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 Genome Tools
Outline
Course Syllabus
 Chapter 01
 Introduction into Bioinformatics
 Chapter 02
 Identification of Genes
 Chapter 03
 Reverse Genetics Approaches
 Chapter 04
 Forward Genetics Approaches
Course Syllabus
 Chapter 05
 Functional Genomics Approaches
 Chapter 06
 Protein-Protein Interactions And Their Analysis
 Chapter 07
 Current Methods of DNA Sequencing
 Chapter 08
 Structure of genomes
Course Syllabus
 Chapter 09
 Genome evolution
 Chapter 10
 Genomics and Systems Biology
 Chapter 11
 Practical Aspects Of Functional Genomics
 Model Organisms,
 PCR and Primer Design
 Literature resources for Chapter 01:
 Bioinformatics and Functional Genomics, 3rd
Edition, Jonathan Pevsner, Wiley-Blackwell, 2015
http://www.bioinfbook.org/php/?q=book3
 Úvod do praktické bioinformatiky, Fatima
Cvrčková, 2006, Academia, Praha
 Plant Functional Genomics, ed. Erich
Grotewold, 2003, Humana Press, Totowa, New
Jersey
Literature
 Syllabus of thecourse
 Definition of Genomics
Outline
 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
 Necessary prerequisite: knowledge of the genome
(sequence) – work with databases
GENOMICS – What is it?
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
• Syllabus of this course
• Definition of genomics
• Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
Outline
 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
• Interface between the biology and computers
• Analysis of proteins, genes and genomes
using computer algorithms and 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
 Bioinformatics in functional genomics
 Processing and analysis of sequencing data
 Identification of reference sequences
 Identification of genes
 Identification of homologues, orthologues and paralogues
 Correlative 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 regulatory networks
Bioinformatics
 Syllabus of this course
 Definition of genomics
 Role of BIOINFORMATICS in FUNCTIONAL GENOMICS
 Databases
 Spectre of „on-line“ resources
Outline
Spectre of on-line Resources
 EBI http://www.ebi.ac.uk/services
Spectre of on-line Resources
 NCBI http://www.ncbi.nlm.nih.gov/
Spectre of on-line Resources
 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
 EMBL
 http://www.ebi.ac.uk/embl/
 GenBank,
 http://www.ncbi.nih.gov/Genbank/GenbankSearch.html
 DDBJ,
 http://www.ddbj.nig.ac.jp
 Include primary datasets – 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
Growth of GenBank
Year
BasepairsofDNA(millions)
Sequences(millions)
1982 1986 1990 1994 1998 2002
J. Pevsner, 
http://www.bioinfbook.org/index.php
Growth of GenBank + Whole Genome Shotgun
(1982-November 2008): we reached 0.2 terabases
Numberofsequences
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
Growth of GenBank
Feb 15 2013
WGS
Interactive concepts in biochemistry, Rodney Boyer, Wiley, 
2002, http://www.wiley.com//college/boyer/0470003790/
Growth of DNA Sequence in Repositories
Year
J. Pevsner, 
http://www.bioinfbook.org/index.php
Growth of DNA Sequence in Repositories
B&FG 3e
Fig. 2-3
Page 22 Year
A vast amount of sequence data has been
generated using next-generation sequencing.
Growth of DNA Sequence in Repositories
B&FG 3e
Fig. 2-3
Page 22 Year
Perhaps 40 petabases (corresponding
to 10 mil. human genomes) of DNA
were generated in calendar year 2014
at major sequencing centers.
 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
 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
GenBank (NCBI) http://www.ncbi.nlm.nih.gov/
Primary Databases
Primary Databases
Primary Databases
Accession
number
Primary Databases
Primary Databases
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
J. Pevsner, 
http://www.bioinfbook.org/index.php
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
J. Pevsner, 
http://www.bioinfbook.org/index.php
RefSeq
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
Primary Databases
Primary Databases
 PROSITE, http://www.expasy.org/prosite/
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
Secondary Databases
 PROSITE, http://www.expasy.org/prosite/
Secondary Databases
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
 PROSITE, http://www.expasy.org/prosite/
Secondary Databases
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
 PRINTS, http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/
Secondary Databases
 Databases of functional or structural motifs, acquired by primary data
(sequences) comparison
 TRANSFAC http://www.gene-regulation.com/
Secondary Databases
Scaffold/Matrix Attached Region transaction Database
 PDB http://www.rcsb.org/pdb/
Structural Databases
 PDB http://www.rcsb.org/pdb/
Structural Databases
 PDB http://www.rcsb.org/pdb/
Structural Databases
Pekárová et al., Plant Journal (2011)
 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
Outline
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome Resources
Genome Resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome Resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome Resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome Resources
 Human Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway
Genome Resources
 The Arabidopsis Information Resource (TAIR) http://www.arabidopsis.org
 TAIR, The Arabidopsis Information Resource, http://www.arabidopsis.org
Genome Resources
 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
 Homology Searching
Outline
 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
 Choosing the right type of alignment using dotplot
 Plotting the sequences against each other (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
 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
 BLAST http://ncbi.nlm.nih.gov/BLAST/
Analytical Tools
 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
E= expectancy
value
 „expectancy value“ provides the number of expected sequence
number with the same or higher similarity whe searching in the
database consisiting of randomly assembled sequences
 the results shows fraction of identical and in case of proteins also
similar sequence positions and/or inserted spaces
BLAST
Basic Local Alignment Search Tool
Primary Databases
BLAST
Basic Local Alignment Search Tool
 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 all possible six-frame 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
 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
 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 exist a lot of specialized versions of BLAST
BLAST
Specialized Versions
 PHI-BLAST (Pattern-Hit Initiated BLAST)
• 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
 Currently there exists a lot of specialized versions of BLAST
BLAST
Specialized Versions
 Example of search by PHI-BLAST
BLAST
Specialized Versions
 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…
Outline
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 http://workbench.sdsc.edu/
Analytical Tools
 VPCR http://grup.cribi.unipd.it/cgi-bin/mateo/vpcr2.cgi
Analytical Tools
 VPCR http://grup.cribi.unipd.it/cgi-bin/mateo/vpcr2.cgi
Analytical Tools
 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 Genome Tools
Outline
 TIGR (The Institute for Genomic Research, http://www.tigr.org/software/)
 Recently part of the J. Craig Venter Institute
Other On-Line Genome
Resources
 Online Mendelian Inheritance in Man (OMIM)
Other On-Line Genome
Resources
 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 Genome Tools
Summary
Discussion