Bioinformatics is a multidisciplinary field that applies computational techniques to manage and analyze biological data, focusing on molecular sequences, structures, and functions. It plays a crucial role in areas such as drug design, DNA analysis, and agricultural biotechnology, utilizing tools like FASTA and BLAST for sequence similarity searches. The document also outlines the historical context and practical applications of bioinformatics in research.

2. bioinformatics
students educators
researchers institutions

3. What is Bioinformatics?
Conceptualizing biology in terms of molecules
and then applying “informatics” techniques
from math, computer science, and statistics
to understand and organize the
information associated with these
molecules on a large scale.

4. What is Bioinformatics?
Bioinformatics involves the technology that uses
computers for storage, retrieval, manipulation, and
distribution of information related to biological
macromolecules.

5. Definition of Bioinformatics
 A multidisciplinary subject.
‘…information technology applied to the management
and analysis of biological data’ (Attwood, T. K)

6. Bioinformatics aims to…
 Collect,
 Organise,
 Store,
 Retrieve,
 Analyse,
….biological data with the use of
computers.

7. Term Bioinformatics was invented by
Paulien Hogeweg and Ben Hesper in
1970
As "the study of informatic processes in
biotic systems".

8. Why do we use Bioinformatics?
 Store/retrieve biological information
(databases)
 Retrieve/compare gene sequences
 Predict function of unknown genes/proteins
 Search for previously known functions of a
gene
 Compare data with other researchers
 Compile/distribute data for other researchers

9. Subfields
The development of computational tools and
databases
The application of these tools and databases in
generating biological knowledge

10. Scope
Tools development:
 Writing software for sequence, structural, and
functional analysis
 Construction and curating of biological databases

11. Tools: Used in three areas
1. Molecular Sequence Analysis
2. Molecular Structural Analysis
3. Molecular Functional Analysis

13. Applications
Drug design
Forensic DNA
analysis
Agricultural
biotechnology

15. • Since Charles Darwin the idea of common origin of species
became widely accepted view, however the level of similarity on
molecular level between distant species remained unclear until
1970s and 1980s.
• At that time the fact that many DNA and particularly protein
molecules retain high similarity, hundreds of millions of years
after separation from the common ancestor, was established.
• This discovery as well as practical needs to search growing DB
lead to development of effective methods of similarity search.
• Two programs, which greatly facilitated the similarity search,
were developed FASTA (Pearson and Lipman 1988) and BLAST
(Altschul et al. 1990).
DB search for similar sequences

16. • The basic step in any similarity search is an alignment of two or
more sequences.
The search provides a list of DB sequences with which a
query sequence can be aligned.
•Then scoring procedure is implemented, which allows to
measure degree of similarity from 100% identity to a loose
similarity.
• A common reason for performing a DB search is to find a
related gene. A matched gene (or any other sequence) may
provide a clue as to function.
• An alternative task can be achieved when a sequence with
known function or role is used as a query for search in a species
genome.
• The search must be fast and sensitive enough.
Basics of similarity searches

17. FASTA
• FASTA is a program for rapid alignment of pairs of
protein and DNA sequences.
• Comparison of all nucleotides or amino acids is
not an option, even for powerful computers,
FASTA instead searches for matching sequence
patterns (“words”) called k-tuples. These patterns
comprise k consecutive matches in the compared
sequences.

18. The ktup value
The ktup (for k-tuples) value stands for the length of the word
used to search for identity.
The higher the ktup value, the less likely you will get a match
unless it is identical.
Lower ktups give a more slower, more sensitive search.
The higher the ktup value, the faster analysis.
FASTA ktups are shorter than BLAST words. 1-2 for
proteins and 4-6 for nucleic acids.

19. •Using k-tuples FASTA builds a local alignment.
• Finally FASTA scores this alignment and output
a list of sequences similar to a query in the
descending order.
FASTA
ATCGAACCTGGATCGTGGCCATCGAACCTGGATCGTGGCCATCGAACCTGGA
GGCGAACCCCTATCGTGGCGTTACCGCCTTATTGACGGCCATCGAACCTGGA
k = 6 k = 8 k = 14
k-tuples
gaps

20. FASTA Format
 A sequence in FASTA format begins with a single-line
description, followed by lines of sequence data.
 The description line is distinguished from the
sequence data by a greater-than (">") symbol in the
first column.
 It is recommended that all lines of text be shorter than
80 characters in length.

21. FASTA Format
 >gi|129295|sp|P01013|OVAX_CHICK GENE X PROTEIN (OVALBUMIN-RELATED)
QIKDLLVSSSTDLDTTLVLVNAIYFKGMWKTAFNAEDTREMPFHVTKQESKPVQMMCM
NNSFNVATLPAEKMKILELPFASGDLSMLVLLPDEVSDLERIEKTINFEKLTEWTNPNTME
KRRVKVYLPQMKIEEKYNLTSVLMALGMTDLFIPSANLTGISSAESLKISQAVHGAFMELSE
DGIEMAGSTGVIEDIKHSPESEQFRADHP FLFLIKHNPTNTIVYFGRYWSP

22. 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.

23.  Examples (all for retinol-binding protein, RBP4):
 X02775 GenBank genomic DNA sequence
 NM_006744 DNA sequence (from a transcript)
 AAC02945 GenBank protein record

24. • Basic Local Alignment Search Tool (BLAST)
•Altschul 1990
•developed as a new way to perform seq. similarity search.
• BLAST is faster than FASTA while being nearly as sensitive.
• The minimal “word” (k-tuple) length is slightly higher than
in FASTA, 3 for proteins and 11 for DNA.
BLAST

25. BLAST
 BLAST is more than a tool to view sequences aligned
with each other or to calculate percent homology, but
a program to locate regions of sequence similarity with
a view to comparing structure and function.

26. A Practical Example
 A researcher might start with a piece of DNA
rather than a literature citation.
 Here we will –
1. Search a DNA database using a piece of DNA
sequence.
2. Use the results of the search to identify relevant
literature.

27. The Experiment
1)Grow
plants
2) Extract
the DNA.
3) Amplify up
the desired
section of DNA.
4) Generate
sequence.

28. A DNA Sequence
>G08_CHEV11Fed.seq
GTCGACGCGCAAATGGTTCTATATCCATACCAATAGCAGTATCGTTGCCA
TTATCACGAATGGAATTAAGTAAAGTTTTCATTCTATCAATAGACTCTAA
AACCACATCCATGATATCTGGAGTTATTTTTAACTCGCCATGTCTTGCTT
TGTTTAAAACATCCTCCATGTGGTGAGTTAACTTTGTTAAAACATCAAAA
TTTAAGAAGCTTGATGATCCTTTAACCGTATGTGCAACACGGAAAATTCT
ATTTAATAATTCTAAATCTTCTGGATTTGATTCAAGCTCTACTAAATCAT
GGTCGATTTGCTCAACAAGCTCAAAAGCTTCAACCAAAAAGTCTTCAAGT
ATTTCTTGCATATCTTCCATATTTTACCCCTGTTCTTGAGATTGATGTTT
TTTAATAACCTTTGCAATTTCATTGAAGAAATCGCTAGCGTTAAATTTGA
CAAGATAGCCTTCTCCACCAGCTTCTTGAACACCTTTCTCATTCATAAAT
TCATTTGATAAAGATGAGTTAAAGACTATAGGAATATCTTTAAATCCGGG
ATCTTCTTTAATGCGTGCAGCGGATCCCGGGTACCTGCAGAATTCAGCTG
CGCCCTTTAGTTCCTAAAGGGTTTTTATCAGTGCGACAAACTGGGATTTT
ATTTATTCAGCAAGTCTTGTAATTCATCCAAAAAACGGCAAACATGAAAG
CCGTCACAAACGGCATGATGCACTTGAATCGATAAGGGAATATAGTATTT
TCCGCCCTCCTCATAATACTTCCCAAACGTAAATATCGGCAGTAGATAGT
The following sequence is in FASTA format.

29. A BLAST Search
 Lets see how to submit a sequence query to the
Genbank database.

30. BLAST Search Screen
Enter sequence.
Select database.
Select BLAST type.

31. BLAST Results

32. BLAST programs
 BLASTp: compares amino acid sequence
query to a protein sequence data base
 BLASTn: compares nucleotide sequence
query to a nucleotide sequence data base
 BLASTx: nucleotide sequence query is
translated and compared to a protein
sequence database