Transcript PowerPoint - Mathematical Sciences

College of Science
Bioinformatics
The University of Texas at El Paso
www.bioinformatics.utep.edu
Bioinformatics Research and
Training at UTEP
Ming-Ying Leung, Ph.D.
Professor, Department of Mathematical Sciences and
Director, Bioinformatics and Computational Science Programs
Director, Bioinformatics Core Facility
Border Biomedical Research Center (BBRC)
The University of Texas at El Paso, El Paso, Texas
College of Science
Bioinformatics
The University of Texas at El Paso
www.bioinformatics.utep.edu
Bioinformatics Program
Interdisciplinary program
supported by departments of
Biological Sciences
Chemistry
Computer Science
Mathematical Sciences
With 24 faculty and staff
members from the Colleges of
Science, Engineering, and
Health Sciences.
Bioinformatics Faculty and Staff
 Biological Sciences: Steve Aley, Igor Almeida, Sid Das,
Krinstine Garza, Kyle Johnson, Carl Lieb, Vanessa
Lougheed, Elizabeth Walsh, Jianying Zhang
 Chemistry: James Becvar, Ricardo Bernal, Mahesh
Narayan, Chuan Xiao
 Clinical Laboratory Science: Delfina Dominguez
 Computer Science: Martine Ceberio, Olac Fuentes,
Vladik Kreinovich,
 Electrical and Computer Engineering: Wei Qian
 Mathematical Sciences: Art Duval, Hamide Dogan,
Ming-Ying Leung, Naijun Sha
 Staff: Oliana Alikaj, Gerardo Cardenas
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Program Goals
1. Training
NSF-Funded Undergraduate Participation
in Bioinformatics Training (UPBiT)
Professional Science Master (PSM) degree
in Bioinformatics
Computational Bioscience for Ph.D. in
Computational Science
Program Goals
2. Research
Biomolecular Sequence Analysis
Ecoinformatics and Phylogenetics
Medical Imaging Informatics
Molecular Structure and Dynamics
Proteomics Data Analysis
Students and Graduates
Current students and trainees:
16 Undergrad trainees in UPBiT, 16 M.S. , and 4 Ph.D. students
in computational bioscience.
Students and Graduates
Over 50 graduates working at:
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Academic Institutions (e.g., Stanford, Vanderbilt, Texas Tech., UTEP)
Research Labs (e.g., Broad Inst., Whitehead Inst., M.D. Anderson)
Government agencies (e.g., NIH, USDA, USAMRIID)
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Industry (Pfizer, Monsanto)
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Biomolecular
Sequence Analysis
Ecoinformatics
and Phylogenetics
Research
Rotations
M
Molecular
Structure and
2 Dynamics
C i
Microarray and
Proteomics Data
Analysis
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Q(w)
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Undergraduate Participation in Bioinformatics Training (UPBiT)
NIH-Funded Bioinformatics Core Facility in the
Border Biomedical Research Center (BBRC)
Analytical
Cytology
Cell
Culture
Study Design &
Data Analysis
DNA
Analysis
Integrate Computational &
Experimental Approaches
Statistical
Bioinformatics
Statistical
Consulting Lab
Biomolecule
Analysis
e.g., RNA structure
prediction
e.g., data
mining
Bioinformatics Computing Lab
BBRC Bioinformatics Core Facility
• Bioinformatics Computing Lab
• Translational Bioinformatics Lab
• Structural Bioinformatics Lab
Specific Aims for
Bioinformatics Core Facility
Provide bioinformatics support to biomedical research:
 Supporting BBRC research clusters through computational
modeling, data management and interpretations, to final
production of and dissemination bioinformatics tools.
 Interfacing with other BBRC core facilities by establishing
protocols and operation manuals integrating computational
components in the experimental procedure.
 Providing consulting services for non-BBRC researchers,
strengthening collaborative efforts for interdisciplinary
research in the border region.
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Computing Resources Development Plan
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Connection with Other BBRC Units
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Software Development
 RNAVLab: Virtual laboratory for RNA sequence analysis and
secondary structure prediction (with DNA sequencing core)
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RNASSA: RNA Secondary Structure Analysis
InversFinder: Finding inversions
Segmenta: RNA Segmentation
Grid and Cloud Computing
 Post-translational modification studies (with Biomolecular
Characterization Core)
 GPI and GPI-anchored protein structure prediction based on mass
spectrometry data (in collaboration with Dr. Clemente Aguilar,
National Institute of Mathematical and Biological Synthesis)
 ISOGlyP (Isoform Specific O-Glycosylation Prediction)
(with Dr. Thomas Gerken as PI, submitted R01 in January 2014)
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Glycosylation Site Prediction Software
(isoglyp.utep.edu)
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Protein Structure, Dynamics
and Interactions
 Establish a standard toolset on Bioinformatics Grid
to facilitate the workflow for computational prediction
of protein structures, interactions, and docking models.
 Current BBRC projects include:
 Searching for cancer-related interactors for tumorassociated antigens from Human Protein Reference Database
(HPRD) and other sources.
 In silico design of nociceptin agonists for Post-Traumatic
Stress Disorder (PTSD) through halogen bonding. Poster to
be presented by Dr. Mahesh Narayan at the Advancing
Computational Biology @ Howard University Symposium.
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Sequence Analysis Projects
 Motif detection to identify potential enhancer
elements critical for learning and memory (led
by Dr. K. Han)
 GPCR prediction and vaccine development
(Collaboration with Dr. Felix Guerrero of the USDAARS and Dr. Kyle Johnson, funded by the USDA-HSI
Program)
 Functional genomic analysis of high-risk
childhood leukemia (BBRC pilot project, led by
Dr. Jeremy Ross)
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Pilot Project: Functional Genomic Analysis of
High-Risk Childhood Leukemia
Jeremy A. Ross, Ph.D., BBRC Research Assistant Professor
Ming-Ying Leung, Ph.D. Director, BBRC Bioinformatics Core
Joseph Knapka, M.S. Student, Bioinformatics
Derrick Oaxaca, M.S. Student, Biological Sciences
Harry Wilson, M.D., Chief Pathologist, El Paso Children’s Hospital
Benjamin Carcamo, M.D., Pediatric Oncologist, El Paso Children’s Hospital
Relevance to Health Disparities: Hispanic children have the highest rates of incidence and death from acute
lymphoblastic leukemia (ALL) in the U.S.
Major Objective: Provide significant insight into the genetic abnormalities driving ALL tumor cell growth and drug
resistance in the Hispanic population.
Approach: 1) Identify protein-encoding somatic mutations using whole-exome sequencing of DNA isolated from
an Hispanic pediatric leukemia patient cohort of new onset or relapsed ALL. 2) Develop and employ a pathwaybased computational pipeline to prioritize functionally relevant sequence variants and aid biological interpretation.
Project Highlight - BBRC “OncoMiner” Pipeline
Read
Alignment
and Variant
Calling
Map reads to
reference
genome and
indentifies
SNP/indels
QC Check
and Data
Cleanup
Remove low
quality reads and
variants located
in introns
Variant
Selection
Select nonsynonymous
mutations for
analysis
Variant
Filtering
Remove dbSNP /
1000 Genomes
redundancies
PROVEAN
Scoring
Prioritize
variants based
upon predicted
functional impact
Gene
Ontology
Group by
biological
process and
molecular
function
Literature
Search
Provide link to
available
PubMed / OMIM
data
Future Plan
 Continue to strengthen bioinformatics support to genomics
and proteomics data analysis.
 Expand software development capabilities for biomedical
informatics and translational bioinformatics research.
 Create portals from Bioinformatics Grid to high
performance computing platforms at UTEP and TACC, and
the RTRN Data Coordinating Center.
 Explore further training and professional opportunities
for students and graduates of the program
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College of Science
Bioinformatics
The University of Texas at El Paso
www.bioinformatics.utep.edu
Contact Us
Email: [email protected]
Web: www.bioinformatics.utep.edu
Facebook: www.facebook.com/Bioinformatics.UTEP
Phone: 915.747.8484