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.
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.
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.
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.
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
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.
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
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.
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
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
International Journal of Computer Science, Engineering and Information Techno...IJCSEIT Journal
In the field of proteomics because of more data is added, the computational methods need to be more
efficient. The part of molecular sequences is functionally more important to the molecule which is more
resistant to change. To ensure the reliability of sequence alignment, comparative approaches are used. The
problem of multiple sequence alignment is a proposition of evolutionary history. For each column in the
alignment, the explicit homologous correspondence of each individual sequence position is established. The
different pair-wise sequence alignment methods are elaborated in the present work. But these methods are
only used for aligning the limited number of sequences having small sequence length. For aligning
sequences based on the local alignment with consensus sequences, a new method is introduced. From NCBI
databank triticum wheat varieties are loaded. Phylogenetic trees are constructed for divided parts of
dataset. A single new tree is constructed from previous generated trees using advanced pruning technique.
Then, the closely related sequences are extracted by applying threshold conditions and by using shift
operations in the both directions optimal sequence alignment is obtained.
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
The Use of K-mer Minimizers to Identify Bacterium Genomes in High Throughput ...Mackenna Galicia
Bioinformatics combines the elements of biology, computer science, and statistics to work with genome sequencing. My project utilizes a sequence analysis technique, k-mer minimizers, to identify bacterium from a shotgun genomic DNA sample. We used the algorithm Bevel to compare DNA sequences against standardized reference genomes in the PATRIC whole genome bacterial database. Bevel is a sequence similarity tool that uses a minimizer database. Minimizers are representative k-mers, subsequences of length k observed to have the minimum hash value across a genomic region and are therefore unique and comparable to that genomic region. The two databases are queried against each other, resulting in a list of positions where two or more sequences match. I am developing two Python applications that first, process the results of the algorithm and secondly, return a score that enable the ranking of bacterium matches. The higher the score, the better the match between the unknown bacteria and the standardized reference genome. The goal of this experiment is to show that minimizers are a fast mean of characterizing bacterial shotgun assembly contigs.
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
Clustal omega is a widely used bioinformatics tool for performing multiple sequence alignment. This ppt contains the concept and types of sequence alignment, algorithms followed by clustal omega, its result interpretation and applications.
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.
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.
(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.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
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.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
2. SEQUENCE ALIGNMENT
It is the way of arranging the
sequence of DNA, RNA, Protein to
identify regions of similarity that may
be a consequence of functional,
structural, or evolutionary relationship
between the sequence.
3. Global Alignment
In global alignment, two sequences to be
aligned are assumed to be generally
similar over their entire length.
Alignment is carried out from beginning
to end of both sequences to find the best
possible alignment across the entire
length between the two sequences.
This method is more applicable for
aligning two closely related sequences of
roughly the same length.
4.
5. Local Alignment
Local alignment, on the other hand, does not
assume that the two sequences in question
have similarity over the entire length.
It only finds local regions with the highest level
of similarity between the two sequences and
aligns these regions without regard for the
alignment of the rest of the sequence regions.
This approach can be used for aligning more
divergent sequences with the goal of searching
for conserved patterns in DNA or protein
sequences. The two sequences to be aligned
can be of different lengths.
6.
7. •It is simplest method of alignment.
•In pairwise alignment sequence there is a
aligning of two sequences.
•It is used in structural, functional and
evolutionary analysis of sequence.
•By pairwise alignment high accuracy result
is obtained.
•It is also used to identify homologous
sequence.
Advantage of Pairwise
alignment
8. Disadvantage of pairwise
alignment
•It is not useful when we align more
than two sequence.
•Pairwise alignment is difficult if we use
long sequences for alignment.
9. •It is also known as the dot plot method.
•It is a graphical way of comparison two
sequence in a two dimensional matrix.
•In a dot matrix two sequences to be
compared are written in the horizontal and
vertical axis of the matrix.
•The comparison is done by scanning each
residue of one sequence for similarity with
all residue in the other sequence.
DOT MATRIX METHOD
11. DYNAMIC PROGRAMING
METHOD
It is the method that determines optimal
alignment by matching two sequence for
all possible pair of character between the
two sequence.
It is similar to dot matrix as,it finds
alignment in a more quantitative way by
converting a dot matrix into scoring
matrix
13. MULTIPLE SEQUENCE ALIGNMENT
•It is a sequence alignment of three or more
biological sequence, generally protein, DNA, or
RNA.
•MSAs require more sophisticated methodologies
than pairwise alignment because they are more
computational complex.
•Most multiple sequence alignment program use
heuristic methods rather than global optimization.
• Because identifying the optimal alignment
between more than a few sequence of moderate
length is prohibitively computational expensive.
14.
15.
16. Advantage of multiple sequence
alignment
•MSA is used for comparing more
than two sequences.
•It is used to identify homologous
residue within sequence.
•To find out identical sequence.
17. Disadvantage of multiple
sequence alignment
•It is more complex method as
compare to pairwise allignment.
•It is more time consuming.
•Due to gap within the sequence it
show error.
•Low accuracy as compare to pairwise
sequence allignment.
18. Online tool for sequence
alignment
There are following online tool for
sequence alignment.
•BLAST
•FASTA
•CLUSTAL OMEGA
19. BASIC STEPS PERFORMED IN BLAST
Open NCBI SITE
All data bases (choosed gene )
Enter the name of gene(thyroid peroxidase)
Click on search
Get list of search result
Get the gene I.D and location
Click on FASTA
Obtained FASTA format and NCBI reference sequence
Run BLAST
34. It is the most commonly used approach
to multiple sequence alignment.
It speeds up the alignment of multiple
sequence through a multistep process.
It first conducts pairwise alignment for
each possible pair of sequences using
the Needleman-Wunsch alignment and
record these similarity scores from the
pairwise comparison.
PROGRESSIVE ALIGNMENT
35. •The scores are then converted into
evolutionary distances to generate a
distance matrix for all the sequence
involved.
•As a result,a phylogenetic tree is
generated using the neighbor-joining
method.
•In the next step,the closest sequence
based on guide tree is aligned with the
consensus sequence using dynamic
programming.
36.
37. •It is based on the idea that an optimal
solution can be found by repeatedly
modifying existing suboptimal solution.
•The procedure starts by producing a low
quality alignment and gradually improves
it by iterative realignment through well
defined procedures until no more
improvement in the alignment can be
achieved.
ITERATION ALIGNMENT
38.
39. It performs multiple alignment through
two sets of iteration.
1.Outer iteration=In this an initial
random alignment is generated that is
used to derive a UPGMA tree
2.Inner iteration=In this the sequence
are randomly divided into two groups
The process is repeated over
many cycles until there is no further
improvement in the overall
alignment scores.
40. •If a residue match is found, a dot is
placed within the graph.
•Otherwise, the matrix position are left
bank.
•When the two sequences have
substantial regions of similarity, many
dots line up to form contiguous diagonal
lines, which reveal the sequence
alignment.
•If there are interruptions in the middle of
a diagonal line, they indicate insertion or
deletion
