BITS: Basics of sequence analysis

Basic bioinformatics concepts,
databases and tools

Module 3
Sequence analysis
Joachim Jacob

http://www.bits.vib.be

Updated Feb 2012
http://dl.dropbox.com/u/18352887/BITS_training_material/Link%20to%20mod3-intro_H1_2012_SeqAn.pdf

In this third module, we will discuss the
possible analyses of sequences

Module 1
Sequence databases and keyword searching

Module 2
Sequence similarity

Module 3
Sequence analysis: types, interpretation, results

In this third module, we will discuss the
possible analyses of sequences

Sequence analysis tries to read
sequences to infer biological properties

AGCTACTACGGACTACTAGCAGCTACCTCTCTG
- is this coding sequence?
- can this sequence bind a certain TF?
- what is the melting temperature?
- what is the GC content?
- does it fold into a stable secondary structure?
…

Tools that can predict a biological
feature are trained with examples
Automatic annotation
vs.
experimentally verified annotations

- Training dataset of sequences (← exp. verified)
- An algorithm defines parameters used for
prediction
- The algorithm determines/classifies whether the
sequence(s) contains the feature (→ automatic
annotation)

The assumption to being able to read
biological function is the central paradigm

DNA → protein sequence → structure →
activity (binding, enzymatic activity,
regulatory,...)

So the premise to do analysis: biological
function can be read from the (DNA)
sequence.

Predictions always serve as a basis for further
experiments.

Analysis can be as simple as measuring
properties or predicting features

Protein
− Metrics (e.g. how many alanines in my seq)
− Modifications and other predictions
− Domains and motifs

DNA/RNA
− Metrics (e.g. how many GC)
− Predicting

Gene prediction

Promotor

Structure

Simple protein sequence analysis


One might be interested in:

pI (isoelectric point) prediction

Composition metrics

Hydrophobicity calculation

Reverse translation (protein → dna)

Occurrence of simple patterns (e.g. does
KDL occurs and how many times)

...
http://en.wikipedia.org/wiki/Hydrophobicity_scales
http://www.sigmaaldrich.com/life-science/metabolomics/learning-center/amino-acid-reference-cha

Protein sequence analysis tools are
gathered on Expasy
http://www.expasy.org/tools (SIB)

Others:

http://www.ebi.ac.uk/Tools/protein.html

http://bioweb.pasteur.fr/protein/intro-en.html

SMS2

Never trust a tool's output blindly

Interpreting depends on the kind of output
When a prediction result is obtained, the question
arises 'Is it true?' (in biological sense)


Programs giving a 'binary' 
Programs giving score/P-
result: 1 or 0, a hit or a miss. value result: the chance that
the 'result' is 'not real' → the
Approach: You should lower, the better
comparing different
prediction programs for Approach: asses the p-value
higher confidence.
E.g. ScanProsite for a motif
E.g. SignalP for signal
peptide prediction.

The basis for the prediction of features
is nearly always a sequence alignment
Based on experimentally verified sequence
annotations, a multiple sequence alignment
is constructed

Different methods exist to capture the information
gained from this multiple sequence alignment

Alignment reveals similar residues
which can indicate identical structure

Same structure,
hence most likely
same function

Most protein pairs with more than 25-
30 out of 100 identical residues were Chances are that
found to be structurally similar. the structure is not
Also proteins with <10% identity can the same
have similar structure.
http://peds.oxfordjournals.org/content/12/2/85.long

The structure of a protein sequence
determines his biological function
Number of Primary = AA chain
Reported
structures Feb 2012: ~ 535 000 in Swissprot
Secondary = structural entities
(helix, beta-strands, beta-sheets,
loops)

Tertiary = 3D
Nov 2011: ~ 80 000 in PDB

Quaternary = interactions

http://en.wikipedia.org/wiki/Protein_structure

Degree of similarity with other
sequences varies over the length
More conserved

Homologous
Histone H1
protein sequences

Protein sequences can consist of
structurally different parts
Domain
part of the tertiary structure of a protein that can exist,
function and evolve independently of the rest, linked to a
certain biological function
Motif
part (not necessarily contiguous) of the primary structure of a
protein that corresponds to the signature of a biological
function. Can be associated with a domain.
Feature
part of the sequence for which some annotation has been
added. Some features correspond to domain or motif
assignments.

Based on motifs and domains, proteins
are assigned to families
Nearly synonymous with gene family
Evolutionary related proteins
Significant structural similarity of domains is reflected in
sequence similarity, and is due to a common ancestral
sequence part, resulting in domain families.

Domains and motifs are represented by
simple and complex methods
domain

Gapped alignment
Motif/domain in silico can be represented by
1. Regular expression / pattern
2. Frequency matrix / profile
3. Machine learning techniques : Hidden Markov Model

http://bioinfo.uncc.edu/zhx/binf8312/lecture-7-SequenceAnalyses.pdf

Regular expressions / patterns are the
simplest way to represent motifs
A representation of all residues with equal probability.

123456 Position: 1. 2. 3. 4. 5. 6.
ATPKAE
KKPKAA [AKT] [AKLT] P [AK] [APT] [ADEKT-]
AKPKAK
TKPKPA
AKPKT-
AKPAAK ? Does this sequence match: AKPKTE
KLPKAD V V V V V V
AKPKAA
Consensus: AKPKAA ? And this sequence: KKPETE
V V V X V V

? And what about this one: TLPATE
For every position the most V V V V V V
Frequently occurring residue

Frequency matrices or profiles include
the chance of observing the residues
For every position of a motif, a list of all amino acids is
made with their frequency. Position-specific
weight/scoring matrix or profile. More sensitive way.
Profile
123456 Position: 1. 2. 3. 4. 5. 6.
ATPKAE
KKPKAA A 0.625 0 0 1/8 6/8 3/8
AKPKAK D 0 0 0 0 0 1/8
TKPKPA E 0 0 0 0 0 1/8
AKPKT- K 0.25 6/8 0 7/8 0 2/8
AKPAAK L 0 1/8 0 0 0 0
KLPKAD P 0 0 1 0 1/8 0
AKPKAA T 1/8 1/8 0 0 1/8 0
Consensus: AKPKA- - 0 0 0 0 0 1/8
Sum 1 1 1 1 1 1
? Query: AKPKTE
? Query: KKPETE
? Query: TLPATE
Example: http://expasy.org/prosite/PS51092 http://prosite.expasy.org/prosuser.html#meth2

How good a sequence matches a
profile is reported with a score

PSWM: scores
123456 Position: 1. 2. 3. 4. 5. 6.
ATPKAE
KKPKAA A 2.377 -2.358 -2.358 0.257 2.631 1.676
AKPKAK D -2.358 -2.358 -2.358 -2.358 -2.358 0.257
TKPKPA E -2.358 -2.358 -2.358 -2.358 -2.358 0.257
AKPKT- K 1.134 2.631 -2.358 2.847 -2.358 1.134
AKPAAK L -2.358 0.257 -2.358 -2.358 -2.358 -2.358
P -2.358 -2.358 0.257 -2.358 0.257 -2.358
KLPKAD T 0.257 0.257 -2.358 -2.358 0.257 -2.358
AKPKAA
Consensus: AKPKA-
? Query: AKPKTE Score = 11.4
? Query: KKPETE Score = 5.0
? Query: TLPATE Score = 4.3

http://prosite.expasy.org/prosuser.html#meth2

A hidden Markov Model takes also into
account the gaps in an alignment
The schematic representation of a HMM

http://www.myoops.org/twocw/mit/NR/rdonlyres/Electrical-Engineering-

Building a HMM from a multiple
sequence alignment

Use HMMER to very sensitively search
protein database with a HMM

You can search with a profile in a
sequence database

Some profile adjustments to the BLAST
protocol exist for particular purposes
PSI-BLAST to identify distantly related proteins
PSI-BLAST (position specific iterated)
After a search result, a profile is made of the similar
sequences, and this is used again to search a database
PHI-BLAST protein with matching of a pattern
PHI-BLAST (pattern hit initiated): you provide a pattern,
which all BLAST results should satisfy.
CSI-BLAST is more sensitive than PSI-BLAST in
identifying distantly related proteins

PSI BLAST http://www.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Web&PAGE=Proteins&PROGRAM=b
PHI BLAST http://www.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Web&PAGE=Proteins&PROGRAM=b
CSI BLAST http://toolkit.tuebingen.mpg.de/cs_blast

Many databases exist that keep patterns,
profiles or models related to function

Motif / domain databases (see NCBI bookshelf for good overview)

http://www.ebi.ac.uk/interpro/ - integrated db

http://expasy.org/prosite/ (motifs)

PFAM – hidden markov profiles (domains)


CDD (Conserved domains database) (NCBI - integrated)

Prodom (domain) (automatic extraction)

SMART (domain)

PRINTS (motif) sets of local alignments without gaps, used as frequency
matrices, made by searching manually made "seed alignments" against
UniProt sequences

Prosite is a database gathering
patterns from sequence alignments

ScanProsite tool : search the
prosite database for a pattern ( present or
not )
Example : [DE](2)-H-S-{P}-x(2)-P-x(2,4)-C>

You can retrieve sequences which correspond to a pattern,
you made up yourself, observed in an alignment or an
known one. The syntax is specific, but not difficult: see
link below!

http://prosite.expasy.org/scanprosite/scanprosite-doc.html#pattern_syntax

Interpro classifies the protein data into
families based on the domain and motifs
Interpro takes all existing motif and domains
databases as input ('signatures'), and aligns
them to create protein domain families. This
reduces redundancy. Each domain is than
given an identifier IPRxxxxxxx.

Uneven size of motifs and families between
families are handled by 'relations' :
parent - child and contains - found in
Families,... Regions, domains, ...

http://www.ebi.ac.uk/interpro/user_manual.html#type

Interpro summarizes domains and motifs
from a dozen of domain databases

http://www.ebi.ac.uk/interpro/databases.html
ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/README.html#2

InterPro entries are grouped in types

Family
Entries span complete sequence

Domain
Biologically functional units

Repeat

Region

Conserved site

Active site

Binding site

PTM site

InterPro entries are grouped in types

You can search your sequence for
known domains on InterProScan

Interproscan http://www.ebi.ac.uk/Tools/pfa/iprscan/

A sequence logo provides a visual
summary of a motif
Creating a sequence logo
Create a nicely looking logo of a motif sequence:
size of letters indicated frequency.

Weblogo - a basic web application to create
colorful logo's

IceLogo - a powerful web application to create
customized logo's

A sequence logo provides a visual
summary of a motif
iceLogo

123456
ATPKAE
KKPKAA
AKPKAK
TKPKPA
AKPKT-
AKPAAK
KLPKAD
AKPKAA
Consensus: AKPKA-

http://www.bits.vib.be/wiki/index.php/Exercises_on_multiple_sequence_alignment#Sequence_logo

There is always a chance that a prediction
of a feature by a tool is false

Number Number
of of
matches matches

True negatives True negatives

True positives True positives

Score Score
Threshold Threshold
False negatives False positives

Ideal situation Reality of the databases

Assessing the performance of categorizing
tools with sensitivity and specificity
PREDICTION
“Confusion matrix” Feature is Feature is
predicted NOT predicted

True False
Sequence contains feature positive Negatives
“Type I
error”
TRUTH

False True
Sequence does NOT contain feature positive negative
“Type II
error”

PREDICTION

Sensitivity
Sequence contains feature True positives/(TP + FN)

TRUTH

False True
Sequence does NOT contain feature positive negative

PREDICTION

Sequence contains feature

TRUTH

Selectivity
Sequence does NOT contain feature
or Specificity
TN/(FP + TN)

PREDICTION

error rate*
TRUTH
FP+FN/total


* misclassification rate

PREDICTION

Accuracy
TRUTH
TP+TN/total


Protein sequences can be searched for
potential modifications

http://www.expasy.org/tools/

e.g. modification (phosphorylation, acetylation,...)

To deal with the confidence in the results, try different
tools, and make a graph (venn diagram) to compare the
results

E.g. predict secreted proteins by signalP and
RPSP, combine results in Venn
− http://bioinformatics.psb.ugent.be/webtools/Venn/
− http://www.cmbi.ru.nl/cdd/biovenn/

Overview SignalPeptide prediction tools: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788353/

Protein sequences can be searched for
secondary structural elements
Based on know structures, machine learning
models of secondary structure elements
are made and can be searched for.
See http://bioinf.cs.ucl.ac.uk/psipred/

In case of multiple analyses on multiple
sequences, mark instead of filter

Starting set of sequences

Worse Better
Analysis filter 1
Analysis filter 1
Analysis filter 2
Analysis filter 3

Analysis filter 2

Analysis filter 3

After performing all analyses on all sequences, different filters
on the results can be applied (e.g. secreted sequence,
! phosphorylated and containing a motif)

NA sequence analyses
GC% http://mobyle.pasteur.fr/cgi-bin/portal.py?#forms::geecee
Melting temperature
For primer development, such as with Primer3

Structure
Codon usage
Codon usage table with cusp
Codon adaptation index calculation with cai
...

A lot of tools can be found at the Mobyle Portal:

Profiles and models are being used to
model biological function in NA seqs
To detect Transcription factor binding sites
TRANSFAC : commercial (BIOBASE, Wolfenbüttel, Germany), started as
work of Edgard Wingender, contains eukaryotic binding sites as
consensus sequences and as PSSMs. Also TRANSCompel with modules of
binding sites.
ooTFD : commercial (IFTI, Pittsburgh PA, USA), started as work of David
Gosh, contains prokaryotic and eukaroytic binding sites as consensus
sequences and as PSSMs.
JASPAR : open access, only representative sets of higher eukaryote binding
sites as PSSMs. Can be searched against sequence or sequence pair at
Consite.
OregAnno : open access, collection of individual eukaryotic binding sites
with their localization in the genome
PAZAR : collection of open access TF databanks

Sequence logos can give an insight in
the important residues of binding sites

DNA: an entry from JASPAR: tata box

A [ 61 16 352 3 354 268 360 222 155 56 83 82 82 68 77 ]
C [145 46 0 10 0 0 3 2 44 135 147 127 118 107 101 ]
G [152 18 2 2 5 0 10 44 157 150 128 128 128 139 140 ]
T [ 31 309 35 374 30 121 6 121 33 48 31 52 61 75 71 ]

The RNA world has the Vienna servers

http://rna.tbi.univie.ac.at/
− secondary structure prediction of
ribosomal sequences
− siRNA design

RNA families can be modeled by
conserved bases and structure

RNA motifs (http://rfam.sanger.ac.uk/search)
Rfam is a databank of RNA motifs and families. It is
made at the Sanger Centre (Hinxton, UK), from a
subset of EMBL (well-annotated standard
sequences excluding synthetic sequences + the
WGS) using the INFERNAL suite of Soan Eddy. It
contains local alignments with gaps with included
secondary structure annotation + CMs.

Some interesting links

Nucleic acid structure
Unafold - Program accessible through webinterface
After designing primers, you might want to check
whether the primer product does (not) adapt a stable
secondary structure.
Some collections of links
− Good overview at http://www.imb-jena.de/RNA.html
− European Ribosomale RNA database (VIB PSB)

Prediction of genes in genomes rely on
the integration of multiple signals
Signals surrounding the gene (transcription factor binding
sites, promoters, transcription terminators, splice
sites, polyA sites, ribosome binding sites,...)
→ profile matching
Differences in composition between coding and
noncoding DNA (codon preference), the presence of
an Open Reading Frame (ORF)
→ compositional analyses
Similarity with known genes, aligning ESTs and (in
translation) similarity with known proteins and the
presence of protein motifs
→ similarity searches

Prediction of genes in genomes rely on
the integration of multiple signals
Signals
Composition
Similarity

e.g. potential methylation sites (profiles)
Alignment of ESTs

GC

Software for prediction genes
EMBOSS
− simple software under EMBOSS : syco (codon frequency), wobble (%GC 3rd base),
tcode (Ficket
statistic : correlation between bases at distance 3)
Examples of software using HMM model of gene :
Wise2 : using also similarity with known proteins
http://www.ebi.ac.uk/Tools/Wise2
GENSCAN : commercial (Chris Burge, Stanford U.) but free for academics, has
models for human/A. thaliana/maize, used at EBI and NCBI for genome
annotation http://mobyle.pasteur.fr/cgi-bin/portal.py?#forms::genscan
GeneMark : commercial (GeneProbe, Atlanta GA, USA) but free for academic
users, developed by Mark Borodovsky, has models for many prokaryotic and
eukaryotic organisms http://exon.gatech.edu

Tutorial on gene prediction http://www.embl.de/~seqanal/courses/spring00/GenePred.00.html

Short addendum about downloading files
FTP, e.g. ftp://ftp.ebi.ac.uk/pub/databases/interpro/

– 'file transfer protocol'
– Most browsers have integrated ftp 'client'
– Free, easy to download files, possibility to
resume after fails
HTTP, e.g. http://www.ncbi.nlm.nih.gov/entrez
Standard protocol for internet traffic,
Slowest method
Aspera – for large datasets (>10GB) downloads
In use in the short read archive (SRA)
Fastest method available currently

Conclusion

 Prediction vs. experimental verified
 Different algorithms need to be compared
 Predictions need to be validated by independent
method

 Software <-> Databases
 Questions? Get social!
→ www.seqanswers.com
→ http://biostar.stackexchange.com
 Always only basis for further wet-lab research

Summary In this third module, we will discuss the possible analyses of sequences
Sequence analysis tries to read sequences to infer biological properties
Tools that can predict a biological feature are trained with examples
The assumption to being able to read biological function is the central paradigm
Analysis can be as simple as measuring properties or predicting features
Protein sequence analysis tools are gathered on Expasy
Never trust a tool's output blindly
The basis for the prediction of features is nearly always a sequence alignment
Alignment reveals similar residues which can indicate identical structure
The structure of a protein sequence determines his biological function
Degree of similarity with other sequences varies over the length
Protein sequences can consist of structurally different parts
Based on motifs and domains, proteins are assigned to families
Domains and motifs are represented by simple and complex methods
Regular expressions / patterns are the simplest way to represent motifs
Frequency matrices or profiles include the chance of observing the residues
How good a sequence matches a profile is reported with a score
A hidden Markov Model takes also into account the gaps in an alignment
Use HMMER to very sensitively search protein database with a HMM
Some profile adjustments to the BLAST protocol exist for particular purposes
Many databases exist that keep patterns, profiles or models related to function
Prosite is a database gathering patterns from sequence alignments
Interpro classifies the protein data into families based on the domain and motifs
Interpro summarizes domains and motifs from a dozen of domain databases
You can search your sequence for known domains on InterProScan
A sequence logo can provide a visual summary of a motif
Protein sequences can be searched for potential modifications
Protein sequences can be searched for secondary structural elements
In case of multiple analyses on multiple sequences, mark instead of filter
Profiles and models are being used to model biological function in NA seqs
Sequence logos can give an insight in the important residues of binding sites
The RNA world has the Vienna servers
RNA families can be modeled by conserved bases and structure
Prediction of genes in genomes rely on the integration of multiple signals

BITS: Basics of sequence analysis

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