OMICS is the comprehensive analysis of the biological system. The technologies which made a revolution such as Genomics, Transcriptomics, Proteomics, Metabolomics and Phenomics, in screening traits and develop novel improved organisms are mentioned here. The presentation gives a brief idea about various OMICS technology used in crop improvement, their steps, techniques used, applications, scope, advantages and disadvantages.
Access to large-scale omics datasets i.e. genomics, transcriptomics, proteomics, metabolomics, phenomics, etc. has revolutionized biology and led to the emergence of systems approaches to advance our understanding of biological processes. With decreasing time and cost to generate these datasets, omics data integration has created both exciting opportunities and immense challenges for biologists, computational biologists, biostatisticians and biomathematicians. Genomics, transcriptomics, proteomics, and metabolomics together they help to bring out the best of characters in plants.
Banoth Madhu: Map based gene cloning in plant. In the process of map-based cloning, one starts with a mutant and eventually identifies the gene responsible for the altered phenotype, allowing the plant to tell you what genes are important in the physiological process of interest and using the genetic relationship between a gene and a marker as the basis for beginning a search for a gene
Genomics and its application in crop improvementKhemlata20
meaning ,definition of genome ,genomics ,tools of genomics ,what is genome sequencing ,methods of genome sequencingand genome mapping ,advantage of genomics over traditional breeding program, examples of some crops whose genome has been sequenced, important points about genomics, work in the field of genomics ,applications of genomics .classification of genomics .different Omics in genomics like Proteomics ,Transcriptomics ,Metabolomics ,Need of genome sequencing
Genomics, proteomics and metabolomics are the three core omics technologies, which respectively deal with the analysis of genome, proteome and metabolome of cells and tissues of an organism.
Access to large-scale omics datasets i.e. genomics, transcriptomics, proteomics, metabolomics, phenomics, etc. has revolutionized biology and led to the emergence of systems approaches to advance our understanding of biological processes. With decreasing time and cost to generate these datasets, omics data integration has created both exciting opportunities and immense challenges for biologists, computational biologists, biostatisticians and biomathematicians. Genomics, transcriptomics, proteomics, and metabolomics together they help to bring out the best of characters in plants.
Banoth Madhu: Map based gene cloning in plant. In the process of map-based cloning, one starts with a mutant and eventually identifies the gene responsible for the altered phenotype, allowing the plant to tell you what genes are important in the physiological process of interest and using the genetic relationship between a gene and a marker as the basis for beginning a search for a gene
Genomics and its application in crop improvementKhemlata20
meaning ,definition of genome ,genomics ,tools of genomics ,what is genome sequencing ,methods of genome sequencingand genome mapping ,advantage of genomics over traditional breeding program, examples of some crops whose genome has been sequenced, important points about genomics, work in the field of genomics ,applications of genomics .classification of genomics .different Omics in genomics like Proteomics ,Transcriptomics ,Metabolomics ,Need of genome sequencing
Genomics, proteomics and metabolomics are the three core omics technologies, which respectively deal with the analysis of genome, proteome and metabolome of cells and tissues of an organism.
Molecular Marker and It's ApplicationsSuresh Antre
Molecular (DNA) markers are segments of DNA that can be detected through specific laboratory techniques. With the advent of marker-assisted selection (MAS), a new breeding tool is now available to make more accurate and useful selections in breeding populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
'Genomics' is nothing but the study of entire genetic compliment of an organism. Plant genomics is study of plant genome. This is my topic of M.Sc. course 'Plant biotechnology'.
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
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
Molecular Marker and It's ApplicationsSuresh Antre
Molecular (DNA) markers are segments of DNA that can be detected through specific laboratory techniques. With the advent of marker-assisted selection (MAS), a new breeding tool is now available to make more accurate and useful selections in breeding populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
'Genomics' is nothing but the study of entire genetic compliment of an organism. Plant genomics is study of plant genome. This is my topic of M.Sc. course 'Plant biotechnology'.
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
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
TOXICOLOGY STUDY IS VERY ESSENTIAL FOR DRUG DISCOVERY. INTERNATIONAL COUNCIL FOR HARMONIZATION (ICH) HAS IMPLEMENTED SOME BASIC RULES AND REGULATION REGARDING THE TOXICITY STUDY ON ANIMAL DURING PRE-CLINICAL TRIAL, WHICH IS A PART OF DRUG DISCOVERY PROCESS. HERE SOME OF THE BASIC TEST ARE DISCUSSED ALONG WITH SOME BASIC CONCEPTS OF GENETICS. HOPE THIS WILL HELP THE STUDENTS TO UNDERSTAND THE TOPIC.
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Crimson publishers-5-MethylcytosineDNA Methylation Patterns among Gut Predomi...CrimsonpublishersMedical
5-MethylcytosineDNA Methylation Patterns among Gut Predominate Commensal Escherichia coli and Lactobacilli from the Balbas and Mazekh Domestic Sheep Breeds by Pepoyan AZ* in Research in Medical &Engineering Sciences
Proteomics and its applications in phytopathologyAbhijeet Kashyap
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
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
Genomics, Transcriptomics, Proteomics, Metabolomics - Basic concepts for clin...Prasenjit Mitra
This set of slides gives an overview regarding the various omics technologies available and how they can be used for improvement in clinical setting or research
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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.
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.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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.
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.
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.
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.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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 .
1. “OMICS IN CROP IMPROVEMENT”
Presented by
Gautham S
MSc. (Ag) Ist Year
Department of Genetics and Plant Breeding
1
2. ֍ OMICS – Comprehensive analysis of the biological system.
֍ Large-scale biology – “OMICS” – Revolution in
screening traits and develop novel improved organisms.
֍ Beyond basic nutrition and the development of functional
foods- for enhanced health quality.
Source : Mochida and Shinozaki, 2010
2
5. Phenotypes produced by an organism
Biomolecules found in an organism
Proteins found in an organism
mRNAs found in an organism
Genes or genetic material in an organism
S
E
T
O
F
5
Source : www.omicsonline.com
6. GENOMICS
֍ Term by Thomas Roderick in 1986.
֍ Genomics : the branch of molecular biology concerned with the structure, function,
evolution and mapping of genomes.
֍ Genome : the complete set of genes or genetic material present in a cell or organism.
(Winklen, 1920)
֍ Studies Intra-genomic phenomena - heterosis, epistasis, pleiotropy etc.
STRUCTURAL FUNCTIONAL
COMPARATIVE MUTATIONAL
GENOMICS
6
Mapping Sequencing Genome analysis
STEPS
7. Genome Mapping
֍ Methods used to identify the locus of a gene and the distances between genes.
֍ Given by Alfred Sturtvent (1915) in Drosophila melanogaster.
֍ Traits mapped : Morphological Characters, Productivity traits, Resistance Traits,
Quality Traits, Agronomic Traits, Special Characters.
֍ To study linkage and recombination.
֍ In India, by the Depart of Biotechnology [DBT] and ICAR.
֍ Done in rice, wheat, maize, chick pea, banana, tomato, Brassica, etc.
7
MAPPING
GENETIC
MAP
CYTOGENETIC
MAP
PHYSICAL
MAP
8. 8
0 150
125
100
75
50
25
Mbp
cM
20 20
30 25
Genetic Map
Physical Map
Cytogenetic Map
DNA Sequence …GATCTGCATGCATGCTAGCTAGTCAGCTAGCTAGAGCTTCGA… Bases
Source : National Human Genome Research Institute
9. Genome Sequencing
֍ Figuring out the order of DNA nucleotides,
or bases in a genome in order of A, C, G, and
T in DNA.
֍ To study various molecular interactions and
aberrations.
֍ Information about genome organisation and
evolution.
GENOMICS
EUKARYOTIC
PROKARYOTIC
9
10. Mapping
Library
creation
Template
preparation
Gel
Electrophoresis
Pre finishing /
Finishing
Data Editing /
Annotation
‘
Make set of smaller clones
from mapped ones
Quality verification,
Biological annotation,
Submission to database
Identify the set of genes
in the region of genome
Purify DNA from smaller
clones. Perform
sequencing chemistries
Determine sequence
from smaller clones
Techniques to produce
high quality sequences
10
Source : www.biotechonweb.com
11. A
P
P
L
I
C
A
T
I
O
N
S
1. Genome size
2. Gene number
3. Gene mapping
4. Gene sequencing
5. Gene cloning
6. QTL Mapping
7. Evolution of Crop
plants
8. Transgenic breeding
9. Marker Assisted
Selection
10. Identification of
DNA Markers
11. Construction of
Linkage maps
L
I
M
I
T
A
T
I
O
N
S
1. Expensive
2. High skilled
3. Limited genes
available
4. Lack of proper
markers
5. Lack of centres
11
12. Achievements and Future scopes
Rice : Nutrient enriched rice – Swarna (GI 43-48) , Doongara (GI 50-56)
High anthocyanin, low glycemic index, Pro-vitamin A (beta-carotene)
Sweet potato : O’ Henry white, 414 Purple
High phenolic acid, high beta- carotene, high anthocyanin
֍ Using MAS : ‘genomics-assisted breeding’ for crop improvement.
֍ Next Generation Sequencing technologies and plant breeding.
֍ Plant comparative genomics : comparing cDNA libraries.
֍ Gene silencing technologies.
Source: http://www.ncbi.nlm.nih.gov , Mochida and Shinozaki, 2010
12
13. TRANSCRIPTOMICS
֍ Study of the Transcriptome.
֍ Transcriptome : complete set of RNA transcripts produced by the genome at any one
time.
֍ Include mRNA, rRNA, tRNA, and other non-coding RNA.
֍ a.k.a Expression Profiling.
֍ To catalogue all species of transcripts.
֍ To determine the transcriptional structure of genes- start sites, 5′ and 3′ ends, splicing
patterns and other PTMs.
֍ To quantify the changing expression levels of each transcript.
Source : Various
13
15. TRANSCRIPT PROFILING
15
Source :
Genomic DNA
Protein binding
Crosslinking
Protein
Immunoprecipitation
Protein digestion
Labelling
Microarray
hybridization
Antibody
16. 16
Applications and Scope
1. Screening target genes
2. Predict gene function
3. Comparative transcriptomics helps in pattern of selection
4. Role of comparative safety assessment of plant products (GMO)
5. Identification of gene involving in stress
6. Understanding symbiotic association
7. Determination of pathogenicity function and Host pathogen interactions
8. Dissection of food quality traits
9. Expression of QTL isolation.
Source : American Chemical Society, 2014
17. PROTEOMICS
֍ Study about structure, function, composition and interaction of Proteome.
֍ Proteome : complete set of protein in a cell at a given time.
֍ By Mark Wilkins et.al in 1990’s.
֍ Helps in determining the proper treatment of diseases.
֍ Identification of Biomarkers.
֍ Pharmacoproteomics : The study of drugs using proteomics.
PROTEOMICS
STRUCTURAL EXPRESSION INTERACTION
17
18. Tools of Proteomics
Peptide Mixture
Protein Mixture Protein
Peptides M S Analysis
M S Data
Identification
separation
separation
digestion digestion
database search algorithms
18
Source : www.proteomics.com
19. TECHNIQUES
2-D GEL
ELECTROPHORESIS
PROTOMAP
MASS
SPECTROMETRY
• Using PAGE –
Poly Acrylamide
Gel Electrophoresis
• Softwares –
BioNumerics2D,
Delta2D, PDQuest,
Progenesis
• O’Farrell and Klose
(1975)
• Protein
Topography and
Migration Analysis
Platform
• Ben Cravatt et.al
19
• Ionizes chemical
species and sorts
them into spectrum
• Based on their
mass-to-charge
ratio.
• Arthur Jeffrey
Dempster (1918)
and F.W. Aston
(1919)
Source: Cristea and Gaskell, 2004
21. Applications and Scope
1. Arabidopsis - the role of GAs during initial stages of seed germination.
2. Barley - cellular mechanisms under lying seed development during grain filling and seed maturation
phases.
3. Rice - novel traits useful for breeding.
4. Maize - unknown novel genes coding for enzymes in metabolic pathways during grain development.
5. Both abiotic and biotic stresses - manifested as the up- or down- regulation of proteins, or their post
translation modification.
6. Salinity stress - plant attempts to restore homeostasis in osmolarity to resume growth and development.
7. Pathogen attack - defence and stress related proteins, metabolic enzymes, translocation and protein
turnover proteins.
8. Decipher the highly complex genetic interactions involved in plant-microbe interactions.
9. For studying symbioses (nitrogen symbiosis, ecto- and endo-mycorrhizal symbiosis) in plants.
Source : Rose et al., 2004
21
22. METABOLOMICS
֍ Study of Metabolome.
֍ Metabolome : collection of all metabolites in a cell, tissue, organ or organism.
֍ Metabolites are ultimate result of cellular pathways.
֍ Look at genotype- phenotype as well as genotype- environ type relationships.
֍ Metabolic profiling : Quantitative study of a group of metabolites, known or unknown, within
or associated with a particular metabolic pathway.
֍ Metabolic fingerprinting : Measures a subset of the whole profile with little differentiation or
quantitation of metabolites.
֍ Monitoring crop quality characteristics
Source : Various
22
24. Applications and Scope
1. Identifying potential biochemical markers optimize trait development in agricultural products and in bio
refining.
2. Differentiate genotypes and phenotypes based on metabolic levels.
3. Differentiating various genotypes and understanding plant responses to biotic and abiotic stresses.
4. Characterization of the novel plant products.
5. Comparison between transgenic and wild-type plants.
6. Improved levels of phytonutrients such as flavonoids and carotenoids.
7. Plant properties are improved - increasing metabolic fluxes into valuable biochemical pathways using
metabolic engineering.
e.g., nutritional value of foods, decreasing the need for pesticide or fertilizer application etc.
8. Into pathways needed for the production of pharmaceuticals in plants.
9. Introducing foreign set of enzymes that lead to the production of desired end products and new metabolites.
Sources : Wishart, 2007
24
25. PHENOMICS
֍ Study of the Phenome.
֍ Phenome : sum total of all phenotypes produced by an organism.
֍ Phenotypes are characterized in a rigorous and formal way, and are linked to the
associated genes and gene variants (alleles).
֍ Genotype –Phenotype map is made to analyse an organism.
25
Source : www.frontiersin.org
26. PHENOMICS
ADVANTAGES
• Identify relation between Genotypes and
Phenotypes.
• Assess pleotropic effects.
• Assess phenotypic quality,
• Study relation of phenotypes with environment.
• Study characters like:
DIS ADVANTAGES
• Time consuming.
• High chance of mistake.
• Effect of environment is very crucial.
• Mutation cannot be taken into account.
26
Source : http://www.phenomecentre.org
Plant height, Leaf area, Chlorophyll content & Photosynthetic efficiency,
Necrosis, Growth rate, Canopy temperature, Ear/panicle size/number,
Salinity/drought/heat /frost tolerance, Root mass/growth, Biomass,
Transpiration rate etc.
27. REFERENCES
1. Aslam, B., 2017, Proteomics: Technologies and Their Applications.
2. Benkeblia, N., 2014, Omics Technologies and Crop Improvement, CRC Press
3. Horgan and Kenny, 2011, ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics.
4. Kumar, S. V., 2012, Proteomics in Agriculture.
5. Mochida and Shinozaki, 2010, Genomics and Bioinformatics Resources for Crop Improvement.
6. Nawar, A., 2013, Proteomics : A biotechnology tool for crop improvement.
7. Setia, R. C., 2018, The Omics technologies and Crop improvement.
8. Wishart, D., 2007, Current Progress in computational metabolomics.
9. https://en.wikipedia.org/wiki
10. https://www.biotecharticles.com/Agriculture-Article/Role-of-Omics-in-Crop-Improvement
11. https://www.genome.gov
12. https://www.metabolon.com
13. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714296
14. https://www.omicsonline.org 27