information technology in plant protection

Information Technology in Plant Protection

Presentation

TÁMOP-4.1.2.A/2-10/1-2010-0012

• Prepared by:– Dr. János Busznyák

GIS tools for Plant Protection

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• Methods of Obtaining Spatial Data– Manual– Geodesy– With the help of Global Positioning– Photogrammetry– Remote Sensing– Manual Map Digitalisation– Scanning Maps– From Digital Files

Digital Mapping Tools for Plant Protection

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• Not only the digital form of the contents of a map ready to be used with a computer.

• No need for segmentation, the elements are of real size, has accurate fitting, has topology, often uses layers and objects.

• Primary Data Obtaining Methods– Measurements (GPS)– Existing Reports

Mostly vector data are obtained from primary data obtaining methods.• From Secondary Sources

– By digitalization, adding automatic or manual vectorization.In the case of georeferencing and vectorization in secondary methods,

the result is also a vector map. If a secondary data collection (scanning) is not followed by vectorization, the result is a digital raster map.

Digital Map

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• Aim– New level of GPS analysis (vector)– New publication possibilities– Lower storage and transfer capacity needs

• Preparatory steps– Digitalization of map sheets– Georeferencing, eliminating distortions, projection convertion (lots

of work)• Pre-processing• Vectorization

– Of areas– Of line-like objects– Of objects

• Post-processing

Raster-Vector Transformation

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• Vectorization– Manual– Semi-automatic– Automatic

Vectorization II.

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• Automatic vectorization of a soil map– Single bit– Low data density

• Automatic vectorization of a topographic map– 8-bit– High data density

Black: convert to lineBlue: segmented pixels

Application of the Automatic Method

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• Coordinates of the shape file vertex points– site,lat,long,name,HOTLINK – 1,38.889,-77.035,Washington

Monument,http://www.nps.gov/wamo

– 2,38.889,-77.050,Lincoln Memorial,c:/ESRI/AEJEE/DATA/WASHDC/linc.jpg

– 3,38.898,-77.036,White House,c:/ESRI/AEJEE/DATA/WASHDC/whse.txt

– 4,38.889,-77.009,Capitol,c:/ESRI/AEJEE/DATA/WASHDC/cap.pdf

ESRI Arc Explorer JEE tutorial

Data Input from Text File

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http://esri.com/

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• With the help of hybrid systems, raster and vector data can be used together. – Vector, raster and attribute data are stored separately, in

the most suitable way for the model.– The operations are carried out by these systems in the

model that is most suitable for the operation in question.– The systems apply a wide variety of vector-raster

transformations before and after the operations.– The GoogleMaps service is based on a hybrid data model.

Hybrid Data Model, Mashup Map

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• Facts that mostly influence data quality:– Origin of data– Geometric accuracy– Accuracy of attribute data – Consistency of attribute data– Topologic consistency– Completeness and validity of data

Data Quality

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• Georeferencing is the process of scaling, rotating, translating and deskewing the image to match a particular size and position.The word was originally used to describe the process of referencing a map image to a geographic location. Source: http://wintopo.com/help/html/georef.htm

• Usual ways:– World file– Header (GeoTiff, GeoJP2…)

Georeferencing

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• Certain image formats include georeferencing information in the header of the image file: – img, – bsq, – bil, – bip, – EXIF– ITT– GeoTIFF– grid

Header

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• Georeferencing information is stored in a separate word file: – The word file contains 6 parameters of an affin

transformation that means a connection between the image coordinate system and that of the world coordinate system.

– The images are stored as raster data, where each cell of the image is identified by a row and coloumn number.

– The name of the word file has to be the same as the image file and be in the same folder.

Word File

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Georeferencing with the Help of 2 Reference Points segítségével

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Graphic Georeferencing - Rubber sheeting

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• Projection, date• Geoid, geoidundulation• Uniform National Projection (UNP - EOV)• Transformation• Base points, base point systems

Projection Systems, Conversion

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• Based on image surface shape– Cylinder projection– Cone projection– Flat projection– Other projection

• Based on image surface axle– Polar (normal)– Transversal (equatorial)– Oblique (not normal difference)

• Based on the contact of the image and base surface – Tangent– Transect

Classification of Projection

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• Systems without projection• Dual projection Hungarian systems• Stereographic projection systems (BUDAPESTI,

MAROSVÁSÁRHELYI)• Oblique Mercator Projection• HÉR, HKR, HDR• EOV• Gauss-Krüger• UTM (Universal Transverse Mercator)• GEOREF (World Geographic Reference System)

Important Projection Systems

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• Reference ellipsoids nearing an area of the Earth surface• The centre of the ellipsoid is that of the Earth• The axis of rotation is that of the Earth’s

– Parameters• Major axis (equatorial radius)• Oblateness (connection between equatorial and polar radius)

• If the centre of the ellipsoid is moved until it fits to the examined area with the least error, we will get the geodesic date.– Bessel (stereographic)– Kraszovszkij (Gauss-Krüger)– Hayford (UTM)– WGS-84 (GPS), – IUGG-67 (EOV)

Important Ellipsoids

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• Geographic Projection– WGS 1984 Datum

• Ortographic Projection– SPHERE Datum

• Eckert IV. Projection– WGS 1984 Datum

Some Interesting Projections

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• GPS measurement gives the height above the ellipsoid (h). When calculating height above sea level(H), geoidundulation has to be taken into consideration.

• Geoidundulation is the separation between the equipotential surface that represents a mean ocean surface and a reference ellipsoid (h=H+N, where N is the value of geoindundulation of the point).

• Geoid: the surface of oceans and seas, if connected by small canals under the land(Listing 1873)

Geoidundulation

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• The starting coordinates have been placed 200km to the South and 650 km to the West. Thus, the Y coordinates are lower than 400, and the X coordinates are always higher than 400, which means they are easy to distinguish.

Uniform National Projection

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• The first elevation of Hungary was carried out based on the Mediterranian base level from 1873-1913.– Height of Nadap main

base point: 173,8385 m. • Baltic base level after

World War II.– Height of Nadap main

base point: 173,1638 m, which is 0,6747 m lower.

Uniform National Elevation Network(EOMA)

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• ETRS89 (OGPSH) points transformed into the Uniform National Projection (EOV) system and back

• The points for the transformation are chosen automatically

• Local transformation based on the common points of the OGPSH and EOV systems

• With 8 common points in Hungary

• With refined Geoidundulation data

Etrs89-Eov-Hivatalos-Helyi-Térbeli-Transzformáció

Transformation

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http://www.gnssnet.hu/letolt3.php

http://www.gnssnet.hu/letolt3.php

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• Database of Altitudinal Base Points• Database of Horizontal Base Points• Database of OGPS Base Points

• Országos GPS Hálózat pontjai (Points of the National GPS Network-OGPSH)

Base Points

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http://www.sgo.fomi.hu/images/bigmap.gif

TÁMOP-4.1.2.A/2-10/1-2010-0012

• Video– Georeferencig (graphical)

• Animation– Georeferencing– Geoidundulation– Shape (create)

Videos and Animations for Chapter 1.

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I. questionIdentify the value of geoid-undulation at the Parliament Building,

Budapest, Hungary with the help of EHT (or any other) software .II. question

Digitalize any map sheet with the help of a scanner. Georeferate it with 3 reference points with the help of GEOREGARCVIEW software.

The necessary coordinates can be obtained from mapservers (eg. Googlemaps).

III. questionDigitalize another map sheet overlapping the previous one with the help of a scanner. Georeferate with 3 reference points with the help of GEOREGARCVIEW software. Open it together with the georeferated file of the previous task with ArcExplorer JEE (or any other) and check its accuracy.The necessary coordinates can be obtained from mapservers (eg. Googlemaps).

Tasks for Chapter 1.

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• Global Positioning– The coordinates of 3

satellites at a given time are needed.

– If time can be measured accurately, then wave spread speed and the time will help calculate how far we are from the satellite.

– In the case of 1 satellite, it will give a sphere surface.

GNSS Device System

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• If there is a connection with 2 satellites, then we are on the sphere of both satellites. The section of the two spheres is a circle.

• The section of the sphere of the third satellite and the circle will be two points, one of which can always be excluded (eg. Points far from the earth surface).

Global Positioning II.

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Differential Correction

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• GNSSNet• NtripCaster IP address,

port: 84.206.45.44:2101

Network RTK in Hungary(2010)

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http://gnssnet.hu/

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• Geotrade GNSS – Host:

www.geotradegnss.hu– Port: 2101

Multi-Base System in Hungary ( 2010)

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http://www.geotrade.hu/geotradegnss

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• Georgikon RTK coverage• DGPS forthe whole

country of Hungary– http://gnss.georgikon.hu– 193.224.81.88:2101

Single-Base System (2010)( 2009

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http://gnss.georgikon.hu/

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Trimble European VRS System

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• CSD (Circuit Switched Data)– Line connected mobile internet - 9,6 kbit/s - 1G

• GPRS (General Packet Radio Service)– Package connected - 115 kbit/s - 2G

• EDGE (Enhanced Data Rates for GSM Evolution) – GPRS reinforcement- 236 kbit/s-os (112-400) - 2,5G

• 3G – 3G mobile network, video call 384 kbit/s - 3G

• HSPA (High-Speed Downlink/Uplink Packet Access)– HSDPA theoretic data transfer speed depending on device and

coverage: up to 21 Mbit/s – 3,5G• 4G LTE (Long Term Evolution)

– 1Gbit/s - 4G

Mobile Internet

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• Video– Trimble VRS system

• Animation– GNSSNet service– Geotrade GNSS– Georgikon GNSS Base


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I. questionFind the data of the accessible satellites of the Galileo andBEIDOU systems at a given time.

II. questionFind the terrain control stations of the Navstar GPS systemat a given time.

III. questionFind the worst measurement site on the Earth’s surface concerning ionosphere state at a topical time. Use the ‘space weather forecast’ of Australia (or any other information source).


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http://www.ips.gov.au/Space_Weather

http://www.ips.gov.au/Space_Weather

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• GNSS Measurement– Planning (almanach)– Realization (online correction: procession too)– Data transfer(exchange formats, RINEX - Receiver

Independent Exchange Format)– Processing (vectors, transformation, error correction)– Network equalization (OGPSH – National GPS Network)

Terrain GNSS Measurement and Processing

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• Guarantee of integrity– GNSS– Way of correction

• Guarantee of nedded accuracy– Accuracy of the Rover device– Way of correction– Satellite constellation– Minimalization of other disturbing facts

Aim of GNSS Measurement Planning

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• GNSS satellite data– Almanach

• Trimble Planning• Leica Satellite Availability• Topcon Occupation Planning

• Receiving correction data– Mobile internet

• Gprs coverage• Style, devices, realization

Devices for Planning

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• Timing – Further in time– Back in time

• General – YUMA formátum,USA Coast Guard Navigációs Központ

(YUMA format, USA Coast Guard Navigation Center)– A dátum és a GPS-hét kapcsolata a GPS-naptárban (the

connection between date and GPS-week in the GPS calendar)

• Trimble• Leica• Topcon

Almanach

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http://www.navcen.uscg.gov/?pageName=gpsAlmanacs

http://www.ngs.noaa.gov/CORS/Gpscal.shtml

http://www.trimble.com/planningsoftware_ts.asp

http://www.leica-geosystems.com/

http://www.topconpositioning.com/

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Trimble Planning

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• Relative – Real time– Radio – Satellite – Internet

• Post-processed– Digital data transfer

Channels of Correction Data

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• Connection to satellites, controller• Connection to correction service• Setting measurement style• Starting measurement• Recording data

Realisation of Measurement

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• Obtain, check and converse existing spatial data

• Set up a measurement plan– Need for accuracy– Available devices and

services – Specialities of the area– Select measurement method

• Places of measurement• Conversion to the format of

the terrain device• Upload data to the terrain

device

Preparation of Measurement

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• Check measurement data• Inspection • Delete, edit• New recording • Data • Export in needed formats• Turn off terrain device

End of Measurement

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• Load data from terrain device– Formats– Give coordinate system and date– Examine data load mistakes– inspection– Delete, edit

• Export to the format of procession

Processing Data

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• Upload data to GIS system– Conversions – Analyses – Interpolations – Model building– Simulation – Statistical analysis– Publication

• Online correction– Procession

• Offline correction– Time of measurement– Obtain correction data– Correction – Check

GIS procession and Analysis of Data

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• EEHHTT software– Data input

• From file• Via keyboard

– Set format of data input– Set data conversion direction– Give coordinates

Checking Transformation

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• Adatgyűjtő– Navigation accuracy

• ArcPad / palmtop with GPS antenna– GPS accuracy

• GPS Pathfinder office / Trimble GeoXH– Geodesic accuracy

• Trimble Survey Controller / Trimble 5800• Data procession

– GPS Analyst– GPS Pathfinder Office– Trimble Geomatics Office

– ArcGIS

Typical Terrain Device System

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Sample• Aim of survey: automatic data collection for 3D relief model• Place of survey: the island of Kányavári, Hungary• Time of survey: 21. December, 2008. 0920h-1530h• Type of survey: RTK; Format of message transfer: CMR+• PDOP mask: 6, elevation cutoff: 10 degrees, antenna: Trimble 5800, hant: 2 m• Coordinate System Hungary Zone Hungarian EOV• Project Datum HD72 (Hungary)• Vertical Datum Geoid Model EGM96 (Global)• Coordinate Units Meters; Distance Units Meters;Height Units Meters

• Name of point DeltaX DeltaY DeltaZ Slope Distance RMS• 25001 13189,539m 1880,080m 11396,001m 17531,898m 0,002m• Name of pointX Y H• 25001 142686.277 505893.164 109.042

Description of Continuous Topographic GPS Survey

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• Take sample• Yield mapping• Sensors • Auto pilot system• Mass flow or sprayer

control • Row control• Seeder control

Basic GPS Elements of Precision Farming

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• 1. GPS survey of field blocks, soil sample taking plan• 2. Take soil sample according to plan every 3-5 acres• 3. Soil examination (extended and holistic) • 4. Make nutrient content maps• 5. Information, services for professional advice, analyses• 6. Agrochemical service• 7. Differentiated fertiliser plan • 8. Differentiated nutrient output, plant number plan • 9. Seeding with base station• 10. Precision herbicid plan (based on Hu, KA, pH map and weed

uptake)• 11. Ffertiliser quantity, upload into professional advice system• 12. Download data from the Internet

Precision Management System (IKR)

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• IKR

Precision Management System

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http://terkepbank.ikr.hu/

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• Spreadsheet

Evaluation of Tillage Experiments

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• Spreadsheet

• GIS software (weed density)

• GIS software (weed density)

Evaluation of Tillage Experiments II.

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3D Model

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• Video– GNSSNet OGPSH

• Animation


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I. questionCreate a forecast for tomorrow 1200hr and 1215hr above 10 degree elevation cutofffor the area of the Helikon strand, Keszthely, Hungary (Lambda = 46 degree 45minutes, Fí = 17 degree 15 minutes, h = 150 m).GDOP=PDOP=HDOPVDOP=TDOP=Number of GPS satellites =Number of Glonass satellites=Number of Galileo satellites=Number of Compass satellites=

II. questionIn the IKR precision management system, which service(s) can use correction GNSSbase data?

III. questionIs soil sample take in the IKR precision management system realized with a yield mapor a grid?


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• Remote Sensing– With the help of remote sensing, objects can be examined that

are not in a direct connection with the sensor.– In a narrow sense, the concept of remote sensing is usually used

for aerial and space images. In a wider sense, it can also be defined for eg. remote measurements or medical applications.

– Remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with the object. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth (both on the surface, and in the atmosphere and oceans) by means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or satellites).

Remote Sensing Device System, 3D Modelling

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• The measurement does not influence the examined object, or change its state.

• It can be used at wavelengths out of the visisble range. The result can be examined in the visible spectrum.

• Objective, exact data can be obtained.• Spatial, several dimension data can be obtained.• Lots of data can be obtained from big areas in a short time.• Areas that can not be reached or examined with other

methods can be examined.

Characteristics of Remote Sensing

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• Active sensors– sense the reflection of their own radiation

• Passive sensors– have no emission

• One or more wavelength range• Images with more than one band are called (depending on the

number of bands) multispectral or hiperspectral.

Clasification of Sensors

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• Geometric – pixel: the space of one point of the image measurable on

the earth surface, its real extension.• Spectral

– the value of radiation from the object• Radiometric

– characterises the colour depth of the pixels• Temporal

– the time interval between the images

Information from Sensors

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• Wavelength, frequency– Visible light (0,4 - 0,7 µm)– Infrared (0,7 µm felett) – Ultraviolet (0,4 µm alatt)

Electromagnetic Spectrum

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• Scatter - Multi path scattering• Occlusion

– Influencing factors• Traveled distance• Radiation energy • Composition of the atmosphere • Size of particles• Wavelength

Atmospheric Effects

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• Chlorophyl absorbs the energy of the wavelengths between 0.45 and 0.67 µm,mostly blue and red colours, thus the colour of the healthy plant is green.

• In an unhealthy plant, the yellow colour together with the green can be caused by red reflection caused by chlorophyl decrease.

• Reflection within the range 0.7 and 1.3 µm highly depends on leaf structure (sort specific), and dramatically increases.

• Effect of stratification, water occlusion bands above 1.3 µm.• Above 1.3 µm, reflection is inversely proportional to the

whole water content of the leaf.

Visible and Infrared Range

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• The reflection curve of plant sorts are identifiable.

• Image correction (atmospheric distortion)

• Sample points• Spectrum

Visible and és Infrared Range II.

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• TM 1 0.45 – 0.52 µm(blue) 30 m• TM 2 0.52 – 0.60 µm(green) 30 m• TM 3 0.63 – 0.69 µm (red) 30 m• TM 4 0.76 – 0.90 µm(near infrared) 30 m• TM 5 1.55 – 1.75 µm(medium infrared) 30 m• TM 6 10.42 – 12.50 µm(thermal infrared) 120 m• TM 7 2.08 – 2.35 µm(middle infrared) 30 m

Spectral Bands and Resolution of Landsat TM

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• ASPRS (ASPRS satellite database)

Planned Objects of Satellite Sensing

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http://www.asprs.org/news/satellites



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• 2002. DLR DAIS, 79 band system• 2006. with the help of AISA DUAL hiperspectral camera,

aerial data collection service was launched by the University of Debrecen (Hungary) and the Ministry of Rural Development. – Senses in a maximum of 498 bands, at the wavelength of

0.45–2.45 micrometres.

Hiperspectral Imaging in Hungary

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• National Aeronautics and Space Administration (NASA) and U.S. Geological Survey (USGS) (1999)

• Images in 7 bands (6 bands 30 m, termal-infra 60 m terrain resolution)

• Sun-synchronic orbit (the satellite travels above a given site at the same local time)

• Circulates at the height of 705 km• Can take images of an area of 185x170 km every 16 days

LANDSAT 5 TM

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• TM 1 0.45 – 0.52 µm differentation of land from plants, mapping of artificial surfaces.

• TM 2 0.52 – 0.60 µm mapping plant cover, identification of artificial surfaces.

• TM 3 0.63 – 0.69 µm differentation of planted surfaces from plantless surfaces, identification of artificial surfaces.

• TM 4 0.76 – 0.90 µm identification of plant sorts, definition of green mass, survey of plant vitality, mapping water surfaces, mapping soil water content.

• TM 5 1.55 – 1.75 µm examination of soil and plant water content, differentation of cloudiness from snow blanket.

• TM 6 10.42 – 12.50 µm mapping heat emission (plant stress, heat pollution)

• TM 7 2.08 – 2.35 µm differentation between rock types, mapping plant eater content

Application of Landsat Images

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• Imaging : central perspective • Photogrammetry: defines the extention of real objects from

the sizes taken from the image– The resulting ortophoto (image data of the Earth surface

obtained by a satellite or aerial data collectors with geographic reference) can comprehensively be used with GPS systems

– During the planning and realisation of imaging, a GPS device system and adequate relief data are needed.

Ortophoto

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• Photogrammetric evaluation is based on stereoscopy with perspectivic mapping between aerial and space images taken using central projection. – The essence of stetoscopy is that given terrain objects are

mapped in different ways in images from different sources. The task of photogrammetry is to measure the difference between parallaxes, and calculate spatial coordinates.

Photogrammetry

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• Differentation of types of vegetation• Cover and yield• Calculatio• Productivity of biomass• Vitality and disease of flora• State of soil

– IMG files• View• Select bands• Colour bands

– Erdas ViewFinder 2.1– http://rst.gsfc.nasa.gov/Front/overview.html– FÖMI oktatóanyag (tutorial of the Institute of Geodesy, Cartography

and Remote Sensing, Hungary)

Remote Sensing Data in Agriculture

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http://rst.gsfc.nasa.gov/Front/overview.html

http://rst.gsfc.nasa.gov/Front/overview.html

http://www.fomi.hu/taverzekeles_oktatoanyag

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• Model of objects• Relief model• Terrain model• Elevation model

– Digital elevation model (DEM) is the topographic visualisation of the earth surface. It is usually used for relief maps, 3D visualisation, waterflow modelling, and in the case of aerial image correction. Applies remote sensing data or traditional land surveying data.

– Raster based elevation model– Vector based elevation model

Application of 3D Models

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• Source elevation data create regular grid cells. The size of the cell is constant within the model. The height of the relevant geographic area can be considered constant in the same grid cell.

• Divides space into triangles not covering one another. – Vertices of every triangle are data

points, with the value of x, y, z. – The points are connected with lines,

which gives Delaunay triangles.– A TIN (Triangulated Irregular Network)

is a complete graph, which keeps its topologic connection with the relevant element (intersection, edge and triangle).

– Input data fit directly into the model.

Raster and Vector Models

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• SRTM (Shuttle Radar Topography Mission 2000) program

– Digital relief of about 80% of the Earth’s surface, with the help of radar system (Endeavour 11 days)

– Radar-interferometry, with two receivers 60 m from one another

– Mapped area: 60 degrees North, 57 degrees south

– Resolution 3 (USA 1) arcsec

Global Relief Model

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http://www2.jpl.nasa.gov/srtm







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• TanDEM-X 2010, (TerraSAR-X)– Mapping of the whole surface of the Earth– Horizontal resolution 12 m, vertical resolution: 2m. – Two-radar remote sensing satellite with stereo microwave

radar device, at the height of 514 km– Polar sun synchronic orbit– Radiowaves emitted from a satellite with the help of

Synthetic-aperture radar (SAR) technique and then reflected from the surface are received with the antenna on the satellite , or the same surface is photographed from two different points.

Global Relief Model II.

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• The digital relief model of Hungary, 5m resolution– 1:10 000 scale EOTR

database was used– A GRID derived from

vectorized level lines.

3D Relief Model

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• Generated from several sources– Level-line digitalization– Digitalization of elevation

points– Import GPS survey points– Correction (aerial photo) – Model generation– Publication

• Generation from direct GNSS measurement

3D Relief Map

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• Video• Animation

– Elevation Model

Videos and animations for Chapter 4.

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I. questionFind an aerial image of your place of living from internet sources.

II. questionFind a space image of your place of living from internetsources.

III. questionMeasure the area of the Kányavári Island (Kányavári-sziget),Hungary on the photos of 1990., 1992. and 2002. Use ErdasViewFinder (or any other IMG viewer). The images can be foundon the remote sensing tutorial website of FÖMI


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• Types of Mapservers– Static webmaps– Dynamically created webmaps– Animated webmaps– personalized webmaps– Open, reusable webmaps– Interactive webmaps– Webmaps suitable for analysis– Collaborative webmaps

Spatial Data Databases

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• Static webmaps– No animation and interactivity– Only created once, infrequently updated– Mostly scanned paper based maps

• Dynamically created webmaps– Created on demand, often from dynamic data sources– Created by server (ArcIMS –ArcSDE)– WMS protocol

Types of Webmaps II.

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• Animated webmaps– Show changes in the map over time (water currents, wind

patterns, traffic info)– Real time, data from sensors– Updated Rregularly or on demand

• Personalized webmaps– Allow user to apply own data filtering, selective content– Personal styling and symbolization– OGC SLD WMS uniform system (Styled Layer Description)

Types of Webmaps III.

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• Open, reusable webmaps• Complex systems, open API(Google Maps, YahooMaps,

BingMaps)• Compatible with API „Open Geospatial and W3C Consortium”

standards• Interactive webmaps• Chengeable parameters • Easy navigation• Events, descriptions, DOM-manipulations

Types of Webmaps IV.

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• Analytic webmaps– Offer GIS-analysis

• Geodata uploaded by user• Geodata provided by server

• Analysis is carried out by a serverside GIS, results of analysis are displayed by the client.

• Collaborative webmaps– Geometric features being edited by one person can not be

changed by any one else at the time. – Quality check is needed before publication

(OpenStreetMap, Google Earth, Wiki- Mapia…).

Types of Webmaps V

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• ‘Institute of Geodesy, Cartography and Remote Sensing’, Hungary

• Földmérési és Távérzékelési Intézet fontosabb adatbázisai

(important databases of the Institute of Geodesy, Cartography and Remote Sensing)

‘FÖMI’

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http://www.fomi.hu/

http://www.fomi.hu/

http://www.fomi.hu/

http://www.fomi.hu/

http://www.fomi.hu/

http://www.fomi.hu/

http://www.fomi.hu/

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• To continuously inform farmers and experts, to provide professional background knowledge for tenders and developments.

• Its knowledge base is based on professional news, events, articles, studies, publications-published in an organised, updated system.

• A further aim of the site is to prepare for online data service (logbooks, electronic submission of data of farmers working on vulnerable areas), to give info on data in connection with agri-environmental management, to publish relevant thematic maps and to ensure agrar forecast.

Hungarian National Rural Network (AIR)

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• 1:200.000 scalegenetic soil map of Hungary

• 40 soil types, 80 sub types, with colours and colour shades

• Physical soil kinds (9 categories) with striping

• Soil formation rock (28 categories) betűjelekkel

AIR Public Map Library

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http://www.air.gov.hu/index.php

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• Obtain, process and store weather data• Apply weather data in the agrometeorology model of the

crop growth monitoring system (Crop Growth Monitoring System, CGMS)

• Process NOAA-AVHRR and SPOT-VEGETATION satellite images using CORINE land coverage data (CORINE Land Cover, CLC)

• Common Research centre– Statistic analysis of data– Quantity forecast– Short time crop yield forecast

MARS (Monitoring Agriculture by Remote Sensing) terményhozam-előrejelző rendszer

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• Monitoring Agriculture by Remote Sensing

MARS

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http://www.marsop.info/

http://www.marsop.info/

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‘FÖMI NÖVMON’ (Plant Monitoring)

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IKR Precision Map Server

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Soil Data Publication (Georgikon Mapserver, Hun)

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• (Infrastructure for Spatial Information in the European Community-INSPIRE)

• ‘The INSPIRE Geoportal provide the means to search for spatial data sets and spatial data services, and subject to access restrictions, view and download spatial data sets from the EU Member States within the framework of the Infrastructure for Spatial Information in the European Community (INSPIRE) Directive.

• Aims at making available relevant, harmonised and quality geographic information to support formulation, implementation, monitoring and evaluation of policies and activities which have a direct impact on the environment.’

• (www.inspire-geoportal.eu)

INSPIRE Geoportal

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• Inspire should be based on the infrastructures for spatial information that are created by the Member States and that are made compatible with common implementing rules and are supplemented with measures at Community level. These measures should ensure that the infrastructures for spatial information created by the Member States are compatible and usable in a Community and transboundary context.

Spatial Data Directive

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• Member States shall ensure that metadata are created for the spatial data sets and services corresponding to the themes listed in Annexes I, II and III, and that those metadata are kept up to date.

Inspire2008 metadata

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• Online access to a collection of geographic data and services• Does not store or maintain data• Metadata, catalogues can be accessed with several search

options• With the help of a map server service, maps and metadata

can be searched for and browsed. • Personal maps can be created from existing data sources.

INSPIRE Geoportal

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• INSPIRE Geoportal

INSPIRE Geoportal Viewer

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http://www.inspire-geoportal.eu/

http://www.inspire-geoportal.eu/

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• ArcExplorer JEE Corine Land Cover mash up map from several sources

• http://vektor.georgikon.hukvsz

• http://geo.kvvm.huclc (80%

transparency)Mashup map: a map that includes another (API), made from several internet sources.

Mashup Mapserver Service

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http://vektor.georgikon.hu/

http://geo.kvvm.hu/

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WebMap and publication

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Website

MapServer

Picture

Video

Web service

HTML

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• Steps of realization : – 1. chose topic– 2. create map, upload

data• a. Create web album,

upload photos• b. Upload video

– 3. create website, embed map

– 4. publish website

Steps of Realization

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• Video– Institute of Geodesy Cartography and Remote Sensing– Hungarian National Rural Netvork– Inspire Geoportal– GoogleMaps service

• Animation


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I. questionMeasure the length of the Belső-tó (‘Inner lake’) of Tihany, Hungary with the help of the topographic map service of the Georgikon Mapserver (or any other mapserver).

II. questionCreate a GoogleMaps map in any agricultural topic with at least 5objects, inserted images and embed it into a website of the sametopic.

III. questionEmbed further mapserver services (Bingmaps, YahooMaps…) intothe website you have created.


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• Prepared by:– Dr. Máté Csák

Plant protection database

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Plant Protection Information


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Plant protection’s databases: Topics

• Database management theory– Information, data– Database models, databases– Database Management Systems

• Relation model– Base of theory– Normalized database– Catalog, data-dictionary

• Plant protection’s databases– Practical problems and their solutions


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Database management - Information• Information technology concepts, words of Latin

origin, which is intelligence, news, messages, information does.

• Definitions:1) In general, the data information, news of which we

consider relevant, and lack of knowledge has decreased. Wikipedia

2) Knowledge gains, the growth of knowledge, and it means reduce uncertainty. SH Atlas

3) The information provided is new data, news which removes uncertainty and consequences. Kalamár-Csák


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Theoretical - Information

The information is the same physical reality of the universe as

matter and energy.


pure informationInformation processing

Meaningful information

DNA-moleculeComputer data input

proteinCalculation results

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Theoretical - Information

Manifestations:• Clearly pronounced– Explicit

– When the information is completely clear to everyone, not in need of explanation.

– For example: the Balaton water at 28 °C• Hidden – Implicit

– The data connection between a method can be displayed.

– For example: statistical calculation (average)


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Theoretical - Data

• The data of an object (any thing that relates to the data), to a specific value (character state, completed forms) for the variable (properties, attributes, characteristic, character).– Therefore be considered as a specific data are

defined, you define what kind of object that is variable, what value are added. The figures represented the value unit is always connected.

• For example: Name: Arvalin LR; Agent: Zinc phosphate; Volume: 4 %


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Theoretical – Data model

• A collection of concepts, which clearly describe the structure of a database.– The structure includes the data type and

their relationship to the restrictive conditions for the data.

– The database conceptual level, logical structure description.


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Entity-Relationship-ER basic elements of data model


ENTITIES

ATTRIBUTES

RELATIONSHIPS

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Data model – ER - Entities

• Entities : are the principal data objects, which all other things to distinguish, and information is to be collected. – Procedures at issue, and whom we want to store

data.– For example: Citizens, Workers, Patients,

Custumers; Plants, Agents, Phenological phase, Harmful; Cars, Goods, Accounts ...

– The entity to a specific value of the occurrence.


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·

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Data model – ER - Attributes

Attributes: • Internal structure of the entities• are characteristics of entities that provide

descriptive detail about them.– Plants of the named individual characteristic

such as : name, Latin name, ...• The property values of an individual's actual

value is determined.• For example: Peach, Prunus persica, …


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Data model – ER – Attributes - Key

• If a property or properties to a group of clearly specifies, that the value which the individual is involved, together they are called keys. – For example: name in Plants


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Data model – ER - Relationship

The relationships: • the external structure of entities,• the represent real-world associations among

one or more entities.• are described in terms of degree,

connectivity, and existence.– For example: Plants-Harmful, Accounts-Goods, ...

• A particular occurrence of a relationship is called relationship instance.


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Datamodel – ER – Relationship - Types

The types of relationships:

•Independent connectivity

•1:1 connectivity

•1:N connectivity

•N:M connectivity


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Adatmodell – ER – Kapcsolatok 1.

1. Independent connectivity– The two entities independent of each

other, if one set of instances, nothing is linked to a single element or another entities.

• For example:• Agent’s Id: Employe’s account


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Data model – ER – Relationship 2.

2. One –to – one connectivity (1:1):

• One of the elements of each set of instances of another entity set exactly one element is linked.

– For example: Agent’s Id: Agent’s name


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Data model – ER – Relationship - 1:1 Connectivity


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3. One-to-many connectivity:

• A set of instances of each element of the B element within the multi-set of instances.

– For example: Aetiologies: Diseases


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Data model – ER – Relationships - 1:N connectivity


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4. Many-to-many connectivity:• A set of intstances of all elements of the B

element within the multi-set of instances, vice versa.

– Például: Plants : Diseases

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Data model – ER – Relationship - N:M connectivity


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Data model - ER definition

• The data model isa finite number set of entity, their finite number set of properties and their set of relationship.


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Data model - TypesDepending on the core 3 is based on storing the physical data model exist.

entity property connectivity•net, hierarchical + - +

•relation + + -

•Object oriented + + +

• + object-relational (mixid data model)


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Databases• Database: some relation to each other in a

structured set of data, stored so that multiply users can access, typically digital form.

• The database is a finite number of entities occur, their are a finite number of property value, and the relationship of the presence data model orgonized as a combination.

• Benefit: you can use many at once. The data are stored "single" only.


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Integrated database

• Linked to all data that are used by different users in different groupings.

• The physical placement of data, centrally, redundancy-free or minimal, controlled redundancy occurs .

• Centrally controlled – data protection, – entering the new data, and– change existing data.


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Database Management System (DBMS)

• A softvare, which provides the connection to the database.

• Allows databases – creation, – query the data, – modification, – maintenance, – large amounts of data on long-term safe

storage.


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• Grouping– According to the number of users

• Single-user• Multi-user

– Job sharing as• A tasking• Client-Server

– Number of storage locations• A stored• Split /shared


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• The system components– Data Definition Language (DDL)

• User level• Conceptual level• Physical storage level

– Data Manipulation Language (DML)– Data Control Language (DCL)


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Database Management System (DBMS) – Operating concept

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DBMS – Operating concept - Explanation

1 Request for information from the database (Application program)2 Request the interpretation and analysis (DBMS: syntax,

existence, rights)3a Executeable→ to operating system3b can not execute → to program4 Contact the exterior container (operating system) 5 The transfer of the requested data (OS, from storage into buffer)6 The passing of data, feedback for a program7 The receipt of data into a program.


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• Two types:– Has a autonomous languages

• Oracle (1977)• DB/2 (1983)• SyBase (1987)• Informix (1981)• Ingres (1980)

– Plug-in type• IDMS (1983)• SQL (1986)


http://hu.wikipedia.org/wiki/Oracle

http://hu.wikipedia.org/w/index.php?title=DB/2&action=edit&redlink=1

http://hu.wikipedia.org/w/index.php?title=SyBase&action=edit&redlink=1

http://hu.wikipedia.org/wiki/Informix

http://hu.wikipedia.org/wiki/Informix



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Relation database model – Theoretical basis

• In 1970 Dr. Edgar F. Codd (IBM) create the Relation Database Model.

• The data model describes the various types of data, their relation, connections, and their privacy procedures.

• The collected data are logically separate entity types, entities (table). Determine that the individual entities, whereas we can clearly identify, and also what additional features (attributes).


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VirKor program – Relation diagram

In VirKor database has seven tables and their properties and relations.


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VirKor program – Relational mode of representation

• Relation of entities (special tables) shows.

• They describe the real world, different entities and their properties.

• Plants table


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VirKor program - Relational mode of representation

• The connection between the entities can be depicted in relations.

• The data management comes true with relational operations.

• Plants – Pests relation


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Relation model – Benefits and disadvantages

Benefits:• Mathematical (set theoretical) based on models• Very close to everyday thinking,• Most flexibly modifiable,• Well-separable, can be made independent the

three level.Disadvantages:• The power delivery is less effective.

– This is not so big trouble already today.


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Relation model – The properties of relations • Is a clear relation in the database;• The specimens were characterized by rows and columns of entity properties;• The same number of colums in each row;• Columns within a clear relation to the name;• Any column in a row add up to a value (if no value is NULL);• Columns in any order;• Not two are the same place;• There are least a combination of columns that uniquely identifies the row.

This is the primery key.• Identify any data:

– Relation name– + column name– + value of primery key


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Relation model – Table, viewsWe do not store each entity value of each property physically.• Base relation. Table

Physically stored.• Virtual relation. View

Contains no data. We create from tables with relational operations.• Materialized view.

Physically stored. We create from tables with relational operations. Change when you change the default tables.

• Snapshot Physically stored. Value of tables, views in a certain moment.

• Queries, the selection result.Relation is not true and only temporarily exist.

• Temporarily tablesTemporarily need that operation, task.


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Relation model – Keys

Ensure data integrity, consistency and relation exemption.

The system automatically checks:• The primary key and foreign key

relations between entities(eg., key of plants in a diseases of plants)– matching– Cascading change– Cascading delete


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PRIMARY KEY• Clearly identify a relation the rows.• The primary key (or part thereof) may not

be (or not) null value, and should not contain unnecessary columns.

• It is important to decide what should be the primary key if you have more options (eg, person identity: identity card number, tax identification, social insurance number)


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Relation model – Integrity

Integrity additional options:• Define a unique index (this columns

will not add the same value in two rows)

• Given specific field conditions must be satisfied(eg, the check number only value possibility)


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Relation model – Indexes

• To expedite the indexed column in the direct and sequential access.– Auto maintenance,– You can always be created, deleted,

– Slows down the change,– Space is needed.


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FOREIGN KEY• A column (combination) in relation to the

link only to add value, eithers as a NULL value, or the referenced tablaóe with one of the primary key values are equal.

• Establish connections between the 1:N relationship. Shall remain valaid for all the changes, data input, deleting.


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Relation model – Foreign key relationship


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Relation model – Normalization

• Normalization is a formal algorithmic process in which the initial data, the negative pattern of consistent application of appropriate rules of succession is logically more transparent better shape form.


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Relation model – Normalization• The previous steps of design entities well

manageable, received a standard take forms.

• Algorithmizable.• Result of:

– The data will be less need for storage;– The elementary data faster and less error-

prone to change;– The database will be logically clearer.


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Relation model – Normalization – Functional dependence• Any relation of attributes values depend on the values of other attributes.• If one of the attributes of the relation R (X), the independent variable is clearly

identified by another attribute (Y), the dependent variable, then we say that Y is functionally dependent on X from the relation R.

• Naturally this is a clear relation to the actual content of R is not only valid, but independent of time, for the whole duration of its existence constraint database.

• Both the X and Y attributes can be complex, that is consist of several columns as well.

• Functional dependence of the usual marked withR.X R.Y

• Maybe this even a functional diagram, dependency diagram is represent with a different name.


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Relation model – Representation of functional dependence


The arrow Z is from.points to an independent attribute of the dependent attribute.Y and Z in the diagram is functionally dependent from X,Y and

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Relation model – Full functional dependence• General terms, it R in relation it Y attribute

functionally if and only if X (composite) is complete attribute, if it is functionally dependent on X from, but does not depend on X has only a real component of his. – If X is not complex, then the functional and the

full functional dependence is the same.– strong– weak


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Relation model – Full dependence

•Be P, Q Í A and P ® Q.•Q full dependent (functionally) from P, P only if Q does not depend on the part of set •Otherwise, the dependence is partial.

– For example:– ORDERITEM {order_id, goods_id, piece}– REPAYMENTS{deptor_id, month, amount, date}– VISIT{visitor_id, date, time, subject, period}


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Relációs modell – Tranzitív függőség

•Depends on the P to S is transitive, if there exists Q Í A, and P ® Q, Q ® S, but the reverse is not true dependecies.

– Példák: P ® Q® S– WORKER {perid, name, class_code, class_name}– ORDERHEADER {order_id, custcode, custname,

custaddres, date, deadline, totalvalue}– VISITOR{id, name, firm, firmname, firmaddres, …}


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Relation model – Normalforms

• The entity’s structural state • 0NF • 1NF• 2NF• 3NF• 4NF


Plant Latin name Deseases …

Apple Malus domestica

Applemosaic, Impetigo, Apple powdery mildew, …

Potato Solanum tuberosum

Staining virus reticulated, Blight, Black rotting, …

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Relation model – First normal form

• First normal form (1NF) is the relation which– Each column has one and only one attribute is

present,– Each row is different,– The order of attributes in each row is same,– There are not repeating fields,– Belongs to each line (at least) a unique key,

from which all the other attributes are functionally dependent.


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Relation model – Normál forms - 1NF - example


Plant Latin name Disease Athology …

Apple Malus domestica Applemosaic virus

Apple Malus domestica Impetigo mushrooms

Apple Malus domestica Apple powdery mildew mushrooms


Staining virus reticulated virus


Blight mushrooms


Black stemrotting bacterium

Wheat Triticum vulgare Pitch staining mushrooms

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Relation model – Normalforms – 1NF - AnomaliesIt can be seen a lot of redundancy (eg Plant and Latin name).Hidden error possibilities (anomalies of change):• Erase anomaly:

– If we erase the removal of the wheat disease Pitch staining• Modify anomaly:

– If the potato into the new name blight disease are renamed, you can either „new” plants will should be modified or anywhere.

• Enter anomaly:– New disease can be entered only if a plant is already ill

(primary key can not be part of a NULL value).


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Relation model – Normalforms – 2NF

• A relation R is in second normal form (2NF) if and only if it is in 1NF and every non-key attribute is fully dependent on the primary key. – Elementary primary key 1NF relations are also automatically in2NF.

Key relations are complex, however, in order to eliminate anomalies int the change we need to2NF.(This is not to removes all the amomalies, but could significantly reduce their number). This is called decomposition of relations.

– The decomposition happens so, that it 1NF from relation with a projection like that 2 NF, we manufacture relations, the primary keys of which the primary key of the original relation, or parts therefor, are those and can only those column that are fully dependent in the new primary key.


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Relation model – Normalforms – 2NF - Example


Plan Latin name



Wheat Triticum vulgare

Plan Desease Latin name PictureApple Apple mosaic virus Apple mosaic virusApple Impetigo Venturia inaequalisApple Apple powdery mildew Podosphaera leucotrichaPotato Virus networking staining Potato leafrollPotato Black stem rotting Erwinia carotovora subsp.

atroseptica

Potato Blight Phytophthora infestansWheat Pitch staining Lidophia graminis

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Relation model – Normalforms – Decomposition (1NF®2 NF)• R(A,B,C,D) before decomposition(1NF)

– PRIMARY KEY(A,B)– R.A ® R.D

• After decomposition(2NF)– R1(A,D)– PRIMARY KEY (A)

• and– R2(A,B,C)– PRIMARY KEY (A,B)– FOREIGN KEY(A), refers to R1


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Relation model – Normalforms – 3NF

• A relation R is in third normal form (3NF) if and only if it is in 2NF and every non-key attribute is non-transitively dependent on the primary key.

• In other words, the 3NF means that only the functional dependence of the primary and the alternative keys can start up.

• Employee of the 2NF relation is not in 3NF, because for example, the class (CLASS) is not the primary or alternate key and other columns (CLASS-NAME), BOSS) is functionally dependent on it.


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Relation model – Normalforms – Decomposition (2NF®3NF)

• The decomposition happens so, that the 2 NF relation, we take the projection, which includes only those attributes that are exclusivly dependent on the primary key. This is primarily key will remain the same. The other new relation (or relations, if more than one relationship), the primary key attribute of an independent relation dismantled, and the columns of his dependent attributes.


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Relation model – 3NF - Example


Plan Latin name



Wheat Triticum vulgare

Plan Disease kép

Apple Apple mosaic virus

Apple Impetigo

Apple Apple powdery mildew

Potato Virus networking staining

Potato Black stem rotting

Potato Blight

Wheat Pitch staining

Disease Latin name Aetiology …

Apple mosaic virus

Apple mosaic virus

virus

Impetigo Venturia inaequalis

fungus

Apple powdery mildew

Podosphaera leucotricha

fungus

Virus networking staining

Potato leafroll virus

Blight Erwinia carotovora subsp. atroseptica

fungus

Black stem rotting

Phytophthora infestans

bacteria

Pitch staining Lidophia graminis fungus

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Relation model – Normalforms – Decomposition (2NF®3 NF)

• General terms, if the A, B, C, D columns (any compound can be) of 2 NF relation– R(A,B,C,D)

• PRIMARY KEY(A)• R.B ® R.C

• 3NF is the decompositions of the re-establishment relations of the following means:

– R1(B,C)• PRIMARY KEY(B)és

– R2(A,B,D)• PRIMARY KEY(A)• FOREIGN KEY(A), refers to R1

• The relation R can be set back at any time is clearly a combination of R1 and R2 (B). It is however, that the splitting is done according to the principle set out above.


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Relation model – Normalforms – Decomposition - 3 NF

Notes:• Not always appropriate to the 3NF

shape (e.g., address and zip code).• Most database management system

enough to 1 NF, and even the primary key is not required!!!


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Catalog, data dictionary

• The database – definition, – relationships, – storage, – how to use maintaining tables,

• views of all.• System administration carry out tasks.


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• Pesticides Register• VirKor – assistant educational material


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Pesticides Register - Records of pesticides• The aim of a database

Task of the ER-chemistry Co. register of pesticides manufactured by the Planning

• The register should include:– the origin of certain pesticides,– the elements needed to produce the drug,– the possible application areas.

• It is assumed that:– a pesticide may be single or multi-component,– more may be used against the pest,– one component can be derived from multiple

suppliers.


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Pesticides Register - Entity types

• Pesticides(id, name, degree of hazard, price)

• Factories (factory code, name)• The fields of application of drugs (pest

, type)• The drug components (compnent

name)• The Transporters (Transporter code,

date, name, address)


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Pesticides Register - Entity type of ER-model

• Pesticides(id, name, hazard, price)• Factories(id, name)• Pests(pestname, type)• Components(name)• Transporters(id, date, name, address)


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Pesticides Register - - ER-diagram


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Pesticides Register - Relations• Where can produce ? Factories:Pesticides (1:N)

– A pesticide plant produces only one, but a plant can produce more gain.

• What do you apply? Pesticides:Pest (M:N)– A pesticide may be used against several pests, and in a pest

can destroy more times.• What are the ingredients? Pesticides:Components

(M:N)– A pesticide consists of several components, but other

substances may also be a component creator.• Where did it come from? Components:Transport (M:N)

– Carry more of a component supplier, but a number of component suppliers will also be distributed


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Pesticides Register - Relation model


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Pesticides Register - Relations

Only the primary keys• Pests(pname, type)• Factories(fid, name)• Components (cname)• Transports(tid, tdate,

tname, taddress)

Primary keys and foreign keys• Pesticedes(pid, name,

hazard, price, fid)• Applies(pid, pname,

term)• Elements(pid, cname,

volume%)• Origines(cname, tid,

tdate, quantity)


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Pesticides Register - Pest table


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Pesticides Register - Pesticide form


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Pesticides Register - Pests form


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Pesticides Register - Factories form


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VirKor program

assistant educational material


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VirKor program - assistant educational material

The Virkor is an assistant educational program, that helps students understand how to recognize diseases of different plants.

• Demonstration boards are modern.• Educational resource for students of

plant doctor.


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VirKor program – How does it works?

• Stored in the database:– plants,– diseases,– these relations.

• The displayed images are stored in a folder (locally or server).


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VirKor program – Diseases of plant: Apple

Apple proliferation phytoplasma Podosphera leuchotricha


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VirKor program - Diseases of plant: Apple

Apple mosaic virus Monolinia fructigena


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VirKor program – One disease on different Plants: Mosaic virus on cucumber and apple.


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VirKor program - Symptoms: Necrosis of tissue


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VirKor program – How it’s made?

• Photographed hand-capture demonstration boards and then were cleaned.

• The boards in the data recorded in an Excel spreadsheet.

• Created the relational database model.

• Developed the application.


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VirKor program – How it’s made? - Data collection - Digitization

Digitization of the demonstration boardsOriginal Cleaned


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VirKor program – How it’s made? - Data collection - Stored

Store the data in a worksheet (1NF)


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VirKor program – How it’s made? - Modify and correct data structure

In this case we supplemented the data with some other properties, for example: add plant parts.


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VirKor program – How it’s made? - Data collection - Analyze the relationship between data

• Functional dependencies:– The Latin names of the plants are

dependants of the Hungarian names.– The same refers to the disease, the

symptoms and the aetiology (e.g. virus).


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VirKor program – How it’s made? – Create a Relation database model


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VirKor program - How it’s made? - Table – Entity: Plants

• Apple and it diseases


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VirKor program - How it’s made? - Table – Entity: Diseases


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VirKor program - How it’s made? - Table – Entity: Plants’ diseases


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VirKor program - How it’s made? - Develop tutor program

• Form of Plants• Setting the properties of each tool• Programming each event

– For example • load image file in the picture box• Change the status of checkboxes• Etc.


AZ ELŐADÁS LETÖLTHETŐ:-

Thank you for your kind attention.

Made by: Máté Csák PhD.

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Bibliography• Quittner P. - Baksa-Haskó G. (2008): Adatbázisok, Adatbázis-

kezelő rendszerek, DE ATC AVK• KUPCSIKNÉ FITUS I. (2004): Adatbáziskezelés, AIFSZ képzés

tananyaga• TÍMÁR L. ET AL. (2007): Építsünk könnyen és lassan

adatmodellt!, Pannon Egyetemi Kiadó, 46/2007, pp. 23-99.• HERNANDEZ, M. J. – Viescas, J. L. (2009): SQL-lekérdezések

földi halandóknak, Kiskapu.• ULLMAN, J. D. – Widom, J. (2008): Adatbázisrendszerek

Alapvetés 2. átdolgozott kiadás, Panem Kiadó.• CZENKY M. (2005): Adatmodellezés - SQL és ACCESS

alkalmazás - SQL Server és ADO, ComputerBooks.


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• Prepared by:– Sándor Nagy

Bioinformatics

203


BioinformaticsBioinformatics -

Databases and homology searching

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Contents

• What does Bioinformatics mean?• Structure and operation of DNA• Bioinformatical databases• Using databases• Exercise


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Definition• Bioinformatics derives knowledge from computer

analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine.


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Fields of Bioinformatics

• Superindividual Bioinformatics uses systematical modelling in order tp know biological systems

• Molecular Bioinformatics does protein and nucleotid analysis and planning

• Computing Bioinformatics is focusing on utilization of biological systems


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Aim of Bioinformatics

Is to decipher the genetically encoded information, which lead us information on the followings:

• 3D sturcture,• Function,• Evolutionary relations.


DNA Protein Function

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Questions answered by Bioinformatics

• In which other creature can we find the actual sequences? (→ ortholog searching)?

• What kind of variatons can occure in a certain creature? (→paralog searching)?

• What is the rate of heterogeity in a certain paralog (→searching polymorphism)?

• Which positions are important in a given sequency (→ evolutionary conserved) ?


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Basics: Structure of the DNA• Double helix, in which nucleotide bases on the two strands

are connected by Hydrogene bonds: A:T - 2, G:C – 3 H-bonds • Base pairing: complemeter nucleotide bases within the long

polymer are: A:T and G:C• replication, • Genetic code- isn’t monotone • Two helical chains each coiled round the same axis, and each

with a pitch of 34 Ångströms (3.4 nanometres) and a radius of 10 Ångströms

• These two strands run in opposite directions to each other and are therefore anti-parallel, 5′ (five prime) and 3′(three prime) ends

• It containes four bases: adenine (A), cytosine (C), guanine (G), thymine (T)

• Structure of DNA:– http://www.youtube.com/watch?v=qy8dk5iS1f0&feature=player_e

mbedded


http://www.youtube.com/watch?v=qy8dk5iS1f0&feature=player_embedded

http://www.youtube.com/watch?v=qy8dk5iS1f0&feature=player_embedded

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Bases: replication of DNA

• Following from the rule of Base pairing: Hydrogene bonds within the double helix can be pulled apart, both strands are templates for the synthesis of a new strand. Result of this process: same structure

• Genetically coded information ensured by the order of nucleotides

• DNA replication:– http://www.youtube.com/watch?v=E8N

HcQesYl8&feature=related


http://www.youtube.com/watch?v=E8NHcQesYl8&feature=related

http://www.youtube.com/watch?v=E8NHcQesYl8&feature=related

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Történeti áttekintés

• Early 50thies– publishing insuline sequence• 1953 Watson-Crick: Structure of DNA• Early 70thies – creating algorithms for

sequenal analysis:– Dot matrix– Local and Global Sequence Alignment– BLAST algoritmus

• 1972 first computer stored databases of proteine sequences

• 1979 GenBank prototype


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Bioinformatical databases

• Gene Bank: NCBI (National Center for Biotechnology Information)– http://www.ncbi.nlm.nih.gov/

• European Molecular Biology Laboratory – European Bioinformatics Institute - EMBL-EBI– http://www.ebi.ac.uk/

• DNA DataBank of Japan – DDBJ– http://www.ddbj.nig.ac.jp/


http://www.ncbi.nlm.nih.gov/

http://www.ebi.ac.uk/

http://www.ddbj.nig.ac.jp/

TÁMOP-4.1.2.A/2-10/1-2010-0012Information Technology in Plant Protection

http://www.ncbi.nlm.nih.gov/

Choosing database

Searching keywords

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Navigation in Database - Gene Bank

• Choosing database:– Pubmed – database of

publications– Protein – database of

proteines– Nucleotide – database of

nucleic acid– Genom – database of whole

genomes– Gene – database of genes


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Navigation in Database - Gene Bank

• Exercise:– Looking for information on Phytophthora

infestans• Database: Taxonomy, Keyword: Phytophthora

infestans• Result of our search (next slide)

– Taxonomic classification– Databse results from GeneBank– References to other resources

• Choosing the following reference link on the result page we can reach all sequences in the Database: Nucleotid – Dirket

• Looking for INF2A gene within the results


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Eligazodás az adatbázisban - Gene Bank

• Eredmény tábla:



Azonosítók

Information on publishing

Information on structure

Gén azonosítás


Aminoacid orderNukleotide sequence

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Navigation in Database- GenBank• Dataformats: :

– Summary – short description, important information

– GenBank – own format of Genbank, detailed data

– FASTA – name+identifiers+sequence most common used format

– ASN.1 – international format


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Navigation in Database- GenBank

• FASTA format:– Advantage:

• commonly used• simple• small

– Disadvantage:• less information


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Navigation in Database- GenBank

• Accession number:– AY693804 - Phytophthora infestans INF2A

(Inf2A) gene, complete cds– Accepted international identifier for

nukelic acids and protein sequences• GI (Genbank Identification) number:

– GI:51832280 - - Phytophthora infestans INF2A (Inf2A) gene, complete cds

– Identifier especially used only by Genbank


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Use for … ?

• To quest genetic information of a given organism

• To compare and check our results• Basis of comparing experiments• Collection of papers and publication• Base of researches


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Exercise:

– Look for the genetic code and proteine sequence of a chosen important causative agent and examine the availability of its’ genome.

– Save the given result in FASTA format, textfile. Keep the saved file, it is required for the exercise next time.


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Content

• Comparing two sequences• Searching homologue sequences –

BLAST• Nucleotide BLAST - BLASTN• Proteine BLAST – BLASTP• Use for what?• Exerxise


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How to compare two sequences – Dot matrix

• Dot Matrix method (Gibbs and McIntyre, 1970): It compares two amino acid or nucleotide sequences in a way of placing the two sequences in a matrix in both vertical and horizontal direction and it draws a dot in case of parity.

• Exceedingly suitable for visual demonstration of mutations, deletions and insertions.


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How to compare two sequences – Global Sequence Alignment• Other well known analytical method is the

Global Sequence Alignment which uses dynamical programming.

• Essence of the process: examining analogy of the sequences with the help of a scoring system on the whole sequence.


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How to compare two sequences – Local Sequence Alignment• Using also the dynamic programming

process.• Essence of the process: examining

analogy of the sequences with the help of a scoring system. It tries to create the best alignment.


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Pair Sequence Similarity Search

• Basic Local Alignment Search Tool (BLAST) the most effective and common process of searching similarity– Peculiarities:

• Fast• Effective sensibility

– Types:• Blastn – for nucleotide sequences• Blastp – for proteins• Blastx – for translated nucleotid sequences

• http://www.ncbi.nlm.nih.gov/blast


http://www.ncbi.nlm.nih.gov/blast

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Pair Sequence Similarity Search

• Types:


BLAST Kereső szekvencia Adatbázis

Blastn Nucleotide Nucleotide

Blastp Proteine Proteine

Blastx 6 frame translated nucleotide Proteine

Tblastn Proteine 6 frame translated nucleotide

Tblastx 6 frame translated nukleotide 6 frame translated nucleotide


Entering sequences - copy - upload

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Most important settings: Blastn – Searching Databases

• The most commonly used one is the not-redundant nucleotide database (chosen one)

• It is possible to narrow searching in case we add taxonomical data in section „Organism”.


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Most important settings: Blastn – program optimalisation

• Megablast: searching analogies with 95% or bigger similarity, very fast.

• D megablast: exceedingly suitable for comparison of species, bit slower.

• Suitable for the comparison of any sequences, it indicates little similarities, slow.


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Most important settings: Blastp – Searching Databases

• The most commonly used one is the not-redundant protein database (chosen one)

• It is possible to narrow searching in case we add taxonomical data in section „Organism”.


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Most important settings: Blastp – program optimalisation

• Blastp: simple searching in protein database.

• PSI-BLAST: searching algorithm with position-specific scoring

• PHI-BLAST: searching with pattern-specific scoring system.


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BLAST result evaluation

• Look for those nucleotide sequences which are similar to AY693804 - Phytophthora infestans INF2A gene.


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Searching parameters

Garphical demonstration of the result

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• Result summary table– Max score

• Bigger value means bigger similarity– Query coverage

• Bigger value means bigger similarity


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• Result summary table– E value – Expected value

• Lower value means higher similarity.– Max ident – maximal query alignment

• Higher value means higher similarity


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• Detailed sequence alignment:


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BLAST on local environment• It is possible to run BLAST program in local environment.

– It is useful in the following cases:• Comparing sequences to local databases• Operations requiring large number of calculations

• ftp://ftp.ncbi.nih.gov/blast/ – Command line running with parameter inputs.– Supporting many operating systems (also 32 and 64 bits

architectures)– Detailed help– First step is the database formatting, next step is similarity

analysis. – It is able to create a lot of output formats


ftp://ftp.ncbi.nih.gov/blast/

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What can we do with it?

• We have an unknown sequence from an unknown source.– What can be the source?– To which gene is similar?– What can be the function of the protein

coded by this sequence?• Use the sequence in FASTA format as

query parameter in the BLAST program. From the result we can answer the questions above.


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Exercise

– Using of the FASTA format sequence saved in the previous presentation search relatives with similarity analysis (BLAST).


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Contents

• Multiple sequence alignments• Examining protein sequences• Protein 3D models• Use for what?• Exercise


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Multiple Sequence Alignment

• Essence:– Trying to fit more sequences at the same

time. Possible differences are estimated by penalties.

• Use:– Searching common peculiars and

parameters– Inserting new sequences - taxonomy– Protein structures– Phylogenetical analysis


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Multiple Sequence Alignment - An example

TTGACATG CCGGGG---A AACCGTTGACATG CCGGTG--GT AAGCCTTGACATG -CTAGG---A ACGCGTTGACATG -CTAGGGAAC ACGCGTTGACATC -CTCTG---A ACGCG******** ?????????? *****• What is the consensus sequence?• In case of differences it is difficult to detect

common patterns. That’s why we use alignment software.


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Multiple sequence alignment

• Types of alignment:– Manual: hand-made, laborious and long

process. Human faults can occur.– Automata: faster, sometimes it doesn’t

consider biological requirement.– Combinated: we gain the best result by

using together the manual and the automata processes. First use the computer then refine it by hand.


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• Most used program for multiple sequence alignment: CLUSTAL W– Downloadable local version:

http://www.clustal.org– WWW version: http://www.ebi.ac.uk/clustalw– Use progressive alignment method– Fast, low memory usage application– More sequence alignment effective– Able to use drawing simple phylogenetic

trees


http://www.clustal.org/

http://www.ebi.ac.uk/clustalw

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• Exercise: Do the multiple alignment on the following sequence:– Sequence file

http://align.bmr.kyushu-u.ac.jp/mafft/online/server/




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• Example:– Examine the catalase sequences of the

plants below:• Paprika (Capsicum annuum)• Tobacco (Nicotiana tabacum)• Tomato (Solanum lycopersicum)• Potato(Solanum tuberosum)

• Sequence file• http://align.genome.jp/


http://align.genome.jp/

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The result with Jalview program.

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Phylogenetics

• The study of evolutionary relatedness among various groups of organisms through molecular sequencing data and morphological data matrices.


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Phylogenetics

• Relation of phylogenetical analysis and sequence alignments. – Sequence alignment determine the similarity and

difference of the aligned sequences. – In case of ortholog sequences: differences of

sequences from different species arises from the mutations collected during their different evolution.

– Number of mutations, namely the rate of the difference between the sequences is connected to the evolutionary distance between the two species: the longer ago the two species separated, the higher sequence difference occur within their ortholog genes.


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Phylogenetics

• Steps of phylogenetic analysis:– 1. Sequence alignment– 2. Definition of evolutionary model– 3. Tree build– 4. Examination of the tree(s)


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Phylogenetics

• Example:– Do the multiple sequence alignment with

the sequence file below and draw the phylogenetic tree.

– Sequence file





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• 1. Sequence alignment


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4. Examination of the tree

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Phylogenetics


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Use for what?

• Function prediction• Relative finding• Identification


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Exercise

– Look for resistance genes with known sequences. Do multiple sequence alignments on them. Evaluate similarities and create a phylogenetic tree.

– What are the consequences of the result?



Bioniformatics - multiple sequence

alignmentProtein sequence analysis

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Content

• Characteristics of the proteins• Protein sequence analysis• Protein 3D models• Practical task


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Characteristics of the proteinsProteins are biochemical compounds consisting of one or more

polypeptides typically folded into a globular or fibrous form, facilitating a biological function

Properties:• 20 amino acid coding triplets• Four types of bases 43 = 64 type of triplets enough 20 amino acid

coding• 61 triplet coding amino acids• 3 stop sign (stop codon) UAA, UAG, UGA• 1 codon sign the start of translation (start codon, methionine AUG)• One triplet coding only one type of amino acid, but the same amino acid

can determinate more triplets degeneration• synonymous triplets: coding the same amino acid• The gene and it has coded protein chain is coo linear• The code is zero overlapped• The genetically code is universal on the living resources.


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Characteristics of the proteins


DNA nucleotide order

RNA nucleotide order

Protein amino acid order

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Characteristics of the proteins


The code table. (Griffiths et al., An Introduction of Genetic analysis, 8th Ed. Fig. 9-8.)

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Group of proteins:Basis of biological activity• Enzymes (pepsin)• Protection proteins (immunoglobulin)• Transport proteins (hemoglobin, mioglobin,

transpherin )• Hormones (insulin, ACTH)• Structure proteins (collagen, elastin,

keratin)• Toxins (snake poison)


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Characteristics of protein sequences

• Protein synthesis video:– http://www.youtube.com/watch?v=NJxobg

kPEAo

• Protein structure video:– http://www.youtube.com/watch?v=lijQ3a8

yUYQ


http://www.youtube.com/watch?v=NJxobgkPEAo

http://www.youtube.com/watch?v=NJxobgkPEAo

http://www.youtube.com/watch?v=lijQ3a8yUYQ

http://www.youtube.com/watch?v=lijQ3a8yUYQ

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Caracteristics of protein sequences

• Structures:– Primary structure: order of amino acids

• „MSASSSSALPPLVPALYRWK”– Secondary structure: spatial structure regularly

repeating local structures. The most common examples are the:

• Alpha helix and Beta sheet– Tertiary structure: the overall shape of a single protein

molecule; the spatial relationship of the secondary structures to one another.

– Quaternary structure: several protein molecules (polypeptide chains), usually called protein subunits in this context, which function as a single protein complex


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• Protein databases:– Protein database: UniProt http://www.uniprot.org/– Protein structure database: ProteinDataBank

http://www.rcsb.org/pdb/home/home.do – Protein interaction database: String http://string.embl.de/


http://www.uniprot.org/

http://www.rcsb.org/pdb/home/home.do

http://string.embl.de/

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Protein sequence analysis

• Analysis primary structure– First of all we examine the distribution

and physical-chemical qualities of aminoacids.

• Example: HCA - Hydrophobic Cluster Analysis acetyl-transpherase protein sequence from fusarium.

– http://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py?form=HCA


http://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py?form=HCA

http://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py?form=HCA

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Fehérje szekvenciák vizsgálata


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• For general similarity trials• We can gain information by general

physical and chemical examinations.

• It is a big challenge for today’s technology to determine the structure and the function of a protein from the DNA sequence.

• For instance several diseases can be defeated if we are able to solve this problem.


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• Prediction according to the dimension:– 1D: amino acid properties, which are

able to write as 1D string. Example: sequence, secondary structure, hydrophobicity

– 2D: distance and contacts between amino acid pairs

– 3D: configuration prediction on the basis of all atom coordinates


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• Common analysis for protein structures:– 3D configuration visualization → we can see some

important properties. – 3D configuration aligning→ similar structure – similar

function.– 3D configuration classifying → line condition, similar

function.– 3D configuration predicting → secondary, tertiary,

quaternary structure prediction.– Small molecules docking → medical product candidate

molecule for known structure molecule.– Protein structure behavior→ molecule-dynamic

simulation.


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Protein 3D models

• In order to understand the function of a proteine, we have to know its 3D structure and quaternary structure. Then we are able to conclude its linking possibilities to other molecules, proteines, enzymes.

• Let’s see an example for acetyl-transpherase protein sequence from fusarium :

– http://www.rcsb.org/pdb/explore/explore.do?structureId=3FP0


http://www.rcsb.org/pdb/explore/explore.do?structureId=3FP0

http://www.rcsb.org/pdb/explore/explore.do?structureId=3FP0

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Analysis of protein synergy

• Database of Interacting Proteins– It collect data about proteines interacting

(bonding) with each others by experimental results.

– It describes about 11 000 interaction of about 6200 proteine.

– Specific details of one interaction: one proteine, the other proteine, interacting regions, experimental methods, dissociation constant, references.

– Example: we can show how interaction-network graphs build (nodes clickable).


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Analysis of protein synergy

• http://dip.doe-mbi.ucla.edu/


http://dip.doe-mbi.ucla.edu/

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Exercise

– Look for the proteine sequence and (if it exists) the 3D structure of an important causative agent in an optional sequence database. Examine the possible linking points.



Bioinformatics- Genomes

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Content

• What is genome?• Genome projects• Genome browser software• Use for what?• Exercise


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Definition of genome?

• The genome: „The genome is the entirety of an organism's hereditary information. It is encoded either in DNA or, for many types of virus, in RNA. The genome includes both the genes and the non-coding sequences of the DNA/RNA.” (Wikipedia)


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Genomics is the discipline of Genome

• Genomics: encompasses a broader scope of scientific inquiry associated technologies than when genomics was initially considered. A genome is the sum total of all an individual organism's genes. Thus, genomics is the study of all the genes of a cell, or tissue, at the DNA (genotype), mRNA (transcriptome), or protein (proteome) levels.– Functional genomics: attempts to make use of the

vast wealth of data produced by genomic projects – Structural genomics: attempts to determine the

structure of every protein encoded by the genome


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Tools of genomics

• Microarray (v. chip): It is a 2D array on a solid substrate (usually a glass slide or silicon thin-film cell) that assays large amounts of biological material using high-throughput screening methods.

• Types: – DNA microarrays (oligonucleotids or cDNA) – Protein microarrays – Cellular microarrays – Tissue microarrays– Antibody microarrays


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Tools of genomics


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Genome projects

• In the last decade several genome projects has started in the world. The aim of these projects is the entire recognition of the genetic code of more and more creatures. In August 2010 genomes of nearly 2500 species are known entirely or partly.

• Important projects:

– http://genome.ucsc.edu


http://genome.ucsc.edu/

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Genome projects

• First successful genome project was the Hemophilus Influenzae in 1995, done by Fleischmann et al. The first plant genome was Arabidopsis thaliana in 2000. The entire exploration of the humane genome was completed in 2003.

• Tools of bioinformatics play an important role in the analytical work following genome sequencing. This time happen assembling the genetically code.


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Genom browsers

• Trough genome browsers we are able to view clear and arranged format of genomic data.

• In some case genome is the starting point of genetically analysis.

• Some genome browsers:– http://genome.ucsc.edu– http://www.ensembl.org – http://ecrbrowser.dcode.org– http://www.ncbi.nlm.nih.gov/mapview/


http://genome.ucsc.edu/

http://www.ensembl.org/

http://ecrbrowser.dcode.org/

http://www.ncbi.nlm.nih.gov/mapview/

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Genome browsers

• Properties of UCSC Genome browser– News and information on the start page– Genome grouping on the basis of class

and genome.– Selectable sequence assembly by date

(tracking able)– Searching by test


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Chromosome number

Position on Chromosome

Base position

Known genes

Mammals conservations


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Genome browsers

• Options under the graphical interface:– Gene and gene probability options– mRNA and EST options– Expressions and regulation– Comparison options– Variations and duplicates


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Genome browsers

• NCBI MapViewer properties– Four level system

• Start page• Genome View• Map View• Sequence View

– Ability for keyword searching– Access to 800 species genomes


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Genome browsers

• NCBI MapViewer


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Exercise

– With the help of genome browser look for the genome of an important causative agent in an optional sequence database. Examine what kind of genes can occur in the surroundings of the one-millionth nucleotide on the second chromosome of this organism.


PRESENTATION CAN BE DOWNLOADED FROM:-

Georgikon Faculty


Prepared by:Dr. János Busznyák - Dr. Máté Csák– Sándor Nagy

information technology in plant protection

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