It contains the information about Immuno informatics, immune cell development, Immunological database management and tools used for immuno informatics, all about Microarray and DNA MIcroArray experiment, Micro Array Data Classificationa and Machine learning Overview.
Functional proteomics, methods and toolsKAUSHAL SAHU
INTRODUCTION
HISTORY
DEFINITION
PROTEOMICS
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOMICS ANALYSIS
TOOLS FOR PROTEOM ANALYSIS
DIFFERENTS METHODS FOR STUDY OF FUNCTIONAL PROTEOMICS
APLLICATIONS
LIMITATIONS
CONCLUSION
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
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.
Functional proteomics, methods and toolsKAUSHAL SAHU
INTRODUCTION
HISTORY
DEFINITION
PROTEOMICS
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOMICS ANALYSIS
TOOLS FOR PROTEOM ANALYSIS
DIFFERENTS METHODS FOR STUDY OF FUNCTIONAL PROTEOMICS
APLLICATIONS
LIMITATIONS
CONCLUSION
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
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.
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.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Being able to identify genes, compare them, analyze them could be applied in various research areas from medical to industrial.
This ppt is designed for Health science and computational biology students to enable you understand the above mentioned topic.
INTRODUCTION
WHAT IS DATA AND DATABASE?
WHAT IS BIOLOGICAL DATABASE?
TYPES OF BIOLOGICAL DATABASE
PRIMARY DATABASE
Nucleic acid sequence database
Protein sequence database
SECONDARY DATABASE
COMPOSITE DATABASE
TERTIARY DATABASE
WHY NEED?
CONCLUSION
REFRENCES
Like personalized medicine, personalized vaccinology aims to provide the right vaccine, to the right patient, at the right time, to achieve protection from disease, while being safe (i.e., free from unintended side effects). Starting with these lines, this presentation will provide overall information related to the vaccinomoic along with the suitable examples and thus will be helpful for the students to understand the basics related to the same.
Bioinformatics: Introduction, Objective of Bioinformatics, Bioinformatics Databases, Concept of Bioinformatics, Impact of Bioinformatics in Vaccine Discovery
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.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Being able to identify genes, compare them, analyze them could be applied in various research areas from medical to industrial.
This ppt is designed for Health science and computational biology students to enable you understand the above mentioned topic.
INTRODUCTION
WHAT IS DATA AND DATABASE?
WHAT IS BIOLOGICAL DATABASE?
TYPES OF BIOLOGICAL DATABASE
PRIMARY DATABASE
Nucleic acid sequence database
Protein sequence database
SECONDARY DATABASE
COMPOSITE DATABASE
TERTIARY DATABASE
WHY NEED?
CONCLUSION
REFRENCES
Like personalized medicine, personalized vaccinology aims to provide the right vaccine, to the right patient, at the right time, to achieve protection from disease, while being safe (i.e., free from unintended side effects). Starting with these lines, this presentation will provide overall information related to the vaccinomoic along with the suitable examples and thus will be helpful for the students to understand the basics related to the same.
Bioinformatics: Introduction, Objective of Bioinformatics, Bioinformatics Databases, Concept of Bioinformatics, Impact of Bioinformatics in Vaccine Discovery
Genomics is the study of an organism's entire genome, which is the complete set of genetic material present in its DNA. This includes all the genes, non-coding regions, and regulatory sequences. Genomics involves sequencing and analyzing the DNA to identify genes, variations (such as single nucleotide polymorphisms or SNPs), and other structural features of the genome.
P4 Medicine: A Vision For Your Molecular HealthSachin Rawat
Medicine is undergoing tremendous change. Unlike today, medicine of tomorrow would be pro-active rather than reactive.Medicine would be personalized to individual patient's genome. It would predict, and hence prevent, diseases even before they manifest. Also, this medicine would require active societal participation to bring it from labs to clinics.
A Wellcome Trust-funded project to extend the Guide to PHARMACOLOGY (www.guidetopharmacology.org) to include data on key immunological data types and associate these to drugs and drug targets. Presented at the ELIXIR-UK All-Hand Meeting, Edinburgh, Nov 2017.
A radiology report serves as an intermediary between a radiologist and referring clinician for suggesting
appropriate treatment to the patients, aimed at better healthcare management. It is essentially a tool
that assists radiologists in conveying their input to the patients and clinicians regarding positive or negative findings on a case. The objective of this paper is to discuss and propose Radiology Information & Reporting System (RIRS), highlight challenges governing its implementation and suggest way forwards
towards its effective implementation across the public sector tertiary care institutions of Pakistan. In the end, it is concluded that the proposed RIRS would potentially offer enormous benefits in terms of cost
savings, reporting accuracy, faster processing and operational efficiency as opposed to the conventionally available manual radiology reporting procedures and systems.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
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 .
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.
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.
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
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.
3. HOW DO WE DEFINE IMMUNOINFORMATICS ?
Immunoinformatics or computational
immunology is a field of science that
encompasses high-throughput genomic and
bioinformatics approaches to immunology.
4. WHAT DOES THE WORD IMMUNOLOGY
MEANS?
Immunology is a branch of biomedical science that
covers the study of all aspects of the immune
system in all organisms.
5. AIM OF IMMUNOINFORMATICS
The field's main aim is to convert immunological
data into computational problems, solve these
problems using mathematical and computational
approaches and then convert these results into
immunologically meaningful interpretations.
6. LET US KNOW MORE ABOUT IMMUNOLOGY
Immunology research is important for
understanding the mechanisms underlying the
defense of human body and to develop drugs for
immunological diseases and maintain health.
Recent findings in genomic and proteomic
technologies have transformed the immunology
research drastically. Sequencing of the human and
other model organism genomes has produced
increasingly large volumes of data relevant to
immunology research and at the same time huge
amounts of functional and clinical data are being
reported in the scientific literature and stored in
clinical records.
7.
8. MORE ABOUT IMMUNOINFORMATICS
1. Recent advances
in bioinformatics or computational biology were
helpful to understand and organize these large
scale data and gave rise to new area that is
called Computational
immunology or immunoinformatics.
2. Computational immunology is a branch of
bioinformatics and it is based on similar concepts
and tools, such assequence alignment and protein
structure prediction tools. Immunomics is a
discipline like genomics andproteomics.
9. 3. It is a science, which specifically
combines Immunology with computer
science, mathematics,chemistry,
and biochemistry for large-scale analysis of
immune system functions.
4. It aims to study the complex protein–protein
interactions and networks and allows a better
understanding of immune responses and their role
during normal, diseased and reconstitution states.
5.Computational immunology is a part of
immunomics, which is focused on analyzing large
scale experimental data.
10. HISTORY
Computational immunology began over 90 years
ago with the theoretic modeling of malaria
epidemiology. At that time, the emphasis was on
the use of mathematics to guide the study of
disease transmission. Since then, the field has
expanded to cover all other aspects of immune
system processes and diseases
11. IMMUNOLOGICAL DATABASE
After the recent advances in sequencing and
proteomics technology, there have been many fold
increase in generation of molecular and
immunological data. The data are so diverse that
they can be categorized in different databases
according to their use in the research. Until now
there are total 31 different immunological
databases noted in the Nucleic Acids Research
(NAR) Database Collection, which are given in the
following table, together with some more immune
related databases.[4] The information given in the
table is taken from the database descriptions
in NAR Database Collection.
12. TOOLS
Immunoinformatics is using the basic bioinformatics
tools such as ClustalW,[34] BLAST,[35] and TreeView, as
well as specialized immunoinformatics tools, such as
EpiMatrix,[36][37] IMGT/V-QUEST for IG and TR sequence
analysis, IMGT/ Collier-de-Perles and
IMGT/StructuralQuery[38] for IGvariable domain structure
analysis.[39]Methods that rely on sequence comparison
are diverse and have been applied to analyze HLA
sequence conservation, help verify the origins of human
immunodeficiency virus (HIV) sequences, and construct
homology models for the analysis of hepatitis B virus
polymerase resistance to lamivudine and emtricitabine.
13. There are also some computational models which
focus on protein–protein interactions and networks.
There are also tools which are used for T and B cell
epitope mapping, proteasomal cleavage site
prediction, and TAP– peptide prediction
15. APPLICATIONS
To determine ALLERGIES:
The use of immunoinformatics tools can be useful
to predict protein allergenicity and will become
increasingly important in the screening of novel
foods before their wide-scale release for human
use. Thus, there is a major develop such as to
make reliable broad based allergy databases and
combine these with well validated prediction tools in
order to enable the identification of potential
allergens in genetically modified drugs and foods.
16. 2. Infectious diseases and host responses
Algorithms have been developed for knowing which
kinds of disease and hosts are present in our body.
3. Immune system function
The affinity of HLA-binding peptides for TAP was
found to differ according to the HLA supertype
concerned using this method. This research could
have important implications for the design of
peptide based immuno-therapeutic drugs and
vaccines.
17. 4. Cancer Informatics
Immunoinformatics have been useful in increasing
success of tumour vaccination.
18. MICROARRAY!!
The most commonly used global gene expression profiling method in
current genomics research is the DNA microarray-based approach.
19. WHAT IS A MICROARRAY?
A microarray (or gene chip) is a slide attached with
a high-density array of immobilized DNA oligomers
(sometimes cDNAs) representing the entire
genome of the species under study.
20. INTERNAL STRUCTURE OF MICROARRAY
1.Each oligomer is spotted on the slide and serves
as a probe for binding to a unique, complementary
cDNA.
2. The entire cDNA population, labeled with
fluorescent dyes or radioisotopes, is allowed to
hybridize with the oligo probes on the chip.
3. The amount of fluorescent or radiolabels at each
spot position reflects the amount of corresponding
mRNA in the cell.
Using this analysis, patterns of global gene
expression in a cell can be examined.
22. MICROARRAY DATA CLASSIFICATION
One of the key features of DNA microarray analysis
is to study the expression of many genes in parallel
and identify groups of genes that exhibit similar
expression patterns.
1.Supervised and Unsupervised Classification
A supervised analysis refers to classification of data
into a set of predefined categories. For example,
depending on the purpose of the experiment,the
data can be classified into predefined “diseased” or
“normal” categories.
23. An unsupervised analysis does not assume
predefined categories, but identifies data categories
according to actual similarity patterns. The
unsupervised analysis is also called clustering,
which is to group patterns into clusters of genes
with correlated profiles.
25. WHAT IS MACHINE LEARNING?
Machine learning is an adaptive process that
enables computers to learn from experience, learn
by example, and learn by analogy.
There are new computational approaches to solve
the computational problems on biological
approaches by handling complex data.
It presents modelling methods, such as supervised
classification, clustering and probabilistic graphical
models for knowledge discovery, as well as
deterministic and stochastic heuristics for
optimization. Applications in genomics, proteomics,
systems biology, evolution and text mining are also
shown.
26. THE EXISTING RESEARCH ON BIOINFORMATICS
THAT HAS APPLIED MACHINE LEARNING
Research , Area, Application Reference
Sequence alignment:
1.BLAST, http://www.ncbi.nlm.nih.gov/BL
2.AST/FASTA, http://www.ebi.ac.uk/fasta33/
Multiple sequence alignment:
1.ClustalW, http://www.ebi.ac.uk/clustalw/
2.MultAlin.http://prodes.toulouse.inra.fr/multalin/multalin.html
3.DiAlignhttp://www.genomatix.de/cgibin/dialign/dialign.pl
o Gene finding
1.Genscan http://genes.mit.edu/GENSCAN.html
2.GenomeScan http://genes.mit.edu/genomescan/
28. WHY MACHINE LEARNING?
There are some biological problems in which
experts can specify only input/output pairs, but not
the relationships between inputs and outputs,such
as the prediction of protein structure and structural
and functional sequences.
This limitation can be addressed by machine
learning methods.
They are able to adjust their internal structure to
produce approximate results for the given
problems.
