CG920 Genomics
Lesson 1
Introduction into Bioinformatics
Jan Hejátko
Functional Genomics and Proteomics of Plants,
Mendel Centre for Plant Genomics and Proteomics,
Central European Institute of Technology (CEITEC), Masaryk University, Brno
hejatko@sci.muni.cz, www.ceitec.muni.cz
 Syllabus of the course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
 GENOME resources
 Analytical tools
 Homologies searching
 Searching of sequence motifs, open reading frames, restriction sites…
 Other on-line genomic tools
Outline
2
Course Syllabus
 Chapter 01
 Introduction to bioinformatics
 Chapter 02
 Identification of genes
 Chapter 03
 Reverse genetics approaches
 Chapter 04
 Forward genetics approaches
3
Course Syllabus
 Chapter 05
 Funcional genomics approaches
 Chapter 06
 Protein-protein interactions and their analysis
 Chapter 07
 Current DNA-sequencing methods
 Chapter 08
 Structural genomics
4
Course Syllabus
 Chapter 09
 Localization of genes and gene products in the cell
 Chapter 10
 Genomics and systems biology
 Chapter 11
 Practical aspects of functional genomics
 Chapter 12
 Tools of systems biology
 Model organisms, PCR and PCR primer design
5
 Literature sources for Chapter 01:
 Bioinformatics and Functional Genomics,
2009, Jonathan Pevsner, Willey-Blackwell,
Hobocken, New Jersey
http://www.bioinfbook.org/index.php
 Úvod do praktické bioinformatiky, Fatima
Cvrčková, 2006, Academia, Praha
 Plant Functional Genomics, ed. Erich
Grotewold, 2003, Humana Press, Totowa, New
Jersey
Literature
6
 Syllabus of this course
 Definition of genomics
Outline
7
 Sensu lato (in the broad sense) – it is interested in STRUCTURE
and FUNCTION of genomes
 Sensu stricto (in the narrow sense) – it is interested in
FUNCTION of individual genes – FUNCTIONAL
GENOMICS
 It uses mainly the reverse genetics approaches
 Condition: knowing the genome (sequence) –
work with databases
GENOMICS – What is it?
8
Genomics is a science discipline that is interested in the
analysis of genomes. Genome of each organism is a
complex of all genes of the respective organism. The genes
could be located in cytoplasm (prokaryots) nucleus (in most
euckaryotic organisms), mitochondria or chloroplasts (in
plants).
The critical prerequisite of genomics is the knowledge of
gene sequences.
Functional genomics is interested in function of individual
genes.
9
3 : 1
Forward („classical“) genetics approaches Reverse genetics approaches
?
Insertional mutagenesis
5‘TTATATATATATATTAAAAAATAAAATAAAA
GAACAAAAAAGAAAATAAAATA….3‘
GENOMICS – What is it?
The role of BIOINFORMATICS in FUNCTIONAL GENOMICS
BIOINFORMATICS
FUNCTIONAL GENOMICS
10
With the knowledge of gene sequences (or the knowledge of the gene files in the
individual organisms, i.e. the knowledge of genomes), Reverse Genetics appears
that allows study their function.
In comparison to ”classical” or Forward Genetics, starting with the phenotype, the
reverse genetics starts with the sequence identified as a gene in the sequenced
genome. The gene identification using approaches of Bioinformatics will be
described later (see Lesson 02).
Reverse genetics uses a spectrum of approaches that will be described in the
Lesson 03 that allow isolation of sequence-specific mutants and thus their phenotype
analysis.
The necessity of having phenotype alterations in the forward genomics approach
introduces important difference between those two approaches. Thus, the gene is no
longer understood as a factor (trait) determining phenotype, but rather as a piece of
DNA characterized by the unique string of nucleotides. i.e. physical DNA molecule.
11
• Syllabus of this course
• Definition of genomics
• Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
Outline
12
 Definiction of bioinformatics (according to NIH Biomedical
Information Science and Technology Initiative Consortium)
Research, development, or application of computational tools and
approaches for expanding the use of biological, medical,
behavioral or health data, including those to acquire, store,
organize, archive, analyze, or visualize such data.
Bioinformatics
13
NIH WORKING DEFINITION OF BIOINFORMATICS AND COMPUTATIONAL
BIOLOGY, July 17, 2000
The following working definition of bioinformatics and computational biology were
developed by the BISTIC Definition Committee and released on July 17, 2000. The
committee was chaired by Dr. Michael Huerta of the National Institute of Mental
Health and consisted of the following members:
Bioinformatics Definition Committee BISTIC Members Expert Members
Michael Huerta (Chair) Gregory Downing
Florence Haseltine Belinda Seto
Yuan Liu
Preamble
Bioinformatics and computational biology are rooted in life sciences as well as
computer and information sciences and technologies. Both of these interdisciplinary
approaches draw from specific disciplines such as mathematics, physics, computer
science and engineering, biology, and behavioral science. Bioinformatics and
computational biology each maintain close interactions with life sciences to realize
their full potential.
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Bioinformatics applies principles of information sciences and technologies to make
the vast, diverse, and complex life sciences data more understandable and useful.
Computational biology uses mathematical and computational approaches to address
theoretical and experimental questions in biology. Although bioinformatics and
computational biology are distinct, there is also significant overlap and activity at
their interface.
Definition
The NIH Biomedical Information Science and Technology Initiative Consortium
agreed on the following definitions of bioinformatics and computational biology
recognizing that no definition could completely eliminate overlap with other activities
or preclude variations in interpretation by different individuals and organizations.
Bioinformatics: Research, development, or application of computational tools and
approaches for expanding the use of biological, medical, behavioral or health data,
including those to acquire, store, organize, archive, analyze, or visualize such data.
Computational Biology: The development and application of data-analytical and
theoretical methods, mathematical modeling and computational simulation
techniques to the study of biological, behavioral, and social systems.
15
• Interface of biology and computers
• Analysis of proteins, genes and genomes
using computer algorithms and
computer databases
• Genomics is the analysis of genomes.
The tools of bioinformatics are used to make
sense of the billions of base pairs of DNA
that are sequenced by genomics projects.
What is bioinformatics?
J. Pevsner,
http://www.bioinfbook.org/index.php
16
 Bioinformatics in functional genomics
 Processing and analysis of sequencing data
 Identification of reference sequences
 Identification of genes
 Identification of homologs, orthologs and paralogs
 Correlation analysis of genomes and phenotypes (incl.
human)
 Processing and analysis of transcriptional data
 Transcriptional profiling using DNA chips or next-gen
sequencing
 Evaluation of experimental data and prediction of new
regulations in systems biology approaches
 Mathematical modelling of gene regualtion networks
Bioinformatics
17
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
Outline
18
Spectre of on-line resources
19
There are many of on-line resources that
could be used.
 EBI http://www.ebi.ac.uk/services
Spectre of on-line resources
20
 NCBI http://www.ncbi.nlm.nih.gov/
Spectre of on-line resources
21
Nowadays, the resources are interconnected and could be accessed via dedicated
web pages.
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
Outline
22
 EMBL
 http://www.ebi.ac.uk/embl/
 GenBank,
 http://www.ncbi.nih.gov/Genbank/GenbankSearch.html
 DDBJ,
 http://www.ddbj.nig.ac.jp
 They include sets of primary data – DNA and protein sequences
 Sequences in databases of „The Big Three“:
 Daily mutual exchange and backup of data
 Works with large amount of data (capacity and software requirements)
 September 2003 27,2 x 106 entries (approx. 33 x 109 bp)
 August 2005 100 x 109 bp from 165.000 organisms
Primary databases
23
Growth of GenBank
Year
BasepairsofDNA(millions)
Sequences(millions)
1982 1986 1990 1994 1998 2002
J. Pevsner,
http://www.bioinfbook.org/index.php
24
Growth of GenBank + Whole Genome Shotgun
(1982-November 2008): we reached 0.2 terabasesNumberofsequences
inGenBank(millions)
BasepairsofDNAinGenBank(billions)
BasepairsinGenBank+WGS(billions)
0
20
40
60
80
100
120
140
160
180
200
1982 1992 2002 2008
J. Pevsner,
http://www.bioinfbook.org/index.php
25
Growth of GenBank
Feb 15 2013
26
WGS
Interactive concepts in biochemistry, Rodney Boyer, Wiley,
2002, http://www.wiley.com//college/boyer/0470003790/
27
Shotgun sequencing allows a scientist to rapidly determine the sequence of very
long stretches of DNA. The key to this process is fragmenting of the genome into
smaller pieces that are then sequenced side by side, rather than trying to read the
entire genome in order from beginning to end. The genomic DNA is usually first
divided into its individual chromosomes. Each chromosome is then randomly broken
into small strands of hundreds to several thousand base pairs, usually accomplished
by mechanical shearing of the purified genetic material. Each of the short DNA
pieces is then inserted into a DNA vector (a viral genome), resulting in a viral particle
containing "cloned" genomic DNA (Fig. 1).
The collection of all the viral particles with all the different genomic DNA pieces is
referred to as a library. Just as a library consists of a set of books that together make
up all of human knowledge, a genomic library consists of a set of DNA pieces that
together make up the entire genome sequence.
28
Placing the genomic DNA within the viral genome allows bacteria infected with the
virus to faithfully replicate the genomic DNA pieces. Additionally, since a little bit of
known sequence is needed to start the sequencing reaction, the reaction can be
primed off the known flanking viral DNA.
In order to read all the nucleotides of one organism, millions of individual clones are
sequenced. The data is sorted by computer, which compares the sequences of all
the small DNA pieces at once (in a "shotgun" approach) and places them in order by
virtue of their overlapping sequences to generate the full-length sequence of the
genome (Fig. 2). To statistically ensure that the whole genome sequence is acquired
by this method, an amount of DNA equal to five to ten times the length of the
genome must be sequenced. (Interactive concepts in biochemistry, Rodney Boyer,
Wiley, 2002, http://www.wiley.com//college/boyer/0470003790/)
29
Arrival of next-generation sequencing:
In two years we have gone from 0.2 terabases to
71 terabases (71,000 gigabases) (November 2010)
J. Pevsner,
http://www.bioinfbook.org/index.php
30
DDBJ/EMBL/GenBank accepts both complete and incomplete
genomes. Whole Genome Shotgun (WGS) sequencing
projects are incomplete genomes or incomplete chromosomes
that are being sequenced by a whole genome shotgun
strategy. WGS projects may be annotated, but annotation is
not required.
The pieces of a WGS project are the contigs (overlapping
reads), and they do not include any gaps. An AGP file can be
submitted to indicate how the contig sequences are assembled
together into scaffolds (contig sequences separated by gaps)
and/or chromosomes. We must have the contig sequences
without gaps as the basic units for all WGS projects.
31
 They include sets of primary data – DNA and protein sequences
 Protein sequences:
 PIR, http://pir.georgetown.edu/
 MIPS, http://www.mips.biochem.mpg.de
 SWISS-PROT, http://www.expasy.org/sprot/
Primary databases
32
 Standard nucleotide sequences acquired by high quality
sequencing
 Types of sequences in primary databases
 ESTs (Expressed Sequence Tags)
 HGTS (High Throughput Genome Sequencing)
- Results of sequencing projects without annotation
 Reference sequences of annotated genomes
 TPAs (Third Party Annotation)
- sequences annotated by third party (by someone else, not the orginal
authors)
Primary databases
33
GenBank (NCBI) http://www.ncbi.nlm.nih.gov/
Primary databases
34
Primary databases
35
Primary databases
36
Accession
number
Primary databases
37
Primary databases
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What is an accession number?
An accession number is label that used to identify a sequence. It is a string of letters
and/or numbers that corresponds to a molecular sequence.
Examples (all for retinol-binding protein, RBP4):
X02775 GenBank genomic DNA sequence
NT_030059 Genomic contig
Rs7079946 dbSNP (single nucleotide polymorphism)
N91759.1 An expressed sequence tag (1 of 170)
NM_006744 RefSeq DNA sequence (from a transcript)
NP_007635 RefSeq protein
AAC02945 GenBank protein
Q28369 SwissProt protein
1KT7 Protein Data Bank structure record
protein
DNA
RNA
Page 27
J. Pevsner,
http://www.bioinfbook.org/index.php
39
NCBI’s important RefSeq project:
best representative sequences
RefSeq (accessible via the main page of NCBI)
provides an expertly curated accession number that
corresponds to the most stable, agreed-upon “reference”
version of a sequence.
RefSeq identifiers include the following formats:
Complete genome NC_######
Complete chromosome NC_######
Genomic contig NT_######
mRNA (DNA format) NM_###### e.g. NM_006744
Protein NP_###### e.g. NP_006735
Page 27
J. Pevsner,
http://www.bioinfbook.org/index.php
40
RefSeq
41
Accession Molecule Method Note
AC_123456 Genomic Mixed Alternate complete genomic
AP_123456 Protein Mixed Protein products; alternate
NC_123456 Genomic Mixed Complete genomic molecules
NG_123456 Genomic Mixed Incomplete genomic regions
NM_123456 mRNA Mixed Transcript products; mRNA
NM_123456789 mRNA Mixed Transcript products; 9-digit
NP_123456 Protein Mixed Protein products;
NP_123456789 Protein Curation Protein products; 9-digit
NR_123456 RNA Mixed Non-coding transcripts
NT_123456 Genomic Automated Genomic assemblies
NW_123456 Genomic Automated Genomic assemblies
NZ_ABCD12345678 Genomic Automated Whole genome shotgun data
XM_123456 mRNA Automated Transcript products
XP_123456 Protein Automated Protein products
XR_123456 RNA Automated Transcript products
YP_123456 Protein Auto. & Curated Protein products
ZP_12345678 Protein Automated Protein products
NCBI’s RefSeq project: many accession number
formats for genomic, mRNA, protein sequences
J. Pevsner,
http://www.bioinfbook.org/index.php
42
Primary databases
43
Primary databases
44
 PROSITE, http://www.expasy.org/prosite/
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
Secondary databases
45
 PROSITE, http://www.expasy.org/prosite/
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
Secondary databases
46
 PROSITE, http://www.expasy.org/prosite/
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
Secondary databases
47
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
 PRINTS, http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/
Secondary databases
48
 TRANSFAC http://www.gene-regulation.com/
Secondary databases
Scaffold/Matrix Attached Region transaction Database
49
S/MARt DB (scaffold/matrix attached region transaction database). This database
collects information about S/MARs and the nuclear matrix proteins that are supposed
be involved in the interaction of these elements with the nuclear matrix.
http://transfac.gbf.de/SMARtDB/index.html)
 PDB http://www.rcsb.org/pdb/
Structural databases
50
 PDB http://www.rcsb.org/pdb/
Structural databases
51
 PDB http://www.rcsb.org/pdb/
Structural databases
Pekárová et al., Plant Journal (2011)
52
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
 GENOME resources
Outline
53
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome resources
54
Genome resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
55
Genome resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
56
Genome resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
57
Genome resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
58
Genome resources
 The Arabidopsis Information Resource (TAIR) http://www.arabidopsis.org
59
 TAIR, The Arabidopsis Information Resource, http://www.arabidopsis.org
Genome resources
60
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
 GENOME resources
 Analytical tools
 Homologies searching
Outline
61
 Global versus local alignment
 Global alignment: only for sequences, which are similar and of
a similar length (BUT can insert spaces into one or both
sequences)
 Local alignment provides identification and comparison even in
case of alignment of regions of sequences with high similarity,
e.g. even in case of change of order of protein domains during
evolution
Cvrčková, Úvod do praktické bioinformatiky
 Global alignment is used mainly in case of multiple alignment
(CLUSTALW, further in the presentation)
Analytical tools
62
 Choosing the right type of alignment using dotplot
 Plotting the sequences (x and y axis)
 Identification of identity in „dot“ of specific size (e.g. 2 bp)
 Filtering the diagonals of lengths lower than a treshold
Cvrčková, Úvod do praktické bioinformatiky
Analytical tools
63
 Examples of sequence alignment using dotplot
 Global alignment: possible only for sequences A and B
 The rest of the sequences underwent change of order of protein
domains and therefore it is neccessary to do a local alignment
 Dotplot can be obtained using BLAST2 (see further in the
presentation)
Cvrčková, Úvod do praktické bioinformatiky
Analytical tools
64
 BLAST http://ncbi.nlm.nih.gov/BLAST/
Analytical tools
65
 Word size: 10-11 bp or 2-3 aa
 Scoring the homology with matrices PAM (Point Accepted Mutation) or
BLOSUM (BLOcks Substitution Matrix)
 Primary similarities (seed matches)
 Expanding the homology regions to the left and to the
right
 Showing the results
MRKEV [delece]
MRKE [záměna]
MRKY [inzerce]
MRAKY M R . K E V
| | | :
M R A K Y
Matice PAM 250
Cvrčková, Úvod do praktické bioinformatiky
BLAST
Basic Local Alignment Search Tool
66
E= expectancy
value
 „expectancy value“ udává předpokládaný počet sekvencí se stejnou
nebo lepší podobnosti při vyhledávání ve stejně velké databázi
složené z náhodných sekvencí
 výsledek udává frakci totožných a u proteinů i podobných pozic,
příp. počet vložených mezer
BLAST
Basic Local Alignment Search Tool
67
Primary databases
68
BLINK is a link to the pre-computed BLAST search results for the respective sequence
(see the next slide).
BLAST
Basic Local Alignment Search Tool
69
 Searching according to source (organism) of sequences, e.g. known
genomes of microorganisms
 Currently there exists a lot of specialized versions of BLAST
 BLASTP
• Given the protein query, it returns the most similar protein
sequences from the protein database.
 BLASTN
• Given the DNA query, it returns the most similar DNA
sequences from the DNA database.
 BLASTX
• Compares the six-frame conceptual translation products of
a nucleotide query sequence (both strands) against a
protein sequence database.
• Other variants, e.g. MEGABLAST, for identification of
identical or very similar sequences (searches long similar
regions of nucleotide sequences)
BLAST
Specialized versions
70
 TBLASTN
• Compares a protein query against the all six reading
frames of a nucleotide sequence database.
 TBLASTX
• Translates the query nucleotide sequence in all six
possible frames and compares it against the six-frame
translations of a nucleotide sequence database.
 Currently there exists a lot of specialized versions of BLAST
BLAST
Specialized versions
71
 PSI-BLAST (Position-Specific Iterated Blast)
• For every alignment, PSI-BLAST creates so-called PSSM
(position specific substitution matrix)
• PSSM takes into account relative frequency of specific
aminoacid residue in a specific position within sequences
identified as similar in first step, which can mean functional
conservation.
• First step: standard BLAST, during which PSI-BLAST
identifies a list of similar sequences with E value better
than minimal value (standard = 0,005)
 Currently there exists a lot of specialized versions of BLAST
BLAST
Specialized versions
72
 PHI-BLAST (Pattern-Hit InitiatedBlast)
• Sequence of motif must be inserted using special syntax:
• [LVIMF] means either Leu, Val, Ile, Met or Phe
• For identification of specific sequence, e.g. motif (pattern)
in sequence of similar protein sequences
• - is spacer (means nothing)
• x(5) means 5 positions in which any residue is allowed
• x(3, 5) means 3 to 5 positions where any residue is allowed
BLAST
Specialized versions
 Currently there exists a lot of specialized versions of BLAST
73
 Example of search by PHI-BLAST
BLAST
Specialized versions
74
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
 GENOME resources
 Analytical tools
 Homologies searching
 Searching of sequence motifs, open reading frames, restriction sites…
Outline
75
 http://workbench.sdsc.edu/
Analytical tools
 http://workbench.sdsc.edu/
Analytical tools
77
 http://workbench.sdsc.edu/
Analytical tools
78
 http://workbench.sdsc.edu/
Analytical tools
79
 http://workbench.sdsc.edu/
Analytical tools
80
 http://workbench.sdsc.edu/
Analytical tools
81
 http://workbench.sdsc.edu/
Analytical tools
82
Analytical tools
 VPCR http://grup.cribi.unipd.it/cgi-bin/mateo/vpcr2.cgi
83
Analytical tools
 VPCR http://grup.cribi.unipd.it/cgi-bin/mateo/vpcr2.cgi
84
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
 PRIMARY, SECONDARY and STRUCURAL databases
 GENOME resources
 Analytical tools
 Homologies searching
 Searching of sequence motifs, open reading frames, restriction sites…
 Other on-line genome tools
Outline
85
 TIGR (The Institute for Genomic Research, http://www.tigr.org/software/)
 Recently part of the J. Craig Venter Institute
Other online genome resources
86
 Online Mendelian Inheritance in Man (OMIM)
Other online genome resources
87