Sequence analysis involves subjecting DNA, RNA, or peptide sequences to analytical methods to understand their features, functions, structures, or evolution. The most basic sequence analysis is sequence alignment, which involves aligning two sequences and measuring their similarity to determine if they are related. Sequence alignment is important for biological inferences like predicting the function and structure of unknown proteins from similar sequences or determining evolutionary relationships between sequences from different species.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. The Needleman–Wunsch algorithm is still widely used for optimal global alignment, particularly when the quality of the global alignment is of the utmost importance.The algorithm essentially divides a large problem (e.g. the full sequence) into a series of smaller problems and uses the solutions to the smaller problems to reconstruct a solution to the larger problem. It is also sometimes referred to as the optimal matching algorithm and the global alignment technique.
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The following slides were prepared by POORNIMA M.S student of II M.Sc., Life Science Bangalore University, Bangalore
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. The Needleman–Wunsch algorithm is still widely used for optimal global alignment, particularly when the quality of the global alignment is of the utmost importance.The algorithm essentially divides a large problem (e.g. the full sequence) into a series of smaller problems and uses the solutions to the smaller problems to reconstruct a solution to the larger problem. It is also sometimes referred to as the optimal matching algorithm and the global alignment technique.
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The following slides were prepared by POORNIMA M.S student of II M.Sc., Life Science Bangalore University, Bangalore
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
1. Sequence Analysis
• Is the process of subjecting a DNA, RNA or
peptide sequence to any of a wide range of
analytical methods to understand its
features, function, structure, or evolution
• Is the process of subjecting a DNA, RNA or
peptide sequence to any of a wide range of
analytical methods to understand its
features, function, structure, or evolution
2. • Given two sequences, we can
– Measure their similarity
– Determine the residue-residue correspondences
– Observe patterns of conservation and variability
– Inter evolutionary relationships
• Given two sequences, we can
– Measure their similarity
– Determine the residue-residue correspondences
– Observe patterns of conservation and variability
– Inter evolutionary relationships
3. Bioinformatics
Sequence Analysis
• The most basic sequence analysis - whether two sequences
are related – sequence alignment. This involves
aligning two sequences
similarity in sequences
sequences are related similarity is by chance
• The most basic sequence analysis - whether two sequences
are related – sequence alignment. This involves
aligning two sequences
similarity in sequences
sequences are related similarity is by chance
4. Bioinformatics
• Is the most basic tool of bioinformatics.
• Sequence similarity must be quantified –
important to identify real similarity from
coincidence.
• Is the most basic tool of bioinformatics.
• Sequence similarity must be quantified –
important to identify real similarity from
coincidence.
5. Bioinformatics
• Finding similarity between sequences is important for
many biological inferences, like
•Finding similar proteins allows us to predict the
function and structure of the unknown protein.
•Similar sequences can come from two species which
share a common ancestor indicating their evolutionary
relationship.
• Locating similar subsequences in DNA allows us to
identify pockets of interest, such as regulatory
elements.etc
• Finding similarity between sequences is important for
many biological inferences, like
•Finding similar proteins allows us to predict the
function and structure of the unknown protein.
•Similar sequences can come from two species which
share a common ancestor indicating their evolutionary
relationship.
• Locating similar subsequences in DNA allows us to
identify pockets of interest, such as regulatory
elements.etc
6. Bioinformatics
• Pairwise sequence alignment
• Local and global alignment
• Multiple sequence alignment
•Clustal W
Sequence Alignment
• Pairwise sequence alignment
• Local and global alignment
• Multiple sequence alignment
•Clustal W
7. •The comparing of two sequences by searching for a series of
individual characters or patterns that are in the same order in
the sequences, ie, the identification of residue-residue
correspondences.
• Local and Global.
• Global alignment, attempts to align the entire sequence. If two
sequences have approximately the same length and are quite
similar, they are suitable for the global alignment.
• Local alignment finds stretches of sequences with high level
of matches.
Pairwise sequence alignment
•The comparing of two sequences by searching for a series of
individual characters or patterns that are in the same order in
the sequences, ie, the identification of residue-residue
correspondences.
• Local and Global.
• Global alignment, attempts to align the entire sequence. If two
sequences have approximately the same length and are quite
similar, they are suitable for the global alignment.
• Local alignment finds stretches of sequences with high level
of matches.
L G P S S K Q T G K G S - S R I W D N
Global alignment
L N - I T K S A G K G A I M R L G D A
- - - - - - - T G K G - - - - - - - -
Local alignment
- - - - - - - A G K G - - - - - - - -
8. Methods of sequence alignment
•Dot plot method
• Dynamic programming approach
• Smith-Waterman algorithm and Needleman-Wunsch
algorithm
•Heuristic methods / k-Tuple Method
• BLAST and FASTA
•Dot plot method
• Dynamic programming approach
• Smith-Waterman algorithm and Needleman-Wunsch
algorithm
•Heuristic methods / k-Tuple Method
• BLAST and FASTA
9. • A dot matrix analysis is a method for comparing two
sequences to look for possible alignment (Gibbs and
McIntyre 1970)
• One sequence (A) is listed across the top of the matrix and
the other (B) is listed down the left side
• Starting from the first character in B, one moves across the
page keeping in the first row and placing a dot in many
column where the character in A is the same
• The process is continued until all possible comparisons
between A and B are made
• Any region of similarity is revealed by a diagonal row
of dots
• Isolated dots not on diagonal represent random matches
Dot matrix analysis
• A dot matrix analysis is a method for comparing two
sequences to look for possible alignment (Gibbs and
McIntyre 1970)
• One sequence (A) is listed across the top of the matrix and
the other (B) is listed down the left side
• Starting from the first character in B, one moves across the
page keeping in the first row and placing a dot in many
column where the character in A is the same
• The process is continued until all possible comparisons
between A and B are made
• Any region of similarity is revealed by a diagonal row
of dots
• Isolated dots not on diagonal represent random matches
10. • Detection of matching regions can be improved by
filtering out random matches and this can be achieved
by using a sliding window
• It means that instead of comparing a single sequence
position more positions is compared at the same time
and, dot is printed only if a certain minimal number of
matches occur
• Dot matrix analysis can also be used to find direct and
inverted repeats within the sequences
Dot matrix analysis
• Detection of matching regions can be improved by
filtering out random matches and this can be achieved
by using a sliding window
• It means that instead of comparing a single sequence
position more positions is compared at the same time
and, dot is printed only if a certain minimal number of
matches occur
• Dot matrix analysis can also be used to find direct and
inverted repeats within the sequences
12. • Nucleic Acids Dot Plots of genes Adh1 and G6pd in the mouse
•http://arbl.cvmbs.colostate.edu/molkit/dnadot/index.html
Dot matrix analysis: two very different sequences
13. • Nucleic Acids Dot Plots of genes Adh1 from the mouse and rat (25 MY)
•http://arbl.cvmbs.colostate.edu/molkit/dnadot/index.html
Dot matrix analysis: two similar sequences
14. • Nucleic Acids Dot Plots of genes Adh1 from the mouse and rat (25 MY)
•http://arbl.cvmbs.colostate.edu/molkit/dnadot/index.html
Dot matrix analysis: two similar sequences sequences; size
of the sliding window increased
15. • Is a highly computationally demanding as well as intensive
method.
• It aligns two nucleotide/protein sequences, explores all possible
alignments and chooses the best alignment (high scoring
alignment) as the optimal alignment.
• Is based on alignment scores.
• It uses gaps to achieve the best alignment.
• Global alignment program is based on Needleman-Wunsch
algorithm and local alignment on Smith-Waterman. Both
algorithms are derivates from the basic dynamic programming
algorithm.
Dynamic programming algorithm for
sequence alignment
• Is a highly computationally demanding as well as intensive
method.
• It aligns two nucleotide/protein sequences, explores all possible
alignments and chooses the best alignment (high scoring
alignment) as the optimal alignment.
• Is based on alignment scores.
• It uses gaps to achieve the best alignment.
• Global alignment program is based on Needleman-Wunsch
algorithm and local alignment on Smith-Waterman. Both
algorithms are derivates from the basic dynamic programming
algorithm.
16. • How are alignments scored?
• Using scoring matrices
•They account for gaps, substitutions, insertions and
deletions.
•For nucleic acids, scoring is simple (only 4 characters are
present, and substitutions do not happen)
•Eg: the scoring scheme used by BioEdit
• How are alignments scored?
• Using scoring matrices
•They account for gaps, substitutions, insertions and
deletions.
•For nucleic acids, scoring is simple (only 4 characters are
present, and substitutions do not happen)
•Eg: the scoring scheme used by BioEdit
Variation Score
Match 2
Mismatch -1
Gap initiation -3
Extending gap by 1 -1
17. • For proteins , the scoring schemes are more complicated because
amino acid substitutions occur frequently, especially among
amino acids with similar physicochemical properties
• Eg: Alanine valine substitutions happen without
significant changes to the protein.
18. Scoring a sequence alignment with a gap
penalty
Sequence 1 V D S - C Y
Sequence 2 V E S L C Y
Score 4 2 4 -11 9 7 Score = sum of amino acid pair scores (26)
minus single gap penalty (11) = 15
As two sequences may differ, it is likely to have non-identical amino
acids placed in the corresponding positions. In order to optimise
the alignment gap(s) may be introduced, which may reflect losses
or insertions, which occurred in the past in the sequences.
Introduction of gaps causes penalties.
Scores gained by each match are not always the same, for instance
two rare amino acids will score more than two common.
19. Derivation of the dynamic programming algorithm
1. Score of new = Score of previous + Score of new
alignment alignment (A) aligned pair
V D S - C Y V D S - C Y
V E S L C Y V E S L C Y
15 = 8 + 7
2. Score of = Score of previous + Score of new
alignment (A) alignment (B) aligned pair
V D S - C V D S - C
V E S L C V E S L C
8 = -1 + 9
3. Repeat removing aligned pairs until end of alignments is reached
1. Score of new = Score of previous + Score of new
alignment alignment (A) aligned pair
V D S - C Y V D S - C Y
V E S L C Y V E S L C Y
15 = 8 + 7
2. Score of = Score of previous + Score of new
alignment (A) alignment (B) aligned pair
V D S - C V D S - C
V E S L C V E S L C
8 = -1 + 9
3. Repeat removing aligned pairs until end of alignments is reached
20. • Consider building this alignment in steps, starting from the initial match (V/V)
and then sequentially adding a new pair until the alignment is complete, at each
stage choosing a pair from all the possible matches that provides the highest
score for the alignment up to that point.
• If the full alignment has the highest possible (or optimal) score, then the old
alignment from which it was derived (A) by addition of the aligned Y/Y pair
must also have been optimal up to that point in the alignment.
• In this manner, the alignment can be traced back to the first aligned pair that
was also an optimal alignment.
• The example, which we have considered, illustrates 3 choices: 1. Match the
next character(s) in the following position(s); 2. Match the next character(s) to a
gap in the upper sequence; 3. Add a gap in the lower sequence.
Description of the dynamic programming algorithm
• Consider building this alignment in steps, starting from the initial match (V/V)
and then sequentially adding a new pair until the alignment is complete, at each
stage choosing a pair from all the possible matches that provides the highest
score for the alignment up to that point.
• If the full alignment has the highest possible (or optimal) score, then the old
alignment from which it was derived (A) by addition of the aligned Y/Y pair
must also have been optimal up to that point in the alignment.
• In this manner, the alignment can be traced back to the first aligned pair that
was also an optimal alignment.
• The example, which we have considered, illustrates 3 choices: 1. Match the
next character(s) in the following position(s); 2. Match the next character(s) to a
gap in the upper sequence; 3. Add a gap in the lower sequence.
21. • It is critical to have reasonable scoring schemes accepted by the scientific
community for DNA and proteins and for different types of alignments
• Matrices for DNA are rather similar as there are only two options purine &
pyrimidine and match & mismatch
• Proteins are much more complex and the number of option is significant
• PAM and BLOSUM matrices are the commonly used scoring matrices for
proteins.
• They are constructed by analysing the substitution frequencies seen in
alignments of known families of proteins.
• Identities are assigned high positive scores. Also some amino acids are
more abundant than others
• Frequently observed substitutions also get positive scores.
• Mismatches or matches that are unlikely to have been a result of
evolution are given negative scores.
Scoring matrices
• It is critical to have reasonable scoring schemes accepted by the scientific
community for DNA and proteins and for different types of alignments
• Matrices for DNA are rather similar as there are only two options purine &
pyrimidine and match & mismatch
• Proteins are much more complex and the number of option is significant
• PAM and BLOSUM matrices are the commonly used scoring matrices for
proteins.
• They are constructed by analysing the substitution frequencies seen in
alignments of known families of proteins.
• Identities are assigned high positive scores. Also some amino acids are
more abundant than others
• Frequently observed substitutions also get positive scores.
• Mismatches or matches that are unlikely to have been a result of
evolution are given negative scores.
22. • These scores form the matrix entries and are represented in log odds scores
• Odds score is the ratio of chance of amino acid substitution due to essential
biological reason to the chance of random substitution.
• PAM- (Point Accepted Mutation) matrix is derived from global alignments of
very similar sequences, so that an observed change will reflect one mutation
• An accepted point mutation is a replacement of one A.A by another,
accepted by natural selection
• There are many different PAMs, which represent different evolutionary
scenarios.
• BLOSUM (blocks substitution matrix ) –dvpd from regions of closely related
proteins that can be aligned without gaps. They calculated the ratio of
observed pairs at any position to the number expected from overall amino acid
frequency.
• Results in the form of log odds score.
• PAM is more suitable for studying quite distant proteins, BLOSUM is for
more conserved proteins of domains
Scoring matrices
• These scores form the matrix entries and are represented in log odds scores
• Odds score is the ratio of chance of amino acid substitution due to essential
biological reason to the chance of random substitution.
• PAM- (Point Accepted Mutation) matrix is derived from global alignments of
very similar sequences, so that an observed change will reflect one mutation
• An accepted point mutation is a replacement of one A.A by another,
accepted by natural selection
• There are many different PAMs, which represent different evolutionary
scenarios.
• BLOSUM (blocks substitution matrix ) –dvpd from regions of closely related
proteins that can be aligned without gaps. They calculated the ratio of
observed pairs at any position to the number expected from overall amino acid
frequency.
• Results in the form of log odds score.
• PAM is more suitable for studying quite distant proteins, BLOSUM is for
more conserved proteins of domains
23. • Gap penalties are subtracted from alignment scores to ensure algorithms
produce biologically sensible alignments without too many gaps
• Gap penalties may be:
• Constant – independent of the length of the gap
• Proportional – proportional to the length of the gap
• Affine – containing gap opening and gap extension contributions.
• Opening a gap should be strongly penalised than extending a gap.
Gap Penalty
• Gap penalties are subtracted from alignment scores to ensure algorithms
produce biologically sensible alignments without too many gaps
• Gap penalties may be:
• Constant – independent of the length of the gap
• Proportional – proportional to the length of the gap
• Affine – containing gap opening and gap extension contributions.
• Opening a gap should be strongly penalised than extending a gap.
24. Scoring matrices: PAM (Percent Accepted Mutation)
Amino acids are grouped according to the chemistry of the side group: (C) sulfhydryl, (STPAG)-small
hydrophilic, (NDEQ) acid, acid amide and hydrophilic, (HRK) basic, (MILV) small hydrophobic, and
(FYW) aromatic. Log odds values: +10 means that ancestor probability is greater, 0 means that the
probability are equal, -4 means that the change is random. Thus the probability of alignment YY/YY is
10+10=20, whereas YY/TP is –3-5=-8, a rare and unexpected between homologous sequences.
25. Scoring matrices: BLOSUM62
(BLOcks amino acid SUbstitution Matrices)
Ideology of BLOSUM is similar but it is calculated from a very different and much larger set
of proteins, which are much more similar and create blocks of proteins with a similar pattern
26. Alignment A: a1 a2 a3 a4
b1 b2 b3 b4
Alignment B: a1 a2 a3 a4 -
b1 - b2 b3 b4
Alignment A: a1 a2 a3 a4
b1 b2 b3 b4
Alignment B: a1 a2 a3 a4 -
b1 - b2 b3 b4
The highest scoring matrix position
is located (in this case s44) and then
traced back as far as possible,
generating the path shown