Transcript Lecture 1

Introduction to
Bioinformatics
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GENERAL INFORMATION
Course Methodology
The course consists of the following components;
i. a series of 10 lectures and 10 mini-exams,
ii. 7 skills classes, each with one programming task,
iii. one final written exam.
•In the lectures the main theoretical aspects will be presented.
•Each lecture starts with a "mini-exam" with three short questions belonging
to the previous lecture.
•In the skills classes (SCs) several programming tasks are performed, one of
which has to be submitted until next SC.
•Finally ,the course terminates with a open-book exam.
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GENERAL INFORMATION
10 lectures and 10 mini-exams
Prologue (In praise of cells)
Chapter 1. The first look at a genome (sequence statistics)
Chapter 2. All the sequence's men (gene finding)
Chapter 3. All in the family (sequence Alignment)
Chapter 4. The boulevard of broken genes (hidden Markov models)
Chapter 5. Are Neanderthals among us? (variation within and between
species)
Chapter 6. Fighting HIV (natural selection at the molecular level)
Chapter 7. SARS: a post-genomic epidemic (phylogenetic analysis)
Chapter 8. Welcome to the hotel Chlamydia (whole genome comparisons)
Chapter 9. The genomics of wine-making (Analysis of gene expression)
Chapter 10. A bed-time story (identification of regulatory sequences)
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GENERAL INFORMATION
mini-exams
* First 15 minutes of the lecture
* Closed Book
* Three short questions on the previous lecture
* Counts as bonus points for the final mark …
* There is a resit, where you can redo individual mini’s
you failed to attend with a legitimate leave
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GENERAL INFORMATION
Skills Class:
* Each Friday one hour hands-on with real data
* Hand in one-a-week – for a bonus point
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GENERAL INFORMATION
Final Exam:
* 10 short questions regarding the course material
* Open book
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GENERAL INFORMATION
Grading:
The relative weights of the components are:
i. 10 mini-exam: B1 bonus points (max 1)
ii. 7 skills class programming task: B2 bonus points (max 1)
iii. final written exam (open-book, three hours): E points (max 10)
Final grade =
min(E + (B1 + B2), 10)
Study Points:
6 ECTS/ 4 NSP
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GENERAL INFORMATION
Course Book:
Introduction to
Computational
Genomics
A Case Studies
Approach
Nello Cristianini,
Matthew W. Hahn
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GENERAL INFORMATION
Additional recommended texts:
• Bioinformatics: the machine learning approach, Baldi & Brunak.
• Introduction to Bioinformatics, Lesk, and: Introduction to Bioinformatics,
Attwood & Parry-Smith.
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Introduction to Bioinformatics.
LECTURES
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Introduction to Bioinformatics.
LECTURE 1:
* Prologue (In praise of cells)
* Chapter 1. The first look at a genome (sequence statistics)
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Introduction to Bioinformatics.
Prologue :
In praise of cells
* Nothing in Biology Makes Sense Except in the Light of Evolution
(Theodosius Dobzhansky)
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GENOMICS and PROTEOMICS
Genomics is the study of an organism's genome and the use of the genes.
It deals with the systematic use of genome information, associated with other
data, to provide answers in biology, medicine, and industry.
Proteomics is the large-scale study of proteins, particularly their structures
and functions.
Proteomics is much more complicated than genomics. Most importantly,
while the genome is a rather constant entity, the proteome differs from cell to
cell and is constantly changing through its biochemical interactions with the
genome and the environment. One organism will have radically different
protein expression in different parts of its body, in different stages of its life
cycle and in different environmental conditions.
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Development
of
Genomics/
Proteomics
Databases
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modern map-makers
have mapped the entire
human genome
Hurrah – we know the
entire 3.3 billion bps of the
human genome !!!
… but what does it mean
???
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Metabolic activity in GENETIC PATHWAYS
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How can we
measure
metabolic
processes
and
gene activity ???
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EXAMPLE:
Caenorhabditis elegans
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Some fine day
in 1982 …
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Boy, do I
want to
map the
activity of
these
genes !!!
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Until recently we lacked tools to measure
gene activity
1989 saw the introduction of the
microarray technique by Stephen Fodor
But only in 1992 this technique became
generally available – but still very costly
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Until recently we lacked tools to measure
gene activity
1989 saw the introduction of the
microarray technique by Stephen Fodor
But only in 1992 this technique
became
Microarray
generally available – but still very costly
Stephen
Fodor
Microarray-ontwikkelaar
Ontwikkelde microarray
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Some fine day many,
many, many years
later …
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Now I’m
almost
there …
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Using the microarray technology we can
now make time series of the activity of
our 22.000 genes – so-called
genome wide expression profiles
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The identification of genetic pathways
from Microarray Timeseries
Sequence of genomewide expression profiles
at consequent instants
become more realistic
with decreasing costs …
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Genomewide expression profiles: 25,000 genes
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Now the problem is to map these
microarray-series of genome-wide
expression profiles into something that
tells us what the genes are actually doing
… for instance a network representing
their interaction
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GENOMICS: structure and coding
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DNA
Deoxyribonucleic acid (DNA) is a nucleic acid that
contains the genetic instructions specifying the biological
development of all cellular forms of life (and most viruses).
DNA is a long polymer of nucleotides and encodes the
sequence of the amino acid residues in proteins using the
genetic code, a triplet code of nucleotides.
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DNA under electron microscope
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3D model of a section of the DNA molecule
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James Watson and Francis Crick
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Genetic code
The genetic code is a set of rules that maps DNA sequences
to proteins in the living cell, and is employed in the process of
protein synthesis.
Nearly all living things use the same genetic code, called the
standard genetic code, although a few organisms use minor
variations of the standard code.
Fundamental code in DNA: {x(i)|i=1..N,x(i) in {C,A,T,G}}
Human: N = 3.3 billion
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Genetic code
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Replication
of
DNA
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Genetic code: TRANSCRIPTION
DNA → RNA
Transcription is the process through which a DNA sequence is enzymatically
copied by an RNA polymerase to produce a complementary RNA. Or, in other
words, the transfer of genetic information from DNA into RNA. In the case of
protein-encoding DNA, transcription is the beginning of the process that
ultimately leads to the translation of the genetic code (via the mRNA
intermediate) into a functional peptide or protein. Transcription has some
proofreading mechanisms, but they are fewer and less effective than the
controls for DNA; therefore, transcription has a lower copying fidelity than
DNA replication.
Like DNA replication, transcription proceeds in the 5' → 3' direction (ie the old
polymer is read in the 3' → 5' direction and the new, complementary
fragments are generated in the 5' → 3' direction).
IN RNA Thymine (T) → Uracil (U)
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Genetic code: TRANSLATION
DNA-triplet → RNA-triplet = codon → amino acid
RNA codon table
There are 20 standard amino acids used in proteins,
here are some of the RNA-codons that code for each amino acid.
Ala A
Leu L
Arg R
Lys K
Asn N
Met M
Asp D
Phe F
Cys C
Pro P
...
Start
Stop
GCU, GCC, GCA, GCG
UUA, UUG, CUU, CUC, CUA, CUG
CGU, CGC, CGA, CGG, AGA, AGG
AAA, AAG
AAU, AAC
AUG
GAU, GAC
UUU, UUC
UGU, UGC
CCU, CCC, CCA, CCG
AUG, GUG
UAG, UGA, UAA
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PROTEOMICS: structure and function
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Protein Structure:
primary structure
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Protein
Structure:
secondary
Structure
a: Alpha-helix,
b: Beta-sheet
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Protein Structure:
super-secondary Structure
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Protein Structure = protein function:
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EVOLUTION and the origin of SPECIES
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Tree of Life
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Phylogenetic relations between
Cetaceans and ariodactyl
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Unsolved problems in biology
Life. How did it start? Is life a cosmic phenomenon? Are the conditions necessary for the origin of
life narrow or broad? How did life originate and diversify in hundred millions of years? Why, after
rapid diversification, do microorganisms remain unchanged for millions of years? Did life start on
this planet or was there an extraterrestrial intervention (for example a meteor from another planet)?
Why have so many biological systems developed sexual reproduction? How do organisms
recognize like species? How are the sizes of cells, organs, and bodies controlled? Is immortality
possible?
DNA / Genome. Do all organisms link together to a primary source? Given a DNA sequence, what
shape will the protein fold into? Given a particular desired shape, what DNA sequence will produce
it? What are all the functions of the DNA? Other than the structural genes, which is the simpler part
of the system? What is the complete structure and function of the proteome proteins expressed by
a cell or organ at a particular time and under specific conditions? What is the complete function of
the regulator genes? The building block of life may be a precursor to a generation of electronic
devices and computers, but what are the electronic properties of DNA? Does Junk DNA function as
molecular garbage?
Viruses / Immune system. What causes immune system deficiencies? What are the signs of
current or past infection to discover where Ebola hides between human outbreaks? What is the
origin of antibody diversity? What leads to the complexity of the immune system? What is the
relationship between the immune system and the brain?
Humanity: Why are there drastic changes in hominid morphology? Why are there giant hominid
skeletons and very small hominid skeletons? Is hominid evolution static? Is hominid devolution
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possible? Are there Human-Neanderthal hybrids? What explains the differences between Human
and Neanderthal Fossils?
Introduction to Bioinformatics.
LECTURE 1:
CHAPTER 1:
The first look at a genome (sequence statistics)
* A mathematical model should be as simple as possible, but not
too simple!
(A. Einstein)
* All models are wrong, but some are useful. (G. Box)
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Introduction to Bioinformatics.
The first look at a genome (sequence statistics)
• Genome and genomic sequences
• Probabilistic models and sequences
• Statistical properties of sequences
• Standard data formats and databases
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.1 Genomic era, year zero
• 1958: Fred Sanger (Cambridge, UK): Nobel prize for
developing protein sequencing techniques
• 1978: Fred Sanger: First complete viral genome
• 1980: Fred Sanger: First mitochrondrial genome
• 1980: Fred Sanger: Nobel prize for developing DNA
sequencing techniques
•1995: Craig Venter (TIGR): complete geneome of
Haemophilus influenza
• 2001: entire genome of Homo sapiens sapiens
• Start of post-genomic era (?!)
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.1 Genomic era, year zero
ORGANISM
DATE SIZE
DESCRIPTION
Phage phiX 74
Human mtDNA
HIV
H. influenza
1978
1980
1985
1995
1st viral genome
1st organelle genome
AIDS retrovirus
1st bacterial genome
H. sapiens
2001 3,500 Mb
5,368 bp
16,571 bp
9,193 bp
1,830 Kb
complete human
genome
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.2 The anatomy of a genome
• Definition of genome
• Prokaryotic genomes
• Eukaryotic genomes
• Viral genomes
• Organellar genomes
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
• Alphabets, sequences, and sequence space
• Multinomial sequence model
• Markov sequence model
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
Alphabets, sequences, and sequence space
4-letter alphabet N = {A,C,G,T} (= nucleoitides)
* sequence: s = s1s2…sn e.g.: s = ATATGCCTGACTG
* sequence space: the space of all sequences (up to a certain
length)
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
Multinomial sequence model
* Nucleotides are independent and identically distributed
(i.i.d),
* p = {pA,pC,pG,pT}, pA + pC + pG + pT = 1
* P(s) 
n
 p(s(i))
i 1
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of genome sequences
Markov
sequence model
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of genome sequences
Markov sequence model
* Probability start state π
* State transition matrix T
n
* P(s)   (s1 ) p(s(i  1), s(i))
i 1
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome:
statistical sequence analysis
• Base composition & sliding window plot
• GC content & change point analysis
• Finding unusual DNA words
• Biological relevance of unusual motifs
• Pattern matching versus pattern discovery
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition H. influenzae
BASE
AMOUNT
FREQUENCY
A
C
G
T
567623
350723
347436
564241
0.3102
0.1916
0.1898
0.3083
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
Haemophilus influenzae type b
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
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Evidence for co-evolution of gene order and recombination rate
Csaba Pál & Laurence D. Hurst
Nature Genetics 33, 392 - 395 (2003)
Figure 3. Sliding-window plot of the number of essential genes
(black line) and standard deviation from chromosomal mean
recombination rate (gray line) along chromosome 9.
Dot indicates the centromere. The windows were each ten genes long, and
one gene jump was made between windows.
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GC content
GC versus AT
Organism
H. influenzae
M. tuberculosis
S. enteridis
GC content
38.8
65.8
49.5
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GC content
•Detect foreign genetic material
•Horizontal gene transfer
•Change point analysis
• AT denatures (=splits) at lower temperatures
• Thermophylic Archaeabacteriae: high CG
• Evolution:
Archaea > Eubacteriae > Eukaryotes
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GC content
Example of very high GC content
Average GC content: 61%
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GC content
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Change points in Labda-phage
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k-mer frequency motif bias
• dimer, trimer, k-mer: nucleotide word of length 2, 3, k
• “unusual” k-mers
• 2-mer in H. influenzae
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k-mer frequency motif bias
2-mer (dinucleotide) density in H. influenzae
A*
C
G
T
*A
C
G
T
0.1202 0.0505 0.0483 0.0912
0.0665 0.0372 0.0396 0.0484
0.0514 0.0522 0.0363 0.0499
0.0721 0.0518 0.0656 0.1189
NB:
freq(‘AT’)  freq(A or T)
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k-mer frequency motif bias
Most frequent 10-mer (dinucleotide) density
in H. influenzae:
AAAGTGCGGT
ACCGCACTTT
Why?
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Unusual DNA-words
Compare OBSERVED with EXPECTED frequency
of a word using multinomial model
Observed/expected ratio:
A*
C
G
T
*A
1.2491
1.1182
0.8736
0.7541
C
G
T
0.8496 0.8210 0.9535
1.0121 1.0894 0.8190
1.4349 1.0076 0.8526
0.8763 1.1204 1.2505
This takes also into account the relative
proportionality pA, pC, pG, pT.
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Unusual DNA-words
Restriction sites: very unusual words
CTAG -> “kincking” of DNA-strand
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genome
signature:
Nucleotide motif
bias in four
genomes
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Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.5 Finding data: GenBank,
EMBL, and DDBJ
• Online databases
•FASTA: a standard data format
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DATABASES
Generalized (DNA, proteins and carbohydrates, 3Dstructures)
Specialized (EST, STS, SNP, RNA, genomes, protein
families, pathways, microarray data ...)
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OVERVIEW OF DATABASES
1. Database indexing and specification of search terms
(retrieval, follow-up, analysis)
2. Archives (databases on: nucleic acid sequences, genome,
protein sequences, structures, proteomics, expression,
pathways)
3. Gateways to Archives (NCBI, Entrez, PubMed, ExPasy,
Swiss-Prot, SRS, PIR, Ensembl)
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Generalized DNA, protein
and carbohydrate databases
Primary sequence databases
EMBL (European Molecular Biology Laboratory nucleotide sequence
database at EBI, Hinxton, UK)
GenBank (at National Center for Biotechnology information, NCBI,
Bethesda, MD, USA)
DDBJ (DNA Data Bank Japan at CIB , Mishima, Japan)
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NCBI: National Center for
Biotechnology information
Established in 1988 as a national resource for molecular biology information,
NCBI creates public databases, conducts research in computational biology,
develops software tools for analyzing genome data, and disseminates
biomedical information - all for the better understanding of molecular processes
affecting human health and disease.
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NCBI - GenBank
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The EMBL Nucleotide Sequence Database (also known as EMBL-Bank) constitutes
Europe's primary nucleotide sequence resource. Main sources for DNA and RNA
sequences are direct submissions from individual researchers, genome
sequencing projects and patent applications.
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EBI: European
Bioinformatics Institute
The European Bioinformatics Institute (EBI) is a non-profit academic organisation that
forms part of the European Molecular Biology Laboratory (EMBL).
The EBI is a centre for research and services in bioinformatics. The Institute manages
databases of biological data including nucleic acid, protein sequences and
macromolecular structures.
Our mission
To provide freely available data and bioinformatics services to all facets of the scientific
community in ways that promote scientific progress
To contribute to the advancement of biology through basic investigator-driven research
in bioinformatics
To provide advanced bioinformatics training to scientists at all levels, from PhD students
to independent investigators
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To help disseminate cutting-edge technologies to industry
What is DDBJ
DDBJ (DNA Data Bank of Japan) began DNA data bank activities in earnest in
1986 at the National Institute of Genetics (NIG).
DDBJ has been functioning as the international nucleotide sequence database
in collaboration with EBI/EMBL and NCBI/GenBank.
DNA sequence records the organismic evolution more directly than other
biological materials and ,thus, is invaluable not only for research in life
sciences, but also human welfare in general. The databases are, so to speak, a
common treasure of human beings. With this in mind, we make the databases
online accessible to anyone in the world
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ExPASy Proteomics Server
(SWISS-PROT)
The ExPASy (Expert Protein Analysis System) proteomics
server of the Swiss Institute of Bioinformatics (SIB) is
dedicated to the analysis of protein sequences and
structures as well as 2-D PAGE
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Generalized DNA, protein
and carbohydrate databases
Protein sequence databases
SWISS-PROT (Swiss Institute of Bioinformatics, SIB, Geneva, CH)
TrEMBL (=Translated EMBL: computer annotated protein sequence
database at EBI, UK)
PIR-PSD (PIR-International Protein Sequence Database, annotated
protein database by PIR, MIPS and JIPID at NBRF, Georgetown
University, USA)
UniProt (Joined data from Swiss-Prot, TrEMBL and PIR)
UniRef (UniProt NREF (Non-redundant REFerence) database at EBI, UK)
IPI (International Protein Index; human, rat and mouse proteome database
at EBI, UK)
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Generalized DNA, protein
and carbohydrate databases
Carbohydrate databases
CarbBank (Former complex carbohydrate structure database, CCSD,
discontinued!)
3D structure databases
PDB (Protein Data Bank cured by RCSB, USA)
EBI-MSD (Macromolecular Structure Database at EBI, UK )
NDB (Nucleic Acid structure Datatabase at Rutgers State University of
New Jersey , USA)
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PROTEIN DATA BANK
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DATABASE SEARCH
Text-based (SRS, Entrez ...)
Sequence-based (sequence similarity search) (BLAST, FASTA...)
Motif-based (ScanProsite, eMOTIF)
Structure-based (structure similarity search) (VAST, DALI...)
Mass-based protein search (ProteinProspector, PeptIdent, Prowl …)
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Search across databases
Help
Welcome to the Entrez cross-database search page
PubMed: biomedical literature citations and abstracts PubMed Central: free, full
text journal articles Site Search: NCBI web and FTP sites Books: online books
OMIM: online Mendelian Inheritance in Man OMIA: online Mendelian Inheritance in
Animals
Nucleotide: sequence database (GenBank) Protein: sequence database Genome:
whole genome sequences Structure: three-dimensional macromolecular structures
Taxonomy: organisms in GenBank SNP: single nucleotide polymorphism Gene:
gene-centered information HomoloGene: eukaryotic homology groups PubChem
Compound: unique small molecule chemical structures PubChem Substance:
deposited chemical substance records Genome Project: genome project information
UniGene: gene-oriented clusters of transcript sequences CDD: conserved protein
domain database 3D Domains: domains from Entrez Structure UniSTS: markers
and mapping data PopSet: population study data sets GEO Profiles: expression
and molecular abundance profiles GEO DataSets: experimental sets of GEO data
Cancer Chromosomes: cytogenetic databases PubChem BioAssay: bioactivity
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screens of chemical substances GENSAT: gene expression atlas of mouse central
nervous system Probe: sequence-specific reagents
New! Assembly Archive recently created at NCBI links together trace data and finished sequence providing
complete information about a genome assembly. The Assembly Archive's first entries are a set of closely related
strains of Bacillus anthracis. The assemblies are avalaible at TraceAssembly
See more about Bacillus anthracis genome Bacillus licheniformis ATCC
14580Release Date: September 15, 2004
Reference: Rey,M.W.,et al.
Complete genome sequence of the industrial bacterium Bacillus licheniformis and
comparisons with closely related Bacillus species (er) Genome Biol. 5, R77 (2004)
Lineage: Bacteria; Firmicutes; Bacillales; Bacillaceae; Bacillus.
Organism: Bacillus licheniformis ATCC 14580
Genome sequence information
chromosome - CP000002 - NC_006270
Size: 4,222,336 bp Proteins: 4161
Sequence data files submitted to GenBank/EMBL/DDBJ can be found at NCBI FTP:
GenBank or RefSeq Genomes
Bacillus cereus ZKRelease Date: September 15, 2004
Reference: Brettin,T.S., et al. Complete genome sequence of Bacillus cereus ZK
Lineage: Bacteria; Firmicutes; Bacillales; Bacillaceae; Bacillus; Bacillus cereus group.
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Organism:
BLAST
NCBI → BLAST Latest news: 6 December 2005 : BLAST 2.2.13 released About
Getting started / News / FAQs
More info
NAR 2004 / NCBI Handbook / The Statistics of Sequence Similarity Scores
Software
Downloads / Developer info
Other resources
References / NCBI Contributors / Mailing list / Contact us
The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity
between sequences. The program compares nucleotide or protein sequences to
sequence databases and calculates the statistical significance of matches. BLAST can
be used to infer functional and evolutionary relationships between sequences as well
as help identify members of gene families. Nucleotide
Quickly search for highly similar sequences (megablast)
Quickly search for divergent sequences (discontiguous megablast)
Nucleotide-nucleotide BLAST (blastn)
Search for short, nearly exact matches
Search trace archives with megablast or discontiguous megablast
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Protein
Protein-protein BLAST (blastp)
Fasta Protein Database Query
Provides sequence similarity searching against nucleotide and protein databases
using the Fasta programs.
Fasta can be very specific when identifying long regions of low similarity especially for
highly diverged sequences.
You can also conduct sequence similarity searching against complete proteome or
genome databases using the Fasta programs.
Download Software
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Kangaroo
MOTIV BASED SEARCH
Kangaroo is a program that facilitates searching for gene and protein patterns
and sequences
Kangaroo is a pattern search program. Given a sequence pattern the program
will find all the records that contain that pattern.
To use this program, simply enter a sequence of DNA or Amino Acids in the
pattern window, choose the type of search, the taxonomy and submit your
request.
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ANALYSIS TOOLS
DNA sequence analysis tools
RNA analysis tools
Protein sequence and structure analysis tools (primary, secondary, tertiary
structure)
Tools for protein Function assignment
Phylogeny
Microarray analysis tools
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MISCELLANEOUS
Literature search
Patent search
Bioinformatics centers and servers
Links to other collections of bioinformatics resources
Medical resources
Bioethics
Protocols
Software
(Bio)chemie
Educational resources
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Introduction to Bioinformatics.
END of LECTURE 1
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