This document summarizes the application of bioinformatics in the agriculture sector. It discusses how bioinformatics is used to analyze vast amounts of agricultural data to develop stronger, more drought and disease resistant crops with improved nutritional quality. Specific applications mentioned include developing renewable energy crops, insect resistant crops using Bt genes, golden rice with increased vitamin A, drought tolerant crops, and using omics data for plant breeding. It also discusses using bioinformatics to study plant diseases, synteny between crops like rice and Arabidopsis, and software/tools used in bioinformatics.

1. College Roll No. 7020/18
University Roll No. 18076583012
Program Name – B.Sc. Life Science
Semester – V
Title of Paper – Bioinformatics
Name of Student - Suraj Singh
Topic – Application of
Bioinformatics in Agriculture
Sector

2. Application of Bioinformatics in
Agriculture Sector

3. Contents
• Branches and Sub-branches of Science
• Introduction
• Crops
• Application in –
Renewable Energy
Insect resistance
Improve Nutritional Quality
Grow in Poorer Soils and Drought Resistant
Plant Breeding
Accelerate Crop Improvement in a Changing Climate
Bioinformatics in Plant Disease Management
• About Arabidopsis
• About Oryza longistaminata
• Clustering of predicted proteins on rice BACs to homologs on different
chromosomes of Arabidopsis.
• Arabidopsis synteny with other plant species
• Circular overview of the 12 assembled O. longistaminata chromosomes.
• Rice – Maize synteny
• Software & Tools
• References

4. Science & Technology
Biology Medicine Engineering Mathematics IT
Bioinformatics
( Management Information System for Molecular Biology )
Data
Collection
Data
mining
Database
search
Analysis Modeling
Discovery, Development and Implementation in algorithms
Serve Agriculture and Health Sector

5. Introduction
• Bioinformatics is widely applied in agricultural
research.
• Since agricultural data are of different types and
huge in collection, its interpretation is difficult; thus
Bioinformatics play big role to analyze the data
properly.
• Collection and storage of plant genetic resource and
wisely application of bioinformatics help to produce
stronger, more drought, disease and insect resistant
crops and improve the quality of livestock making
them healthier, more disease resistant and more
productive.

6. Crops
• Comparative genetics consists of the model and
non-model plant.
• Species can reveal an organization of their genes
with respect to each other which further use for
transferring information from the model crop
systems to other food crops.
• Arabidopsis thaliana (water cress) and Oryza sativa
(rice) are examples of available complete plant
genomes (Proost et al 2009).

7. Renewable Energy
• Plant based biomass is one of the best resource for obtaining
energy by converting it into biofuels such as ethanol which
could be used to drive the vehicles and fly the planes.
• Biomass based crop species such as maize (corn), switch grass
and lignocellulosic species like Trifolium , straw are widely
used for biofuel production.
• We could detect sequence variants in biomass-based crop
species to maximize biomass production and recalcitrance.
• Recently, genome of Eucalyptus grandis has been released
which is also one of major resource of biomass components
and all the genes take part in conversion of sugars into
biomass components have already been deciphered,
therefore bioinformatics provides great insight into
mechanisms and pathways responsible for this conversion so
that in future we can enhance production of biomass
components in eucalyptus and other relevant plants (Bisby et
al 1993).

8. Insect resistance
• Bacillus thuringiensis (or Bt)
is a Gram-positive, soil-dwelling
bacterium, commonly used as a biological pesticide.
• It’s genes ( cri gene ) control a number of serious
pests that have been successfully transferred to
cotton, maize and potatoes.
• These crops are known as Bt crops.
• This new ability of the plants to resist insect
outbreak may reduce the amount of insecticides
being used.

9. Improve Nutritional Quality
• Scientists have recently succeeded
in transferring genes into rice to
increase levels of Vitamin A, iron
and other micronutrients.
• Bioinformatics tool helped to produce such golden
rice that can fight against vitamin A deficiencies.
• This work could have a profound impact in reducing
occurrences of blindness and anemia caused by
deficiencies in Vitamin A and iron respectively
(Paine et al 2005).
• Scientists have inserted a gene from yeast into the
tomato, and the result is a plant whose fruit stays
longer on the vine (Fraser et al 2009).

10. Grow in Poorer Soils and Drought Resistant
• Progress has been made in developing cereal varieties
that have a greater tolerance for soil alkalinity, free
aluminium and iron toxicities.
• These varieties allow agriculture to succeed in poorer
soil areas, thus adding more land to the global
production base.
• Research is in progress to produce crop varieties
capable of tolerating reduced water conditions (Wang
et al 2004).
• Data obtained from such intensive research are huge
which are difficult to analyse by a single scientist.
• Bioinformatics help in a greater amount to solve such
problems.

11. Plant Breeding
• The goal of plant genomics is to understand the genetic
and molecular basis of all biological processes in plants.
• This understanding is fundamental to allow efficient
exploitation of plants as biological resources in the
development of new cultivars with improved quality
and reduced economic and environmental costs.
• An omics data can now be envisioned as a highly
important tool for plant improvement.
• The ability to examine gene expression allows us to
understand how plants respond to and interact with
the internal and external stimuli.
• These data may become crucial tool of future breeding
decision management systems (Langridge 2011).

12. Accelerate Crop Improvement in a Changing Climate
• The change in climate and increase in population will
increase pressure on our ability to produce sufficient
food.
• The breeding of novel crops and the adaptation of
current crops to the new environment are required to
ensure continued food production.
• Advances in genomics offer the potential to accelerate
the genomics based breeding of crop plants.
• However, relating genomic data to climate related
agronomic traits for use in breeding remains a huge
challenge, and one which will require coordination of
diverse skills and expertise.
• Bioinformatics, when combined with genomics has the
potential to help maintain food security in the face of
climate change through the accelerated production of
climate ready crops (Batley and Edwards 2016).

13. Bioinformatics in Plant Disease Management
• Pathogen trait is considered as a primary interest of plant
bioinformatics.
• The contribution of bioinformatics advances made possible
the mapping of the entire genomes of many organisms in just
over a decade.
• The current efforts to determine gene and protein functions,
have improved the ability to understand the root causes of
plant diseases and find new cures.
• Furthermore, many future bioinformatics innovations will
likely be spurred by the data and analysis demands of the life
sciences.
• Bioinformatics have many practical applications in current
plant disease management with respect to the study of host
pathogen interactions, understanding the disease genetics
and pathogencity factor of a pathogen which ultimately help
in designing best management options.

14. About Arabidopsis
Arabidopsis thaliana - small flowering plant
• widely used as a model organism in plant biology.
• member of the mustard (Brassicaceae) family, which includes cultivated species such as cabbage and radish.
• not of major agronomic significance,
• important advantages for basic research in genetics and molecular biology.
• Small genome (114.5 Mb/125 Mb total) has been sequenced in the year 2000 ( SequenceViewer, AGI ).
• Extensive genetic and physical maps of all 5 chromosomes (MapViewer).
• A rapid life cycle (about 6 weeks).
• Prolific seed production and easy cultivation in restricted space.
• Efficient transformation methods utilizing Agrobacterium tumefaciens.
• A large number of mutant lines and genomic resources many of which are available from StockCenters.
• The 1001 Genomes Project for Arabidopsis thaliana associated with it.
• TAIR collects and makes available the information arising from it.
About Oryza longistaminata
• Oryza longistaminata (AA genome type) is a wild rice, Perennial, tall (2 m or more), erect, and rhizomatous
grass; ligule of lower leaves >15 mm, acute or 2-cleft; panicles open to intermediately open; spikelets 4.5-11.4
mm long and 2-3 mm wide, awned (2-5 cm long); anther 1.5-8.2 mm long.
• A whole genome shotgun assembly (i.e. Illumina sequence) of O. longistaminata was generated by Professor
Wen Wang (Kunming Institute of Zoology, Chinese Academy of Sciences) in collaboration with BGI-Shenzhen.
• The genome assembly was composed of 135,973 scaffolds spanning 344.6 Mb with a N50 scaffold size of 62.4
kb.
• Using this assembly, the Arizona Genomics Institute (AGI) selected scaffolds and contigs that were syntenic to
the short arm of chromosome 3 of O. sativa ssp.japonica, and the order and orientation of each scaffold/contig
was confirmed using Genome Puzzle Mater software (GPM, unpublished) to produce a Chr3S pseudomolecule.
• The final O. longistaminata chromosome 3 short arm resulted in a single scaffold of 14,404,039 bp composed of
4,724 contigs.

15. Arabidopsis thaliana Oryza longistaminata

16. Clustering of predicted proteins on rice BACs to homologs on different chromosomes of Arabidopsis.
Hong Liu et al. Genome Res. 2001;11:2020-2026
Clustering of predicted proteins on rice BACs to homologs on different chromosomes of Arabidopsis. Each row is for a rice BAC, and the size of starburst indicates the number of rice proteins on the
BAC showing homology with Arabidopsis proteins on a specific chromosome. The number after the starburst is the total number of hits. Following are the number of putative proteins on each BAC.
Because of removing overlapping genes on neighboring clones, some of those BACs contain fewer proteins; these are indicated in the following list by an asterisk. The actual number is indicated
following the original one. On Chromosome 1: AP002818(25),AP002882(32),AP003338(17),*AP002867(25)(14), *AP002747(30)(18), AP002541(31),*AP002868(23)(21),AP002487(11),
AP003046(24),AP003233(27), AP002909(21),AP002538(20),AP002526(27), AP002913(24),*AP002483(26)(9),AP003311(30),AP002872(32), AP002540(34),*AP002522(30)(15),AP003045(32),
*AP002539(36)(9), AP002521(31),AP002912(29),AP002523(23),*AP002524(27)(17),AP003118(22),AP003047(23),*AP002484(25)(21), *AP002486(28)(25), *AP002816(24)(7),*AP002836(21)(18),
*AP002743(26)(22), *AP002746(34)(21),AP002537(29),AP003244(19),AP003105(27), AP001551(31),*AP002093(27)(7),*AP002092(33)(32), *AP003104(32)(15), AP001633(36),AP002094(25),
*AP001800(27)(13), *AP002835(21)(16),AP002902(27),AP001859(23), AP001550(26), *AP002481(26)(19),AP001539(29),*AP002855(23)(19),AP003210(21),AP003074(34),AP002865(28),
*AP001278(30)(23), *AP000816(12)(10),*AP000492(33)(9),AP000570(33),*AP002525(25)(21),*AP002817(24)(15),*AP001366(27)(24),AP001383(27), *AP001080(25)(21), *AP000969(23)(17),
*AP001073(29)(18), AP001081(31),*AP000837(18)(12),AP000836(24),AP001072(21), AP000815(25),AP003018(25),AP003020(26),AP002070(30),*AP002480(28)(26),AP002482(34),
AP002820(12),AP003140(30),AP003578(20),AP002881(15), AP002871(27), AP002953(23),AP002971(25),AP002869(35),AP003143(20),AP002870(21),AP003021(31),AP002914(19),AP002968(21),
AP003144(18),AP003054(16),AP003075(18),AP002908(24),AP003048(21), AP002844(27),AP002866(34),AP002897(21),AP002843(27),AP002819(25),AP002744(36),AP002839(32), AP002910(33),
AP003053(14),AP003023(25), AP002901(33),AP002899(28), AP002972(22), AP003076(40),AP003106(29),AP003073(33).On Chromosome 2: AP000366(3), AP000367(20).On Chromosome 3:
AP000615(28).On Chromosome 6: *AP001129(35)(28),AP000616(28),AP001552(25), AP001389(27), AP001168(27),*AP000391(27)(24),AP000559(28),AP002542(32),AP000399(32),AB026295(35),
AB023482(32),AP002071(27), AP002069(29), AP003044(26).On Chromosome 8: AP000364(25).

17. Arabidopsis synteny with other plant species

18. Circular overview of the 12 assembled O. longistaminata chromosomes.

19. Rice – Maize synteny
The Plant Journal, Volume: 38,
Issue: 3, Pages: 396-409, First
published: 14 April 2004, DOI:
(10.1111/j.1365-313X.2004.02058.x)

20. Software & Tools
• Homology searching ( e.g., BLAST )
• Sequence alignment ( e.g., ClustalW )
• Phylogenetics ( e.g., PHYLIP )
• Functional patterns ( e.g., HMMER )
• Gene prediction ( e.g., GeneFinder )
• RNA structure ( e.g., MFOLD )
• Protein structure ( e.g., JPred )

21. References :
• https://globaljournals.org/GJSFR_Volume17/3-Role-of-Bioinformatics-in-Crop.pdf
Role of Bioinformatics in Crop Improvement By Ujjawal Kumar Singh Kushwaha, Indra
Deo, Jai Prakash Jaiswal & Birendra Prasad
• https://www.arabidopsis.org/
• https://images.app.goo.gl/5tj7f5skhefGt4E78
• https://www.google.com/imgres?imgurl=https://4.bp.blogspot.com/d8B_KL8hvo/WcZxIYy_8
mI/AAAAAAAADu4/FFKEMsKnVJ0y69CE1x1cgosJD0q6nCcgCLcBGAs/s1600/GoldenRiceinthefi
eld710px.jpg&imgrefurl=http://uplbgenews.blogspot.com/2017/09/golden-rice-alleviating-
globalvitamin.html&tbnid=n8DqsSiH0QiN_M&vet=1&docid=ch3z8lxpgppeHM&w=710&h=30
0&itg=1&hl=en-US&source=sh/x/im#imgrc=n8DqsSiH0QiN_M&imgdii=SJHGbNrDCTBcDM
• https://www.nature.com/articles/s42003-018-0171-y
Assembling the genome of the African wild rice Oryza longistaminata by exploiting
synteny in closely related Oryza species
• https://plants.ensembl.org/Oryza_longistaminata/Info/Annotation/
Oryza longistaminata Assembly and Gene Annotation
• https://en.wikipedia.org/wiki/File:Bt-toxin-crystals.jpg
• https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-313X.2004.02058.x
New in silico insight into the synteny between rice (Oryza sativa L.) and maize (Zea
mays L.) highlights reshuffling and identifies new duplications in the rice genome