Matthew Weber

B.S. Molecular and Cellular Biology (2016)

M.S. Bioinformatics (2018)

Department of Crop Sciences, UIUC

CompGen fellow

advised by Dr. Matthew Hudson

Mutation profiles of cancer

Mr. Weber is developing machine learning methods to effectively stratify cancers

based on the statistical properties of mutations found in afflicted individuals.

Cancer stratification is predictive of disease outcomes, drug response and drug metabolism.

Effective computational approaches based on total data acquired to-date can make this process cheaper in the clinic.

Matt collaborates with the Ontario Institute for Cancer Research to make sure his models are realistic

Junyu Li

B.S. Molecular and Cellular Biology (2017)

minor in Computer Science

SPIN fellow

Genomic variant calling by assembly

Junyu and Mr. K are focusing on a method to detect genomic variants by assembly.

They are employing the software Cortex-var, which constructs de-novo genome assembly

on multiple sequencing samples, and then compares the resultant de Bruijn graphs

to detect where they diverge, indicating a potential variant. This could be a good method

for detecting novel variants, especially repeats and complex rearrangements in complex genomes,

such as polyploid plants and cancer.

Junyu and Mr. K work as an interdisciplinary team. Junyu provides the expertise in

math and computer science to automate the Cortex-var workflow and interpret the algorithm.

Mr. K is using his strong background in genomics to interpret, clean-up and validate the output.

Matthew Kendzior

B.S. Crop Science (2016)

Plant Biotechnology, Molecular Biology

M.S. Bioinformatics (2018)

Department of Crop Sciences, UIUC

Graduate Fellow in the College of ACES

advised by Dr. Matthew Hudson 

Jennie Zermeno

BS Integrative Biology (2017)

Benchmarking performance and accuracy of genomic variant calling software

Jennie and Tiffany collaborate to document our efforts in benchmarking variant calling on HPC systems.

We have run variant calling experiments on 500 genomes in parallel, on Blue Waters,

to identify performance bottlenecks when using the GATK best practices workflow.

Jennie is documenting this work in a publication.

We have also tested a number of alternative software, such as Isaac, Genalice, Sentieon,

as well as Dragen - a hardware solution.

Tiffany is documenting the pros and cons of each of these excellent approaches in a separate manuscript.

Tiffany Li

BS Integrative Biology (2018)

minor in Computer Science

Angela Chen

M.S. Statistics (2017)

Department of Statistics, UIUC

CompGen fellow

advised by Dr. Alexander Lipka

Accurate and scalable GWAS algorithms

Angela and Khory are collaborating to improve the scalability and parallelization

of the statistical software TASSEL5, widely used for conducting genome wide association studies (GWAS) in plants.

Angela is writing a manuscript to demonstrate that her new stepwise epistatic model selection procedure

has greater statistical power compared to other methods. However, the Java-based TASSEL5 cannot be

easily parallelized across multiple nodes in a computational cluster, to run on modern, relevant datasets,

which tend to be very large, such as the Alzheimer's SNP panel.

Khory is providing the expertise in computer science to convert this Java code

into C++ and parallelize it in HPC environment.

Khory Wagner

advised by Dr. Vologymyr Kindratenko

NCSA Industry

program

Ryan Chui

B.S. Biochemistry (2016)

M.S. Bioinformatics (2017)

Department of Computer Science, UIUC

Noah Flynn

B.S. Bioengineering, Mathematics (2017)

minor in computer science

SPIN fellow

Evolution of molecular networks and persistence of organisms

Construct and compare gene, metabolic and signaling networks from organisms across the tree of life.

The goal of the project is to provide support for the general framework of persistence strategies.

It postulates that persistence is achieved by biological systems via a tradeoff of traits that serve either

economy, flexibility, or robustness. In this project we want to figure out how economy, flexibility, and robustnessdetermine and quantify the molecular

mechanisms that underlie these persistence strategiesof a biological system can be quantified. Will analysis of the biomolecular networks

allow us to differentiate between organisms of differing economy, flexibility, and robustness, and

subsequently classify unknown, newly discovered, or modified organisms within such predefined

classes?

Aishwarya Raj

B.S. Biochemistry (2019)

minor in Bioinformatics

Illinois Informatics Institute fellow

Ellen Nie

BS. Biochemistry (2018),

minor in Computer Science

Big data network transfers for genomics

Ellen is benchmarking the network transfers of genomic data across multiple sites.

She wants to understand the limitations of modern network backbone for big data genomics,

and to facilitate correct configuration of the endpoints to resolve those limitations.

Ellen is looking at the sites of our collaborators in Toronto, South Africa, Sudan, and the UK.

Nainika Roy

B.S. Molecular and Cellular Biology (2017)

minor in Informatics and Chemistry

SPIN fellow

Data formats and data structures in computational genomics

Matthew Hudson

Bioinformatics

Crop Science

HPCBio, Carver Biotechnology Center

Elliott Rodriguez

Computer Science

SPIN fellow

NCSA Scientific Software and Applications + University of Khartoum

Portable variant calling workflow

H3Africa Consortium

Azza Ahmed

Computer Science

Bioinformatics

Morgan Taschuk

Bioinformatics

OICR

Paul Hatton

University of Birmingham