Download A bioinformatika elméleti alapjai 4

This course is sponsored by the
International Centre for Genetic
Engineering and Biotechnology
Welcome
Bioinformatics:
Computational approaches to
biological information
Organizer:
Sándor Pongor
Leonardo Marino-Ramirez, Christoph W. Sensen,
Laurent Falquet, Sándor Pongor
Teaching staff: Stefan Grabuschnig, János Juhász
Secretariat:
Elisabetta Lippolis
Chiara Alberti
Giorgia Danelon
Computer system manager:
Dario Palmisano
Diego Soldano
Trieste, 26-30 June, 2017
Computational approaches to biological information
Trieste, May 23 - 27, 2016
 Theoretical intro: Sándor Pongor
 Sequence database searching, theory and practice (Leonardo
Marino)
 Multiple alignment, tree building (Christoph Sensen)
 Next Generation Sequencing (Laurent Falquet)
 Genome annotation (Christoph Sensen)
 Chip-seq, RNA-seq (Leonardo Marino-Ramirez)
BIOINFORMATICS
INFORMATICS
Model, description and visualization
The subjects: Molecular structures
MARTKQTARK
STGGKAPRKQ
LATKAARKSA
Sequences
CIPKWNRCGPKMDGVPCCEPYTCTSDYYGNCS
Extended sequences
(e.g. disulphide-topologies)
Diagrams (hydrophobicity plots, helical circles)
Domain-cartoons
(sec. str. cartoons)
3D structures
3D cartoons
Core data-types
tassfvvswvsasdtvsgfrvey
elseegdepqyldlpstatsvni
pdllpgrkytvnvyeiseegeqn
lilstsqttapdappdptvdqvd
dtsivvrwsrprapitgyrivys
psvegsstelnlpetansvtlsd
lqpgvqynitiyaveenqestpv
fiqqettgvprsdkvppprdlqf
vevtdvkitimwtppespvtgyr
vdvipvnlpgehgqrlpvsrntf
aevtglspgvtyhfkvfavnqgr
eskpltaqqatkldaptnlqfin
etdttvivtwtpprarivgyrlt
vgltrggqpkqynvgpaasqypl
rnlqpgseyavslvavkgnqqsp
rvtgvfttlqplgsiphyntevt
ettivitwtpaprigfklgvrps
qggeaprevtsesgsivvsgltp
gveyvytisvlrdgqerdapivk
SEQUENCES
3-D
GENOMES
TEXT
A structural model
Relationships
Substructures
Structure
Entity-relationship model
Pongor, Nature, 1987
Core data groups
-GAA-
CONSENSUS
STRUCTURES
TREES
NETWORKS
A structural model
Relationships
Substructures
Structure
Entity-relationship model
Pongor, Nature, 1987
Generalized structure
Relationships
Substructures
Structure
Susbstructures, relations, rules = onthology
Entity-relationship model
Pongor, Nature, 1987
Core operations
 Simplification + annotation
 Comparison
 Aggregation
Annotation: providing sg with notes, adding notes to sg
SEQUENCES
 Model: Chemical
structure
 Description: Series
of characters
 Simplified and/or
extended
visualization
IFPPVPGP
Domain A
Domain B
SEQUENCES
Domain A
Domain B
001-200
DOMAIN
PROTEASE A
205-230
DOMAIN
TRANSMEMBRANE
250-350
DOMAIN
SIGNAL BINDING
TABULAR DESCRIPTION: FEATURE TABLE, PTT TABLE
Leonardo Marino
ANNOTATING GENOME SEQUENCES
Gene 1
Christoph Sensen
Gene 2
Genome annotation .ptt table
RNAseq, CHIPseq: MAPPING READS TO
REFERENCE GENES OR GENOMES
~ NUMERICAL ANNOTATION
Leonardo Marino
SIMPLIFICATION OF 3D STRUCTURES
 Model: 3D chemical
structures
 Description: 3D
coordinates
 Simplified and/or
extended
visualization
(xi, yi, zi)n
Domain A
Some molecules are more equal then others…
…”This figure is purely diagrammatic. The two ribbons
symbolize the the phosphate-sugar chains, and the
horizontal rods the pairs of the bases holding the chains
together. The vertical line marks the fibre axis”
Protein visualization
Input: atomic 3D coordinates and sequence.
Structures As Database Records
Identification
Name of protein
Organism
Function
Cross-references
...
Domain structure
Sec. structure
Disulphides
….
ANNOTATIONS
CIPKWNRCGPKMDGVPCCEPYTCTSDYYGNC
Sequence (structure)
qfinetdttvivtwtpprarivgyrltvgllseeg
depqyldlpstatsvnipdllpgrkytvnvyeise
egeqnlilstsqttapdappdptvdqvddtsivvr
wsrprapitgyrivyspsvegsstelnlpetansv
tlsdlqpgvqynitiyaveenqestpvfiqqettg
vprsdkvppprdlqfvevtdvkitimwtppespvt
gyrvdvipvnlpgehgqrlpvsrntfaevtglspg
vtyhfkv
Database record, fields
SEQUENCE
OR STRUCTURE
Core operations 2
 Comparison
The concept of similarity I
Shared parts
Shared context
...easier if modular
The concept of similarity II
…Easy for humans, hard for computers
Similarity in bioinformatics:
Important properties
 Quantitative: we need a similarity score and a method
to calculate significance
 Alignment (finding matches between sequences,
between structures, etc.)
 Aggregation (adding small similarities together).
Similarity scores and significance:
 A score is a number. Higy score is
high similarity. No inherent „scale”.
 A score can be scaled if we know the
probabilities of random similarities.
This gives significance: what is the
probability of finding this number by
chance? The smaller the better
Alignment
 Finding the best match between two sequences
 Finding exact matches is easy. In biology we need approximate
matches, and that is difficult.
The result:
1)A similarity score (number), with
significance
2) An alignment pattern
RGD
RGD...W
Substructure identity ~ similarity
”The similarity of objects can be best described as
partial identities of components and relationships
Erich Goldmeier, The similarity of perceived forms, 1936
Which alignment is better?
 The one with a higher score
 The one with a „nicer” motif..
Core Operations 3
 Aggregation
Why do we need aggregation?
 Biological objects are large and complex (genomes, proteomes,
metagenomes, pathway data, etc.)
 Often, measuring instruments can only collect data on small
pieces (next generation sequencing reads, peptide spectra in
proteomics)
 Computational analysis of small fragments is accurate.
Why do we need aggregation?
(in other words)
 Only simple objects can be easily located by similarity, say we
easily find a 3 amino acid motif in a sequence or in a 3D
structure.
 Unfortunately, most objects in bioinformatics are
COMPLICATED, like genomes, proteomes, metagenomes,
pathways, even ordinary protein or gene sequences.
 There is one general trick: We divide a complex object into
simple parts (like characteristic motifs), identify individual parts
by simple numerical means, and then AGGREGATE the results.
 Not elegant, but works, even with very complex problems.
Aggregating local sequence similarities
Sequence 1
Sequence 2
 Are these two sequences related by evolution? (are they
homologous?) Only probabilistic answers...
 We need aggregate scores, i.e. probabilities for finding
combinations by chance...
Leonardo Marino
BLAST
Examples for aggregation in bioinformatics
 Single proteins, genes: constructing protein/gene similarity from
local similarities (BLAST) Inferring homolgy.
 Proteomics: Constructing protein similarities from peptide
fragment similarities. Inferring protein presence.
 Genomics1: Aggregating a long sequence from short reads
(next generation sequencing). Inferring a genome.
 Genomics2: Putting protein similarities together into pathways.
 Metagenomics: Inferring a microbial community from species
similarities.
The human mind is good at aggregating noisy
signals
Edgar Rubin’s vase
(~1915, Copenhagen)
Kanizsa’s Triangle
(~1955, Trieste)
The human mind is good at aggregating noisy
signals according to structures
 Contour recognition principles
 In bioinformatics, computers do
this in an abstract space of data,
and without human intuition.
  Filtering, search space
reduction is useful when designing
bioinformatics tools.
Psychology of vision.
SUMMARY: Core data types
tassfvvswvsasdtvsgfrvey
elseegdepqyldlpstatsvni
pdllpgrkytvnvyeiseegeqn
lilstsqttapdappdptvdqvd
dtsivvrwsrprapitgyrivys
psvegsstelnlpetansvtlsd
lqpgvqynitiyaveenqestpv
fiqqettgvprsdkvppprdlqf
vevtdvkitimwtppespvtgyr
vdvipvnlpgehgqrlpvsrntf
aevtglspgvtyhfkvfavnqgr
eskpltaqqatkldaptnlqfin
etdttvivtwtpprarivgyrlt
vgltrggqpkqynvgpaasqypl
rnlqpgseyavslvavkgnqqsp
rvtgvfttlqplgsiphyntevt
ettivitwtpaprigfklgvrps
qggeaprevtsesgsivvsgltp
gveyvytisvlrdgqerdapivk
A structural model
Relationships
Substructures
Structure
Entity-relationship model
Pongor, Nature, 1987
SUMMARY: Core operations
 Simplification + annotation
 Comparison
 Aggregation
Models are human constructs...
THIS IS NOT A PIPE!
Models are human constructs...
THIS IS NOT A MOLECULE
Bioinformatics:
Computational approaches to
biological information
Organizer:
Sándor Pongor
Leonardo Marino-Ramirez, Christoph W. Sensen,
Laurent Falquet, Sándor Pongor
Teaching staff: Stefan Grabuschnig, János Juhász
Secretariat:
Elisabetta Lippolis
Chiara Alberti
Giorgia Danelon
Computer system manager:
Dario Palmisano
Diego Soldano
Trieste, 26-30 June, 2017