The topic of this lecture is
Bioinformatics Tools for Assessing Genome
Variability of Animal Genetic Resources.
The lecture is part of Module 2,
Conservation and Sustainable
Utilization of Animal Genetic Resources.
The creation of this presentation was
supported by the Erasmus+ KA2 grant
as part of the project ISAGREED
Innovation of Content and Structure
of Study programs in the management of
animal genetics and food resources
using digitalization.
What is bioinformatics and what is its
importance? Bioinformatics is
an interdisciplinary field that combines
biology, computer science,
statistics, and mathematics to
analyze and interpret biological data
using computational tools and algorithms.
In animal genetics, it plays a crucial
role in obtaining and analyzing
extensive genomic data, such as
DNA sequences, to gain
insight into the genetic makeup of
biological process of animals.
In animal genetics, bioinformatics
helps identify and characterize
genes responsible for specific traits,
diseases, or abnormalities in animals,
for example. It can help identify gene
associated with poor color,
milk production, growth rate
or sustainability to disease.
It allows researchers to compare and
analyze genomes of different animal
species, understand their
evolutionary relationships and
identify common genetic elements.
This can provide insights into the
diversity and evolution of animal
species. Bioinformatics
helps in developing new breeding
strategies and improving breeding
practices. By analyzing
genetic data, it helps identify
animals with desirable traits for
breeding programs, improves traits
such as productivity, disease resistance,
or adaptability.
Bioinformatics supports the protection
and conservation of endangered animal
species by studying her
genomes and identifying
genetic markers for monitoring
populations, assessing genetic
diversity, and assessing
in captive breeding programs.
How are the data used for
bioinformatics analysis, internet
connection, computer
programs, and often data are needed.
Data such as DNA, RNA, or protein
sequences are used, also genomic,
transcriptomic, proteomic, metabolomic,
phylogenetics, or structural data.
Data for bioinformatics analysis are
initially uploaded to online
databases. There were about
2,000 databases available online in
January 2024.
The most significant sequences
databases are GenBank, ANA,
UniProt, and the genome database
Ensembl.
On servers, where the
databases are located,
there are tools for searching,
aligning, and analyzing bioinformatic
data. Pairwise sequence
alignment is used to identify
regions of similaritythat may
indicate functional, structural
and or evolutionary relationships
between two biological sequences,
proteins or nucleic acid.
Multiple sequence alignment, MSA,
is the alignment of three or more
biological sequences of similar length.
From the output of MSA applications,
homology can be inferred and the
evolutionary relationship between
sequences can be studied.
The basic tool for aligning two
sequences is BLAST, Basic
Local Alignment Search Tool, on a
server NCBI.
BLAST searches for areas of
similarity between biological sequences.
The program compares nucleotide or
protein sequencesThe sequence
databases and calculate
statistical significance. It is
possible to compare your own sequence
with database sequences in Genbank.
It is possible to compare specific 2
sequences. The primary
focus is on local alignment,
also available for global alignment.
Another tool for pairwise sequence
alignment is on the European
EMBL-ABI server, and it is
EMBOSS.
When do you use local or global
alignment? Local, using
Smith-Waterman algorithmis used
for more different, evolutionary
distant sequences. It is
limited to assigning unique segments
and stops where the sequence
diverge significantly.
Global alignment using
Niedelmann-Wunsch algorithm is the
most suitable for
sequences that are similar and
approximately the same length.
attempt to align sequences over their
entire length even at the cost of
introducing gaps into
one or both sequences.
We will demonstrate a modal alignment
process. We want to
determine which two sequences A
and B or C and D
are more similar to each other.
align the sequences over their entire
length. It is write
them into two rows placed below each
other so that identical positions,
bases or amino acids are aligned.
Each pair and null pair will be
assigned a value. For example,
1 for match and
0 for mismatch.
Both alignments show that the first pair
of sequences, A and B, have eight
match and two mismatch. And the
second pair of sequences, C and D,
have 17 match and three mismatch.
However, which pair of
sequences is more similar?
It is necessary to calculate normalized
similarity values, score. We
can compare the similarity of pairs of
sequences of different lengths,
multiply the number of matches by their
value 1, and add to
it the number of mismatches
multiplied by their value 0.
The normalized score is determined
by dividing the calculated value by the
length of the alignment. In our
case, the alignment of sequences
C and D has a higher score,
so they are more similar.
In another example, we
align two sequences of different lengths.
If we determine the score for
an alignment sequence, it would have a
value of 6.
After alignment, the score increases to
9. The score increases by
inserting gaps. Gaps
increase the number of aligned identical
residues.
There are many online tools for multiple
sequence alignment .
Among the oldest is Clustal, but
others have been gradually developed such
as MAFT, T-Cofee, MASL,
K-ALIGN, or COBALT.
Each was developed for different types of
sequences and their lengths.
The principle of MSA, aligning
three or more sequences, is similar to
that of BLAST, based on pairwise
alignment. However, the
calculations are more complex. This
can reveal mutations,
substitutions or insertion-deletions.
These comparisons are used to derive
evolutionary relationships through
phylogenetic analyses. It can highlight
homologous features between
sequences. The results may be a
phylogenetic tree expressing
evolutionary distances between sequences.
And thank you for your attention.