SlideShare a Scribd company logo
1 of 1
Download to read offline
Scientfc thinking with computatonal skills
Developing an undergraduate bioinformatcs course
Kate L Hertweck, Ph.D, Department of Biology, The University of Texas at Tyler
ABSTRACT
A challenge for undergraduate science educators is
meetng increasing demands for students to handle,
analyze, and interpret large-scale biological data.
Bioinformatcs (BIOL 4306/4106) is a newly developed
course being taught in spring 2015 through the
Department of Biology. During the lecture and lab,
students are taught how to integrate biological content
knowledge with computatonal methods. The use of
technology and focus on critcal scientfc thinking make
the class challenging but tractable for students from a
variety of educatonal backgrounds. Assessments are
designed to help students develop practcal,
transferable skills they can apply to a variety of
professions.
IMPLEMENTATION
●
Lecture highlights concepts and theory related to
analysis of large biological datasets, like from
genomic sequencing projects.
●
Lab focuses on technical skills, like implementng
computer code and statstcal analyses, to solve
problems.
●
Assessment is based on homework and projects,
rather than exams, to focus on applicaton of skills.
●
All materials developed for lab are publicly available:
htps://github.com/BioinformatcsSpring2015
●
Another new class, Bioinformatcs for Research, will
be ofered in Fall 2015 to assist Biology graduate
students in performing scientfc research.
●
Describe the scope of bioinformatcs research and
applicatons
●
Design and implement bioinformatcs pipelines to answer
pre-defned questons from a variety of biological
disciplines
●
Validate results from bioinformatcs algorithms using
hypothesis testng, correctng for multple comparisons,
etc.
●
Characterize the limitatons of data to answer questons
of interests
●
Obtain resources to learn new languages and algorithms
●
Code basic scripts to accomplish the goals above
COURSE OBJECTIVES COURSE CURRICULUM SUMMARY
Bioinformatcs Framework: fundamental skills
and knowledge for analyzing biological data
●
Data in biology
●
Workfows and pipelines
●
Statstcal inference
●
Data visualizaton
Applied Bioinformatcs: general topics that apply
learning from Bioinformatcs Framework
●
Sequence searching
●
Phylogenetcs and clustering
●
Genome assembly
●
Comparatve genomics
PRE-CLASS SURVEY RESULTS
●
All students were biology majors
●
All students agreed (strongly or moderately) that
they wanted to improve their data analysis
capabilites
●
Some students had previous experience with
computer programming.
●
Most students believed computatonal skills
would be important to their future careers
BIOINFORMATICS
Software
DNA sequences Genomic data
Computer programming
Statistics and
visualization

More Related Content

Viewers also liked

SeqinR - biological data handling
SeqinR - biological data handlingSeqinR - biological data handling
SeqinR - biological data handling
pau_corral
 
regex-presentation_ed_goodwin
regex-presentation_ed_goodwinregex-presentation_ed_goodwin
regex-presentation_ed_goodwin
schamber
 
Hertweck Evolution 2014
Hertweck Evolution 2014Hertweck Evolution 2014
Hertweck Evolution 2014
Kate Hertweck
 

Viewers also liked (20)

Evolution of transposons, genomes, and organisms (Hertweck Fall 2014)
Evolution of transposons, genomes, and organisms (Hertweck Fall 2014)Evolution of transposons, genomes, and organisms (Hertweck Fall 2014)
Evolution of transposons, genomes, and organisms (Hertweck Fall 2014)
 
SeqinR - biological data handling
SeqinR - biological data handlingSeqinR - biological data handling
SeqinR - biological data handling
 
Hertweck bbl2012
Hertweck bbl2012Hertweck bbl2012
Hertweck bbl2012
 
Hertweck uva2012
Hertweck uva2012Hertweck uva2012
Hertweck uva2012
 
Phylolecture
PhylolecturePhylolecture
Phylolecture
 
Hertweck AB3ACBS presentation
Hertweck AB3ACBS presentationHertweck AB3ACBS presentation
Hertweck AB3ACBS presentation
 
Transposable elements of Agavoideae
Transposable elements of AgavoideaeTransposable elements of Agavoideae
Transposable elements of Agavoideae
 
iEvoBio Hertweck presentation 2012
iEvoBio Hertweck presentation 2012iEvoBio Hertweck presentation 2012
iEvoBio Hertweck presentation 2012
 
Poster
PosterPoster
Poster
 
Poster
PosterPoster
Poster
 
regex-presentation_ed_goodwin
regex-presentation_ed_goodwinregex-presentation_ed_goodwin
regex-presentation_ed_goodwin
 
Hertweck Monocots V Presentation
Hertweck Monocots V PresentationHertweck Monocots V Presentation
Hertweck Monocots V Presentation
 
Hertweck Evolution 2014
Hertweck Evolution 2014Hertweck Evolution 2014
Hertweck Evolution 2014
 
Getting More Phylotastic
Getting More PhylotasticGetting More Phylotastic
Getting More Phylotastic
 
Phylogenetics in R
Phylogenetics in RPhylogenetics in R
Phylogenetics in R
 
Careers in Botany
Careers in BotanyCareers in Botany
Careers in Botany
 
Evolution 2012
Evolution 2012Evolution 2012
Evolution 2012
 
Bayesian Divergence Time Estimation – Workshop Lecture
Bayesian Divergence Time Estimation – Workshop LectureBayesian Divergence Time Estimation – Workshop Lecture
Bayesian Divergence Time Estimation – Workshop Lecture
 
Phylogeny in R - Bianca Santini Sheffield R Users March 2015
Phylogeny in R - Bianca Santini Sheffield R Users March 2015Phylogeny in R - Bianca Santini Sheffield R Users March 2015
Phylogeny in R - Bianca Santini Sheffield R Users March 2015
 
Chamberlain PhD Thesis
Chamberlain PhD ThesisChamberlain PhD Thesis
Chamberlain PhD Thesis
 

Similar to Developing an undergraduate bioinformatics course

APrimeronMolecularBiologySpring2016Syllabus
APrimeronMolecularBiologySpring2016SyllabusAPrimeronMolecularBiologySpring2016Syllabus
APrimeronMolecularBiologySpring2016Syllabus
Yvette Tran
 
A Primer on Molecular Biology, Spring 2016 Syllabus
A Primer on Molecular Biology, Spring 2016 SyllabusA Primer on Molecular Biology, Spring 2016 Syllabus
A Primer on Molecular Biology, Spring 2016 Syllabus
Billal Ahmed
 
ICIS Module Spec - BI3D23 PROJECT
ICIS Module Spec - BI3D23 PROJECTICIS Module Spec - BI3D23 PROJECT
ICIS Module Spec - BI3D23 PROJECT
Daniel Band
 
PrimerSyllabusFall2016
PrimerSyllabusFall2016PrimerSyllabusFall2016
PrimerSyllabusFall2016
Yvette Tran
 
A collaborative model for bioinformatics education: combining biologically i...
A collaborative model for bioinformatics education:  combining biologically i...A collaborative model for bioinformatics education:  combining biologically i...
A collaborative model for bioinformatics education: combining biologically i...
Elia Brodsky
 
Simon Cotterill introduces the ePET e-portolio system
Simon Cotterill introduces the ePET e-portolio systemSimon Cotterill introduces the ePET e-portolio system
Simon Cotterill introduces the ePET e-portolio system
JISC Netskills
 

Similar to Developing an undergraduate bioinformatics course (20)

Omics Logic - Bioinformatics 2.0
Omics Logic - Bioinformatics 2.0Omics Logic - Bioinformatics 2.0
Omics Logic - Bioinformatics 2.0
 
Introduction to IB Diploma Biology
Introduction to IB Diploma BiologyIntroduction to IB Diploma Biology
Introduction to IB Diploma Biology
 
APrimeronMolecularBiologySpring2016Syllabus
APrimeronMolecularBiologySpring2016SyllabusAPrimeronMolecularBiologySpring2016Syllabus
APrimeronMolecularBiologySpring2016Syllabus
 
50_Research methodology and Biostatistics.pdf
50_Research methodology and Biostatistics.pdf50_Research methodology and Biostatistics.pdf
50_Research methodology and Biostatistics.pdf
 
July 14, 2016 Webcast for the Bioinformatics MS at NYU Tandon Online
July 14, 2016 Webcast for the Bioinformatics MS at NYU Tandon OnlineJuly 14, 2016 Webcast for the Bioinformatics MS at NYU Tandon Online
July 14, 2016 Webcast for the Bioinformatics MS at NYU Tandon Online
 
Res1 Methods of Research Outline
Res1 Methods of Research OutlineRes1 Methods of Research Outline
Res1 Methods of Research Outline
 
Research Data Management for young researchers & PhD students the case of Es...
Research Data Management for young researchers & PhD students the case of Es...Research Data Management for young researchers & PhD students the case of Es...
Research Data Management for young researchers & PhD students the case of Es...
 
A Primer on Molecular Biology, Spring 2016 Syllabus
A Primer on Molecular Biology, Spring 2016 SyllabusA Primer on Molecular Biology, Spring 2016 Syllabus
A Primer on Molecular Biology, Spring 2016 Syllabus
 
ICIS Module Spec - BI3D23 PROJECT
ICIS Module Spec - BI3D23 PROJECTICIS Module Spec - BI3D23 PROJECT
ICIS Module Spec - BI3D23 PROJECT
 
Blended learning programs
Blended learning programs Blended learning programs
Blended learning programs
 
Online Graduate Programs in Bioinformatics at NYU
Online Graduate Programs in Bioinformatics at NYUOnline Graduate Programs in Bioinformatics at NYU
Online Graduate Programs in Bioinformatics at NYU
 
Lecture-on-Writing-Research-Paper.ppt
Lecture-on-Writing-Research-Paper.pptLecture-on-Writing-Research-Paper.ppt
Lecture-on-Writing-Research-Paper.ppt
 
Preparation for NBA
Preparation for  NBAPreparation for  NBA
Preparation for NBA
 
Lbrce atal fdp_data_science
Lbrce atal fdp_data_scienceLbrce atal fdp_data_science
Lbrce atal fdp_data_science
 
PrimerSyllabusFall2016
PrimerSyllabusFall2016PrimerSyllabusFall2016
PrimerSyllabusFall2016
 
INSTRUCTIONAL DESIGN BIOMOLECULE ANALYSIS.pdf
INSTRUCTIONAL DESIGN BIOMOLECULE ANALYSIS.pdfINSTRUCTIONAL DESIGN BIOMOLECULE ANALYSIS.pdf
INSTRUCTIONAL DESIGN BIOMOLECULE ANALYSIS.pdf
 
Bioinformatic core facilities discussion
Bioinformatic core facilities discussionBioinformatic core facilities discussion
Bioinformatic core facilities discussion
 
Aligning Nuclear Physics Computing Techniques with Non-Research Physics Careers
Aligning Nuclear Physics Computing Techniques with Non-Research Physics CareersAligning Nuclear Physics Computing Techniques with Non-Research Physics Careers
Aligning Nuclear Physics Computing Techniques with Non-Research Physics Careers
 
A collaborative model for bioinformatics education: combining biologically i...
A collaborative model for bioinformatics education:  combining biologically i...A collaborative model for bioinformatics education:  combining biologically i...
A collaborative model for bioinformatics education: combining biologically i...
 
Simon Cotterill introduces the ePET e-portolio system
Simon Cotterill introduces the ePET e-portolio systemSimon Cotterill introduces the ePET e-portolio system
Simon Cotterill introduces the ePET e-portolio system
 

Recently uploaded

SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
CaitlinCummins3
 
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
中 央社
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
中 央社
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
Krashi Coaching
 

Recently uploaded (20)

ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
 
Dementia (Alzheimer & vasular dementia).
Dementia (Alzheimer & vasular dementia).Dementia (Alzheimer & vasular dementia).
Dementia (Alzheimer & vasular dementia).
 
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
 
Championnat de France de Tennis de table/
Championnat de France de Tennis de table/Championnat de France de Tennis de table/
Championnat de France de Tennis de table/
 
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
 
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptx
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio App
 
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING IIII BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the life
 
How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptx
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge App
 
An overview of the various scriptures in Hinduism
An overview of the various scriptures in HinduismAn overview of the various scriptures in Hinduism
An overview of the various scriptures in Hinduism
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024
 

Developing an undergraduate bioinformatics course

  • 1. Scientfc thinking with computatonal skills Developing an undergraduate bioinformatcs course Kate L Hertweck, Ph.D, Department of Biology, The University of Texas at Tyler ABSTRACT A challenge for undergraduate science educators is meetng increasing demands for students to handle, analyze, and interpret large-scale biological data. Bioinformatcs (BIOL 4306/4106) is a newly developed course being taught in spring 2015 through the Department of Biology. During the lecture and lab, students are taught how to integrate biological content knowledge with computatonal methods. The use of technology and focus on critcal scientfc thinking make the class challenging but tractable for students from a variety of educatonal backgrounds. Assessments are designed to help students develop practcal, transferable skills they can apply to a variety of professions. IMPLEMENTATION ● Lecture highlights concepts and theory related to analysis of large biological datasets, like from genomic sequencing projects. ● Lab focuses on technical skills, like implementng computer code and statstcal analyses, to solve problems. ● Assessment is based on homework and projects, rather than exams, to focus on applicaton of skills. ● All materials developed for lab are publicly available: htps://github.com/BioinformatcsSpring2015 ● Another new class, Bioinformatcs for Research, will be ofered in Fall 2015 to assist Biology graduate students in performing scientfc research. ● Describe the scope of bioinformatcs research and applicatons ● Design and implement bioinformatcs pipelines to answer pre-defned questons from a variety of biological disciplines ● Validate results from bioinformatcs algorithms using hypothesis testng, correctng for multple comparisons, etc. ● Characterize the limitatons of data to answer questons of interests ● Obtain resources to learn new languages and algorithms ● Code basic scripts to accomplish the goals above COURSE OBJECTIVES COURSE CURRICULUM SUMMARY Bioinformatcs Framework: fundamental skills and knowledge for analyzing biological data ● Data in biology ● Workfows and pipelines ● Statstcal inference ● Data visualizaton Applied Bioinformatcs: general topics that apply learning from Bioinformatcs Framework ● Sequence searching ● Phylogenetcs and clustering ● Genome assembly ● Comparatve genomics PRE-CLASS SURVEY RESULTS ● All students were biology majors ● All students agreed (strongly or moderately) that they wanted to improve their data analysis capabilites ● Some students had previous experience with computer programming. ● Most students believed computatonal skills would be important to their future careers BIOINFORMATICS Software DNA sequences Genomic data Computer programming Statistics and visualization