Supporting Scientific Collaboration Online

SCOPE Workshop at

San Diego Supercomputer CenterMarch 19-22, 2008

Problem Spaces as an

Open Participatory Learning Infrastructure

E-Science

Web 2.0

Open Educational Resources

Engaged Science

Learning

Scholarship of

Teaching

A community model for teaching and learning science which integrates and acts on the role of technology in education.

Existing Technological

Resources

Building a Community of Teaching and

Learning

Workshop Structure Overview• Introductions to working with:

– a Problem Space– an Investigative Case– OERCommons– Visualization tools– CaseIT– Planting Science

• 2 small group projects w/ presentations– Integrate OER, E-science, and/or Web 2.0 resources into a

teaching project of your choice.– Describe ways you will build and/or teach with

collaborative tools and resources.

Investigations of HIV-1 Env Evolution

Evolutionary Bioinformatics:

Microbial analyses from sequence to structure to function to ecology

SCOPE Workshop at SDSCMarch 19-22, 2008

1. Virus docks with receptors on host cell (CD4 + co-receptor)

2. Reverse transcription: viral RNA DNA

3. Viral DNA inserts into host’s DNA

4. Viral RNA transcribed & proteins assembled

5. New virions bud from host cell, killing it

Life Cycle of HIV

HIV Virus

The HIV Genome

HIV env Gene

gp120 V3 region sequence

Nucleic Acid Sequence

CTAGCAGAAGAAGAGGTAGTAATTAGATCTGCCAATTTCACAGACAATGCTAAAATCATAATAGTACAGCTGAATGCATCTGTAGAAATTAATTGTACAAGGCCCAACAACAATACAAGAAAAGGTATACATATAGGACCAGGGAGAGCATTTTATGCAACAGGAGAAATAATAGGAGATATAAGACAAGCACATTGTAACATTAGTAGAGAAAAATGGAATAATACTTTAAACCAGGTAGTTACAGAATTAAGGGAACAATTTGGGAATAAAACAATAACCTTTAATCACTCCTCAGGAGGGGACCCAGAAATTGTAATGCACAGTTTTAATTGTGGAGGGGAATTTTTCTATTGTAAT

------------------------------------------------------------------------

Amino Acid Sequence

LAEEEVVIRSANFTDNAKIIIVQLNASVEINCTRPNNNTRKGIHIGPGRAFYATGEIIGDIRQAHCNISREKWNNTLNQVVTELREQFGNKTITFNHSSGGDPEIVMHSFNCGGEFFYCN

gp 120 Structure

The Markham et al.HIV-1 env Sequence Dataset

• Longitudinal study of 15 HIV+ patients from Baltimore

• Patients came in at 6-month intervals (“visits”) over 4-year period

• Focused on the 3rd variable loop of the env gene (285 bp)

• Each visit: sampled ~10 viral sequences and measured CD4 levels

Rich data set for analyzing patterns of HIV evolution and their correlation

with rates of T-cell decline

Summary of the data set

• Subjects: 15

• Number of visits: 3-9

• Number of clones per visit: 2-18

• Total number of sequences available: 666

• CD4 cell counts for each visit

Possible Investigations

• What is the pattern of HIV evolution within an individual? – Do the number of clones over time change in

any regular way?– Do certain clones appear to survive (leave

descendents) over time while other disappear (go extinct)?

Possible Investigations

• What is the pattern of HIV evolution within the env sequence? – Are there particular positions in the sequence

that are more or less likely to mutate? – Are there different rates of synonymous (silent)

and non-synonymous mutations?

User Web BrowserOutput to User

User Input

Format Translator, Query Engine and Program Driver

Workbench Server

Results to User

User Instructions and queries

Application Programs

(May have varyinginterfaces and be written in different

languages)Results

Instructions

Information Sources(May be of

varying formats)

Information

Queries

NCSA Computational Biology Group

The NCSA Information Workbench - An Architecture for Web-Based Computing