This document discusses multiple sequence alignment techniques. It begins with definitions of key terms like homology, similarity, and conservation. It then describes pairwise alignment and its applications. The rest of the document focuses on multiple sequence alignment methods like progressive alignment, iterative refinement, tree alignment, star alignment, and using genetic algorithms. It provides examples and explanations of popular multiple sequence alignment tools like Clustal W and T-Coffee.
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
INTRODUCTION.
NCBI.
EMBL.
DDBJ.
CONCLUSION.
REFERENSE.
The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health.
The NCBI is located in Bethesda, Maryland and was founded in 1988 through legislation sponsored by Senator Claude Pepper.
The NCBI houses a series of databases relevant to biotechnology and biomedicine. Major databases include GenBank for DNA sequences and PubMed, a bibliographic database for the biomedical literature.
All these databases are available online through the Entrez search engine.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Sequence homology search and multiple sequence alignment(1)AnkitTiwari354
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
INTRODUCTION.
NCBI.
EMBL.
DDBJ.
CONCLUSION.
REFERENSE.
The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health.
The NCBI is located in Bethesda, Maryland and was founded in 1988 through legislation sponsored by Senator Claude Pepper.
The NCBI houses a series of databases relevant to biotechnology and biomedicine. Major databases include GenBank for DNA sequences and PubMed, a bibliographic database for the biomedical literature.
All these databases are available online through the Entrez search engine.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Sequence homology search and multiple sequence alignment(1)AnkitTiwari354
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...journal ijrtem
process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...IJRTEMJOURNAL
BLAST is most popular sequence alignment tool used to align bioinformatics patterns. It uses
local alignment process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
This paper presents a literature survey conducted for research oriented developments made till. The significance of this paper would be to provide a deep rooted understanding and knowledge transfer regarding existing approaches for gene sequencing and alignments using Smith Waterman algorithms and their respective strengths and weaknesses. In order to develop or perform any quality research it is always advised to conduct research goal oriented literature survey that could facilitate an in depth understanding of research work and an objective can be formulated on the basis of gaps existing between present requirements and existing approaches. Gene sequencing problems are one of the predominant issues for researchers to come up with optimized system model that could facilitate optimum processing and efficiency without introducing overheads in terms of memory and time. This research is oriented towards developing such kind of system while taking into consideration of dynamic programming approach called Smith Waterman algorithm in its enhanced form decorated with other supporting and optimized techniques. This paper provides an introduction oriented knowledge transfer so as to provide a brief introduction of research domain, research gap and motivations, objective formulated and proposed systems to accomplish ultimate objectives.
Describes about the major neurodegenerative disorders such as Dementia,Alzhimers disease,Parkinsons disease,Amyotrophic lateral sclerosis,etc.Their causes,symptoms and preventative measures.
Describes about the importance of vitamins in our daily activities , classification of vitamins,various sources of vitamins and also about the problems which occurs due to the deficiency of vitamins.
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.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
(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.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
3. Terminology
Homology - Two (or more) sequences have a common
ancestor
Similarity - Two sequences are similar, by some
criterias. It does not refer to any evolutionary process,
just to a comparison of the sequences by some method
Conservation - Changes at a specific position of an
amino acid in a sequence that preserve the physico-
chemical properties
4.
5. Gaps
Positions at which a letter is paired with a
null are called gaps
Gap scores are typically negative
Since a single mutational event may cause
the insertion or deletion of more than one
residue
which may led to the fomation of Gaps
6.
7. Identity-The extent to which two (nucleotide
or amino acid) sequences are invariant.
Motif- The biological factor which is used as a
model for studies.it may be a functional or
structural domain, active site, phosphorylation
site etc.
Profile- A quantitative motif description -
assigns a degree of similarity to a potential
match
8.
9. Pairwise alignment
The process of lining up two or more
sequences.
Inorder to achieve ,maximal level of
identity
For the purpose of assessing the degree of
similarity and the possibility of homology.
10.
11. It is used to find, whether the two proteins,or
nucleic acids are related structurally or
functionally.
It is used to identify, domains or motifs that
are shared between proteins.
It is the basis of BLAST searching .
It is used in the analysis of genomes.
12. In Pairwise alignment protein sequences
can be more informative than DNA.
protein is more informative: because
many amino acids share related
biophysical properties.
13. UniProt
UniProt is a comprehensive, high-quality and
freely accessible database of protein sequence
and functional information.
Many entries being derived from genome
sequencing projects.
It contains a large amount of information about
the biological function of proteins derived
from the research literature.
14. Dot plot
In Bioinformatics a Dot plot is a graphical
method that allows the comparison of
two biological sequences.
Which,identify regions of close similarity
between them. It is a kind of recurrence plot.
15. These were introduced by Gibbs and McIntyre in
1970.
They are two-dimensional matrices that have the
sequences of the proteins being compared along the
vertical and horizontal axes.
Individual cells in the matrix can be shaded black if
residues are identical so that matching sequence
segments appear as runs of diagonal lines across the
matrix.
16. A dot plot of a human zinc finger transcription factor.showing
regional self-similarity. The main diagonal represents the sequence's
alignmentwith itself; lines off the main diagonal represent similar or
repetitive patterns within the sequence.
17.
18. A Multiple Sequence Alignment (MSA) is a
basic tool for the sequence alignment of two or
more biological sequences.
Generally Protein, DNA, or RNA.
In many cases, the input set of query sequences
are assumed to have an evolutionary
relationship.
By which they share a lineage and are
descended from a common ancestor.
19. Compare all sequences pairwise.
Perform cluster analysis on the pairwise data to
generate a hierarchy for alignment.
This may be in the form of a binary tree or a
simple ordering.
20. Build the Multiple Alignment by first aligning the
most similar pair of sequences.
Then the next most similar pair and so on.
Once an alignment of two sequences has been
made, then this is fixed.
Thus for a set of sequences A, B, C, D having
aligned .
A with C and B with D the alignment of A, B, C,
D is obtained by comparing the alignments of A
and C with that of B and D using averaged scores
at each aligned position.
21. An example of Multiple Alignment
• VTISCTGSSSNIGAG-NHVKWYQQLPG
• VTISCTGTSSNIGS--ITVNWYQQLPG
• LRLSCSSSGFIFSS--YAMYWVRQAPG
• LSLTCTVSGTSFDD--YYSTWVRQPPG
• PEVTCVVVDVSHEDPQVKFNWYVDG--
• ATLVCLISDFYPGA--VTVAWKADS--
• AALGCLVKDYFPEP--VTVSWNSG---
• VSLTCLVKGFYPSD--IAVEWWSNG--
22. Applications of MSA
Detecting similarities between sequences(closely or
distinctly related).
Detecting conserved regions or motifs in sequences.
Detection of structural homologies.
Thus, assisting the improved prediction of secondary
and teritiary structures of proteins.
23. Making patterns or profiles that can be further
used to predict new sequences falling in a given
family.
Inferring evolutionary trees or linkages.
25. Progressive Alignment Method
The most widely used approach to multiple sequence
alignments
Also known as the Hierarchical or Tree method
Developed by Paulien Hogeweg and Ben Hesper in
1984.
Progressive alignment builds up a final MSA by
combining pairwise alignments beginning with the
most similar pair and progressing to the most
distantly related.
26. All progressive alignment methods require two
stages.
First stage in which the relationships between the
sequences are represented as a tree, called a guide
tree.
Second step in which the MSA is built by adding
the sequences sequentially to the growing MSA
according to the guide tree.
28. Clustal W
The Clustal series of programs are widely used in
molecular biology
For the multiple alignment of both nucleic acid and
protein sequences and for preparing phylogenetic trees.
Works by progressive alignment: it aligns a pair of
sequences then aligns the next one onto the first pair.
Most closely related sequences are aligned first, and then
additional sequences and groups of sequences are added,
guided by the initial alignments
29. Uses alignment scores to produce a phylogenetic
tree.
Aligns the sequences sequentially, guided by the
phylogenetic relationships indicated by the tree.
30. T-Coffee
T-Coffee
(Tree based Consistency Objective Function For
alignment Evaluation)
It has advanced features to evaluate the quality of the
alignments
It produces alignment in the aln format (Clustal)
But can also produce PIR, MSF, and FASTA format.
The most common input formats are supported
(FASTA, PIR)
31. A set of methods to produce MSAs while reducing the errors
inherent in progressive methods are classified as "iterative"
They work similarly to progressive methods but repeatedly
realign the initial sequences as well as adding new sequences
to the growing MSA
Barton and Sternberg formulated this method for MSA.
Different iterative methods used in Bioinformatics are of
DIALGIN,MUSCLE(multiple sequence alignment by log-
expectation),etc
32. TREE ALIGNMENT
In computational phylogenetics, Tree Alignment is
used to analyse a set of sequences with evolutionary
relationship using a fixed tree.
Essentially,Tree Alignment is an algorithm for
optimizing phylogenetic tree
To be specific, phylogenetic tree shows an
evolutionary relationship between different species
and taxa joined together are assumed to have the
same ancestor.
33. In MSA ,DNA,RNA, and proteins sequences are
usually generated and they are assumed to have
evolutionary relationship .
Generally ,heuristic algorithm and tree alignment
graph are also adopted to solve multiple sequence
alignment problems.
34. Tree Alignment Graph
Roughly ,tree alignment graph aims to align
trees into a graph.
And finally synthesis them to develop statistics.
35. TAG is a combination of a set of aligning trees.
It can store conflicting hypotheses evolutionary
relationship and synthesize the source trees to develop
evolutionary hypotheses.
Therefore ,it is a basic method to solve other
alignment problems.
37. STAR ALIGNMENT
Star phylogeny is an another form of Tree Alignment
The Star Alignment is also used to analyse a set of
sequences with evolutionary relationship using a fixed
Star .
Instead of score , Star algorithm uses the cost notation .
38.
39. An algorithm is a set of instructions that is
repeated to solve a problem.
A genetic algorithm conceptually follows steps
inspired by the biological processes of
evolution.
Genetic Algorithms follow the idea of
SURVIVAL OF THE FITTEST.
Originally developed by John Holland (1975)
40. Also known as Evolutionary Algorithms, genetic
algorithms demonstrate self organization and
adaptation similar to the way that the fittest
biological organism survive and reproduce.
Generally applied to spaces which are too large.
The method learns by producing offspring that are
better and better as measured by a fitness function.
41. Steps in Simple GA
Initialize population.
Evaluate population.
Select parents for reproduction.
Perform crossover and mutation.
Evaluate offspring.
42. Outline of the Basic Genetic
Algorithm
Selection-Select two parent chromosomes
from a population according to their fitness.
Crossover-With a crossover probability cross
over the parents to form a new offspring.
Mutation-With a mutation probability mutate
new offspring at each locus (position in
chromosome).
43. Accepting -Place new offspring in a new
population
Replace-Use new generated population
for a further run of algorithm
Test-If the end condition is satisfied, stop
and return the best solution in current
population.
