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
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
Biotechnology information system in india (btis net)KAUSHAL SAHU
Introduction to Bioinformatics
Bioinformatics in India
Biotechnology Information System Network
Objective
Structure of BTISnet in India
Apex centre
Centre of excellence
Research activities proposed to be undertaken by the CoEs
Distributed information centers(DICs)
Sub-Distribution
Sub-DIC National Institute of Technology, Raipur
BIF for Biology Teaching Through Bioinformatics (BTBI)
EMBnet India Node
Future planning
Conclusion
Reference
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.
Bioinformatics is the branch of life science that deals with the use of mathematical, statistical and computer methods to analyze biological and biochemical data.
Types of Bioinformatics (see the slides)
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
WHAT IS BIOINFORMATICS?
Computational Biology/Bioinformatics is the application of computer sciences and allied technologies to answer the questions of Biologists, about the mysteries of life. It has evolved to serve as the bridge between:
Observations (data) in diverse biologically-related disciplines and
The derivations of understanding (information)
APPLICATIONS OF BIOINFORMATICS
Computer Aided Drug Design
Microarray Bioinformatics
Proteomics
Genomics
Biological Databases
Phylogenetics
Systems Biology
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
jarvis is a concept to easy life. it is future. it's just a life assistant which makes life easier.it is technically a smart speech recognition system.
it is use by the tony stark in iron man movies
inspired by iron man......:P
Biotechnology information system in india (btis net)KAUSHAL SAHU
Introduction to Bioinformatics
Bioinformatics in India
Biotechnology Information System Network
Objective
Structure of BTISnet in India
Apex centre
Centre of excellence
Research activities proposed to be undertaken by the CoEs
Distributed information centers(DICs)
Sub-Distribution
Sub-DIC National Institute of Technology, Raipur
BIF for Biology Teaching Through Bioinformatics (BTBI)
EMBnet India Node
Future planning
Conclusion
Reference
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.
Bioinformatics is the branch of life science that deals with the use of mathematical, statistical and computer methods to analyze biological and biochemical data.
Types of Bioinformatics (see the slides)
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
WHAT IS BIOINFORMATICS?
Computational Biology/Bioinformatics is the application of computer sciences and allied technologies to answer the questions of Biologists, about the mysteries of life. It has evolved to serve as the bridge between:
Observations (data) in diverse biologically-related disciplines and
The derivations of understanding (information)
APPLICATIONS OF BIOINFORMATICS
Computer Aided Drug Design
Microarray Bioinformatics
Proteomics
Genomics
Biological Databases
Phylogenetics
Systems Biology
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
jarvis is a concept to easy life. it is future. it's just a life assistant which makes life easier.it is technically a smart speech recognition system.
it is use by the tony stark in iron man movies
inspired by iron man......:P
A smart house is a house that has highly advanced automatic systems for lighting, temperature control, multi-media, security, window and door operations, and many other functions.
Event: Plant and Animal Genomes conference 2012
Speaker: Sandra Orchard
InterPro is an open-source protein resource used for the automatic annotation of proteins, and is scalable to the analysis of entire new genomes through the use of a downloadable version of InterProScan, which can be incorporated into an existing local pipeline. InterPro integrates protein signatures from 11 major signature databases (CATH-Gene3D, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY, and TIGRFAMs) into a single resource, taking advantage of the different areas of specialization of each to produce a resource that provides protein classification on multiple levels: protein families, structural superfamilies and functionally close subfamilies, as well as functional domains, repeats and important sites. The InterPro website has been improved, following extensive community consultation and a new version of InterProScan promises improved speed, ease of implementation as well as additional functionalities.
Bioinformatics is a science field that is similar to but distinct from biological computation, while it is often considered synonymous to computational biology.
Genomic Big Data Management, Integration and Mining - Emanuel WeitschekData Driven Innovation
Thanks to Next Generation Sequencing (NGS), a technology that is lowering the cost and time of reading DNA, we are faced with huge amounts of biomedical data. These data are continuously collected by research laboratories, and often organized through world-wide consortia, which are releasing many public data bases. One of the main aims of bioinformatics is to solve fundamental issues in biomedicine research (e.g., how cancer occurs) starting from big genomic data and their analysis. In this talk I will give an overview of big genomic data management, integration, and mining.
International Journal on Bioinformatics & Biosciences (IJBB)ijbbjournal
International Journal on Bioinformatics & Biosciences (IJBB) is a Quarterly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Bioinformatics & Biosciences.
Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. It is an interdisciplinary field, which harnesses computer science, mathematics, physics, and biology
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
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Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
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.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
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2. • Introduction
• History
• Need for bioinformatics
• Computational evolutionary biology
• Success
• Software and tools
CONTENTS
3. • Bioinformatics is the application of Information
technology to store, organize and analyse the
vast amount of biological data.
• The stored data is available in the form of
sequences and structures of proteins and
nucleic acids (the information carrier).
• The biological information of nucleic acids is
available as sequences while the data of
proteins is available as sequences and
structures
INTRODUCTION
4. • Sequences are represented in single dimension
where as the structure contains the three
dimensional data of sequences.
5. Biologists
collect molecular data:
DNA & Protein sequences,
gene expression, etc.
Computer scientists
(+Mathematicians, Statisticians, etc.)
Develop tools, soft wares, algorithms
to store and analyze the data.
Bioinformaticians
Study biological questions by
analyzing molecular data
The field of science in which biology, computer science
and information technology merge into a single
discipline .
6. • By course of 10 years starting from 1981,
following events occurred…
• 579 human genes had been mapped.
• Invented a method for automated DNA
sequencing.
• The Human Genome organization (HUGO) was
founded. This is an international organization of
scientists involved in Human Genome Project.
• The first complete genome map was published
for the bacteria Haemophilus influenza .
HISTORY
7. • After 10 years…
• By 1991, a total of 1879 human genes had been
mapped.
• In 1993, Genethon , a human genome research
center in France Produced a physical map of the
human genome.
• After 3 years…
• Genethon published the final version of the
Human Genetic Map. This concluded the end of
the first phase of the Human Genome Project.
8. • Bioinformatics was fuelled by the need to create
huge databases.
• GenBank and EMBL and DNA Database of
Japan.
• They store and compare the DNA sequence
data coming from the human genome and other
genome sequencing projects.
• Today, bioinformatics enhances protein structure
analysis, gene and protein functional
information, data from patients, pre-clinical and
clinical trials, and the metabolic pathways of
numerous species.
9. • The first bioinformatics databases were constructed
a few years after the first protein sequences began
to become available.
• Now, A huge variety of divergent data resources of
different types and sizes are now available either in
the public domain information through
Internet(www.ncbi.nlm.nih.gov).
• All of the original databases were organized in a
very simple way with data entries being stored in flat
files, as a single large text file. Re-write - Later on
lookup indexes were added to allow convenient
keyword searching of header information.
10. • Bioinformatics uses many areas of computer
science, statistics, mathematics and engineering to process
biological data.
• Complex machines are used to read in biological data at a much
faster rate than before.
• Analyzing biological data may involve algorithms in artificial
intelligence, soft computing, data mining, image processing,
and simulation.
• The algorithms in turn depend on theoretical foundations such
as discrete mathematics, control theory, system theory, information
theory, and statistics.
• Commonly used software tools and technologies in the field
include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATL
AB, and spreadsheet
• the development of new algorithms (mathematical formulas) and
NEED FOR BIOINFORMATICS
11. • Evolutionary biology is the study of the origin
and species, as well as their change over
time. Informatics has assisted evolutionary
biologists by enabling researchers.
COMPUTATIONAL EVOLUTIONARY
BIOLOGY
13. • 2) Analysis of regulation
• One can then apply clustering algorithms to that
expression data to determine which genes are
co-expressed
14. • 3) Analysis of protein expression
• Bioinformatics is very much involved in making
sense of protein microarray and HT MS data.
• involves the problem of matching large amounts
of mass data against predicted masses from
protein sequence databases.
15. • 4) Analysis of mutations in cancer
• Bioinformaticians continue to produce
specialized automated systems to manage the
sheer volume of sequence data produced, and
they create new algorithms and software to
compare the sequencing results to the growing
collection of human genome sequences
and germline polymorphisms
17. • 6) High-throughput image analysis
• Computational technologies are used to
accelerate or fully automate the processing,
quantification and analysis of large amounts of
high-information-content biomedical imagery.
• accuracy, simple objective and high speed
18. • Open-source bioinformatics software
• Many free and open-source software tools have
existed and continued to grow up till now.
• The range of open-source software
packages includes titles such
as Bioconductor, BioPerl, Biopython, BioJava, BioR
uby, Bioclipse, EMBOSS, .NET Bio, Taverna
workbench, and UGENE.
• In order to maintain this tradition and create further
opportunities, the non-profit Open Bioinformatics
Foundation have supported the annual
Bioinformatics Open Source Conference (BOSC)
SOFTWARE AND TOOLS
19. • Web services in bioinformatics
• The main advantages is that end users do not
have to deal with software and database
maintenance overheads.
20. • Bioinformatics workflow management
systems
• A Bioinformatics workflow management
system is a specialized form of a workflow
management system designed specifically to
compose and execute a series of computational
or data manipulation steps, or a workflow, in a
Bioinformatics application.
21. • Rosalind
• Rosalind is an educational resource and web
project for learning bioinformatics
through problem solving and computer
programming.