1. Open Source Projects for Undergraduate Research Experiences
Open Science, Open Data,
Open Source Projects for Undergraduate Research Experiences
Kam D. Dahlquist, Ph.D.
Department of Biology
Loyola Marymount University
Both relatively new to LMU
Dondi’s background in medical informatics, data visualization, person-computer interactions
During my postdoc I had served as project manager for GenMAPP, want to extend features of GenMAPP, especially for other species
I am not a software developer (last time I took a computer science class was AP Pascal in high school), but I’ve had a lot of experience interacting with developers
I’m proud of GenMAPP, especially that it is user-friendly for biologists, and is relatively bug free (result of my extensive testing)
However, I never would have been standing up in this community to talk about it because although we believed strongly that GenMAPP should be free-of-charge, we were slow to make the source code available (it is now available on SourceForge)
It has only been my collaboration with Dondi that I have been educated as to what Open Source software development truly means (Cathedral and Bazaar)
This is the perfect forum for talking about our work because, while I am using the fruits of XMLPipeDB for GenMAPP as first imagined, we designed this project to have components that are resusable for other purposes and that the bioinformatics developer community is our target audience
BioQUEST/HHMI/CaseNet Summer Workshop
June 13, 2015

2. Outline An open science ecosystem enhances student learning
Quick example: XMLPipeDB project in a Biological Databases course
Longer example: GRNmap project in Biomathematical Modeling course
Potential research projects for BioQUEST participants
Challenges are also opportunities
Computer literacy
Data literacy
Information literacy

3. Open Science Ecosystem
Source
Code
Open Access
(creative commons)
Open Data
Open Pedagogy
Open Science
(open process)
Reproducible
Research
Research
Integrity
Citizen
Science
With thanks to John Jungck

4. Open Science Pedagogy Adds Open Source Values and Tools to Problem Spaces
Students solve an authentic research problem.
They investigate large, publicly available datasets.
They return the products of their research to the scholarly community.
Image:

5. Official Open Source Definition (http://opensource.org)
Free redistribution
Source code
Derived works
Integrity of the author’s
source code
No discrimination against
persons or groups
No discrimination against
fields of endeavor
Distribution of license
License must not be
specific to a product
License must not
restrict other software
License must be
technology-neutral

6. Open Source Values Mirror STEM Curricular Reform
Active Learning Pedagogy
Open Source Practices & Tools
Source code is available, modifiable, and long-lived
Authentic problem to solve with realistic complexity
Central code repository; version control; provenance of code
Accountability to a developer and user community
Participatory and collaborative work; peer review
Task and bug trackers; continuous integration; test-driven workflows
Responsibilities accompany rights
Responsibility and ownership of the learning process
Documentation: in-line, user manual, web site, wiki

7. Pedagogy Implemented on Course Wikis
Team-taught and cross-listed
BIOL/CMSI 367: Biological Databases
BIOL/MATH 388: Biomathematical Modeling
Single instructor
BIOL 368: Bioinformatics Laboratory
BIOL 478: Molecular Biology of the Genome
(wet lab, mostly offline)
data analysis:
Weekly assignments leading up to final research project
All projects involve exploration of DNA microarray data

8. Pedagogy Implemented on Course Wikis
Team-taught and cross-listed
BIOL/CMSI 367: Biological Databases
BIOL/MATH 388: Biomathematical Modeling
Single instructor
BIOL 368: Bioinformatics Laboratory
BIOL 478: Molecular Biology of the Genome
(wet lab, mostly offline)
data analysis:
Weekly assignments leading up to final research project
All projects involve exploration of DNA microarray data

9. GenMAPP-compatible Gene Database Visualize data
Biological Databases Team Final Project:
create a gene database for a bacterial species
PostgreSQL
Intermediate
Database
OVERALL DATA FLOW
Automatically building relational databases from an XML schema (XSD).
XML source files: UniProt, Gene Ontology and Uniprot -> GO associations.
Outputs a populated gene database for species of interest.
GenMAPP has no idea we supply it with gene databases which are 10 years newer…
GenMAPP-compatible
Gene Database
Visualize data
Microarray data

10. Each Student on the Team is Assigned a Specific Role
Coder
Project Manager
MODULARIZED
XSD – DB module
Auto generates the code to take xml schema and create java code/objects and sql queries.
Compiles a compiler to compile java jars.
GenMAPP Builder: an application which uses XMLPipeDB libraries to build gene databases
UNIPROT/GODB
Java jars (libraries) placed within the application GenMAPP Builder.
They parse the xsd and create the sql queries that insert records into PostgreSQL database.
XMLPipeDB Utilities
1. xml import to PostgreSQL
2. configure database
3. tally engine
4. ad-hoc queries
Export to GenMAPP GDB (specific to GenMAPP Builder)
1. exports all data and relations for a single species into a gene database
XSD – DB
Auto generates the code to take xml schema and create java code and sql queries.
Avoids manual revising of code in response to scheme change.
Simply provide it with the updated xsd and it updates the java libraries automatically.
The java source is based on xml tags
It auto edits when a new xsd is read in.
JacksB opensource library converts xml tag -> java object
Hibernate opensource library converts java object -> into sql relational table coverter
These two libraries combined comprise a library to convert xml -> relational database
UNIPROTDB, GODB
Contain the canned libraries created by XSD – DB
“Read xml and Write relational”
XMLPipeDB utilities are separate from the java jars because they each have their own ui.
Each utility calls jars for functionality when required.
Import xml source files into relational database
Configure the relational database
Count tags in xml vs records in relational database
Perform ad-hoc queries if desired
GenMAPP Builder
Application built on XMLPipeDB libraries and custom export functionality.
Export functionality generates the species gene database which can then be used with GenMAPP
Quality
Control
Data Analysis

11. Student Products Are Shared with the Scientific Community

12. Pedagogy Implemented on Course Wikis
Team-taught and cross-listed
BIOL/CMSI 367: Biological Databases
BIOL/MATH 388: Biomathematical Modeling
Single instructor
BIOL 368: Bioinformatics Laboratory
BIOL 478: Molecular Biology of the Genome
(wet lab, mostly offline)
data analysis:
Weekly assignments leading up to final research project
All projects involve exploration of DNA microarray data

13. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network

14. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network

15. Central Dogma of Molecular Biology (simplified)
DNA
Transcription
mRNA
Figure: 11.6
Caption:
In the cells of some organisms, DNA is found only in a membrane-bound structure called the nucleus. In these cells, proteins are manufactured outside the nucleus. Biologists proposed that the information coded in DNA is carried from inside the nucleus to ribosomes outside the nucleus by a molecule called messenger RNA (mRNA).  Question: Why was the choice of the term messenger appropriate?
Translation
Protein
Freeman (2003)

16. And Now in the “omics” Era…
Genome
Transcription
Transcriptome
Figure: 11.6
Caption:
In the cells of some organisms, DNA is found only in a membrane-bound structure called the nucleus. In these cells, proteins are manufactured outside the nucleus. Biologists proposed that the information coded in DNA is carried from inside the nucleus to ribosomes outside the nucleus by a molecule called messenger RNA (mRNA).  Question: Why was the choice of the term messenger appropriate?
Translation
Proteome
Freeman (2002)

24. Budding Yeast, Saccharomyces cerevisiae, is
an Ideal Model Organism for Systems Biology
Small genome of
~6000 genes
Extensive genome-
wide datasets
readily accessible
Molecular genetic
tools available
01_32_model eucaryote.jpg
Alberts et al. (2004)

25. Environmental Changes and Stresses
All organisms must respond to changes in the
environment pH
oxygen availability
pressure
osmotic stress
temperature (heat and cold)
Some changes in the environment cause cellular damage and trigger a “stress response”
damage from reactive oxygen species
damage from UV radiation
sudden and/or large change in temperature (increase or decrease)

26. Cold Shock Is an Environmental Stress that Is Not Well-Studied
Increases in temperature (heat shock)
response very well-characterized
proteins denature due to heat
induction of heat shock proteins (chaperonins), that assist in protein folding
conserved in all organisms (prokaryotes, eukaryotes)
Decreases in temperature (cold shock)
response less well-characterized
decrease fluidity of membranes
stabilize DNA and RNA secondary structures
impair ribosome function and protein synthesis
decrease enzymatic activities
no equivalent set of cold shock proteins that are conserved in
all organisms

27. Yeast Respond to Cold Shock by Changing Gene Expression
Cold shock temperature range for yeast is 10-18°C
Previous studies indicate that the cold shock response can be divided into:
Late response genes – 12 to 60 hours
General environmental stress response genes (ESR) are induced
Regulated by the Msn2/Msn4 transcription factors
Early response genes – 15 minutes to 2 hours
Genes unique to cold shock are induced, such as genes involved in ribosome biogenesis and membrane fluidity
Which transcription factors regulate this response is unknown

28. Transcription Factors Control Gene Expression by Binding to Regulatory DNA Sequences
Activators increase gene expression
Repressors decrease gene expression
Transcription factors are themselves proteins that are encoded by genes

29. Experimental Design and Methods

30. Yeast Cells Were Harvested for Microarrays Before, During, and After a Cold Shock and During Recovery

31. Mixture of labeled cDNA from two samples
4 replicates of each experiment with dye swaps
wt and transcription factor deletion strains

32. DNA Microarray One spot = one gene Green = decreased
relative to control
Red =
increased
Yellow =
no change
in gene
expression
Figure: 16.10a, lower
Caption:
(a) To monitor changes in gene expression, investigators spot thousands of short, single-stranded DNA sequences from coding sequences onto a glass plate. By probing this microarray with labeled cDNAs synthesized from MRNAs, researchers can identify which coding sequences are being transcribed.
Freeman (2002)

33. Gene Expression Changes Due to Cold Shock
Return to Pre-shock Levels During Recovery
t30/t0 cold shock
t60/t0 cold shock
Four sets of biological replicates were performed
Dye orientation was swapped for two sets of replicates
t90/t0 recovery
t120/t0 recovery

34. Steps Used to Analyze DNA Microarray Data
1. Quantitate the fluorescence signal in each spot
2. Calculate the ratio of red/green fluorescence
3. Log2 transform the ratios
4. Normalize the ratios on each microarray slide
5. Normalize the ratios for a set of slides in an experiment
6. Perform statistical analysis on the ratios
7. Compare individual genes with known data
8. Pattern finding algorithms/clustering
Modeling the dynamics of the gene regulatory network
Visualizing the results

35. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
Excel,
stem
New experimental questions
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network

36. And so on…

37. Within-strain ANOVA Reveals How Many Genes Had Significant Changes in Expression at Any Timepointwt
Δgln3
p < 0.05
2378/6189 (38.42%)
1864/6189 (30.11%)
p < 0.01
1527/6189 (24.67%)
1008/6189 (16.29%)
p < 0.001
860/6189 (13.90%)
404/6189 (6.53%)
p <
460/6189 (7.43%)
126/6189 (2.04%)
B-H p < 0.05
1656/6189 (26.76%)
913/6189 (14.75%)
Bonferroni p < 0.05
228/6189 (3.68%)
26/6189 (0.42%)
We performed a statistical analysis on the wild type and the
Purpose of this part was to determine which genes have a log fold change that is different than zero at one or more time points?
Before moving onto clustering we want to perform a sanity check to make sure that we performed the data analysis correctly. This slide shows the number of genes that are significantly changed at arioous p value cutoffs.
This data table showed that the data analysis was performed corrected. The wild type and the dgln3 followed the same pattern where the

38. A Modified T Test Was Used to Determine Significant Changes in Gene Expression at Each Timepoint
wild type
Number of Genes whose Expression Changes
Cold Shock
Recovery
t15
t30
t60
t90
t120
Increased
p < 0.05
439 (7%)
668 (11%)
609 (10%)
398 (6%)
191 (3%)
Decreased
331 (5%)
517 (8%)
411 (7%)
249 (4%)
59 (1%)
Total
770 (12%)
1185 (19%)
1020 (17%)
647 (10%)
250 (4%)

39. Short Time Series Expression Miner (stem) Software Clusters Genes with Similar Profiles
Expression (log2 fold change)
Time (minutes)

41. Short Time Series Expression Miner (stem) Software Clusters Genes with Similar Profiles
Expression (log2 fold change)
Time (minutes)
Gene Ontology categories assigned to clusters:
Ribosome biogenesis
Zinc ion homeostasis
Hexose transport
Endomembrane system
Protein and vesicle transport
Negative regulation of nitrogen
compound process

42. The Transcription Factor Gln3 Regulates Genes Involved in Nitrogen Metabolism
Yeast differentiate between preferred and
non-preferred nitrogen sources.
When the nitrogen source is poor, Gln3 localizes to the nucleus and activates genes required to utilize the poor nitrogen source.
The Dgln3 strain is impaired for growth at cold temperatures:
Doubling time at 13°C of 15 hours vs. 8.3 hours for wild type.
A microarray experiment was performed on the Dgln3 strain.

43. Gln3 Target Genes Were Extracted from the YEASTRACT Database
37 out of 164 (23%) have significantly different expression profiles in the wild type versus the Dgln3 strain

44. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
YEASTRACT,
Excel
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network

45. Genome-wide Location Analysis has Determined the Relationships between Transcription Factors and their Target Genes in Yeast
Does not show whether activation or repression occurs
Shows topology, but not the behavior of the network over time
Data found in YEASTRACT database
Lee et al. (2002)

46. A Transcriptional Network Controlling the Cold Shock Response
Assumptions made in our model:
Each node represents one gene encoding a transcription factor.
When a gene is transcribed it is immediately translated into protein; a node represents both the gene and the protein it encodes.
An edge drawn between two nodes represents a regulation relationship, either activation or repression, depending on the sign of the weight.

47. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network
GRNmap (Windows-only)

48. GRNmap: Gene Regulatory Network Modeling and Parameter Estimation
Parameters are estimated from DNA microarray data from wild type and transcription factor deletion strains subjected to cold shock conditions.
Weight parameter, w, gives the direction (activation or repression) and magnitude of regulatory relationship.

49. The “Worst” Rate Equation is:
Define alpha and beta and K

50. Optimization of the 92 Parameters Requires
the Use of a Regularization (Penalty) Term
Plotting the least squares error function showed that not all the graphs had clear minima.
We added a penalty term so that MATLAB’s optimization algorithm would be able to minimize the function.
θ is the combined production rate, weight, and threshold parameters.
a is determined empirically from the “elbow” of the L-curve.
Least Squares Residual
Define alpha and beta and K
Parameter Penalty Magnitude

51. Forward Simulation of the Model Fits the Microarray Data
Define alpha and beta and K
Forward Simulation of the Model Fits the Microarray Data

52. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
Generate gene regulatory network
Visualizing the results
GRNsight
Modeling dynamics of the network

53. GRNsight Rapidly Generates GRN graphs Using Our Customizations to the Open Source D3 Library
Adobe Illustrator: several hours to create
GRNsight: 10 milliseconds to generate, 5 minutes to arrange
GRNsight: colored edges for weights reveal patterns in data

54. The First Round of Modeling Has Suggested Future Experiments

55. Systems Biology Workflow
DNA microarray data:
wet lab-generated or published
Statistical analysis,
clustering,
Gene Ontology, term enrichment
New experimental questions
Generate gene regulatory network
Visualizing the results
Modeling dynamics of the network

56. 95% of Bioinformatics is Getting Your Data into the Correct File Format
Exposes deficiencies in computer literacy skills in so-called “digital natives”
When you leave your comfort zone, it is,
by definition, uncomfortable
Emphasis on research process
Teamwork
Electronic lab notebook
Keeping track of files and code
Trouble-shooting problems that arise in the research process: bugs, data issues, etc.

57. Summary An open science ecosystem enhances student learning
Quick example: XMLPipeDB project in a Biological Databases course
Longer example: GRNmap project in Biomathematical Modeling course
Potential research projects for BioQUEST participants
Challenges are also opportunities
Computer literacy
Data literacy
Information literacy

58. Acknowledgments Ben G. Fitzpatrick LMU Math John David N. Dionisio
LMU Computer Science
Special thanks to John Jungck & Sam Donovan
Juan Carrillo, Natalie Williams, K. Grace Johnson, Kevin Wyllie, Kevin McGee
Monica Hong, Nicole Anguiano, Anindita Varshneya, Trixie Roque, (Tessa Morris)