Sequence Analysis (II)

Yuh-Shan Jou (周玉山 )jou@ibms.sinica.edu.tw

Institute of Biomedical Sciences, Academia Sinica

Other Areas to Cover

• Genomic Data

• Annotation

• Common Domains prediction WWW

• Other Useful Genome Browsers

But first, some vocabulary...

YACs Yeast Artificial Chromosomes Yeast linear vector to propagate large DNA inserts (100 kb to Mb) Uses yeast centromere and telomeres to propagate insert as a chromosome

BACs Bacterial Artificial Chromosomes E. coli circular plasmid designed to carry large inserts (100-300 kb), single

copy (reduces occurrence of chimeric clones)

Cosmid E. coli circular plasmid holds 5 to 50 kb inserts, multicopy

Plasmids E. coli circular vectors designed to propagate DNA inserts (~1 to 10,000 bp)

Usually have origin of replication and antibiotic resistance marker (pUC8)

M13 E. coli phage adapted for DNA sequencing. Can clone small DNA inserts in

double stranded plasmid version, and convert to single strand version for sequencing

cDNA complementary DNA DNA synthesized from RNA using reverse transcriptase (an RNA-

dependent DNA polymerase)

EST Expressed Sequence Tag Single pass DNA sequence run of a cDNA insert in a plasmid from one end

ORF Open Reading Frame A region of at least 100 codons that is uninterrupted by stop

codons and thus potentially encodes a protein

SNP Single Nucleotide Polymorphism A single base that differs among members of a population. Can be detected by “genotyping”by PCR. Responsible for much trait diversity in

populations (physical appearance, diseases, drug response).

Satellite Marker Short tandem repeat (CACACACACACACACACAC, eg.) with length polymorphisms in a population (10 CA’s vs 25, eg.). Can be detected by genotyping. Often used for screening affected populations for disease genes(LOD scores).

STS Sequenced Tag Site Short (~500 bp) segment of DNA of known sequence mapped to location

EnsemblDatabase and Web

Browser

Erin Pleasance

Canada’s Michael Smith Genome Sciences Centre, Vancouver

Lecture 7.1 7

www.ensembl.org

What is Ensembl?• Joint project of EBI and Sanger

• Automated annotation of eukaryotic genomes

• Open source software

• Relational database system

• Web interface “The main aim of this campaign is to encourage

scientists across the world - in academia, pharmaceutical companies, and the biotechnology and

computer industries - to use this free information.”- Dr. Mike Dexter, Director of the Wellcome Trust

TPMD: http://tpmd.nhri.org.tw

Nucleic Acids Research, 2005, Vol. 33, Database issue D174-D177

Ensembl components

Genes(GeneView, TransView,

ExonView, ProtView)

SNPs and Haplotypes(SNPView, GeneSNPView,

HaploView, LDView)Diseases

(DiseaseView)

Markers(MarkerView)

Families(DomainView,

FamilyView

Functions(GOView)

GenomeSequence(ContigView)

ComparativeGenomics

(ContigView, MultiContigView,SyntenyView, GeneView)

Search tools:Data:

SequenceSimilarity

(BLAST, SSAHA)

Text(TextView)

Anything(EnsMart)

OtherAnnotations

Chromosomes(ChromoView, KaryoView,

CytoView, MapView)

Example 1: Exploring Caspase-3

• Aim to demonstrate basic browsing and views• Caspase-3 is a gene involved in apoptosis (cell

suicide)• We will look at:

– Gene annotation

– SNPs

– Orthologs and genome alignments

– Alternative transcripts and EST genes

Example 1: Exploring Caspase-3

http://www.ensembl.org

Go to human homepage

Species-specific homepage

Statistics of current release

Site map

Finding the tool/view: Site Map

Text SearchSpecies-specific homepagecaspase-3Gene

Click Back to

GeneView

ContigView

ProteinView

ExonViewTransView of transcript

SNPView

ExportView

GeneView

Orthologs predicted by sequence

similarity and synteny

GeneDAS: Get data from external sources

GeneView

Links to external databases

On the same page, information provided for each transcript individually

GeneView

GeneSNPView

Other SNP/Haplotype tools• SNPView

• ProteinView (protein sequence with SNP markup)

• LDView: View linkage disequilibrium (only limited regions)

• HaploView: View haplotypes (only limited regions)

GeneViewClick Back to

ContigView

Sequence contigs

Chromosome and bands

ContigView: Detailed View

Other tracks: Aligned

sequences etc.

Gene annotations

See other tracks, options

in menusTargetted gene

predictions(2 alternative transcripts)

EST genes

Genscan predictions

ContigView

MultiContigView

Rat ortholog

DNA sequence homology

Other Comparative

Genomics Tools• Saw gene orthology,

DNA homology• Other view is

SyntenyView• Also access

comparative genomics through EnsMart

Data Mining with EnsMart

• Allows very fast, cross-data source querying• Search for genes (features, sequences, etc.) or

SNPs based on– Position; function; domains; similarity; expression;

etc.

• Accessible from Ensembl website (MartView) as well as stand-alone

• Extremely powerful for data mining

Example 2: EnsMart

• A new disease locus has been mapped between markers D21S1991 and D21S171. It may be that the gene involved has already been identified as having a role in another disease. What candidates are in this region?

Example 2: EnsMart

• EnsMart is based on BioMart

• http://www.ensembl.org/Multi/martview

OR

• http://www.ebi.ac.uk/BioMart/martview

EnsMart: Choosing your dataset

EnsMart: Filtering

D21S1991

D21S171

21

EnsMart: OutputNote you can

output different types of information

eg. sequences

EnsMart: Output

Sequence Similarity Searching

• Use SSAHA for exact matches (fast)

• Use BLAST for more distant similarity (slow)

EnsEMBL BLAST

Lecture 5.1 38

The ideal annotation of “Gene”

Ideal Gene

All mRNAs

All proteins

All structures

All SNPs

All clones

• All protein modifications• Ontologies • Interactions (complexes, pathways, networks)•Expression (where and when, and how much)•Evolutionary relationships

Promoter(s)

gene number in the human genome

• Consortium 30.000 ~ 40.000 2001

• Celera 27.000 ~ 38.000 2001

• Consortium+Celera 50.000 Hogenesch et al. 2001

• DBsearches 65.000 ~ 75.000 Wrigth et al., 2001

• HumanGenomeSciences 90.000 ~ 120.000 Haseltine, 2001

• Consortium Build 34 35,000 ~ 40,000 April, 2003

• Consortium Build 35 20,000 ~ 25,000 Nature 431:931, 2004

Human Genome Project -- “Why sequence junk?!”

• 90% of human genome (3.3x109) in finished status, ie 99% of euchromatin.

• 45% of the genome are repeat sequences.

• 5% of the genome encodes genes (1.5% is coding).

• 35,000 ~ 40,000 genes with multiple splicing products per gene (build 34).

• Finish at April, 2003 & single chromosome papers published one by one.

• The entire human genome was finished again Oct. 2004.Build 35 assembly with 2.85 billion nucleotides interrupted by only 341 gaps. It covers 99% of the euchromatic genome with an error rate of 1 / 100,000 bases. The human genome seems to encode only 20,000–25,000 protein-coding genes. (Nature 431:931-945, 2004).

Cost of Genome sequencing: average US $1 per base.= 3.3 billion US dollars to sequence the human genome.

Ab initio gene identification

• Goals– Identify coding exons– Seek gene structure information– Get a protein sequence for further analysis

• Relevance– Characterization of anonymous DNA genomic

sequences– Works on all DNA sequences

Gene Finding on the Web

GRAIL: Oak Ridge Natl. Lab, Oak Ridge, TN– http://compbio.ornl.gov/grailexp

ORFfinder: NCBI– http://www.ncbi.nlm.nih.gov/gorf/gorf.html

DNA translation: Univ. of Minnesota Med. School– http://alces.med.umn.edu/webtrans.html

GenLang– http://cbil.humgen.upenn.edu/~sdong/genlang.html

BCM GeneFinder: Baylor College of Medicine, Houston, TX

– http://dot.imgen.bcm.tmc.edu:9331/seq-search/gene-search.html– http://dot.imgen.bcm.tmc.edu:9331/gene-finder/gf.html

Promoter “Exon 1” | “Intron 1” | “Exon 2” | “Intron 2” | “Exon 3” | “Intron 3” | “Exon 4”

Transcription

Splicing

Translation

polyApolyAcapcap

polyApolyA

StopStop

GU AG GU AG

NucleusNucleus

CytoplasmCytoplasm

GU AG

DNA

Primary transcript

MaturemRNA

StartStart

capcap

Poly-A SignalPoly-A Signal

Relative PerformanceClaverie 1997 Rogic 2000Sn (%) Sp (%) Overall Overall

Individual ExonsMZEF 78 86 0.79HEXON 71 65 0.64SorFind 42 47 0.62GRAIL II 51 57 0.47

Gene StructureGENSCAN 78 81 0.86 0.91FGENES 73 78 0.74 0.83GRAIL II/Gap 51 52 0.66 GeneParser 35 40 0.54HMMgene 0.91

What works best when?

• Genome survey (draft) data:expect only a single exon in any given stretch of contiguous sequence

– BLASTN vs. dbEST (3’ UTR)– BLASTX vs. nr (protein CDS)

• Finished data: large contigs are available, providing context– GENSCAN– HMMgene

Lecture 5.1 46

Things we are looking to annotate?

• CDS

• mRNA

• Alternative RNA

• Promoter and Poly-A Signal

• Pseudogenes

• ncRNA

Pseudogenes• Could be as high as 20-30% of all genomic sequence predictions

could be pseudogene

• Non-functional copy of a gene– Processed pseudogene

• Retro-transposon derived• No 5’ promoters• No introns• Often includes polyA tail

– Non-processed pseudogene• Gene duplication derived

– Both include events that make the gene non-funtional• Frameshift• Stop codons

• We assume pseudogenes have no function, but we really don’t know!

Noncoding RNA (ncRNA)

• ncRNA represent 98% of all transcripts in a mammalian cell

• ncRNA have not been taken into account in gene counts• cDNA• ORF computational prediction• Comparative genomics looking at ORF

• ncRNA can be:– Structural– Catalytic– Regulatory

09_04.jpgNoncoding RNA database: http://biobases.ibch.poznan.pl/ncRNA

Non-encoding genes:

The total number of ncRNAs are still unknown due to difficulty of predicting ncRNA from genome sequences.

Noncoding RNA (ncRNA)

• tRNA – transfer RNA: involved in translation

• rRNA – ribosomal RNA: structural component of ribosome, where translation takes place

• snoRNA – small nucleolar RNA: functional/catalytic in RNA maturation

• Antisense RNA: gene silencing

http://rfam.wustl.edu

http://rna.tbi.univie.ac.at/

Input (sequence only)

Fold Algorithm

RNA or DNA parameters

Target temperature

Advanced fold options

Output formats

Link to your previous run

Email (necessary for large sequences)

Output in bracket notation

Output - PostScript

Free energy (∆G)

Enthalpy (∆S)

Melting (de-hybridization) temperature

RNAalifold:Predicts consensus secondary structures for

sets of aligned RNA (ClustalW files).

Information from the alignment:

1. Conserved nucleotide pairs are shown normally.

2. Pairs with consistent mutations, which support the structure, are marked by circles.

3. Pairs with inconsistent mutations are shown in two shades of gray.

Graphical Representation – Sequence Logo

• Horizontal axis: position of the base in the sequence.

• Vertical axis: amount of information.

• Letter stack: order indicates importance.

• Letter height: indicates frequency.

• Consensus can be read across the top of the letter columns.

http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html

Tools on the Web for motifs• MEME – Multiple EM for Motif Elicitation.

http://meme.sdsc.edu/meme/website/• metaMEME- Uses HMM method

http://meme.sdsc.edu/meme• MAST-Motif Alignment and Search Tool

http://meme.sdsc.edu/meme

• TRANSFAC - database of eukaryotic cis-acting regulatory DNA elements and trans-acting factors. http://transfac.gbf.de/TRANSFAC/

• eMotif - allows to scan, make and search for motifs in the protein level. http://motif.stanford.edu/emotif/

Websites for Promoter findingPromoter Scan: NIH Bioinformatics (BIMAS)

http://bimas.dcrt.nih.gov/molbio/proscan/

Promoter Scan II: Univ. of Minnesota & Axyx Pharmaceuticals

http://biosci.cbs.umn.edu/software/proscan/promoterscan.htm

Signal Scan: NIH Bioinformatics (BIMAS)http://bimas.dcrt.nih.gov:80/molbio/signal/index.html

Transcription Element Search (TESS): Center for Bioinformatics, Univ. of Pennsylvaniahttp://www.cbil.upenn.edu/tess/

Search TransFac at GBF with MatInspector, PatSearch, and FunSitePhttp://transfac.gbf-braunschweig.de/TRANSFAC/programs.html

TargetFinder: Telethon Inst.of Genetics and Medicine, Milan, Italy

http://hercules.tigem.it/TargetFinder.html

TFs play a significant role in differentiation in a number of cell

types

The fact that ~ 5% of the genes are predicted to encode

transcription factors underscores the importance of

transcriptional regulation in gene expression (Tupler et al. 2001

Nature. 409:832-833)

The combinatorial nature of transcriptional regulation and

practically unlimited number of cellular conditions significantly

complicate the experimental identification of TF binding sites on

a genome scale

Understanding the transcriptional regulation is a major

challenge

Transcriptional regulatory regionTranscriptional regulatory region

1st exonU1

GU

SR

TSS

GTFsRNAP II

5’ 3’

core promoter region (~100bp)

activator

repressor

70K

regulatory promoter region

1st exon1st exonU1

GUU1

GU

SRSR

TSS

GTFsRNAP II

5’ 3’

core promoter region (~100bp)core promoter region (~100bp)

activator

repressor

70K70K

regulatory promoter region

regulatory promoter region

Computational approaches to identify potential regulatory Computational approaches to identify potential regulatory

elements and modules, and derive new, biologically relevant and elements and modules, and derive new, biologically relevant and

testable hypothesistestable hypothesis

Transcriptional regulatory module

• cis-regulatory elements are sequence-specific regions transcription factors bind

AGGCTA

AGGCTA

CGGTTAAG

CGGTTAAG

GCTAACGC

GCTAAGCG• TFs combinatorially

associate with each other to form modules and regulate their target genes

Mammalian Promoter Dadabase (MPromDb) (http://bioinformatics.med.ohio-state.edu)

MPromDb 1.0 (Mammalian Promoter Database)

(http://bioinformatics.med.ohio-state.edu)

Click here to search the database

Human, mouse & ratSearch by gene symbol; Genbank Acc.Num; Unigene/LocusLink ID; TF binding site name

MPromDb 1.0 (Mammalian Promoter Database)

(http://bioinformatics.med.ohio-state.edu)

MPromDb 1.0 (Mammalian Promoter Database)

(http://bioinformatics.med.ohio-state.edu)

BAX gene promoter with TF binding site annotations, with supporting evidence from 3 PubMed records

MPromDb (Mammalian Promoter Database)

Promoter sequences with annotations of experimentally supported TF binding

sites

Promoter sequences with annotations of computationally predicted TF binding

sitesA platform for statistical analysis & pattern recognition, to predict TF binding

sites in uncharacterized promoters, and model combinatorial association of TF

binding sites

A platform for comparative genomics, to reveal conserved regions across

genomes of different species

Identification of core-promoters

Identification of all the human-mouse-rat homologues pairs

Modeling and identification of TF binding sites & modules

Specific databases of protein sequencesand structures

Swissprot PIR TREMBL (translated from DNA) PDB (Three Dimensional Structures)

Primary Secondary Tertiary Quaternary

Amino acid sequence

Alpha helices &

Beta sheets,

loops.

Packing of secondaryelements.

Packing of several polypeptide chains

Protein Structure

Structure Prediction: Motivation

• Hundreds of thousands of gene sequences translated to proteins (genbanbk, SW, PIR)

• Only about 28000 solved structures (PDB)

• Goal: Predict protein structure based on sequence information

Structure Prediction: Motivation

• Understand protein function– Locate binding sites

• Broaden homology– Detect similar function where sequence differs

• Explain disease– See effect of amino acid changes– Design suitable compensatory drugs

Prediction Approaches

• Primary (sequence) to secondary structure– Sequence characteristics

• Secondary to tertiary structure– Fold recognition– Threading against known structures

• Primary to tertiary structure– Ab initio modelling

Secondary structure prediction

• AGADIR - An algorithm to predict the helical content of peptides • APSSP - Advanced Protein Secondary Structure Prediction Server • GOR - Garnier et al, 1996 • HNN - Hierarchical Neural Network method (Guermeur, 1997) • Jpred - A consensus method for protein secondary structure prediction at

University of Dundee • JUFO - Protein secondary structure prediction from sequence (neural

network) • nnPredict - University of California at San Francisco (UCSF) • PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader,

MaxHom, EvalSec from Columbia University • Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction • PSA - BioMolecular Engineering Research Center (BMERC) / Boston • PSIpred - Various protein structure prediction methods at Brunel University • SOPMA - Geourjon and Delיage, 1995 • SSpro - Secondary structure prediction using bidirectional recurrent neural

networks at University of California • DLP - Domain linker prediction at RIKEN

http://searchlauncher.bcm.tmc.edu/

Multiple Sequence Alignment

ClustalW Algorithm

• Compute pairwise alignment for all the pairs of sequences. • Use the alignment scores to build a phylogenetic tree such

that • similar sequences are neighbors in the tree• distant sequences are distant from each other in the tree.

• The sequences are progressively aligned according to the branching order in the guide tree.

• http://www.ebi.ac.uk/clustalw/

Progressive Sequences Alignment (Higgins and Sharp 1988)

ClustalW InputFast

alignment?

Scoring matrix

Alignment format

Fast alignment

options

Gap scoring

Phylogenetic trees

Input sequences

ClustalW Output (1)

Input sequences

Pairwise alignment scores

Building alignment

Final score

ClustalW Output (2)

Sequence names Sequence positions

Match strength in decreasing order: * : .

Phylogenetic Trees

• Represent closeness between many entities– In our case, genomic or protein sequences

human

chimpmonkey

Observed entity

Unobserved commonality

Distance representation

N Y L S N K Y L S N F S N F L S

N K/- Y L S N F L/- S

N K/- Y/F L/- S

Progressive Sequence Alignment (Protein sequences example)

MSA Approaches• Progressive approach

CLUSTALW (CLUSTALX) PILEUP

T-COFFEE

• Iterative approach: Repeatedly realign subsets of sequences.

MultAlin, DiAlign.

• Statistical Methods:Hidden Markov Models

SAM2K

• Genetic algorithmSAGA

Multiple Alignment tools on the Web (Some URLs)

EMBL-EBIhttp://www.ebi.ac.uk/clustalw/

BCM Search Launcher: Multiple Alignment

http://dot.imgen.bcm.tmc.edu:9331/multi-align/multi-align.html

Multiple Sequence Alignment for Proteins (Wash. U. St. Louis)http://www.ibc.wustl.edu/service/msa/

Editing Multiple Alignments

• There are a variety of tools that can be used to modify a multiple alignment.

• These programs can be very useful in formatting and annotating an alignment for publication.

• An editor can also be used to make modifications by hand to improve biologically significant regions in a multiple alignment created by one of the automated alignment programs.

GCG alignment editors

• Alignments produced with PILEUP (or CLUSTAL) can be adjusted with LINEUP.

• Nicely shaded printouts can be produced with PRETTYBOX

• GCG's SeqLab X-Windows interface has a superb multiple sequence editor - the best editor of any kind.

SeqVu

Editors on the Web• Check out CINEMA (Colour

INteractive Editor for Multiple Alignments)– It is an editor created completely in

JAVA (old browsers beware)– It includes a fully functional version

of CLUSTAL, BLAST, and a DotPlot module

http://www.bioinf.man.ac.uk/dbbrowser/CINEMA2.1/

Questions:

1. Download protein seq and predict domain, secondary structure and post-translational modifications.

2. Download all SARS virus genome and perform MSA.