Agricultural Bioinformatics Research Unit's Educational Program
Agricultural BioinformaticsResearch Unit'sEducational ProgramApplication Guidelines FY2022Notice! All lectures will be conducted in Japanese.
1. IntroductionThe importance of bioinformatics in the fields of agricultural and life scienceshas significantly increased. As a concrete methodology, bioinformatics hasbecome indispensable in addressing today's critical social issues of food, theenvironment, and life, and as a means of integrating fragmented disciplines,and because of this education in both the fundamental and applied aspectsof the discipline is called for. Our program provides hands-on education inbioinformatics and conducts cutting-edge research in agricultural and lifesciences related to bioinformatics. Over the past 18 years, more than 7,300people have taken courses and earned credits in this program. Through oureducational and research activities, we aim to form a social collaboration andinternational base for agricultural bioinformatics.2. Application ProceduresThis program provides graduate-level classes premised on an undergraduatelevel knowledge of life sciences. Also, since many classes use computers,students are expected to already have some knowledge of basic computeroperations. For these reasons, we envision graduate students majoring in thenatural sciences and working adults involved in research and developmentrelated to agriculture and life sciences (including medicine and pharmacology).If you are a student at the University of Tokyo, please register through UTAS.If you are not a student at the University of Tokyo, please apply from theapplication form on the website from May 10 to June 20.Only those whohave passed the selection process may participate in the program. Successfulapplicants will be notified by email by June 30.Program guidance (for University of Tokyo students)From Monday, April 1, A video of the course guidance will be distributed.The video address will be announced on our website.1
3. Classes (see page 6 and after for details)Generally, classes offered in this program have three categories.Fundamentals:Introduction to Biological Sequence Analysis, Introduction to GenomeInformatics, Introduction to Biostatistics, and Introduction to StructuralBioinformatics are mainly for those who have no research experience usingbioinformatics. You will learn how to use various life science databases andbioinformatics tools and will also be taught the basic elements of statistics.Methodologies:The Methodologies classes (Knowledge Information Processing, SequenceStatistics and Mathematical Biology, Molecular Modeling and Simulation,Omics Analysis, Functional Genomics, Introduction to Systems Biology, FieldInformatics) are based on the Fundamentals classes and provide instructionon various experimental methods (transcriptome analysis, mass spectrometry,etc.) and computer applications (pattern recognition, machine learning,statistical model and model selection, molecular modelling).Advanced Topics:In the Special Lectures on Agricultural Bioinformatics, researchers fromcompanies and universities will give lectures and provide practical training ontheir cutting-edge research topics. Our aim is to provide feedback in this wayon the practical applications of bioinformatics. In Research Exercise onAgricultural Bioinformatics, you will receive research guidance from ourprogram faculty members.2
4. List of lecture coursesCourse CodeCourse Namesemester*/credit3912135Introduction to Biological Sequence AnalysisS1・13912136Introduction to Genome InformaticsS1・13912103Introduction to BiostatisticsS1・13912139Introduction to Structural BioinformaticsS1・13912137Knowledge Information ProcessingSP・13912105Sequence Statistics and Mathematical Biology3912106Molecular Modeling and Simulation3912138Omics Analysis3912108Functional GenomicsS1・13912109Introduction to Systems BiologyS1・13912157Field InformaticsS1・13912111Special Lectures on Agricultural Bioinformatics IS1・13912112Special Lectures on Agricultural Bioinformatics IIS1・13912140Special Lectures on Agricultural Bioinformatics IIIS1・13912141Special Lectures on Agricultural Bioinformatics IVS1・13912142Research Exercise on Agricultural BioinformaticsW・1*―S1・1―Semester for registering courses using UTASThe four fundamental subjects are Intermediate Subjects. University of Tokyoundergraduate students can add these to their course credits.060700130 Introduction to Biological Sequence Analysis060700140 Introduction to Genome Informatics060700150 Introduction to Biostatistics060700160 Introduction to Structural BioinformaticsUniversity of Tokyo graduate students who complete and pass the followingcourses may count them toward the credits required for completion of ology informaticsS1・148101123 Computational biophysicsS1・14917490Advanced Lectures in Applied Computer Science XVIIS1・14917491Advanced Lectures in Applied Computer Science XVIIIS1・14917891Advanced Lectures in Applied Computer Science 13AS1・13
5. NotesFor University of Tokyo students As in the previous year, all course lectures will be conducted online(Zoom) this year. All lectures will be conducted in Japanese. Lectures for University of Tokyo students (students who can registerthrough UTAS) will generally take place 17:15-18:45 and 19:00-20:30.Changes in a course schedule will be announced on UTAS or ITC-LMS assoon as possible. Please make sure to prepare for online classes. The Zoom URL and information about the lectures will be posted on UTASor ITC-LMS. If you receive an error message when you register at UTAS even thoughthe lecture schedules do not overlap, please register one of the coursesat UTAS and submit an additional registration form to the EducationalSection of the graduate school to which you belong. In the Graduate School of Agricultural and Life Sciences of the Universityof Tokyo, and in many other graduate schools of the University of Tokyo,credits for some courses taken in this program can be added to the creditsrequired to complete a master's degree. For details, please refer to thecurrent year's Graduate School Handbook. If you are interested in taking the course “Research Exercise onAgricultural Bioinformatics,” please contact the program office first.For students outside of the University of Tokyo If you are not a student at the University of Tokyo, please apply duringthe application period. Applications submitted outside this period will notbe accepted. For those who are not students at the University of Tokyo, only those whohave passed the selection process will be allowed to attend. Successfulapplicants will be notified by email by June 30 (Wed). Students who pass the selection process will be required to watch lecturesthat will be distributed on-demand from July 5(Tue). Outside students will not be able to attend course lectures on the samedays and times as University of Tokyo students. Please refer to the coursedetails for the lectures that will be delivered on-demand. The information about the lectures will be posted on dedicated slack.4
Before attending a lecture, please install the necessary software on yourPC by referring to the software installation page on our website. Details of the on-demand streaming will be sent to you via email. You canwatch a lecture as many times as you like during the on-demandstreaming period, and you may also ask questions to the teacher. Credits earned by taking this program cannot be used to complete amaster's degree at another university or to apply for a degree from theNational Institution for NIAD-QE Evaluation.For all students Those who have acquired at least 8 credits will be recognized ashaving completed the specialized education course and will beawarded a "Certificate of Completion."5
6. Course sIntroduction to Biological Sequence Analysis(Course code:3912135/060700130)Kentaro Shimizu, Kenro Oshima4/6 (Wed), 4/13 (Wed), 4/20 (Wed), 4/27 (Wed)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 7/5 to 10/31,Assignment submission until 8/29)Course objectives / OverviewThis course focuses on the use of databases and basic analysis methodsfor life sciences. The use of sequence and functional databases will beintroduced, and basic methods such as homology search, motif analysis,programming, and phylogenetic analysis will be explained in a hands-onformat.Schedule1) Sequence database and homology search2) Genome Database and Programming3) Various methods for predicting functions from sequences4) Molecular evolution and phylogenetic tree constructionTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksThe following books are helpful:Hiroyuki Fuji (ed.), Understanding Bioinformatics, Kodansha, 2018.H. Bono, Life Science Data Analysis, MEDSi、2021Notes on taking the courseStudents should be able to use a Windows PC or Mac.OtherIf you have never accessed various databases related to bioinformatics,please take this course.6
uction to Genome Informatics(Course code:3912136/060700140)Koji Kadota, Yuichi Kodama, Hiroshi Mori4/5(Tue), 4/12 (Tue), 4/19 (Tue), 4/26 (Tue)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 7/5 to 10/31,Assignment submission until 8/29)Course objectives / OverviewIn this course, we will discuss the general trends in the fields of genomeinformation analysis, public sequence databases, and metagenomicanalysis. Included are lectures on general trends in the field of genomeinformation analysis, public nucleotide sequence databases in general, andpractical training on metagenomic analysis.Schedule1) General introduction to genome information analysis system (4/5:Kadota)2) Public databases in life sciences in general (4/12: Kodama)3) Basics and Applications of Metagenomic Analysis (4/19, 4/26: Mori)Teaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksH. Bono, Life Science Data Analysis, 2nd Edition, MEDSi, 2021Nagata, N., Introduction to Bioinformatics Reading by Evolution, MorikitaPublishing, 2019.Hiroyuki Fuji (ed.), Understanding Bioinformatics, Kodansha, 2018.Notes on taking the courseWe use RStudio. Please install R and RStudio. Please install various Rpackages by referring to ITC-LMS (for University of Tokyo students) orslack (for outside students).7
ction to Biostatistics(Course code:3912103/060700150)Hiroyoshi Iwata4/8 (Fri), 4/15 (Fri), 4/22 (Fri), 5/6 (Fri)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 7/5 to 10/31,Assignment submission until 8/29)Course objectives / OverviewThis course teaches introductory biostatistics using the statistical analysissoftware R. This course is designed for students who are using R for thefirst time and will focus on practical training using laptop computers.Schedule1) Visualizing data in R2) Regression analysis, analysis of variance3) Principal component analysis, multidimensional scaling4) Hierarchical cluster analysis, non-hierarchical cluster analysisTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksReference books for further study will be introduced during the course.Notes on taking the courseStudents should be able to use a Windows PC or Mac.OtherWe use RStudio. Please install R and RStudio. Please install various Rpackages by referring to ITC-LMS (for University of Tokyo students) orslack (for outside students).8
uction to Structural Bioinformatics(Course code：3912139/060700160)Tohru Terada, Koji Nagata, Kentaro Shimizu4/7 (Thu), 4/14 (Thu), 4/21 (Thu), 4/28 (Thu)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 7/5 to 10/31,Assignment submission until 8/29)Course objectives / OverviewThis course instructs students on how to use a protein 3D structuredatabase and illustrates its applications. This course also explainsinformation processing methods used in 3D structure determination.Schedule1) Use of 3D structure database and visualization of 3D structure data2) Informatics of 3D structure determination by X-ray crystallography3) Information extraction from 3D structure database4) 3D structure modelingTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksNone in particularNotes on taking the courseStudents should be able to use a Windows PC or Mac.9
CourseInstructorKnowledge Information Processing(Course code:3912137)Minoru Asogawa9/9 (Fri), 9/16 (Fri), 9/30 (Fri), 10/7 (Fri), 10/14 (Fri),Lecturedates10/21 (Fri), 10/28 (Fri)*University of Tokyo students only*Lecture time is 17:15-18:45, unlike other lectures.OutsidestudentsOn-demand distribution available(Distribution period from 11/1 to 1/23,Assignment submission until 12/26)Course objectives / OverviewThis course provides an introduction to pattern recognition and machinelearning using bioinformatics data.Schedule1) Neural network basic, 2) Discrimination analysis and applicationexamples, 3) Analysis method for trained neural network, deep learningand correlation analysis, 4) Clustering analysis and principal componentanalysis, 5) Kernel based learning (SVM), 6) Decision tree, and 7) HiddenMarkov model.Teaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationIn the previous year, the course grade was determined on the basis of fourreport assignments. This evaluation method may be altered depending onthe student's proficiency level.Notes on taking the courseStudents should be able to use R system.OtherR is mainly used. Please install R and RStudio. Please install various Rpackages by referring to ITC-LMS (for University of Tokyo students) orslack (for outside students). It's assumed that you have already masteredthe primary usage of R (or RStudio). Please refer to http://www.iu.a.utokyo.ac.jp/ kadota/r seq.html#users guide and understand the basics.10
ar Modeling and Simulation(Course code:3912106)Tohru Terada5/12 (Thu), 5/19 (Thu), 5/26 (Thu), 6/9 (Thu)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewThis course teaches students about the molecular orbital, molecularmechanics, molecular dynamics, Monte Carlo, and complex modelingmethods. This course also provides training in each of these methods.Schedule1) Molecular orbital method2) Molecular mechanics method3) Molecular dynamics method4) Monte Carlo method5) Complex modeling methodTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksThe following book is helpful:Andrew R. Leach, Molecular Modeling: Principles and Applications, PrenticeHall, 2001.Notes on taking the courseStudents should be able to use a Windows PC or Mac.11
CourseFunctional Genomics (Course code:3912108)InstructorsKoji Kadota, Yasuhiro Tanizawa, Koichi HigashiLecture5/10 (Tue), 5/17 (Tue), 5/24 (Tue), 5/31 (Tue)dates*University of Tokyo students onlyOn-demand distribution availableOutsidestudents(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewThis lecture focuses on methods used in the fields of genome analysis andtranscriptome analysis, including basic concepts of k-mer analysis and itsuse (de novo assembly and genome size estimation), genome annotation,and RNA-seq data analysis.Schedule1) Genome analysis: Bacterial genome analysis and its surroundings(5/10: Tanizawa)2) Genome Analysis: Basics and applications of K-mer analysis (5/17:Kadota)3) Genome Analysis: Chromosome structure analysis centered on Hi-Cdata (5/24: Higashi)4) Transcriptome Analysis: (5/31: Higashi)Teaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksH. Bono (ed.), RNA-Seq Data Analysis Iron Plate Recipes for Wet Labs,Yodosha, 2019.Hiroyuki Fuji (ed.), Understanding Bioinformatics, Kodansha, 2018.H. Bono, Life Science Data Analysis, MEDSi, dyPythonBioinformatics Analysis, Yodosha, 2021Notes on taking the courseWe use RStudio. Please install R and RStudio. Please install various Rpackages by referring to ITC-LMS (for University of Tokyo students) orslack (for outside students).12
ction to Systems Biology (Course code:3912109）Masanori Arita7/1 (Fri), 7/15 (Fri), 7/22 (Fri)*University of Tokyo students only*Lecture time is 13:00-17:00, unlike other lectures.On-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / canalysesforomicsinformation to understand life as a system.ScheduleSeven lectures in 3 days. The course explains the underlying ideas andmethods of dimensional reduction and sparse modeling in big- and omicsdata, and network analysis. This course also covers recent research trendswith concrete examples.Teaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksBernhard O. Palsson, Systems Biology: Simulation of Dynamic Network States,Cambridge University Press, 2011Uri Alon, An Introduction to Systems Biology: Design Principles of BiologicalCircuits, Chapman & Hall/CRC, 2019Notes on taking the courseStudents should be able to use a Windows PC or Mac.OtherCellDesigner and R software programs are necessary. Please install beforeattending the class.13
CourseInstructorsLecturedatesOutsidestudentsField Informatics(Course code:3912157）Yoshihiro Ohmori, Takeshi Izawa, Hiroyoshi Iwata, Wei Guo,Naoyuki Sotta6/8(Wed), 6/15 (Wed), 6/22 (Wed), 6/29 (Wed)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewThe quantity and quality of crop production are not determined solely bygenomic information but are determined as a result of interactions withenvironmental conditions of field, such as weather, soil, nutrient, andmicroorganisms, etc. that change spatially and temporally. In this lecture,we will introduce various research methods using images, genome,transcriptome, and ionome information obtained from the field.Schedule (This course provides omnibus lectures covering topics below)6/8: Image analysis in agriculture6/15: Genome Wide Association Study and Genomic Prediction6/22: Field-transcriptome analysis6/29: Measurement of plant traits from image (The first half)Machine learning using ionome -classification- (The second half)Teaching MethodsThis course is conducted online using Zoom, and students can watchrecorded lectures. We will use R and RStudio for the hands-on practice(also will use MATLAB Online on Jun 8th).Grade EvaluationEvaluation is based on the submission of report on any topic of interesttreated in the course.Reference BooksReference books for further study will be introduced during the course.Notes on taking the courseStudents should be able to use a Windows PC or Mac. Please install R andRStudio. Field informatics relates to lectures, Introduction to Biostatistics,Special Lectures on Agricultural Bioinformatics II and III.14
CourseInstructorLecturedatesOutsidestudentsSpecial Lectures on Agricultural Bioinformatics I(Course code:3912111)Jianqiang Sun6/6 (Mon), 6/13 (Mon), 6/20 (Mon), 6/27 (Mon)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewSmart agriculture enables high quality and large crop production byutilizing advanced technologies such as ICT and IoT, which are currentlybeing promoted worldwide. One of the technologies that supports thefoundation of smart agriculture is programming languages. Python is oneof the easiest programming languages to learn and has a wide range ofapplications. In this course, we will cover the basics of Python andintroduce examples of the latest practical applications of Python inagriculture as well as in molecular biology fields.Schedule1) Basic data type, basic syntax (if, for, while)2) Text and file processing3) Data analysis (NumPy and Pandas)4) Data visualization (matplotlib)Teaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference Web yo-ipp.github.io/Notes on taking the courseStudents are expected to be able to operate one of the following systems:Windows, macOS, or Linux. Anaconda 2020.10 (Python 3.8) must beinstalled on the system in advance.15
CourseInstructorLecturedatesOutsidestudentsSpecial Lectures on Agricultural Bioinformatics II(Course code:3912112)Gen Sakurai, Hiroyoshi Iwata, Yoshihiro Ohmori6/3 (Fri), 6/10 (Fri), 6/17 (Fri), 6/24 (Fri)*University of Tokyo students onlyOn-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewIn this lecture, to learn the basics of building a simulation model in eachcrop cultivation system, we will learn the method of mathematicallymodeling and stimulating the growth of plants, the relationship betweenplants and meteorology, and plant-physiological phenomena. We aim touse this lecture as a starting point for constructing mathematical modelsof biological phenomena such as plant growth and the dynamics ofminerals in plants. Lectures will include instruction on importantmathematical models relating to agrometeorology, plant growth, and thedynamics of minerals and coding exercises on the formulas.Schedule1. Formulation of plant light reception and transpiration2. Formulation of photosynthesis and plant growth3. Basics of water potential, diffusion equation, and the dynamics of waterin soil4. Mathematical modeling of water flow in plantsTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksReference books for further study will be introduced during the course.Notes on taking the courseWe use RStudio. Please install before attending the class.16
l Lectures on Agricultural Bioinformatics III(Course code:3912140)Noboru Koshizuka, Shinsuke Kobayashi, Yoshihiro Ohmori5/13 (Fri), 5/20 (Fri), 5/27 (Fri)*University of Tokyo students only*Lecture time is 13:00-17:00, unlike other lectures.On-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewIn this course, students will learn to write a simple data processing programfor environmental monitoring using data from environmental sensorsinstalled in the greenhouse of the Institute for Sustainable Agro-ecosystemServices (UTokyo ISAS). The goal is to be able to write data processingprograms in Python, handle data in JSON and CSV formats, and performstatistical processing and graphing.ScheduleDay 1 (4 hours): Learn to write Python with Jupyter Notebook.Day 2 (4 hours): Learn to process JSON and CSV data.Presentation of exercise tasks (e.g., Visualize, etc.)Day 3 (4 hours): Presentation of assignments from Day 2.Supplementary lectureTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Notes on taking the courseUse Google Colaboratory (Colab: Python execution environment in thecloud). In this lecture, the content will be understandable even for studentswho are new to programming, but it is desirable to have some programmingexperience regardless of language.This course is related to the Methodology "Field Informatics" and theAdvanced Topics "Special Lectures on Agricultural Bioinformatics II."Therefore, we recommend that you also take them.17
CourseInstructorsLecturedatesSpecial Lectures on Agricultural Bioinformatics IV(Course code:3912141)Hideaki Nojiri, Haruo Suzuki, Koji Yahara, Masami Shintani,Naohiro Noda, Motomu Matsui6/7(Tue), 6/14(Tue), 6/16(Thu), 6/21(Tue), 6/28(Tue)*University of Tokyo students only*Lecture time is 13:15-16:40, unlike other lectures.OutsidestudentsOn-demand distribution available(Distribution period from 8/2 to 12/26,Assignment submission until 10/31)Course objectives / OverviewIn this course, we will learn how informatics is used in microbiology andhow interesting findings have been obtained. The course will coverinformatics to explore diverse microbial phenomena and their functionalmechanisms, including the analysis to understand the contents ofpopulations and various analyses to understand the expression patterns ofgenomic functions.Schedule1. bialpopulations-using the command line2. How to utilize bioinformatics in environmental microbial research3. Reproducible bioinformatics using R language4. From microbial flora analysis to standardization of biometric technology5. Understand the behavior of plasmids that promote the evolution andadaptation of microorganisms using bioinformaticsTeaching MethodsThis course is conducted using Zoom, and students can watch recordedlectures.Grade EvaluationStudents are evaluated based on the completion of homework assignments.Reference BooksNo special preparation is required. In some cases, literature for furtherstudy in the field will be introduced during the course.Notes on taking the courseStudents should be able to use a Windows PC or Mac.18
CourseResearch Exercise on Agricultural Bioinformatics(Course code:3912142)Instructors Program facultyLecturedatesTo be announced by the instructorsCourse objectives / OverviewThis is a special seminar in which program faculty members provideresearch guidance assistance.ScheduleBased on an agreement with the supervisor of the laboratory to which thestudent belongs, a faculty member of this program will provide researchguidance assistance on bioinformatics.Teaching MethodsThis is an exercise with research guidance.Grade EvaluationStudents are required to present their research at a review meeting usuallyheld in February of each year. If, as a result of the oral examination, it isrecognized that the student has effectively applied bioinformatics toresearch in agriculture and life sciences or has contributed to thedevelopment of the field of bioinformatics, credit and a certificate will begiven.OtherStudents who wish to take this course must contact the program office.19
7.Program facultyProgram faculty includes full-time faculty members of the AgriculturalBioinformatics Research Unit, which is the main organizer of the program, aswell as adjunct faculty and lecturers belonging to other universities andresearch institutes.Unit RepresentativeDean, Graduate School ofAgricultural and Life SciencesNobuhiro TsutsumiUnit FacultyAssoc. Prof.Koji Kadotakoji.kadota@gmail.comAssoc. Prof.Yoshihiro Ohmori ayohmori@g.ecc.u-tokyo.ac.jpAdjunct faculty (in charge of administration and lectures)Assoc. Prof.Tohru TeradaDepartment of BiotechnologyProfessorKentaro ShimizuDepartment of BiotechnologyProfessorHideaki NojiriDepartment of BiotechnologyProfessorKoji NagataDepartment of Biological ChemistryProfessorMasanori AritaNational Institute of GeneticsAssoc. Prof.Hiroyoshi IwataDepartment of Agricultural andEnvironmental BiologyCooperating herKenro OshimaHosei UniversityMinoru AsogawaNEC CorporationJianqiang SunNational Agriculture and Food ResearchOrganizationProgram office staffAcademic Support StaffAya MiuraProject Academic SpecialistTomoko Terada20
8. Contact information, program secretariatIn addition to questions about courses, we also accept inquiries about how touse bioinformatics in your own research. Please feel free to contact us by email.Graduate School of Agricultural and Life Sciences, Agricultural BioinformaticsResearch Unit, The University of Tokyo1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, JapanSecretariat contact: Aya MiuraEmail: info@iu.a.u-tokyo.ac.jpURL: https://www.iu.a.u-tokyo.ac.jpYayoi Campus Map: The program office is located on the first basement floorof the Faculty of Agriculture Building 2 (Room 14-2).21
Informatics, Introduction to Biostatistics, and Introduction to Structural Bioinformatics are mainly for those who have no research experience using bioinformatics. You will learn how to use various life science databases and . The use of sequence and functional databases will be introduced, and basic methods such as homology search, motif .
Bioinformatics Crash Course Ian Misner Ph.D. Bioinformatics Coordinator UMD Bioinformatics Core . Bioinformatics!Core The Plan Monday – Introductions – Linux and Python Hands-on Training Tuesday – NGS Introduction – RNAseq with Sailfish (Dr. Steve Mount, CBCB) – RNAse
SECTION-A: Attempt any five questions. SECTION-B: Attempt any five questions. SECTION–A Short Answer type Questions: (60-80 Words) 5 5 25 Marks 1. What is the role of internet in bioinformatics? 2. How bioinformatics assist in drug designing? 3. Write a short note on Internet Protocol (IP). 4. What is Pattern mining? 5.
volumes of biological information in bioinformatics database. They also provide some bioinformatics tools for database search and data acquire. With the explosion of sequence information available to researchers, the challenge facing bioinformatics and computational biologists is to aid in biomedical researches and to invent efficient toolkits.
tronics, Physics, Statistics, or Business Informatics. 8 LUM RAMABAJA Bachelor’s Student in Bioinformatics ‘Bioinformatics is a truly interesting field. The program has inspired me to apply what I have learned and help people by starting a company that diagnoses malaria.’ To The Point KRISTINA PREUER BSc MSc Graduate in Bioinformatics
Bioinformatics, Stellenbosch University Many bioinformatics tools and resources are available on the command-line interface These are often on the Linux platform (or other Unix-like platforms such as the Mac command line). They are essential for many bioinformatics and genomics applications.
Bioinformatics is an interdisciplinary area of the science composed of biology, mathematics and computer science. Bioinformatics is the application of information technology to manage biological data that helps in decoding plant genomes. The field of bioinformatics emerged as a tool to facilitate biological discoveries more than 10 years ago.
Bioinformatics Bioinformatics is the combination of biology and information technology. The discipline encompasses any computational tools and methods used to manage, analyze and manipulate large sets of biological data. Essentially, bioinformatics has three components: The creation of databases allowing the storage and
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