova, Italy URL: http://protein.bio.unipd.it/ Repeat proteins Why are they
Pointwise Exact Bootstrap Distributions of Cost Curves
Pointwise Exact Bootstrap Distributions of Cost Curves Charles Dugas and David Gadoury University of Montréal 25th ICML Helsinki July 2008 Dugas, Gadoury (U Montréal) Cost curves July 8, 2008 1 / 24 Outline
Chapter 5. Proteomics and the analysis of protein sequence Ⅱ
Proteomics Chapter 5. Proteomics and the analysis of protein sequence Ⅱ 1 Pairwise similarity searching (1) Figure 5.5: manual alignment One of the amino acids in the top sequence has no equivalent and
HIDDEN MARKOV MODELS FOR REMOTE PROTEIN HOMOLOGY DETECTION
From THE CENTER FOR GENOMICS AND BIOINFORMATICS Karolinska Institutet, Stockholm, Sweden HIDDEN MARKOV MODELS FOR REMOTE PROTEIN HOMOLOGY DETECTION Markus Wistrand Stockholm 2005 All previously published
Bioinformatics 1--lectures 15, 16. Markov chains Hidden Markov models Profile HMMs
Bioinformatics 1--lectures 15, 16 Markov chains Hidden Markov models Profile HMMs target sequence database input to database search results are sequence family pseudocounts or background-weighted pseudocounts