Award Abstract # 1564894
Bioinformatics Training with Industry Support and Engagement (BITWISE)

NSF Org: DUE
Division Of Undergraduate Education
Recipient: SAINT LOUIS UNIVERSITY
Initial Amendment Date: April 1, 2016
Latest Amendment Date: April 1, 2016
Award Number: 1564894
Award Instrument: Standard Grant
Program Manager: Jill Nelson
DUE
 Division Of Undergraduate Education
EDU
 Directorate for STEM Education
Start Date: May 1, 2016
End Date: April 30, 2022 (Estimated)
Total Intended Award Amount: $649,681.00
Total Awarded Amount to Date: $649,681.00
Funds Obligated to Date: FY 2016 = $649,681.00
History of Investigator:
  • Michael Goldwasser (Principal Investigator)
    goldwamh@slu.edu
  • John Kennell (Co-Principal Investigator)
  • Gerardo Camilo (Co-Principal Investigator)
  • David Letscher (Co-Principal Investigator)
  • Tae Hyuk Ahn (Co-Principal Investigator)
Recipient Sponsored Research Office: Saint Louis University
221 N GRAND BLVD
SAINT LOUIS
MO  US  63103-2006
(314)977-3925
Sponsor Congressional District: 01
Primary Place of Performance: Saint Louis University
MO  US  63103-2006
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): JNBLLTBTLLD8
Parent UEI: JNBLLTBTLLD8
NSF Program(s): S-STEM-Schlr Sci Tech Eng&Math
Primary Program Source: 1300XXXXDB H-1B FUND, EDU, NSF
Program Reference Code(s): 9150, 9178, SMET
Program Element Code(s): 153600
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

The Bioinformatics Training with Industry Support and Engagement (BITWISE) project will provide financial, academic, and career support for academically talented, low-income students while enrolled in a new interdisciplinary Master of Science program in Bioinformatics and Computational Biology at Saint Louis University. The project will leverage the university's midtown location within a vibrant community of biotechnology industries in the Greater St. Louis area. Community partners provide a broad network for placing students into internships while completing their master's degrees and into careers upon graduation. Local industries will invite groups of BITWISE scholars on location through a series of field trips during the academic year, and industry mentors will participate in on-campus activities such as research seminars and career panels.

Direct outcomes of the project will include improved educational opportunities for both the BITWISE scholars and future generations of students who follow a similar educational pathway, and the enrichment of the biotechnology workforce in a field of great local and national need. Further impact of the project will include establishing and assessing a model for a robust university-industry partnership in support of a professionally-oriented master's degree in an interdisciplinary STEM field, and for using both common courses and extracurricular activities to develop a cohesive interdisciplinary cohort from an initial population of students who arrive with diverse backgrounds ranging over more discipline-specific undergraduate fields of study.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Ahmad Rajeh, Kyle Wolf, Courtney Schiebout, Tim Kosfeld, Richard J. DiPaolo, and Tae-Hyuk Ahn "iCAT: Diagnostic Assessment Tool of Immunological History using High-Throughput TCR Sequencing" F1000Research , v.10 , 2021 https://doi.org/10.12688/f1000research.27214.1
Alison Mullis, Zhaolian Lu, Yu Zhan, Tzi-Yuan Wang, Judith Rodriguez, Ahmad Rajeh, Ajay Chatrath, Zhenguo Lin. "Parallel Concerted Evolution of Ribosomal Protein Genes in Fungi and Its Adaptive Significance" Molecular Biology and Evolution , v.37 , 2020 , p.455 https://doi.org/10.1093/molbev/msz229
Eliza Dhungel, Yassin Mreyoud, Ho-Jin Gwak, Ahmad Rajeh, Mina Rho, and Tae-Hyuk Ahn "MegaR: An interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning" BMC Bioinformatics , v.22 , 2021 , p.25 https://doi.org/10.1186/s12859-020-03933-4
Helen Richards, Yunge Wang, Tong Si, Hao Zhang, Haijun Gong "Intelligent Learning and Verification of Biological Networks" Advances in Artificial Intelligence, Computation and Data Science , v.31 , 2021 , p.3 http://doi.org/10.1007/978-3-030-69951-2_1
Mariah Hassert, Kyle J. Wolf, Ahmad Rajeh, Courtney Shiebout, Stella G. Hoft, Tae-Hyuk Ahn, Richard J. DiPaolo, James D. Brien, Amelia K. Pinto "Diagnostic differentiation of Zika and dengue virus exposure by analyzing T cell receptor sequences from peripheral blood of infected HLA-A2 transgenic mice." PLOS Neglected Tropical Diseases , v.14 , 2020 , p.e0008896 https://doi.org/10.1371/journal.pntd.0008896
McMillan J, Lu Z, Rodriguez JS, Ahn TH, Lin Z "YeasTSS: An Integrative Web Database of Yeast Transcription Start Sites" Database: The Journal of Biological Databases and Curation , v.2019 , 2019 , p.baz048 https://doi.org/10.1093/database/baz048
Wolf K, Hether T, Gilchuk P, Kumar A, Rajeh A, Schiebout C, Maybruck J, Buller RM, Ahn TH, Joyce S, DiPaolo RJ. "Identifying and Tracking Low-Frequency Virus-Specific TCR Clonotypes Using High-Throughput Sequencing." Cell Reports , v.25 , 2018 , p.2369 https://doi.org/10.1016/j.celrep.2018.11.009
Yassin Mreyoud, Myoungkyu Song, Jihun Lim, Tae-Hyuk Ahn "MegaD: Deep Learning for Rapid and Accurate Disease Status Prediction of Metagenomic Samples" Life , v.12 , 2022 , p.669 https://doi.org/10.3390/life12050669
Yassir Mreyoud and Tae-Hyuk Ahn "Deep Neural Network Modeling for Phenotypic Prediction of Metagenomic Samples" Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '20) , 2020 https://doi.org/10.1145/3388440.3414921
Yongil Yang, Cory Gardner, Pallavi Gupta, Yanhui Peng, Cristiano Piasecki, Reginald J. Millwood, Tae-Hyuk Ahn, C Neal Stewart Jr "Novel Candidate Genes Differentially Expressed in Glyphosate-Treated Horseweed (Conyza canadensis)" Genes , v.12 , 2021 , p.1616 https://doi.org/10.3390/genes12101616
Zeyu Zhang, Madison Pope, Nadia Shakoor, Robert Pless, Todd C. Mockler, Abby Stylianou "Comparing Deep Learning Approaches for Understanding Genotype x Phenotype Interactions in Biomass Sorghum" Frontiers in Artificial Intelligence , 2022 , p.https://w https://doi.org/10.3389/frai.2022.872858
(Showing: 1 - 10 of 11)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The project supported 28 S-STEM scholars while enrolled in an interdisciplinary Masters of Science program in Bioinformatics and Computational Biology. At the conclusion of the project, 23 scholars have graduated from the program (though one lost scholarship eligibility), while four are entering their final academic year. One scholar withdrew from the academic program (to work full-time in a bioinformatics industry).  Among the 22 students who completed the program as S-STEM Scholars, 7 have subsequently entered PhD programs in STEM fields, while 14 are employed in a STEM industry.The cohort of scholars had a combined GPA of 3.77 while in the program.  The scholars also contributed as co-authors on eleven refereed publications and four poster presentations at professional meetings. 

The intellectual merit of the project was to analyze various educational and co-curricular support structures, especially in light of a professionally-oriented master's degree which enrolls students from a wide variety of undergraduate disciplines.  An evaluation of the various interventions concluded that the use of Supplemental Instruction, with a second-year student supporting first-year students in their introductory sequence, was most well received by students, both because of the academic support and the building of a cohort. 

The broader impact of the project is most directly reflected in the successful development of the cohort of scholars, and the resulting enrichment of the workforce.  The project also strengthened this relatively new academic program and established pathways that will be used by many future students.  The sustainability of the academic program is demonstrated by the Fall 2022 intake of students, which was the largest cohort to date for the program, despite students no longer being supported as NSF S-STEM scholars.

 


Last Modified: 08/27/2022
Modified by: Michael H Goldwasser

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