Download Introduction to bioinformatics

BioInformatics - What and Why?
The following power point
presentation is designed to give
some background information on
Bioinformatics.
This presentation is modified from information supplied by Dr.
Bruno Gaeta, and with permission from eBioInformatics Pty
Ltd (c) Copywright
The need for bioinformaticists.
The number of entries in data bases of gene sequences is
increasing exponentially. Bioinformaticians are needed to
understand and use this information.
GenBank growth
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
Genome sequencing projects, including the human genome project
are producing vast amounts of information. The challenge is to use
this information in a useful way
Publically available genomes (April 1998)
COMPLETE/PUBLIC
Aquifex aeolicus
Pyrococcus horikoshii
Bacillus subtilis
Treponema pallidum
Borrelia burgdorferi
Helicobacter pylori
Archaeoglobus fulgidus
Methanobacterium thermo.
Escherichia coli
Mycoplasma pneumoniae
Synechocystis sp. PCC6803
Methanococcus jannaschii
Saccharomyces cerevisiae
Mycoplasma genitalium
Haemophilus influenzae
COMPLETE/PENDING PUBLICATION
Rickettsia prowazekii
Pseudomonas aeruginosa
Pyrococcus abyssii
Bacillus sp. C-125
Ureaplasma urealyticum
Pyrobaculum aerophilum
ALMOST/PUBLIC
Pyrococcus furiosus
Mycobacterium tuberculosis H37Rv
Mycobacterium tuberculosis CSU93
Neisseria gonorrhea
Neisseria meningiditis
Streptococcus pyogenes
Terry Gaasterland, Siv Andersson, Christoph Sensen
http://www.mcs.anl.gov/home/gaasterl/genomes.html
Bioinformatics impacts on all aspects of
biological research.
”..We must hook our individual computers into the
worldwide network that gives us access to daily changes
in the databases and also makes immediate our
communications with each other. The programs that
display and analyze the material for us must be improved
- and we must learn to use them more effectively. Like the
purchased kits, they will make our life easier, but also like
the kits, we must understand enough of how they work to
use them effectively…”
Walter Gilbert (1991)
“Towards a paradigm shift in biology” Nature News and Views 349:99
Promises of genomics and
bioinformatics

Medicine





Knowledge of protein structure facilitates drug design
Understanding of genomic variation allows the tailoring
of medical treatment to the individual’s genetic make-up
Genome analysis allows the targeting of genetic
diseases
The effect of a disease or of a therapeutic on RNA and
protein levels can be elucidated
The same techniques can be applied to
biotechnology, crop and livestock
improvement, etc...
What is bioinformatics?


Application of information technology to the
storage, management and analysis of
biological information
Facilitated by the use of computers
What is bioinformatics?

Sequence analysis


Molecular modeling


Geneticists obtain information about the evolution of organisms by
looking for similarities in gene sequences
Ecology and population studies


Crystallographers/ biochemists design drugs using computer-aided
tools
Phylogeny/evolution


Geneticists/ molecular biologists analyse genome sequence
information to understand disease processes
Bioinformatics is used to handle large amounts of data obtained in
population studies
Medical informatics
 Personalised medicine
Sequence analysis: overview
Sequencing project
management
Nucleotide
sequence
analysis
Sequence
entry
Sequence database
browsing
Manual
sequence
entry
Nucleotide sequence file
Search for protein
coding regions
Search databases for
similar sequences
Design further experiments
Restriction mapping
PCR planning
coding
non-coding
Protein
sequence
analysis
Translate
into protein
Search databases for
similar sequences
Sequence comparison
Search for
known motifs
RNA structure
prediction
Create a multiple
sequence alignment
Edit the alignment
Molecular
phylogeny
Search for
known motifs
Predict
secondary
structure
Sequence comparison
Multiple sequence analysis
Format the alignment
for publication
Protein sequence file
Protein family
analysis
Predict
tertiary
structure
Gene Sequencing: Automated chemcial
sequencing methods allow rapid generation of
large data banks of gene sequences
Database similarity searching: The BLAST program has been written
to allow rapid comparison of a new gene sequence with the 100s of
1000s of gene sequences in data bases
Sequences producing significant alignments:
(bits)
Value
gnl|PID|e252316 (Z74911) ORF YOR003w [Saccharomyces cerevisiae]
112
gi|603258 (U18795) Prb1p: vacuolar protease B [Saccharomyces ce... 106
gnl|PID|e264388 (X59720) YCR045c, len:491 [Saccharomyces cerevi... 69
gnl|PID|e239708 (Z71514) ORF YNL238w [Saccharomyces cerevisiae]
30
gnl|PID|e239572 (Z71603) ORF YNL327w [Saccharomyces cerevisiae]
29
gnl|PID|e239737 (Z71554) ORF YNL278w [Saccharomyces cerevisiae]
29
7e-26
5e-24
7e-13
0.66
1.1
1.5
gnl|PID|e252316 (Z74911) ORF YOR003w [Saccharomyces cerevisiae]
Length = 478
Score = 112 bits (278), Expect = 7e-26
Identities = 85/259 (32%), Positives = 117/259 (44%), Gaps = 32/259 (12%)
Query: 2
QSVPWGISRVQAPAAHNRG---------LTGSGVKVAVLDTGIST-HPDLNIRGG-ASFV 50
+ PWG+ RV
G
G GV
VLDTGI T H D
R
+ +
Sbjct: 174 EEAPWGLHRVSHREKPKYGQDLEYLYEDAAGKGVTSYVLDTGIDTEHEDFEGRAEWGAVI 233
Query: 51
PGEPSTQDGNGHGTHVAGTIAALNNSIGVLGVAPSAELYXXXXXXXXXXXXXXXXXQGLE 110
P
D NGHGTH AG I + +
GVA + ++
+G+E
Sbjct: 234 PANDEASDLNGHGTHCAGIIGSKH-----FGVAKNTKIVAVKVLRSNGEGTVSDVIKGIE 288
Sequence comparison:
Gene sequences can be aligned to see similarities
between gene from different sources
768 TT....TGTGTGCATTTAAGGGTGATAGTGTATTTGCTCTTTAAGAGCTG
||
||
|| | | ||| | |||| |||||
||| |||
87 TTGACAGGTACCCAACTGTGTGTGCTGATGTA.TTGCTGGCCAAGGACTG
.
.
.
.
.
814 AGTGTTTGAGCCTCTGTTTGTGTGTAATTGAGTGTGCATGTGTGGGAGTG
| | |
| |||||| |
|||| | || |
|
136 AAGGATC.............TCAGTAATTAATCATGCACCTATGTGGCGG
.
.
.
.
.
864 AAATTGTGGAATGTGTATGCTCATAGCACTGAGTGAAAATAAAAGATTGT
||| | ||| || || |||
|
||||||||| ||
|||||| |
173 AAA.TATGGGATATGCATGTCGA...CACTGAGTG..AAGGCAAGATTAT
813
135
863
172
913
216
Restriction mapping: Genes can be
analysed to detect gene sequences that can be
cleaved with restriction enzymes
50
AceIII
AluI
AlwI
ApoI
BanII
BfaI
BfiI
BsaXI
BsgI
BsiHKAI
Bsp1286I
BsrI
BsrFI
CjeI
CviJI
CviRI
DdeI
DpnI
EcoRI
HinfI
MaeIII
MnlI
MseI
MspI
NdeI
Sau3AI
SstI
TfiI
Tsp45I
Tsp509I
TspRI
100
150
200
250
1
2
1
2
1
2
1
1
1
1
1
2
1
2
4
1
2
2
1
2
1
1
2
1
1
2
1
2
1
3
1
CAGCTCnnnnnnn’nnn...
AG’CT
GGATCnnnn’n_
r’AATT_y
G_rGCy’C
C’TA_G
ACTGGG
ACnnnnnCTCC
GTGCAGnnnnnnnnnnn...
G_wGCw’C
G_dGCh’C
ACTG_Gn’
r’CCGG_y
CCAnnnnnnGTnnnnnn...
rG’Cy
TG’CA
C’TnA_G
GA’TC
G’AATT_C
G’AnT_C
’GTnAC_
CCTCnnnnnn_n’
T’TA_A
C’CG_G
CA’TA_TG
’GATC_
G_AGCT’C
G’AwT_C
’GTsAC_
’AATT_
CAGTGnn’
PCR Primer Design:
Oligonucleotides for use in the polymerisation
chain reaction can be designed using computer
based prgrams
OPTIMAL primer length
MINIMUM primer length
MAXIMUM primer length
OPTIMAL primer melting temperature
MINIMUM acceptable melting temp
MAXIMUM acceptable melting temp
MINIMUM acceptable primer GC%
MAXIMUM acceptable primer GC%
Salt concentration (mM)
DNA concentration (nM)
MAX no. unknown bases (Ns) allowed
MAX acceptable self-complementarity
MAXIMUM 3' end self-complementarity
GC clamp how many 3' bases
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
20
18
22
60.000
57.000
63.000
20.000
80.000
50.000
50.000
0
12
8
0
Gene discovery:
Computer program can be used to recognise the
protein coding regions in DNA
0
1,000
2,000
3,000
4,000
0
1,000
2,000
3,000
4,000
2.0
1.5
1.0
0.5
-0.0
2.0
1.5
1.0
0.5
-0.0
2.0
1.5
1.0
0.5
-0.0
Plot created using codon preference (GCG)
RNA structure prediction: Structural
features of RNA can be predicted
A
C
G
U
A
G
A
U
G
C
U
A
C
A
U
A
C
A
C
G
G
GU
C
G
U GA
A
U
U C
U
A
G
U
G
C
G
G
G
U
A
A
C
C
G
UC
G
U
C
C
A
G
G
U
A
G
U
G
CG
A
U
C
C
U
G
C
G
C
C
A
C
Protein structure prediction:
Particular structural features can be recognised in protein
sequences
50
100
50
100
5.0
KD Hydrophobicity
-5.0
10
Surface Prob.
0.0
1.2
Flexibility
0.8
1.7
Antigenic Index
-1.7
CF Turns
CF Alpha Helices
CF Beta Sheets
GOR Turns
GOR Alpha Helices
GOR Beta Sheets
Glycosylation Sites
Protein
Structure :
the 3-D structure of
proteins is used to
understand protein
function and
design new drugs
Multiple sequence alignment:
Sequences of proteins from different organisms can be
aligned to see similarities and differences
Alignment formatted using MacBoxshade
Phylogeny inference: Analysis of sequences
allows evolutionary relationships to be determined
E.coli
C.botulinum
C.cadavers
C.butyricum
B.subtilis
B.cereus
Phylogenetic tree constructed using the Phylip package
Large scale bioinformatics:
genome projects

Mapping

Identifying the location of
clones and markers on the
chromosome by genetic
linkage analysis and physical
mapping


Using database searches,
pattern searches, protein
family analysis and structure
prediction to assign a function
to each predicted gene
Data mining
Searching for relationships and
correlations in the information
Sequencing
Assembling clone sequence
reads into large (eventually
complete) genome sequences
Gene discovery
Identifying coding regions in
genomic DNA by database
searching and other methods
Function assignment

Genome comparison
Comparing different complete
genomes to infer evolutionary
history and genome
rearrangements
Challenges in bioinformatics

Explosion of information




Need for faster, automated analysis to process large
amounts of data
Need for integration between different types of
information (sequences, literature, annotations, protein
levels, RNA levels etc…)
Need for “smarter” software to identify interesting
relationships in very large data sets
Lack of “bioinformaticians”


Software needs to be easier to access, use and
understand
Biologists need to learn about the software, its
limitations, and how to interpret its results