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

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

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

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: http://www.bioquest.org/bedrock/problem_spaces/

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

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

Pedagogy Implemented on Course Wikis Team-taught and cross-listed BIOL/CMSI 367: Biological Databases https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page BIOL/MATH 388: Biomathematical Modeling http://www.openwetware.org/wiki/BIOL398-04/S15 Single instructor BIOL 368: Bioinformatics Laboratory http://www.openwetware.org/wiki/BIOL368/F14 BIOL 478: Molecular Biology of the Genome (wet lab, mostly offline) data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis Weekly assignments leading up to final research project All projects involve exploration of DNA microarray data

Pedagogy Implemented on Course Wikis Team-taught and cross-listed BIOL/CMSI 367: Biological Databases https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page BIOL/MATH 388: Biomathematical Modeling http://www.openwetware.org/wiki/BIOL398-04/S15 Single instructor BIOL 368: Bioinformatics Laboratory http://www.openwetware.org/wiki/BIOL368/F14 BIOL 478: Molecular Biology of the Genome (wet lab, mostly offline) data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis Weekly assignments leading up to final research project All projects involve exploration of DNA microarray data

GenMAPP-compatible Gene Database Visualize data Biological Databases Team Final Project: create a gene database for a bacterial species http://xmlpipedb.cs.lmu.edu/ 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

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

Student Products Are Shared with the Scientific Community http://sourceforge.net/projects/xmlpipedb/

Pedagogy Implemented on Course Wikis Team-taught and cross-listed BIOL/CMSI 367: Biological Databases https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page BIOL/MATH 388: Biomathematical Modeling http://www.openwetware.org/wiki/BIOL398-04/S15 Single instructor BIOL 368: Bioinformatics Laboratory http://www.openwetware.org/wiki/BIOL368/F14 BIOL 478: Molecular Biology of the Genome (wet lab, mostly offline) data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis Weekly assignments leading up to final research project All projects involve exploration of DNA microarray data

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

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

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)

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)

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)

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)

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

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

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

Experimental Design and Methods

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

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

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)

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

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

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

And so on…

Within-strain ANOVA Reveals How Many Genes Had Significant Changes in Expression at Any Timepoint wt 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 < 0.0001 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

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%)

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

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

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.

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

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

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)

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.

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)

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.

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

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

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

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

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

The First Round of Modeling Has Suggested Future Experiments

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 http://www.openwetware.org/wiki/Dahlquist:BioQUEST_Summer_Workshop_2015

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.

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

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)