This document introduces bioinformatics and discusses some of its key concepts and applications. It defines bioinformatics as an interdisciplinary field that combines computer science, statistics and engineering to study and process biological data. It describes some basic cell components like DNA, RNA and proteins, and how genetics and the genetic code work. It also provides a brief history of bioinformatics, highlighting projects like the Human Genome Project. Finally, it outlines several applications of bioinformatics like phylogenetic analysis, drug design, microarray analysis and protein-protein interaction networks.
Genomic databases are referred to as online repositories of genomic variants, described for a single (locus-specific) or more (general) genes or specifically for a population or ethnic group (national/ethnic).
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
Genomic databases are referred to as online repositories of genomic variants, described for a single (locus-specific) or more (general) genes or specifically for a population or ethnic group (national/ethnic).
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
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.
description of functional genomics and structural genomics and the techniques involved in it and also decribing the models of forward genetics and techniques involved in it and reverse genetics and techniques involved in it
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
Slides contain information about why bioinformatics appeared,
who bioinformaticians are, what they do, what kind of cool applications and challenges in bioinformatics there are.
Slides were prepared for the Bioinformatics seminar 2016, Institute of Computer Science, University of Tartu.
this presentation is about bioinformatics. the contents of bioinformatics are as under:
1.Introduction to bioinformatics.
2.Why bioinformatics is necessary?
3.Goals of bioinformatics
4.Field of bioinformatics
5.Where bioinformatics help?
6.Applications of bioinformatics
7.Software and tools of bioinformatics
8.References
Lecture delivered by T. Ashok Kumar, Head, Department of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil, Thuckalay, INDIA. UGC Sponsored National Workshop on BIOINFORMATICS AND GENOME ANALYSIS for College Teachers on August 11 & 12, 2014. Organized by Centre for Bioinformatics, Department of Zoology, NMCC.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
INTRODUCTION
DEFINITION OF BIOINFORMATICS
HISTORY
OBJECTIVES OF BIOINFORMATICS
TOOLS OF BIOINFORMATICS
BIOLOGICAL DATABASES
HOMOLOGY AND SIMILARITY TOOLS (SEQUENCE ALIGNMENT)
PROTEIN FUNCTION ANALYSIS TOOLS
STRUCTURAL ANALYSIS TOOLS
SEQUENCE MANIPULATION TOOLS
SEQUENCE ANALYSIS TOOLS
APPLICATION
CONCLUSION
REFERENCES
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble and analyze the function and structure of genomes
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.
description of functional genomics and structural genomics and the techniques involved in it and also decribing the models of forward genetics and techniques involved in it and reverse genetics and techniques involved in it
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
Slides contain information about why bioinformatics appeared,
who bioinformaticians are, what they do, what kind of cool applications and challenges in bioinformatics there are.
Slides were prepared for the Bioinformatics seminar 2016, Institute of Computer Science, University of Tartu.
this presentation is about bioinformatics. the contents of bioinformatics are as under:
1.Introduction to bioinformatics.
2.Why bioinformatics is necessary?
3.Goals of bioinformatics
4.Field of bioinformatics
5.Where bioinformatics help?
6.Applications of bioinformatics
7.Software and tools of bioinformatics
8.References
Lecture delivered by T. Ashok Kumar, Head, Department of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil, Thuckalay, INDIA. UGC Sponsored National Workshop on BIOINFORMATICS AND GENOME ANALYSIS for College Teachers on August 11 & 12, 2014. Organized by Centre for Bioinformatics, Department of Zoology, NMCC.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
INTRODUCTION
DEFINITION OF BIOINFORMATICS
HISTORY
OBJECTIVES OF BIOINFORMATICS
TOOLS OF BIOINFORMATICS
BIOLOGICAL DATABASES
HOMOLOGY AND SIMILARITY TOOLS (SEQUENCE ALIGNMENT)
PROTEIN FUNCTION ANALYSIS TOOLS
STRUCTURAL ANALYSIS TOOLS
SEQUENCE MANIPULATION TOOLS
SEQUENCE ANALYSIS TOOLS
APPLICATION
CONCLUSION
REFERENCES
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble and analyze the function and structure of genomes
I started studying English, my TOEIC score was 400 points or less.I didn’t know how to improve my English skill but I tried some method to study English. I will introduce how to study English.
CentOS 7 was officially released in July, 2014.
There are many significant changes in it. So you have to learn how they are if you want to use it or smoothly migrate settings from the previous version.
Bioinformatics for beginners (exam point of view)Sijo A
. The term bioinformatics is coined by…………………………….
Paulien Hogeweg
2. What is an entry in database?
The process of entering data into a computerised database or spreadsheet.
3. Define BLASTp
BLAST- Basic Local Alignment Search Tool
It is a homology and similarity search tool.
It is provided by NCBI.
It is used to compare a query DNA sequence with a database of sequences.
4. What is Ecogenes?
Ecogene is a database and website and it is developed to improve structural and functional annotation of E.coli K-12 MG 1655.
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.
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.
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.
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.
Richard's aventures in two entangled wonderlandsRichard 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...!
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.
2. Table of Contents
● Definition
● Recent History
● Basics
● Applications
3. Definition
Wikipedia:
Bioinformatics an interdisciplinary field that develops methods and
software tools for understanding biological data. As an
interdisciplinary field of science, bioinformatics combines
computer science, statistics, mathematics and engineering to
study and process biological data.1
1 http://en.wikipedia.org/wiki/Bioinformatics
4. Basics
● What is a cell and how does it work?
The nucleus
Mitochondrion
The lysosome
ER - Electroplasmatic reticulum
The DNA
Ribosome
http://www.wormbook.org/chapters/www_intromethodscellbiology/cellfig10.jpg
Cytoplasm
mRNA
5. Basics
● Genetics
o 1953 Watson & Crick uncovered DNA α-helix structure
o ~ 3’000’000’000 base pairs in human genome
o living organisms inherit their genetic properties from
their parents
o Molecular clock hypothesis
o Through comparing DNA in an alignment we can
deduce a similarity measure and a common ancestor
6. Basics
o DNA is packed very tightly
o Genetic Code - 3 bases code for one amino-acid
o Hundreds of amino-acids code for one protein
29lifescience.wikispaces.com/file/view/ChromosomeStructure.jpg/57692504/659x261/ChromosomeStructure.jpg
7. Basics
● Translation from mRNA to a protein
The nucleus
Mitochondrion
The lysosome
ER - Electroplasmatic reticulum
The DNA
Ribosome
http://www.wormbook.org/chapters/www_intromethodscellbiology/cellfig10.jpg
Cytoplasm
mRNA
11. Recent History
● 1978 - Nussinov Algorithm
● 2000 - Human Genome Project
● 2010 - 1000 Genomes Project
● 2014 - First time synthesis of an
artificial yeast chromosome
http://www.sciencemag.org/content/291/5507/F1.medium.gif
12. Applications
● Sequence:
o Phylogeny
o Gene Finding
● Structure:
o Drug Design
o Protein Dynamics
● Systems:
o Microarrays
o Protein Network Inference
13. Applications - Phylogeny
● DNA Sequence
similarities
● (Re-)construct the
evolutionary history
● Problem: Multiple
Sequence Alignment
Phylogenetic Tree of Life
14. Applications - Phylogeny
● Example:
o Given a set of taxa X={x1,x2,...,xn} and a distance function d(xi,xj),
reconstruct an evolutionary tree
1 2
4
5
3
8
9
6 7
1 2 3 4 5
UPGMA
15. Applications - Drug Design
● What is Drug Design?
o Design:
Deliberate creative act
o Drug Design:
Design of a drug for a specific (medical) application
16. Applications - Drug Design
● Key question:
What to put into a pill?
● Problem: Molecular dynamics and optimization
17. Applications - Drug Design
● Drug Discovery Pipeline
Biol.
Data
Target ID Lead ID Optimization Testing Approval
Accelerated by
Bioinformatics
19. Applications - Microarrays
● Samples labeled
o Experimental
o Control
o When same expression
level:
Yellow
https://genome.unc.edu/images/microarray.jpg
20. Applications - PPIs
● Combining multiple
experiments to a graph
● Problem:
Network Inference
21. Applications
● Solution to high complexity / runtime problems:
Use heuristical methods and dynamic programming
● Metropolis Monte Carlo
● => Blast
25. Applications
● How hard / complex are these
problems?
● Multiple Sequence Alignment:
o NP-complete - O(length#sequences)
● Molecular Dynamics simulation:
o naive scales O(#particles2)
Editor's Notes
Simple Model of a cell - a lot of things going on in different parts of the cell and what kind of cell we are looking at
Care most about Nucleus with DNA, Ribosomes and Cytoplasm
on the right is a less simplified version of a cell
1869 dicovered nuclein in Tuebingen, 1953 Watson and Crick
Next: History
Simple Model of a cell - a lot of things going on in different parts of the cell and what kind of cell we are looking at
Care most about Nucleus with DNA, Ribosomes and Cytoplasm
on the right is a less simplified version of a cell
Simple Model of a cell - a lot of things going on in different parts of the cell and what kind of cell we are looking at
Care most about Nucleus with DNA, Ribosomes and Cytoplasm
on the right is a less simplified version of a cell
Simple Model of a cell - a lot of things going on in different parts of the cell and what kind of cell we are looking at
Care most about Nucleus with DNA, Ribosomes and Cytoplasm
on the right is a less simplified version of a cell
Simple Model of a cell - a lot of things going on in different parts of the cell and what kind of cell we are looking at
Care most about Nucleus with DNA, Ribosomes and Cytoplasm
on the right is a less simplified version of a cell
Human Genome Project 300 Mio $ - 13 years - new technologies have emerged and we are even able to replicate an artificial yeast chromosome - next: Applications
MSA - length#sequences complexity,
Clustering problem, reconstruction of trees - NEXT Another application of Bioinformatics
NP-complete problem - Dynamic Programming to the rescue
Disease we want to cure, like a special kind of cancer. Target is a special cancer supressor protein p53, examine possible substances that increase effectiveness of p53 - try to optimize them
10 - 15 years and costs of up to 870 Million US$ - NEXT Application Microarrays
