Qin, UTC fall 2021 courses

 CPSC 4180, Prog Lang Adv Data Analytics, 42589 

CPSC 5180, Prog Languages for Adv Data subject CPSC,  42590

BIOL 4999R, 02, Data Science for Biologists, 43510

 

BIOL 5010,  Data Science for Biologists   , 44107 

Gradescope

 

automatic grading tools

Gradescope

https://www.gradescope.com/ 

Peer-led Team Learning (PLTL)

 Eric Voss, SIU Edwardsville.  

A Little Help from My Friends: Peer Led Team Learning Before and After COVID-19

Friday, November 13, 2020

3:00 – 4:30 pm:  Active Learning Strategies in STEM Education

Peer-led Team Learning (PLTL) is a model of active learning that introduces peer-led workshops as an integral part of undergraduate STEM courses.  Students who have done well in the course are recruited and trained to become peer-leaders.  The peer-leaders meet with small groups of six to ten students each week for one hour to discuss, debate, and engage in problem solving related to the course material.  PLTL originated in a General Chemistry course at the City College of New York in the 1990s.  Early evidence of improved student attitudes and performance led to further study and development of PLTL by a national team, which resulted in more widespread adoption of PLTL in a variety of science, mathematics, and engineering courses.  Very early on, several SIUE chemistry faculty members attended PLTL training sessions sponsored by the National Science Foundation and subsequently implemented PLTL workshops into the SIUE on-sequence General Chemistry courses.  Since then, implementation has expanded into all first-year chemistry courses and several biology courses.  Due to COVID‑19, PLTL workshops have transitioned from face-to-face to online synchronous sessions, with new challenges and opportunities.  Student performance data, student attitudes, peer-leader training methods, workshop material development, scheduling, space allocation, institutionalization, and sustainability of PLTL will be discussed in this webinar. Questions prior to the webinar? 

autograder, codeHS

 autograder

https://readwritecode.blog/these-are-the-autograders-you-ve-been-looking-for-bda0fa8fd8a

visualizing your code

visualizing your code
http://www.pythontutor.com/

four complex parameters draw an elephant

Drawing an elephant with four complex parameters
American Journal of Physics 78, 648 (2010); https://doi.org/10.1119/1.3254017Jürgen Mayer

Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany

Khaled Khairy

https://aapt.scitation.org/doi/10.1119/1.3254017


Fourier waves in R
http://www.di.fc.ul.pt/~jpn/r/fourier/fourier.html

autism, ADHD, neural diverse

Neurodiversity: autism and ADHD. Autism is an expensive diagnosis, many kids from poor family are only diagnosed with ADHD.

Hispanic family expect kids to generate income after high-schools.

school bus

data camp academic course offer

https://www.datacamp.com/groups/computational-genomics/courses/active

Select create free account, group name, number of student

NIBLSE, bioinformatics core competencies

https://qubeshub.org/groups/niblse/resourcecollection/core_competencies#2

This set of bioinformatics core competencies for undergraduate life scientists is informed by the survey results of more than 1,200 people, analysis of 90 syllabi addressing bioinformatics across institutions and diverse departments, and discussion among experts across academia and industry. The bulleted lists contain examples illustrating the competencies.

1. Explain the role of computation and data mining in addressing hypothesis-driven and hypothesis-generating questions within the life sciences: It is crucial for students to have a clear understanding of the role computing and data mining play in the modern life sciences. Given a traditional hypothesis-driven research question, students should have ideas about what types of data and software exist that could help them answer the question quickly and efficiently. They should also appreciate that mining large datasets can generate novel hypotheses to be tested in the lab or field.
   
   • What hypotheses can one ask based biometric data being compiled (Fitbit, Google, etc.)
   • Understand the role of various databases in identifying potential gene targets for drug development
2. Summarize key computational concepts, such as algorithms and relational databases, and their applications in the life sciences: In order to make use of sophisticated software and database tools, students must have a basic understanding of the underlying principles that these tools are based upon. Students are not expected to be experts in multiple algorithms or sophisticated databases, but currently the vast majority of life sciences majors never take a programming or database course, and have essentially zero exposure to how these tools work. This must change.
   
   • Be exposed to how data is organized in relational databases
   • Be able to modify the search parameters to achieve biologically meaningful results
   • Understand underlying algorithm(s) employed in sequence alignment (e.g. BLAST)
3. Apply statistical concepts used in bioinformatics: Many biology curricula contain statistics, either as a standalone biostatistics course or as part of other courses such as capstone research courses. The primary distinction with regard to bioinformatics has to do with the statistics of large datasets and multiple comparisons.
   
   • Drug trials: Interpretation of well designed drug trial data
   • Transcriptomics: Understand the statistical modelling used to identify differentially expressed genes; Understand how genes implicated in cancer are identified using panels of sequenced tumor and WT cell lines or biopsies
   • Sequence similarity searching: Understand that there is a probability of finding a given sequence similarity score by chance (the p-value); The size of the database searched affects the probability that they would see that particular score in a particular search (the expectation, or e-value).
4. Use bioinformatics tools to examine complex biological problems in evolution, information flow, and other important areas of biology: This competency is written broadly so as to encompass a variety of problems addressed using bioinformatics tools, from understanding the evolutionary underpinnings of sequence comparison and homology detection, to the distinctions between genomic sequences, RNA sequences, and protein sequences, to the interpretation of phylogenetic trees. We want to emphasize that bioinformatics tools can be used to teach existing parts of the curriculum such as the central dogma or phylogenetic relationships, thus integrating the bioinformatics into the curriculum as opposed to adding it on as an addition to an already overfull curriculum (and thus forcing decisions about what topic to remove to make room). The point of saying “complex” biological problems is that students should be able to work through a problem with multiple steps, not just perform isolated tasks.
   
   • Employ gene ontology tools (e.g., Mapman, GO, KEGG).
   • Understand protein sequence, structure, and function, using a variety of tools
   • Understand gene structure, genomic context, alternative splicing using genome browsers
   • Understand concept of homology
5. Find, retrieve, and organize various types of biological data: Given the numerous and varied datasets currently being generated from all of the ‘omics fields, students should develop the facility to: identify appropriate data repositories; navigate and retrieve data from these databases; and organize data relevant to their area of study (in flat files or small local stand-alone databases).
   
   • Store and interrogate small datasets using spreadsheets or delimited text files.
   • Navigate and retrieve data from genome browsers
   • Retrieve data from protein and genome databases (PDB, UniProt, NCBI)
6. Explore and/or model biological interactions, networks and data integration using bioinformatics: Modeling of biological systems at all levels, from cellular to ecological, is being facilitated by technological (e.g., sequencing, biochemical, genetics) and algorithmic advances. These models provide novel insights into the perturbations in systems causative of disease, interactions of microbes with various eukaryotic systems, and how metabolic networks respond to environmental stresses. Students should be familiar with the techniques used to generate these analyses, have the ability to interpret the outputs, and use the data to generate novel hypotheses.
   
   • Cell Biology: predict impact of gene knockout on cell-signaling pathway
   • Transcriptome: Analysis of transcriptomic data (RNA-Seq) available from SRS using Galaxy
   • Ecological: Analysis of microbial sequence data using QIIME on Galaxy
7. Use command-line bioinformatics tools and write simple computer scripts: The majority of the datasets students should be familiar with and be able to interact with (e.g., genomic and proteomic sequences, BLAST results, RNASeq and resulting differential expression data) are text files. The most powerful and dynamic way to interact with these datasets is through the command line or shell scripting, both of which are readily acquired skills. Students need to have the flexibility to manipulate their own data, and to create and modify complex data processing and analysis workflows.
   
   • Write simple unix shell scripts to manipulate files
   • Apply RNASeq analyses using R (STAR, Tophat, DESeq2) to open source data sets (SRS)
   • Build and run statistical analyses using R or Python scripts
   • Run BLAST using command line options
8. Describe and manage biological data types, structure, and reproducibility: This competency addresses two distinct concerns: 1) each of the varied ‘omics fields produce data in formats particular to its needs, and these formats evolve with changes in technologies and refinements in downstream software; and 2) all experimental data is subject to error and the user must be cognizant of the need to verify the reproducibility of their data. The first concern highlights the requirement for students to develop an awareness of and ability to manipulate different data types given the versioning of formats. The second points to the need for caution, to carry out appropriate statistical analyses on their data as part of normal operating procedures and report the uncertainty of their results, and to provide the relevant information to enable reproduction of their results. Sometimes students have the tendency to assume that anything they retrieve from an online database must be correct; they need to be taught that this is not always the case.
   
   • Reproducibility: Compare reproducibility of biological replicate data (e.g.transcriptomic data) using statistical tests (Spearman).
   • Formats: Understand the various sequence formats used to store DNA and protein sequences (FASTA, FASTQ); Understand the representation of gene features using Gene Feature Format (GFF) files; Mass-Spec
9. Interpret the ethical, legal, medical, and social implications of biological data: The increasing scale and penetrance of human genetic and genomic data has greatly enhanced our ability to identify disease-related loci, druggable targets, and potential for gene replacements with developing techniques. However, with this information also comes many ethical, legal, and social questions which are often outpaced by the technological advances. As part of their scientific training, students should debate the medicinal, societal and ethical implications of these information sets and techniques.
   
   • How does the scientific community protect against the falsification or manipulation of large datasets?
   • Who should have access to this data, and how should it be protected?
   • What are the implications, good and bad, of being able to walk into a doctor’s office and have your genome sequenced and analyzed in minutes?

*** R learning materials and computational biology

Online courses

Github applied computational genomics
https://github.com/quinlan-lab/applied-computational-genomics

https://github.com/BenLangmead/comp-genomics-class


R programing at Coursera
https://www.coursera.org/learn/r-programming

Data camp https://www.datacamp.com/ 
 introduction, intermediate, and advanced R

Statistics and R
https://www.edx.org/course/statistics-r-harvardx-ph525-1x

http://genomicsclass.github.io/book/pages/classes.html
https://courses.edx.org/courses/HarvardX/PH525.1x/1T2015/info

Quantitative biology workshop
https://www.edx.org/course/quantitative-biology-workshop-mitx-7-qbwx-2

Introduction to Bioconductor: annotation and analysis of genomes and genomics assays
https://www.edx.org/course/introduction-bioconductor-annotation-harvardx-ph525-5x

https://cgondro2.une.edu.au/Rcourse.htm
http://www.springer.com/us/book/9783319144740 . R book, primer to analysis of genomics data using R


Books and articles
An introduction to statistical learning with applications in R
http://www-bcf.usc.edu/~gareth/ISL/index.html

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 
• • 
    

References:
http://hongqinlab.blogspot.com/2013/10/useful-r-materials-for-teaching.html
http://hongqinlab.blogspot.com/2015/06/ngs-tutorials.html

Carl Zimmer, personal genome

60G raw data

http://scopeblog.stanford.edu/2017/03/09/what-can-you-learn-from-your-own-genome-science-writer-carl-zimmer-found-out/

active learning, bioinformatics teaching

https://flxlexblog.wordpress.com/2015/08/31/active-learning-strategies-for-bioinformatics-teaching-2


https://figshare.com/articles/Active_learning_strategies_for_bioinformatics_teaching/1541056

UTC BGE course plan

Programming for genome analysis

  Cross-list 4999 in both biology and computer science. 

  Python Bioinformatics programming. 

  Systems biology

  Bioinformatics Programming :: Programming for Genome Analysis.

  Computational biology program at UTC. 

  Human genome analysis. 

Recruit students to help with lab. cross-listing 4999. 

spelman biology curricula

Competence-based core curriculum. reading, writing, talking and doing science.

SAWOK, science as a way of knowing
http://www.spelman.edu/docs/academics-biology/bio_sawok-classes_spring2012.pdf?sfvrsn=0

Morehouse
http://www.morehouse.edu/academics/bio/pdf/Biology-Dept-Handbook-082514.pdf


Gene and genealogy resources
http://dna-explained.com/category/education/

Thalassemia

Thalassemia module References

https://en.wikipedia.org/wiki/Thalassemia

http://www.healthline.com/health/thalassemia#Symptoms3

http://www.ncbi.nlm.nih.gov/pubmed/22445352

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656091/pdf/ppat.1003327.pdf

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666573/pdf/ejhg2014131a.pdf

Cognitive Load Theory and Computer Science Education John Sweller,

Cognitive Load Theory and Computer Science Education 

John Sweller, Emeritus Faculty, University of New South Wales 

Cognitive load theory uses our knowledge of human cognitive architecture to devise instructional procedures, most of which are directly relevant to computer science education. There are several basic aspects of human cognition that are critical to instructional design. First, based on evolutionary educational psychology, cognitive load theory assumes that most topics taught in educational and training institutions are ones that we have not specifically evolved to learn. Such topics require biologically secondary knowledge rather than the biologically primary knowledge that we have evolved to acquire. Second, these instructionally relevant topics require learners to acquire domain-specific rather than generic cognitive skills. Third, while generic cognitive knowledge does not require explicit instruction because we have evolved to acquire it, domain-specific concepts and skills that provide the content of educational syllabi, do require explicit instruction. These three factors interact with the well-known capacity and duration constraints of working memory and the unlimited capacity and duration characteristics of long-term memory to delineate a cognitive architecture relevant to instructional design. The working memory limits do not apply to biologically primary, generic knowledge acquired without explicit instruction but do apply to the biologically secondary, domain-specific knowledge that requires explicit instruction and that is relevant to computer science education. Human cognition when dealing with such knowledge constitutes a natural information processing system that has evolved to mimic the architecture of biological evolution. Cognitive load theory uses this architecture to generate a large range of instructional effects concerned with procedures for reducing extraneous working memory load in order to facilitate the acquisition of knowledge in long-term memory. This talk reviews the theory and indicates the instructional implications relevant to computer education. 

https://www.youtube.com/watch?v=0xm9_g699fg

note taking for computational experiments

Many students did not to take notes during computational experiments.

bio233 20150929Tue review of exam 1

bio233 20150929Tue review of exam 1

go over exam 1

make up exercises for part 2. Group exercise.

A few student sat alone, but I asked them to join a group nearby, they were welcomed by the groups.
In one group, students were explaining how to do unit conversions and scale conversions.

In another group, students were exchanging answers, and they were trying to gaming the test as a group. The group that were gaming the system quickly got all the correct answers. Unfortunately, they have missed the point of the entire makeup practice.

The students clearly enjoyed the group work to redo the exam.

MSPnet academy, computational thinking

MSPnet Academy: Infusing Computational Thinking into Science Education Presenters: Irene Lee, Maureen Psaila-Dombrowski, Paige Prescott Sep 21, 2015 at 2:00 PM (Eastern) 
Description: The Santa Fe Institute has been developing programs and curricula that infuse computational thinking into Science education for the past 12 years. In this webinar we will describe how the study of Complex Adaptive Systems through computer modeling and simulation fits into existing science frameworks and classes, share information about our program and curricula, and describe the professional development needed to prepare Science teachers to address the computational thinking practices presented in the NRC framework and NGSS. Capacity is limited and access is first-come, first served. Please RSVP to confirm your attendance. Early admission (10 minutes prior to the scheduled starting time) will be granted to those who respond "yes". 
To RSVP, go to http://hub.mspnet.org/wr.cfm/454/202819/D8SBEHAc40Zv86aCYBGE.

biological topics for computer science courses (in progress)

Coalescent simulation
Agent based simulation, infectious disease
Colony growth on agar plate
Gene networks. Graph model
phylogeny and evolutionary tree. Linked list
String: sequence analysis
OOP: genbank file, ORF, start and stop signals.
Arrays: genes and their lengths