Congratulations to Tomáš Brůna (PhD), Penghao Xu (PhD), and Sreenath Srikrishnan (MS), who were recognized as the 2022 Outstanding Students in Bioinformatics at Georgia Tech. Penghao Xu is being awarded the “Mark Borodovsky Prize in the College of Sciences.” Tomáš Brůna and Sreenath Srikrishnan are being awarded Outstanding Students in Bioinformatics awards from the J. Leland Jackson Fellowships fund in Bioinformatics. Each student will receive a monetary award.
The PhD awards nominations were reviewed by an interdisciplinary committee of faculty members, including Matt Torres (College of Sciences), Kostas Konstantinidis (College of Engineering), and Xiuwei Zhang (College of Computing). The Master’s student nominations were reviewed by Bioinformatics faculty Greg Gibson, King Jordan, and Joe Lachance.
Tomáš Brůna is an August 2022 graduate of the PhD program in Bioinformatics. Tomáš Brůna research advisor was Professor Mark Borodovsky. Tomáš’ research focused on three main topics, each of which describes a novel gene prediction algorithm: GeneMark-EP+ (Brůna et al., 2020), an unsupervised gene prediction algorithm that uses homologous cross-species proteins to guide its model training and gene prediction steps. Second, BRAKER2 (Brůna et al., 2021), a fully automated protein homology-based gene prediction pipeline that integrates GeneMark-EP+ with AUGUSTUS, an accurate gene finder that requires supervised training. Finally, he developed GeneMark-ETP+, a self-training gene prediction algorithm that simultaneously utilizes diverse information streams—genomic, transcriptomic, and protein homology—throughout all stages of its model training and gene prediction. According to Professor Borodovsky, “During all the years of my work at Georgia Tech, since 1990, I have rarely, if ever, seen so capable student with spectacular productivity. In addition to all his intellectual abilities and meticulous organization of research work, Tomáš is kind-hearted, which makes him a great member of a team.” Tomáš will be working as a Genome Data Scientist at Lawrence Berkeley National Laboratory.
Penghao Xu is a PhD student in the lab of Professor Francesca Storici. His research focuses on ribonucleotide incorporation in genomic DNA. Among other projects, Penghao developed the RESCOT toolkit using the simulated annealing heuristic and stochastic tunneling method and significantly increased the ribose-seq coverage from 40.41% to 95.84%. The first rNMP libraries we generated only contain ~5,000 rNMPs. With the help of RESCOT, the improved ribose-seq can capture more than 400,000 rNMPs, which is 80x more. The improvement of Ribose-seq provides high-quality data to analyze rNMP incorporation characteristics and facilitated more than 10 research projects in the Storici lab. This work by Xu and Storici is published in Theoretical Computer Science. Professor Storici says, “Penghao is a brilliant student. He has high creativity, is very thoughtful and quick in addressing problems computationally. He is certainly very enthusiastic about bioinformatics and open to new challenges. Almost every week at our lab meeting he presents something new, new data, new scripts, new analyses, or a new idea. His presentations are well designed, clear, rigorous, and stimulating. Penghao has demonstrated a remarkable capacity to solve computational problems.”
Sreenath Srikrishnan is a second-year MS Bioinformatics student, working in the Machine Learning and Bioinformatics (MLB) Lab under the direct supervision of Prof. Peng Qiu. His research aims to improve the accuracy and robustness of prognostic methods in cancer survival and subtype prediction to enable personalized treatments and a better understanding of drug responses for different survival subtypes. In his research, he processes, analyzes, and integrates high-throughput multi-omics data using deep learning methods, to predict cancer survival outcomes and subtypes. He used autoencoder neural networks to reduce the dimensionality of the multi-omics data, thus enabling more accurate and faster models for cancer survival analysis. He is expanding on the latest developments in deep learning and multi-omics to build workflows for cancer survival subtyping in individual cancers and identify important cancer biomarkers for survival prognosis. During his time in the MLB lab, he also led journal club discussions on the latest cutting-edge cancer research and applications of machine learning in bioinformatics. Sreenath recently completed a Bioinformatics Internship in Oncology with Illumina. Professor Qiu says, “Sreenath is smart and hardworking. His research work in my lab is on a very challenging topic, and he has shown wonderful ability to quickly learn new things and adapt them for his project.”
Congratulations to our Outstanding Bioinformatics Students!
Lisa Redding, email@example.com