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.

1. 1
Seminar incharge
Dr. Farzana Jabeen,
Professor &HOD
Dr. D.Shivani
Professor

3. 3

4. Transgenic breeding
4
Domestication &
selection
Hybridization
breeding
Mutation breeding
Omics Approaches

6. 6

7. 7

8. 8

10.  Roche 454
 Ion torrent
 Illumina Solexa
 SOLiD
Second generation
or NGS methods
 Helicos
 SMRT
 Nanopore
Third generation
methods
First generation
methods
 Sanger chain
termination
 Maxam-Gilbert
chemical
degradation

11. EVENTS YEAR
• Discussion in scientific community for sequencing genome 1984-1990
• Human genome project proposed 1986
• Human genome project initiated Oct .1,1990
• Haemophilu influenzae genome sequenced 1995
• E.coli genome sequenced 1997
• Yeast (Saccharomyces cerevisiae) genome sequenced 1999
• Worm (Caenorhabditis elegans) genome sequenced 1999
• Arabidopsis thaliana(a weed) genome sequenced 2000
• Human genome working draft published (90% genome
sequenced)
June 26,2001

12. Genome Size and GeneNumber in some cropplants
CROPS GENOME
SIZE(Mb)
GENE
NO.
YEAR REFERENCE
1) Rice (Oryza sativa) 370 40577 2002 Goff, et al.,
2) Corn (Zea mays) 2500 >32000 2009 Schnable, P et al.
3) Sorghum
(Sorghum bicolor )
700 34496 2009 Paterson , A et al
4) Wheat
(Triticum urartu)
392 - 2013 Ling HQ et al
5) Arabidopsis
(Arabidopsis thaliana)
120 27417 2000 -
6) Soybean
(Glycine max)
950 46430 2010 Haung, et al.
7) Pigeon pea
(Cajanus cajan)
833 48680 2011 Varshney , et al,
8)Potato
(Solanum tuberosum)
844 39031 2011 PGS Constorium
9)Flax
(Linum usitatissimum)
686 43384 2012 Wang,et al

15. Genome editing
Megancleases
ZFN (Zinc Finger Nuclease)
TALEN (Transcription Activator Like Effector
Nuclease)
CRISPR/CAS9
CRISPR/Cpf1
Base Editing

16. 16

17. 17

18. Groundnut aflatoxin management
ojiewo et al. 2020

19. Varshney et al. 2020 19

20. • Prediction of gene-to-phenotype
relationhips
• Drought tolerance improvement
in crop plants
• Improving salt tolerance in plant
• Crop improvement through
modification of plant’s own
genome
• Improvement of biotic stress
resistance
20

21. 21

22. Beside rice the database has
information on barley,
Brachypodium, foxtail millet,
maize, oats, pearl millet, rye,
wheat and sorghum. In the
recent version, information
from other plant species like
Glycine, Musa, Solanum,
Brassica, Arabidopsis, Vitis,
Populus etc., have also been
included.
http://www.gramene.org/

23. Initially the database started with the data from only two plant species
viz., Arabidopsis and rice. Later, PlantGDB was published as a resource
for comparative genomics across 14 plant species.
http://www.plantgdb.org/

24. The Phytozome v7.0 contains data and analyses for 25 plant genomes, 18
of which are sequenced, assembled and partially or completely
annotated. However, the recent version (Phytozome v10) contains 47
sequenced and annotated plant genomes.
http://www.phytozome.jgi.doe.gov/pz/

25. CoGe address four issues, (i) single platform to store multiple versions
of multiple genomes from multiple organism (ii) rapid identification of
sequences of interest in genomes of interest (with associated
information) (iii) comparison of multiple genomic regions using any
algorithms and (iv) visualization of the results for easy and quick
identification of "interesting" patterns.
http://www.genomevolution.org/CoGe/

26. • Epigenetic processes
 Mechanisms other than changes
in DNA sequence that cause
effect in gene transcription and
gene silencing
 two mechanisms, DNA
methylation and histone
modification.

27. • Transcriptome
• The transcriptome is the complete set of transcripts in
a cell and their quantity, for a specific developmental
stage or physiological condition.
• The transcriptome is the set of all RNA molecules,
including mRNA, rRNA, tRNA, and other non-
coding RNA produced in one or a population of cells.
• Unlike the genome, which is roughly fixed for a given
cell line (excluding mutations), the transcriptome can
vary with external environmental conditions.

28. - Expressed sequences and genes of a genome
- Gene regulation and regulatory sequences
- Function and interaction between genes
- Functional differences between tissues and cell types
- Identification of candidate genes for any given process or disease
- Relationship between tissues
- Identification of tissue specific promoters
- Co-regulation and functional relationship of gene products (regulation
networks and functional pathways)
28

29. • Global transcriptomics
• Hybridization-based approaches
– fluorescently labelled cDNA with custom-
made microarrays
– commercial high-density oligo microarrays
• Sequence-based approaches
– Sanger sequencing of cDNA or EST
libraries
– serial analysis of gene expression (SAGE)
– cap analysis of gene expression (CAGE)
– massively parallel signature sequencing
(MPSS)
29

30. Application of transcriptomics inplant breeding
 1- Transcriptome assembly and
profiling
 2- Small RNA characterization
 3- eQTL
30

31. RNAi
• Co-suppression or post-trascriptional gene
silencing (PTGS) in plants.
• RNA interference (RNAi) is a mechanism that
inhibits gene expression at the stage of
translation or by hindering the transcription of
specific genes.
• Testing Hypotheses of Gene Function.
• Target Validation
• Gene Redundancy
• Functional screening
31

32. Rice
• OsBADH2 by RNA interference
leads to significantly increased
2- acetyl-1-pyrroline production.
• have found that the altered
expression levels of OsBADH2
gene influence aroma
accumulation, and the prevalent
aromatic allele probably has a
single evolutionary origin.
32
Niu et al., 2008

33. amylose content in wheat
• Amylose content was markedly
increased in the durum wheat
transgenic lines exhibiting SBE-IIa
gene silencing.
• silencing of SBE-IIa genes in
durum wheat causes obvious
alterations in granule morphology
and starch composition, leading to
high amylose wheat.
33
Francesco et al., 2010

34. low glucocinolate in brassica
• Targeted silencing of BjMYB28 homologs provided significant
reduction, without altering the desirable nonaliphatic glucosinolate pool,
both in leaves and seeds of transgenic plants.
• Molecular characterization of single-copy, low glucosinolate
homozygous lines confirmed significant down-regulation of BjMYB28
homologs vis-a-vis enhanced accumulation of BjMYB28-specific
siRNA pool.
• DET1 suppression in B. napus can increase the levels of carotenoids and
reduce the levels of sinapate esters simultaneously in the seeds, thus
enhancing their overall nutritional value. (Wei et al., 2009 )
• BnFAD2 gene was efficiently down-regulated and mediated by its RNAi
gene, and oleic acid composition in transgenic rapeseeds (Tian et al.,
2011 )
34

35. • The silencing of ghSAD-1 and/or
ghFAD2-1 to various degrees enables the
development of cottonseed oils having
novel combinations of palmitic, stearic,
oleic, and linoleic contents (Liu et al.,
2002 )
• RNAi-knockdown of -cadinene synthase
genes was used to engineer plants that
produced ultra-low gossypol cottonseed
(ULGCS). (Rathore et al., 2011)
35

36. • The term Proteome was first coined in
1994 by Marc Wilkins.
• Proteome refers to complete set of
proteins expressed in a given cell at a
given time.
• Study of Proteome is termed as
proteomics

37. General classification of proteomics
.

38. • It aims to study the dynamic protein products of the genome and their
interactions.
• Major two techniques used in proteomics are
1. 2-D electrophoresis
For seperation of complex protein mixtures
2. Mass spectrometry
Identification and structural analysis
AIM OF PROTEOMICS

39. • 1 gene is no longer equal to one protein
• 1 gene = how many proteins ?? (never known)
• The genome of an organism is essentially
invariant in all its cells, proteomes vary among
different cell types and also vary with time.
• protein product of a single gene may exist in
multiple forms because of posttranslational
modifications, the existence of mutants, and the
formation of splice variants.
1 gene = 1 protein ??
No ! One genome, Many
proteins..

40. Genomics do not have answer to all of our questions
 The behaviour of gene products is difficult or impossible to predict from gene sequence.
 Even if a gene is transcribed, its expression may be regulated at the level of translation.
 Protein products are subject to further control by posttranslational modifications, varying half-
lives, and compartmentalization in protein complexes
Proteomics is a field that promises to bridge a gap between genome sequence and cellular
behaviour..

41. What proteomics can answer
• Protein identification
• Protein Expression studies
• Protein function
• Protein – protein interaction
• Protein Post-Translational modification

42. Experimental Work flow
Protein identification
(Sequest & Mascot)
Proper sample collection and storage
Sample pre-processing
Protein identification LC-MS/MS
Immuno depletion/protein concentration etc
In gel Tryptic digestion/ in-solution digestion
Bioinformatic Analysis
Protein separation by
1D SDS-PAGE/ 2DE
Or
Fractionation by LC

43. Organ Specific protein expression…
43

44. Applications of proteomics

46. Databases
• Dataanalysissearchengines
• Sequest
• MASCOT
• X tandam
• Peaks
• Proteindatabases
• NCBS
• Swiss port

47. METABOLOMICS
• The metabolome consists of
small molecules (e.g. lipids or
vitamins) that are also known as
metabolites (Claudino et al.,
2007).
• Metabolites are involved in the
energy transmission in cells
(metabolism)
• Metabolic phenotypes are the by-
products of interactions between
genetic, environmental, lifestyle
and other factors.

48. Since metabolome is closely tied to
genotype of an organism, its
physiology and its environment (what
the organism or breathes),
metabolomics
opportunity to
phenotype as
eats
offers
look
well
a unique at
genotype- as
genotype-
envirotype relationships

49. Metabolomics
Steps in metabolomic techniques
sample
preparation
sample
extractio
n
chromatography
detectiondata
analysis

50.  Separation Techniques
 Gas Chromatography (GC)
 Capillary Electrophoresis (CE)
 High Performance Liquid Chromatography (HPLC)
 Ultra Performance Liquid Chromatography (UPLC)
 Combination of Techniques
 GC-MS
 HPLC-MS
 Detection Techniques
 Nuclear Magnetic Resonance Spectroscopy (NMR)
 Mass Spectrometry (MS)
Techniques


51. 
Metabolomic Profiling

52. 
Why Metabolomics is so Difficult
2x105
Chemicals
Metabolomics
Proteomics
20 Amino acids
Genomics
4 Bases
ChemicalDiversity
The Pyramid of Life

53. • METABOLOMICS FOR IMPROVEMENT OF FRUITS
• In recent years, the Kiwifruit has gained popularity due to its distinct appearance and the health benefiting
nutrients such as vitamin C and fiber.
• Total of 51 metabolites were detected during development and ripening and are responsible for quality and
flavor of Kiwifruit
• In Kiwifruit, application of synthetic cytokinin N-(2- chloro-4-pyridyl) significantly increases fruit size,
and affects the ripening processes by altering the accumulation pattern of metabolites such as amino acids,
sugars, organic acids etc.
- Wen et al., 2015.

54. • Phenomics term given by Gerlai, 2002.
• The term phenomic refers to sum total of phenotypes at various levels
ranging from molecules to organs and the whole organism.
• Study of plant growth, architecture, performance and composition
using high throughput methods of data acquisition and analysis.

55. HIGH THROUGHPUT PHENOTYPING
• The process of phenotyping which is automated, simultaneous and non-destructive, which
analyses the plant growth, morphology and physiology.

57. • Visual observation
• Requires an examination of
thousands of lines
• Relatively subjective
• Do not easily capture dynamic
aspects of complex traits
• Destructive methods of
measurements
• High cost and labour
• High-throughput screens,
Multiple camera units
• Quantitative results
• Less subjective
• Long term storage of images
• Non-destructive measurement
• Monitor growth dynamics
• Cost effective on large scale

58. Measurement of leaf area
• Morphometric method
• Optical flow method
• Particle /marker tracking method
Plant biomass estimation
• 3-digital imaging technique
• Hyperspectral imaging
• Non-optical method (electrical determination of water
content of plant , portable nuclear magnetic resonance
device)
Seed and fruit phenotyping
• 3-D Laser –scanning technology
• Visual imaging
• NIR Spectroscopy
GLOSS OF PHENOTYPIC TRAITS

59. Analysis of root system
• Rootreader 2D
• Smartreader
• Rootreader 3D
Analysis of shoot system
• Hyperspectral imaging
• Visible imaging
Analysis of chemical content
• Mass spectrometry and Gas chromatography (amino
acids present in fresh plant material )
• Liquid chromatography
• Flow cytometry
• NIR Spectroscopy

60. Analysis of physiological parameters
• SPAD chlorophyll meter
• Fluorescent imaging (chlorophyll fluorescence show negative
co-relation with photosynthetic activity)
Assessment of water use
• CID (carbon isotope discrimination technique used in wheat)
• Leaf and canopy temperature ( higher in case of decreased
transpiration rate)
• SPAD chlorophyll meter
Assessment of soil water content
• Mobile NIR
• Visual spectrophotometer
27

62. • Identifying genetic basis of complex traits
Yield
Abiotic stress: Drought, Salinity
Mineral nutrient deficiencies and toxicities
Biotic stresses
Heavy metal tolerance eg. Aluminium (Al) tolerance
• Transgenics
Identify effective transgene events
Identify transgenes performance across environments and genetic
backgrounds
Identify transgene optimization strategies for improving gene
performance
Forward genetic studies using large numbers of plants
Screening of mapping, association and mutant populations or populations
of wild relatives of crops

63. PATHOGENOMICS
 Pathogen infections are among the leading causes of infirmity and mortality among
humans and other animals and plants..
 The study of Pathogenomics attempts to utilize genomic and metagenomics data
gathered from high through-put technologies (e.g. sequencing or DNA microarrays), to
understand microbe diversity and interaction as well as host-microbe interactions
involved in disease states.
IMMUNOMICS
 Immunomics is the study of immune system regulation and response to pathogens using
genome-wide approaches.
 With the rise of genomic and proteomic technologies scientists have been able to
visualize biological networks and infer inter-relationships between genes and/or proteins;
recently, these technologies have been used to help better understand how the immune
system functions and how it is regulated.
63

64. Case Study
64

65. Materials and methods
• The association panel consisted of a diverse collection of 190 rice cultivars
including both standard salt-tolerant (Pokkali) and salt-sensitive (IR29) varieties.
• Experiment in greenhouse conditions
• Genotyping –
• Phenotyping –
• Number of tillers per plant
• Number of panicles per plant
• Filled grains
• Unfilled Filled grains
• Software analysis - TASSEL 5.0
 112,565SNPs

66. 66

67. Results
Trait Number of QTLs Chromosome no
Number of tillers per
plant
4 1,4,8,10
Number of panicles per
plant
4 2,6,10,12
Filled grains 2 2,4
Unfilled Filled grains 5 1,7,8,11,12
Total 15
 448SNPs – Four salt stress related traits
 If the lead SNP and following SNPs were within 1 Mb, these
SNPs considered into same QTL
 Finally 15 QTLs were found
Conclusion: GWAS provides understanding of salt tolerance mechanisms of rice at the flowering stage,
which can help improve yield productivity under salinity via gene cloning and genomic selection.

68. 68

69. Slmlo-tracrRNA-CAS9
Agrobacterium mediated transformation
Hygromycin resistant calli
Regeneration media
Grow plants
DNA extraction
PCR analysis
DNA sequencing of PCR products
24 plants
Procedure followed
15 Mutant plants

70. Generating non transgenic slmlo1 tomato lines
70
(a) The SlMlo1 locus was targeted by two sgRNAs; (b) T0 tomato transformants were tested for
the presence of deletions using the PCR band shift assay; (c) Selected T0 transformants
genotyped using the PCR band shift assay alongside wild type (WT) (d) SlMlo1 sequencing
reads from selected T0 transformants
(Nekrasov et al., 2017)

71. • I
71
(e) Leaves of tomato plants inoculated with Oidium neolycopersici (5 weeks post inoculation);
(f) PCR genotyping of the T1 generation for the presence T-DNA and the slmlo1 mutation. The
agarose gels presented in panels (b and c) were cropped.
Illumina sequencing data
(Nekrasov et al., 2017)

72. Summary
• Demonstrated by whole-genome sequencing that ‘Tomelo’
is non-transgenic, i.e. it does not carry any foreign DNA
sequences.
• Generated Tomelo in only 9.5 months from the DNA
transformation step to recovery of second generation
transgene-free segregants
72(Nekrasov et al., 2017)

73. 73

74. 74

75. Materials and methods
• 100 replicates of each line in green house conditions
• Seedlings at 40–60 days old,
• at 8 time points (0, 6, 12, 24, 36, 48, 72, and 96 h) after SBPH
infestation.
• SPSS Statistics ver. 22.0
75

76. 76
Resuts and Discussion

77. Resuts and Discussion
77

78. • Analysis of enzyme activities indicates that Pf9279-4 rice plants
defend against SBPH through the activation of the pathway of the
salicylic acid (SA)-dependent systemic acquired resistance.
• Higher SOD activity in combination with lower CAT activity observed
in Pf9279-4 may potentially lead to a higher level of H2O2, as SOD
catalyzes the conversion of O2 into H2O2.
• The higher concentration of H2O2 can be one of the potential factors
that contributes to Pf9279-4 resistance to SBPH.
• Higher levels of GSH, GSH-px, and IDH in Pf9279-4 after SBPH
infestation may represent a better capacity of maintaining redox
homeostasis during SBPH infestation.
78
Resuts and Discussion

79. 79

80. CONCLUSION
• Genomics, transcriptomics, proteomics, and metabolomics together they help to bring
out the best of characters in plants
• Genomic study or finding chromosome location, phenotypic analysis by QTL
mapping, genome-wide association studies (GWAS), etc. are being practiced along
with the development o genome editing by CRISPRCas9 or a variety of crop plants
under stress conditions from the past few years.
• Study of transcriptome profling, and microarray-based studies could detect the
signifcant alteration of gene expression and some rare novel transcript to map out the
physiological pathways.
• metabolite studies, its structure, and its function which reciprocate in many important
biological signalling cascades
• The designing of genotypes of crop species have become easy by using various tools
that help to identify relative alleles at a loci in a single population.
• This has enabled designing of better crop/plant varieties.
80

81. • Reduction in cost of technology usage.
• Development of bioinformatic tools for data analysis and storage of databases.
• Human resource development for an overall purview of technology to apply in
crop breeding.
• capacity building of young scientists is required in breeding to handle, analyze and
interpret the enormous data sets from omics.
• Strengthening of international parternership by leveraging existing capabilities and
data sharing.
• the use of high-throughput methods that enable comprehensive profiling of entire
organism, should provide insight into the stringency of target recognition inherent
in each system.
81

82. Group Resources Databases
Genome Genome sequence, gene
annotation
PlantGDB, Phytozome, CoGE,
PLAZA
Molecular markers, DNA
variation, Quantitative Trait
Locus
Gramene, Phytozome, PIP
database, NCBI dbSNP, CSGRqtl,
SorGSD
Genome Re-sequencing (GIGA)n DB
Focused gene family
database
GRASSIUS
Transcriptome Full length cDNAs, ESTs PlantGDB, Phytozome, NCBI
dbEST
Non-coding RNA NRDR
microRNA PMRD, miRBase
Proteome Proteome / modificome profile GreenPhylDB, Phytozome
Subcellular localization Gramene, Phytozome, PlantGDB
Metabolome Metabolic Map SorghumCyc
Modified from Mochida and Shinozaki (2010)

83. 'OMICS' DATAREPOSITORIES

84. References
• Haifei Hu, Armin Scheben and David Edwards. Advances in Integrating Genomics and Bioinformatics
in the Plant Breeding Pipeline.agriculture.75-93
• Rajeev k.v, pallavi Sinha, Vikas k.s. and Aravind Kumar 5Gs for crop genetic improvement, Curr Opin
Plant Biol (2020).
• Anoushka Kotra, Neetu Jabalia and Nidhee Chaudhary, Significant Role of Genomics in Crop
Breeding: An Overview. International Journal of Basic and Applied Biology. 50-54.
• Pocket K No. 15 ‘Omics’ Sciences: Genomics, Proteomics, and Metabolomics. http://www.isaaa.org/kc
• Mohamed Shorbagi. Metabolomics review. Research gate.net
• K. Chandrasekhar , A. Dileep , D. Ester Lebonah , J. Pramoda Kumari, A Short Review on Proteomics
and its Applications
• Organ specific proteome analysis for identification of abiotic stress response mechanism in crop .
SetsukoKomatsu1* and Zahed Hossain2 .frontiers in plant science
• Proteomic responses of fruits to environmental stresses Zhulong Chan. frontiers in plant science
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