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 .
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 .
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.
In bioinformatics and biochemistry, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes. The format also allows for sequence names and comments to precede the sequences.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
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 sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
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.
In bioinformatics and biochemistry, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes. The format also allows for sequence names and comments to precede the sequences.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
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 sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
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.
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
In silico drugs analogue design: novobiocin analogues.pptx
BLAST : features, types,algorithm, working etc.
1. BLAST
(Basic Local Alignment Search Tool)
• Developed by Steven Altschul and Samuel
Karlin in 1990.
• Compares nucleotide/aminoacid sequences
• Is a heuristic method.
• Is a fast but approximate method of alignment.
• Locates local alignments/short matches called
words
2. Uses of BLAST:
• Search a database for sequences similar
to an input sequence.
• Identify previously characterized
sequences.
• Find phylogenetically related sequences.
• Identify possible functions based on
similarities to known sequences.
4. Types of BLAST:
blastp: compares a protein sequence against a protein
sequence database.
blastn: compares a nucleotide sequence against a
nucleotide sequence database.
blastx: compares a six frame translation of a
nucleotide sequence against a protein database
tblastn: compares a protein sequence against a six
frame translation of a nucleotide database
tblastx: compares a six frame translation of a
nucleotide sequence against a six frame translation of
a nucleotide database.
5. How BLAST works
•Blast searches begin with a query sequence that
will be matched against sequence databases
specified by the user.
•Begins by breaking down the query sequence
into a series of short overlapping “words”
•Default word size for BLAST N is 28 nucleotides
•Default word size for BLAST P is 3 amino acids
•Results obtained depend on the scoring matrix
used.
•BLOSUM 62 matrix is the default scoring matrix
for BLASTP
7. The BLASTP algorithm
• Query sequence is broken into all possible 3-letter
words using a moving window
• Numerical score is calculated for each word by adding
up the values for the amino acids from the BLOSUM62
matrix
• Words with a score of 12 or more are collected into the
initial BLASTP search set.
• The search set is broadened by adding synonyms that
differ from the words at one position.
• Only synonyms with scores above a threshold value are
added to the search set. NCBI BLASTP uses a default
threshold of 10 for synonyms
8.
9. Contd….
Using this search set, BLAST scans a database
and identifies word hits/matches that score
above the threshold.
These short matches serve as seeds. The BLAST
algorithm attempts to extend the match in the
immediate sequence neighborhood
BLAST keeps a running raw score, using scoring
matrices, as it extends the matches. Each new
amino acid either increases or decreases the raw
score
Penalties are assigned for mismatches and for
gaps between the two alignments.
10. • In the NCBI default settings, a gap brings an initial
penalty of 11, which increases by 1 for each missing
amino acid.
• Once the score falls below a set level, the
alignment ceases and blast stops trying to extend
the alignment.
• An extended sequence alignment that was initially
seeded by a word hit is produced -called an hsp, or
high-scoring segment pair
Contd….
11. Contd….
All HSPs that have a cumulative score above the
threshold score are reported in BLAST results.
Raw scores are then converted into bit scores by
correcting for the scoring matrix used
12.
13. The Blast output
Includes a table with the bit scores (S) for each alignment and its E-
value, or “expect score”
the score (S) is a measure of the quality of an alignment (calculated as
the sum of substitution and gap scores for each aligned residue)
E-value (E), or expectation value is a measure of the significance of the
alignment. The E-value is the number of different alignments, with
scores equivalent to or better than S, that are expected to occur in a
database search by chance.
The lower the E-value, the more significant the alignment result.
Alignments with the highest bit scores and lowest E-values are listed at
the top of the table.
15. The query sequence - numbered red bar at the top of the figure. Database hits are
shown aligned to the query, below the red bar. Of the aligned sequences, the most
similar are shown closest to the query. In this case, there are three high scoring
database matches that align to most of the query sequence. The next twelve bars
represent lower-scoring matches that align to two regions of the query, from about
residues 3–60 and residues 220–500. The cross-hatched parts of the these bars
indicate that the two regions of similarity are on the same protein, but that this
intervening region does not match. The remaining bars show lower-scoring
alignments. Mousing over the bars displays the definition line for that sequence to
be shown in the window above the graphic.
16. One-line descriptions in the BLAST report
Each line is composed of four fields: (a) the gi number, database designation, accession
number, and locus name for the matched sequence, separated by vertical bars (appendix 1);
(b) a brief textual description of the sequence, the definition. This usually includes
information on the organism from which the sequence was derived, the type of sequence
(e.G., mRNA or DNA), and some information about function or phenotype. The definition
line is often truncated in the one-line descriptions to keep the display compact; (c) the
alignment score in bits. Higher scoring hits are found at the top of the list; and (d) the e-
value, which provides an estimate of statistical significance. For the first hit in the list, the
gi number is 116365, the database designation is sp (for SWISS-PROT), the accession
number is P26374, the locus name is RAE2_HUMAN, the definition line is rab proteins, the
17. A pairwise sequence alignment from a BLAST report
The alignment is preceded by the sequence identifier, the full definition line, and the length of the matched
sequence, in amino acids. Next comes the bit score (the raw score is in parentheses) and then the E-value. The
following line contains information on the number of identical residues in this alignment (Identities), the
number of conservative substitutions (Positives), and if applicable, the number of gaps in the alignment.
Finally, the actual alignment is shown, with the query on top, and the database match is labeled as Sbjct,
below. The numbers at left and right refer to the position in the amino acid sequence. One or more dashes (–)
within a sequence indicate insertions or deletions. Amino acid residues in the query sequence that have been
masked because of low complexity are replaced by Xs (see, for example, the fourth and last blocks). The line
between the two sequences indicates the similarities between the sequences. If the query and the subject
