GRA Projects

Project Year: PROJECT YEAR: COMPUTATIONAL BIOLOGY FACULTY RESEARCH AWARDS, SUMMER 2024
Student Name Faculty Mentor Title
Appaji, Yashas Sam Brown Automating and Enhancing a Machine Learning Analysis Pipeline to Predict Ecological Niches of Pseudomonas Aeruginosa
Avula, Pavithra May Wang Optimizing Brain Metastasis Diagnosis through Multimodal Modeling: Integrated MRI Imaging and Clinical Data Analysis
Chebbi, Rohini Manoj Bhasin (Emory) GSEA Based Pathway Enrichment on Single Cell Transcriptomic Data for Effective Drug Repurposing
Gaur, Hina Rishi Kamaleswaran (Emory/Duke) Elucidating the Role of Donor Red Blood Cell Metabolism in Necrotizing Enterocolitis Risk Among Neonates
Jaishankar, Anirudh May Wang Deep Learning aided Neural Correlate Visualization of Brain Metastases
Kewale, Vidhya Amirali Aghazadeh Mutation effect modeling for inhibition of the drug efflux pump NorA from Methicillin-Resistant Staphylococcus Aureus
Kramer, Jeff King Jordan Integrating Genetic Ancestry and Social Determinants of Health to Combat Type 2 Diabetes Disparities
Lal, Ayushi Yuhong Fan Role of H1 Histones in Embryogenesis: A Single-Cell RNA Sequencing Approach
Li, Boxuan Sam Brown Machine Learning Approaches to Differentiate Epidemic and Non-Epidemic Strains of Pseudomonas Aeruginosa
Lucas, Madison Kostas Konstantinidis Characterization of Escherichia coli genome diversity in the gut microbiome of FMT patients and the emergence of pathogenic genomes
Natarajan, Kamala Rishi Kamaleswaran (Emory/Duke) Investigating the Role of Lysophosphatidylcholine (LPC) in Post-operative Acute Respiratory Distress Syndrome (ARDS) Patients Following Trauma: A Lipidomic Approach
Patil, Hosala Reddy Farzaneh Najafi Unraveling Motor Timing Adaptation in Wild-Type Mice: Behavioral and Neural Investigation
Singh, Hargobind Lynn Kamerlin Multiple faces of Alpha synuclein: Determining the catalytic activity through MD simulations
Srinivasan, Ramya Melissa Kemp A Canonical Agent-Based Approach to Modeling Antiviral Extracellular Vesicle (EV) Activity as a means to a Potential Host-Directed, Myeloid-Targeted Immunotherapy
Su, Linjun Denis Tsygankov Computational Analysis and Modeling Risk Factors Associated with Transcatheter Aortic Valve Replacement (TAVR)
Tandra, Tamarin Greg Gibson Identifying Patterns of Coherence and Incoherence Between Single-Cell eQTL Effect and Differential Gene Expression in Systemic Lupus Erythematosus
Vora, Khushi Tony Pan (Emory) Computational Optimization of Single-Cell Transcriptomic Data Integration
Yu, Jinyu Yunan Luo Transfer Learning to Optimize Protein Thermal Stability Prediction
Project Year: PROJECT YEAR: COMPUTATIONAL BIOLOGY FACULTY RESEARCH AWARDS, SPRING 2024
Student Name Faculty Mentor Title
Anand, Mridul Brandon Dixon Leveraging Tissue Engineered Lymphatic Malformation Models and Patient Genomics for the Development of Personalized Medicine Approaches
Banerjee, Biswajit Anton Petrov Unpacking the Metabolic Sandwich: A Comprehensive Exploration of Biochemical Pathways, Enzyme Structures, and Evolutionary Relationships
Bindra, Mehak Xiuwei Zhang Integration and Comparison of Single Cell RNA-Sequencing Data from Multiple Species
Chaudhry, Palak Eliver Ghosn (Emory) MITOMATIC: Single-cell lineage tracing approach for resolving human immune development across gestational ages and tissues
Chebbi, Rohini Manoj Bhasin (Emory) Drug repurposing using transcriptomic bulk RNA-seq data
Choi, Chang Woo "Chris" Eliver Ghosn (Emory) Implementation of Automated Projection Pursuit Clustering (APPC) Pipeline to Identify and Compare Immune Cell Subsets in Multidimensional Flow Cytometry Data
Diskalkar, Sarth Lynn Kamerlin Enhancing Protein Microenvironment Learning Through Quality Training Data for Predictive Mutational Analysis
Fogel, Lauren Raquel Lieberman Computational Workflow for Analyzing Deep Saturation Mutagenesis Library Data of Myocilin to Catalog All Glaucoma Causing Variants
Gaur, Hina Rishi Kamaleswaran (Emory/Duke) Characterization of Rapid Recovery Phenotypes in ARDS Patients
Godbole, Meghna Yuhong Fan A Signal quantifier pipeline for ScRNA-Seq
Govindarajan, Sandhya Peng Qiu scRNA -seq Data Analysis to determine key cell types interacting in the Gut Inflammatory disease IBD
Gyimah-Asamoah, Bengy Sam Brown A Deep Learning Approach to Classify De Novo Strains of Pseudomonas aeruginosa Within Cystic Fibrosis Patients
Hachad, Tarek Joe Lachance Deciphering Tuberculosis Susceptibility Across Genetic Ancestry in Africa: A Polygenic Risk Score Approach
Hang, Emily Greg Gibson Positive Assortative Mating in Face Shape: Do Couples Tend to Look More Alike than Different?
Jain, Neha May Wang (Georgia Tech) and David Gutman (Emory) Protein Protein Interactions due to Genomic Variation in The Major Histocompatibility Complex
Kewale, Vidhya Amirali Aghazadeh Mutation effect modeling for inhibition of the drug efflux pump NorA from Methicillin-Resistant Staphylococcus Aureus
Khimani, Asma May Wang Uncovering Gene-Disease Associations and Biological Functions Utilizing Machine Learning Approaches
Kim, Ji Woo "Jenna" Deqiang Qiu Voxel-based Lesion-Symptom Mapping Analysis to study the White Mater Hyperintensities-Neuropsychological Relationships
Kokil, Prerna Francesca Storici Analysis of length of base pairs around ribonucleoside monophosphates in human cells
Lagwankar, Asmita Patrick McGrath and Todd Streelman Analysing the telencephalon of cichlids to understand the neural basis of social behaviour in vertebrates
Leung, Ho Yeung "Ozi" Peng Qiu In silico CITE-seq model for protein prediction in the human blood sample and immuno-age prediction Pipeline
McKee, Anna Andrew Hong (Emory) Transcription Factor Network Analysis of Two SMARCB1-Deficient Cancers
Meadows, Nia Joe Lachance Do polygenic score shifts reflect actual differences in traits, and are they due to natural selection?
Mohan, Anoch Tim Read (Emory) Quantitation of Antimicrobial Resistance Genes Burden from SRA and ENA database using the Gene Read Abundance from Bacteria (GRAbBa) Pipeline
Mohana Krishna, Anagha Nick Housley Transcriptional Atlas of Human Ovarian Cancer Using Single-nuclei RNA Sequencing and Spatial Transcriptomics
Nag, Shivank Matt Torres Ensemble Classifier to Predict Functional Post-Translational Modifications in Proteins with Updated SAPH-ire TFx
Natarajan, Vishva Matt Torres Elucidating the Dynamic Role of Phosphorylation on Drug Binding Affinity
Nguyen, Thanh Long "Lucas" King Jordan Ancestry-Enriched Genetic Variants and its Association with Health Determinants
Nitta, Yusaku "Nick" Jeff Skolnick Developing a Python-based Computational Tool for Protein-Protein Interface Alignment
Patil, Hosala Reddy Farzaneh Najafi Behavioral and Neural Mechanism in Autism Spectrum Disorder
Patil, Hrishikesh Sam Brown Validating genomic machine learning predictions using a transcriptomic analysis in epidemic strains of Pseudomonas aeruginosa
Rajasekar, Shreya Dhwani Batra (CDC) Comparative Analysis of Taxonomic Profiling Tools for Metagenomic Analyses
Ranbhor, Siddhi Todd Streelman Understanding the Transcriptional and Epigenomic Landscape of Complex Social Behavior in Lake Malawi Cichlids
Sanjeeva Reddy, Prarthana Pamela Peralta-Yahya Deorphanization of the orphan receptor OR2C3
Sankar Ramalaxi, Gautham Amirali Aghazadeh Identifying Structural Features in Prokaryotic Short Gene Prediction using Protein Language Models
Silverman, Marc Lily Cheung Plant SWEETs
Sinha, Agniruudrra Lynn Kamerlin Computational Design of Novel β-Lactamases Using Large Language Models
Somadasan, Meenakshi Rishi Kamaleswaran (Emory/Duke) Spatial Omics Analysis of Tfh & Non-Tfh Interactions in Viremia
Srinivasan, Ramya Melissa Kemp A Canonical Agent-Based Approach to Modeling Antiviral Extracellular Vesicle (EV) Activity as a means to a Potential Host-Directed, Myeloid-Targeted Immunotherapy
Su, Linjun Denis Tsygankov Computational Analysis and Modeling Risk Factors Associated with Transcatheter Aortic Valve Replacement (TAVR)
Tandra, Tamarin Greg Gibson Identifying Patterns of Coherence and Incoherence Between Single-Cell eQTL Effect and Differential Gene Expression in Systemic Lupus Erythematosus
Verma, Ishika Francesca Storici Mutational Signature Analysis of Saccharomyces cerevisiae DNA Sequencing Data
Vinod, Suraksha Greg Gibson Influence of Ancestry Proportion and Geographic Variables on Polygenic Risk Scores
Vora, Khushi Tony Pan (Emory) Computational Optimization of Single-Cell Transcriptomic Data Integration
Vummadi, Harshini Anton Petrov Programmatic Support and Adaptation for the Visualization and Data Manipulation of Mitoribosomal Proteins
Wijeyesekera, Charith Melissa Kemp Incorporation of Metabolite-based Communication Data into a Cellular Communication Model
Project Year: Computational Biology Faculty Research Awards, Fall 2023
Student Name Faculty Mentor Title
Aggarwal, Bhavay Saurabh Sinha Designing Graph Neural Networks for the Analysis of Subcellular Spatial Transcriptomics

BACKGROUND 
The advent of scRNA-seq has had a profound impact on genomic and biomedical research. With the ability to profile gene expression at cellular resolution, we are able to significantly increase our understanding of cell types and cell states within different complex biological systems. At the same time, there are many questions that scRNA-seq technology cannot answer satisfactorily. Many biological processes, such as tissue development and cell signaling, are regulated in a spatially specific manner. To understand these processes, we need access to the spatial information of gene expression within tissues. Spatial transcriptomics is a groundbreaking molecular profiling method that provides single cell-resolution expression measurements with spatial resolution. The data generated from spatial transcriptomics experiments typically consists of gene expression levels associated with specific spatial coordinates. This spatially resolved transcriptomic data has been shown to improve our ability to identify cell types, infer cell-cell interactions, and study the organization of tissues and organs at a molecular level. The latest advance in high throughput single-cell transcriptomics is the emergence of spatial transcriptomics technologies with single-molecule resolution. Such subcellular spatial transcriptomics (henceforth “SST”) technologies provide not only the transcript abundance of each gene in a cell, but they also provide the precise subcellular locations of those transcripts, thus materializing a true spatial map of the transcriptome. Figure 1: Visualization of the graph generated for a randomly sampled cell. The nodes are colored based on the gene. 

Our lab has been working on tools for the analysis of SST (subcellular spatial transcriptomics) data and has developed the “Intracellular Spatial Transcriptomic Analysis Toolkit” (InSTAnT). This work has been described in a manuscript that is currently in revision, and I have contributed to this manuscript. InSTAnT is a toolkit that detects gene pairs and modules that co-localize within cells using specialized statistical tests. InSTAnT was used to discover several novel cell type-specific gene pair co-localizations in the brain, and its promising results demonstrate potential for future research.

Boysen, Joanne Yunan Luo Language Model-Based Deep Neural Network Protein Fitness and Annotation Prediction

Background & Question 
Advancements in DNA sequencing technologies, particularly next-generation sequencing, have accelerated the discovery of numerous genes from an extensive variety of species. The increased number of resulting protein sequences creates an opportunity to expand protein engineering, but also presents a challenge as many gene product molecular functions are poorly annotated. Protein engineering holds great promise for a wide range of human endeavors, such as the development of therapeutics drugs and gene editing, through producing protein variants that enhance the original function or are entirely novel [1]. Machine learning (ML) has been increasingly coopted for protein engineering via computational, physics based rational design. A critical component of employing ML for protein engineering is the development of a model that predicts the fitness of a protein given its sequence. This method has already yielded several algorithms that can successfully predict the effects of a mutation on function given evolutionary information of homologous sequences [2][3]. Protein language models (PLM) have been found to generate state-of-the-art representations of biological properties and achieve impressive prediction performance in protein prediction related tasks [4]. 

During Spring of 2023 I found that using a Multilayer Perceptron taking as input a PLM embedding of a mutated amino acid with a protein sequence can accurately predict protein function (Fig. 1). However, the Deep Mutational Scanning (DMS) assay data used to train my novel Multilayer Perceptron was produced in vitro and is thus subject to errors and noise as are all in vitro experiments [5]. To execute data valuation, cull harmful data points, and ultimately improve the quality of training data I will integrate a novel data quality valuation method into the previously developed language model-based deep neural network protein fitness.

Cara, Brendon Matt Torres Employing Enseble Methods in Machine Learning for Improved Prediction of PTM Functionality in Proteins

INTRODUCTION 
Machine learning plays a crucial role in computational biology, enabling the efficient analysis of large datasets for generating reliable predictions [1][2][6]. The Torres Lab's SAPH-ire TFx model has shown promise in predicting the functional significance of PTMs in proteins from different eukaryotic families, potentially aiding in disease identification and treatment [1][2][3]. To enhance the robustness of PTM functionality predictions and gain insights into different prediction approaches, exploring multiple machine learning algorithms through ensemble techniques is important [6][10]. Combining diverse algorithms allows for more reliable outcomes and evaluation of each model's prediction strategies [6][10]. To address the evolving nature of biological data and prevent a decrease in accuracy caused by conceptual drift, continuous model updates are necessary [4][5] . 

As a trainee in the Torres lab, my goal is to improve the current pipeline by integrating ensemble techniques, including the utilization of different models such as One Class SVM alongside SAPHire TFx, and incorporating them with the previous cross-validation pipeline. This comprehensive approach involves cross-comparing the results between the models during the cross-validation process and retraining models if accuracy falls below a specific threshold. By implementing these advancements, I aim to develop a robust framework for analyzing and interpreting PTM functionality, gaining insights into each model's decision patterns, and ensuring more accurate predictions. These efforts will address the challenges posed by evolving PTM datasets and contribute to advancements in the field.

Choi, Jiyeong Joe Lachance Revealing Evolutionary Features of Genetic Variants that Replicate Well Across Ancestries with Machine Learning Model

Background 
Since genome-wide association study (GWAS) became available, researchers have identified numerous associations between human disease and genetic variants. It allows us to understand how human genetic traits are related to complex diseases and to identify novel genes related to specific diseases [1]. However, there is a lack of diversity in human genetics studies. Many of them, especially GWAS studies, focus on European populations. Some traits share similar genetic effects on phenotypes regardless of people’s ancestral heritage. For example, skin color is highly heritable and has several replicating SNPs. Fifty-nine pigmentation-associated SNPs were identified in both Africans and Europeans [3]. On the other hand, other traits might have genetic effects on specific traits depending on ancestry groups. With these traits, the application of European-biased results to the non-European population will produce less accurate predictions [2]. In dealing with this limitation, we would like to see if there is replicating single nucleotide polymorphism (SNP) across populations and apprehend their functional and evolutionary features. Also, we would like to build the database with annotation of the variants. A machine learning method, then, will be generated to predict the likelihood of getting phenotypes when the information is given. This study can bring more insight into genetic patterns on phenotypes by ancestry group, allowing further studies to make more accurate predictions.

Du Plessis, Isabelle Joel Kostka Characterizing Viral Populations in the New Zealand Chatham Rise

Background & Question 
The ocean is estimated to contain 10 to the 30 viruses(1) , making them the most abundant biological entities in aquatic ecosystems(2). Relatively little is known about marine viruses, and most viral populations discovered are novel. Virus-host interactions play a key role in ocean biogeochemistry with 20% of marine bacteria lysed daily by viruses(3). Additionally, some viruses harbor auxiliary metabolic genes that can modulate their host’s metabolism and impact carbon, nitrogen, and sulfur cycling(4). While microbial populations can impact the environment, environmental conditions also influence microbial composition. Prokaryotic genomes have been shown to be affected by environmental factors. Microbial genomes exhibit higher GC content in waters with higher amounts of phosphate and nitrate, and lower temperatures, salinity, and oxygen levels(5). Viruses are closely tied to their hosts, having shown to be synchronized with microbial processes in the surface layer of the ocean(6), but the relationship between environmental factors and viral genomic composition remains to be elucidated. In addition, viral diversity, reproductive strategies, and auxiliary metabolic genes have been shown to vary with available nutrients and depth(7).

Hang, Emily Greg Gibson Positive Assortative Mating in Face Shape: Do Couples Tend to Look More Alike than Different?

BACKGROUND AND QUESTION 

Positive assortative mating, the tendency for individuals to choose mates that are more similar to themselves in phenotype than would be expected by chance, has been identified in humans for traits such as educational attainment, body mass index (BMI), dietary factors, and other physical measurements1,2,3. Facial phenotypic similarity between couples is another trait that has been observed across the world, and while there have been many scientific studies examining why this could be the case—reasons including implicit egotism, familiarity effect, and even game theory4,5,6—far fewer studies have aimed to quantitatively show that couples look similar to each other. This study aims to measure phenotypic facial similarity within couples through the use of 3D images acquired at Georgia Tech in The Facial Expression project.

Jain, Neha May Wang Interpretable Genomic Clustering to Find Phenotypic and Lifestyle Cohorts Among Ancestry Specific Populations vs General Population

Background 
Autoimmune conditions occur when one’s immune system starts recognizing their own body’s cells as foreign and begins attacking these healthy cells [14]. The exact reason behind autoimmune conditions is unknown, but it is thought to be a mix of both environmental and genetic factors. The genetic factors to most autoimmune conditions are poorly understood, as most autoimmune conditions are thought to be polygenic diseases, with their genetic susceptibilities being theorized to be different combinations of hundreds or thousands of alleles [2]. 

The Major Histocompatibility Complex (MHC) of the human genome, a vast region of over 8 thousand single nucleotide polymorphisms (SNPs) on chromosome 6 which encodes both cell surface proteins essential for cell recognition and the adaptive immune response in all vertebrate species, is thought to host many candidate genes for factors causing autoimmune disease [3]. Due to the polygenic nature of autoimmune conditions, many complex allelic interactions contribute to developing these conditions [3]. Machine learning techniques allow us to identify and search for nonlinear relationships among these many complex interactions in our genome [4], and these techniques can be used to robustly find different patterns in different minority cohorts. Thus, machine learning is extremely helpful in exploring the genetic underpinnings of autoimmune conditions and finding better associations between these autoimmune conditions and both genetic and environmental conditions.

Kokil, Prerna Francesca Storici Analysis of DNA Motifs Around Ribonucleoside Monophosphates in Human Cells

BACKGROUND AND QUESTION 
Ribonucleotides are RNA precursors incorporated during replication. They are embedded in the form of rNMPs (ribonucleoside monophosphate) and are the most frequent form of DNA aberrations, as shown in Figure 1. There are approximately two misincorporated rNMPs/kb in a DNA strand (Huang, Ghosh, & Pommier, 2015). However, there are mechanisms of removing these rNMPs misincorporated in the genomic DNA. A prominent pathway is known as Ribonucleotide Excision Repair (RER) by Ribonuclease (RNase) H2 (Cornelio et al., 2017). Due to lack of RNase H2 function, rNMP incorporation increases in genomic DNA. There are few effects of rNMPs some being positive. For example, rNMPs help mating type switching in fission yeast, thus aiding reproduction during its life-cycle (Yao et al., 2013). However, there are also effects deemed harmful. In humans, a mutation in RNase H2 has been associated with Aicardi-Goutières syndrome, which is a severe childhood neuroinflammatory autoimmune disorder (Uehara et al., 2018). It is characterized by abnormal development or destruction of the white matter that affects the brain, spinal cord, and immune system.

Although there is an abundance of rNMPs in DNA, the sites of the rNMP incorporation are still poorly characterized in the human genome (Balachander et al., 2020). Last fall, my project was directed towards finding: What consensus sequences, or motifs, are around the ribonucleotides in both the wildtype (WT) and knock-out (KO) cell lines in Saccaromyces cerevisiae libraries? In addition, I found transcription factor (TF) sites near rNMPs in both the wildtype (WT) and knock-out (KO) libraries. However, the code could be improved to accommodate for more motifs among the cell lines and the position of the motifs with respect to the rNMP site. This project will help us better understand rNMP incorporation in the human genome. Once we know what motifs are present, we can better predict the locations that are more prone to rNMP incorporation and thus potentially more prone to damage.

Menuey, Jay Landon King Jordan Reproducible Bioinformatics Through the Lens of Ancestry Inference

Background 
In the last couple of decades, a reproducibility crisis has emerged in science. Many recent scientific studies are nearly impossible to reproduce, causing their results to be less credible. This reproducibility problem has extended to bioinformatics as well. Genetic ancestry refers to the population groups that their genes are derived from and is objective in nature. These population groups are inferred from the person’s sequenced genome. In this study, I will perform large-scale ancestry inference on a sample dataset and create a comprehensive guide for anybody learning the process to ensure reproducibility of the methods. 

The ultimate goal is to apply this method to several datasets which are a part of CODIGO (https://codigo.biosci.gatech.edu/), the Consortium for Genomic Diversity, Ancestry and Health in Colombia. This is a collaborative project with the National Institutes of Health (NIH) that is centered around increasing the wealth and diversity of genetic information around admixed genomes and support bioinformatics research in Colombia. The Colombian population is a result of an admixture between Europeans, Native Americans, and Africans. In addition, their ancestry is heavily dependent on where they are from in Colombia geographically. This creates a fascinating amount of diversity within the country and its population that is not yet fully understood [1-4]. 

Michael Pham Yunan Luo Evaluating Protein-Protein Networks and Interactions with Rate-Distortion Techniques

Background and Question
Real-world Networks are complex, comprising vast webs of interconnected elements performing a diverse array of social and biological functions. Common among many networks is the pressure to be efficiently compressed either in the brain or in genetic code (1). Biological networks among molecular and cellular components are encoded at various scales in genetic material (2-5), and evolution uses these encoding to propagate network topologies in a surviving species. To answer the questions on what makes one network more compressible than another, we adapt tools from information theory to quantify the compressibility of a network.

Two ways to reduce the amount of information in the sequence is lossless and lossy compression, with lossless compression, the data originally in the file remains after compression, and all the information is restored while in our framework we will be using lossy compression, where the only the reference to recent data is saved which reduces the overall size without losing quality and is important for large scale biological networks. There are several studies done on directed networks however, few studies exist by using “real-world biological data”, therefore there is little information on complex biological networks. Some applied applications include identifying disease genes and drug targets, dysregulated pathways, and discover cell physiology in normal and disease states.

Morin, Kathryn Melissa Kemp Flux balance analysis of head and neck cancer cell lines to characterize the impact of physiological media on metabolic profile

BACKGROUND AND QUESTION 
Current commercial cell growth medias are formulated to provide cell cultures with necessary nutrients. While these formulations can successfully keep cells alive in vitro, they often contain a nutrient profile that differs from that which is available to cells in the in vivo environment. The nutrient composition of commercial media, therefore, can cause “metabolic artifacts” [1] in cancer cells. These artifacts include reversing the urea cycle due to their arginine concentrations, a phenomenon which is “not observed in vivo” [1]. Plasmax is a physiological media which aims to better mimic the in vivo environment and was shown to not cause metabolic artifacts in breast cancer cell lines. The Kemp Lab is interested in verifying these results in two matched head and neck cancer cell lines that reflect radiation-sensitive and radiationresistant phenotypes. Artifacts in the metabolic profiles of cells can impact the perceived efficacy of potential treatments when tested, as the cells may respond differently than they would in the in vivo environment. If the metabolic profiles of head and neck cancer cell lines differ greatly when exposed to Plasmax as opposed to commercial media, cell culture practices should be changed accordingly to create an in vitro environment that most closely matches the in vivo environment.

Mullins, Lee Ellen Kristen Knipe, CDC Development of Modular and Standardized Nextflow Workflows for Oxford Nanopore Data Analysis

BACKGROUND AND QUESTION 
Nanopore technology (ONT) is a third-generation sequencing technology becoming increasingly competitive in the sequencing market. Following Sanger and NGS sequencing methods, which rely on fragmentation and subsequent amplification to read DNA, third-generation sequencing technologies sequence a single DNA molecule directly. 

This advancement in sequencing technology comes with several key benefits. First, ONT technology can produce extremely long reads, making it a powerful tool for de novo genome assembly, structural variant identification, and traversing repetitive genomic regions. [1] Secondly, ONT has created compact, cost-effective sequencing devices such as the MinION and Flongle. These portable devices allow field-based, real-time data generation, providing valuable resources for rapid diagnostics and outbreak tracking. 

Historically, ONT has had much higher sequencing error rates than NGS sequencing (ONT, 1/50 bp-1/100 bp and Illumina, 1/1000 bp). [2,3] However, advances in equipment and software have drastically improved ONT’s error rate performance; some researchers recently claim they have achieved fidelities rivaling Illumina’s. With these advances, ONT is starting to present a viable alternative to NGS sequencing, especially in cases of de novo assembly, as well as when cost and time represent a critical factor.

Pellebon, Jasmyn King Jordan Developing a Predictive Modeling Framework for Mental Health Outcomes

BACKGROUND AND QUESTION
Mental health disorders pose a significant challenge to public health and individual well-being. Early identification and prediction of mental health outcomes can lead to targeted interventions and improved treatment outcomes. This research proposal aims to develop a predictive modeling framework for mental health outcomes, schizophrenia, bipolar disorder, anxiety disorder, and major depressive disorder.

Schizophrenia is a severe and chronic mental health disorder characterized by a range of symptoms, including hallucinations, delusions, disorganized thinking, and impaired cognitive functions. It affects approximately 1% of the global population, making it one of the most prevalent psychiatric disorders worldwide. Schizophrenia has a significant impact on the lives of individuals affected and their families. The symptoms of schizophrenia can be distressing and debilitating, leading to impairments in social and occupational functioning. The disorder often results in a decreased quality of life, increased risk of unemployment, poverty, homelessness, and increased reliance on healthcare services. Individuals with schizophrenia are at an increased risk of developing comorbidities such as substance use disorders, depression, anxiety disorders, and physical health conditions. The presence of comorbidities further complicates the management of schizophrenia and requires integrated and multidisciplinary approaches.

Royer, Charlotte Kostas Konstantinidis Analysis of ESKAPEE Pathogens in a Longitudinal Study of Mother and Child Fecal Microbiomes

Background and Question
Enteric pathogens are major causes of mortality worldwide, especially in children in industrializing countries (1–3). Bacteria such as Enterococcus faecium, Escherichia coli, Salmonella enterica, and viral pathogens such as Norovirus, can be responsible for these infections. These microbes represent major global threats to human health, with E. faecium and E. coli being members of the “ESKAPEE” group (E. faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and E. coli), pathogens with high infection and mortality rates and high rates of antibiotic resistance (4). Early infection with enteric pathogens can have profound effects on child development, including both cognitive and physical, and chronic downstream effects such as diarrhea, which can significantly alter the patient’s microbiome or result in death. There have been some recent efforts to characterize the impacts of enteric pathogens, such as E. coli, on childhood infections (1,3,5), which have uncovered unexpected co-occurrence of bacterial pathogens and commensals in the gut, as well as other effects, but the routes of infection in children and the effects of specific pathogens on microbiome changes remain largely unknown. We therefore aim to characterize enteric infections in early childhood via use of metagenomics, namely the route of acquisition, and how timing of infection affects subsequent growth and development of both the child and its microbiome. Metagenomic sequencing is untargeted in this application, allowing for a comprehensive analysis of all microbes within a sample. This will allow us to not only examine specific enteric pathogens of interest, but also the overall microbiome profile of study subjects.

Sankar Ramalaxmi, Gautham Amirali Aghazadeh Identifying Structural Features in Prokaryotic Short Gene Prediction using Protein Language Models

ABSTRACT
Prokaryotic gene prediction is largely misconceived as a solved problem in bioinformatics. There are many challenges to be solved: 1) the existing tools can hardly detect short genes (<180 nucleotides) although they are highly sensitive in finding long genes, their sensitivity decreases noticeably in finding shorter genes and 2) the false positive prediction in these gene annotation methods gives rise to hypothetical proteins, which may or may not be accurate; therefore, it is crucial to explore the reason behind these false positives. This proposal aims to investigate the potential benefits of leveraging the predicted 3D structure of potentially encoded proteins to enhance the accuracy of ProtiGeno, a transformer-based deep learning tool for prokaryotic gene prediction developed from our prior project. The objective is to acquire the 3D structure of both coding and noncoding regions in prokaryotes using state-of-the-art techniques such as AlphaFold2 and ESMFold. By doing so, we seek to explore the correlation between secondary structure patterns and their role as distinguishing features in the prediction of prokaryotic genes. This study has the potential to advance our understanding of gene prediction methodologies and contribute to the refinement of ProtiGeno's predictive capabilities.

Singh, Akshita Pamela Peralta-Yahya Developing a Workflow to Detect Agonists for GPR119 Using Machine Learning Techniques

BACKGROUND
G-protein-coupled receptors (GPCRs) are seven transmembrane receptors that couple with Gproteins on activation by external stimuli to trigger intracellular signaling cascades [1]. GPCRs comprise the largest family of membrane receptors that are prominent drug targets (35% of all approved drugs exploit GPCR signal transduction) [2]. GPCRs are thus clinically relevant targets for the development of new as well as repurposed drugs. 

This study focuses on identifying ligands for a class A cannabinoid receptor-like GPCR called GPR119 (glucose-dependent insulino tropic receptor) [3]. Pancreatic β and intestinal enteroendocrine L cells show significant levels of GPR119 expression on activation by oleoylethanolamide (OEA) [3, 4]. GPR119 participates in feeding behavior and glucose homeostasis signaling cascades via G-protein (specifically Gs) coupling, thus triggering the cyclic AMP pathway, and causing adenylate cyclase activation [4]. GPR119 activation initiates the release of incretins from the intestine and insulin from the pancreas and is also responsible for weight decline and reduced food intake in rats [4]. Determining pharmacologically relevant agonist targets for GPR119 is essential to identify lead compounds for management of metabolic disorders like type 2 diabetes and obesity [3,4]. GPR119 ligands are also determined to be a viable novel treatment for metabolic-associated fatty liver disease

Snyder, Hannah Joel Kostka Characterization of Viral Populations Associated to Salt Marsh Cordgrass Spartina Alterniflora

Background:
Salt marshes are areas of intertidal grasslands. They are important for coast maintenance, erosion control,
carbon regulation, and they supply raw materials and food. One plant that dominates the North American
wetlands is Spartina alterniflora. This smooth cordgrass plays a large role in wetland maintenance through soil stabilization and nutrient recycling [1]. In salt marshes, there is a gradient in the phenotypic height of S.
alterniflora, wherein the plants located closer to the ocean are taller while the plants growing closer to the
shoreline are shorter [2]. Plants of this species with different height phenotypes have different microbial
populations [2, 3]. These populations are predominantly composed of sulfur-oxidizing and sulfate-reducing
bacteria [3]. The bulk soil metagenomes of bulk sediment, rhizosphere, and endosphere associated with S.
alterniflora sediment compartments of tall and short phenotypes were obtained by the Kostka lab and their
microbiome was analyzed [3].

To improve our understanding of the ecological interactions within the salt marsh associated to S. alterniflora, we are studying the viral populations found in the different sediment compartments and plant phenotypes. Viruses are the most prominent biological entities in the ocean and impact their surrounding ecosystems [4]. Through the lytic cycle, viruses infect the surrounding microbial populations, replicate within them, and then lyse the host while their progeny burst into the environment. Through this process, the viruses regulate the population densities of surrounding microbes, impacting the diversity of microbial populations [5,6]. In the lysogenic cycle, the genome of temperate phage integrates into its host’s and replicates with it, with no lysis occurring. These temperate phages can benefit their host by carrying Auxiliary Metabolic Genes (AMGs) which can play a role in boosting host metabolism [4]. With this project, we aim to characterize the viral populations associated with S. alterniflora, and the role they play in the maintenance of S. alterniflora in salt marshes. We intend to do so by studying the prevalence, taxonomy, host associations, and gene composition of viral populations in the three different sediment compartments at the different height phenotypes, and analyzing the relationship between the viruses, the microbial populations, and S. alterniflora.

Srinivasan, Varsha King Jordan Integrated Risk Prediction Tool for Colorectal Cancer (CRC)

Objective
To create a tool that considers clinical risk factors for colorectal cancer and polygenic risk scores (PRSs) derived from large-scale genomic data in patient risk stratification to predict the risk of individuals contracting CRC with improved discriminatory accuracy between both incident and prevalent case and control subjects. This study has three main objectives: (1) develop meta & ancestry-specific PRSs for UK Biobank and All of US cohorts, (2) create a Cox-clinical risk score for CRC using identified risk factors, (3) computing an integrated risk prediction tool (IRT) using both clinical data and genetic information to improve risk stratification and this tool would be made available for use on the R Shiny App, (4) finding the gain in accuracy of prediction when genetic data are added to clinical risk factors.

Background
Colorectal cancer (CRC) is among the most prevalent and preventable forms of cancer worldwide. There is increased awareness of a strong genetic component to CRC risk, with the identification of several high penetrance alleles that predict increased CRC susceptibility. [1] Although risk factors often influence cancer development, most do not directly cause cancer. Over the past decade, genome-wide association studies (GWASs) for sporadic CRC, which constitutes most cases, have identified ~60 association signals at over 50 loci.

Predictive genetic testing is the use of a genetic test in an asymptomatic person to predict future risk of disease. These tests represent a new and growing class of medical tests, differing fundamentally from conventional medical diagnostic tests. The hope underlying such testing is that early identification of individuals at risk of a specific condition will lead to reduced morbidity and mortality through targeted screening, surveillance, and prevention. [2]

Suri, Anirudh Rishi Kamaleswaran Using Clinical Data and Waveform Data to Assess Patients in the ICU

Background and Question
Sepsis is a life-threatening condition due to the body’s extreme reaction to an infection.
It is a cascade of immunological reactions triggered by an initial infection. The primary
organs where sepsis attacks originate are the lungs, urinary tract, and gastrointestinal
tract. If sepsis is not treated within an advisable duration it can lead to tissue damage,
organ failure, and even death [1]. The number of sepsis occurrences is steadily
increasing (close to 200,000 yearly in the US). Sepsis occurs when biological/
immunological chemicals released in the bloodstream to fight an infection trigger
inflammation in neighboring tissues and organs. These cascades of changes lead to
organ dysfunction and frequently even death. In such conditions, the immune system no
longer fights against the pathogen, instead begins to turn against itself. This makes
medication extremely difficult and tricky. Septic shock is the most severe degree of
sepsis and is diagnosed when a patient’s blood pressure drops to dangerously low
levels. Sepsis also has immense economical implications in the US as well- it is the
number 1 cost of hospitalization in the US owing to the high nursing and medical cost,
and sepsis costs approximately $62 billion annually (only a small proportion) [2].

Sepsis-triggered inflammation is one of the leading causes of death among patients
admitted to an ICU rather than the primary infection.

Acute Respiratory Distress Syndrome (ARDS/ ARS) is a devastating complication of
severe sepsis. Both Sepsis and ARDS fundamentally have the same underlying
characteristics- inflammation and endothelial dysfunction. Patients diagnosed with
sepsis-induced ARDS have higher fatality and lower survival rates. [3]

Tambe, Saanika Joe Lachance Exploring the Genetics of Sub-Saharan Africans by Reconstructing Regional Allele Ages and Recombination Maps

BACKGROUND AND QUESTION
The Lachance Lab works with novel Prostate Cancer (PCa) Biological Datasets of men of African Ancestry (AA). These data sets have been largely made available by Men of African Descent and Carcinoma of Prostate (MADCaP) consortium. Our aim is to understand and further study the high prevalence of PCa in men of African Ancestry. Despite men of African Ancestry (AA) having the highest mortality rate from PCa, we have little knowledge about the variants associated with it. Preliminary research conducted in the lab has discovered novel GWAS (Genome-Wide Association Studies) hits. Additionally, considerable heterogeneity was discovered in the genetic architecture of PCa within West, East and South Africa.

To gain a deeper insight into the heterogeneity observed with our Genome Wide Association Studies (GWAS) results, Linkage Disequilibrium (LD) is crucial as it allows the identification of genetic markers that tag the causal variants [1]. LD is the difference between frequency of a particular combination of alleles at two loci that is observed and the expected frequency for random association. In a population where there is no mutation, selection of specific gene combinations will result in LD. However, genetic recombination breaks this LD [2].

Genetic recombination is an important process that gives rise to novel allele combinations enabling evolution in species [3]. Recombination rates are highly variable across species and populations [4]. The recombination rate is represented as the ratio of genetic distance and physical distance that forms a recombination map. Genetic recombination maps are essential for the evolutionary analysis [5] of populations, one important application being the estimation of allele ages. Presently, there are only a limited number of recombination maps available for the sub-Saharan African population for West Africa [6] and the Khoe San population of South Africa [5]. Therefore, we need accurate genetic recombination maps to study the heterogeneity in the African populations.

Previously, I found various tools that are publicly available which could be used to generate accurate and reliable genetic recombination maps. For initial trial, I used HapMap [7] and Thousand Genomes Project [6] data as input for these tools. I aim to find the best tools, most-suited to create recombination maps with MADCaP Datasets.
 

Vaz, Joel Markus Joe Lachance Polygenic Risk Score Modeling for Prostate Cancer in African Populations

BACKGROUND
The incidence of prostate cancer (CaP) varies among different populations. Individuals of African American ancestry face a 75% higher incidence risk than non-Hispanic Whites1. Among males in African populations, CaP ranks as the leading cause of cancer-related deaths. The factors contributing to this disparity involve a combination of genetic and non-genetic factors, many of which are not yet fully understood2. Conducting genetic studies in Africa presents challenges due to limited infrastructure and resources, leading to lower diagnosis rates. Additionally, variations in medical care access, risk assessment, and lifestyle factors within the African continent emphasize the need for subpopulation studies3.

Given the disease’s geographical and ethnic diversity and its higher mortality rate compared to other populations, it is crucial to identify specific characteristics unique to certain regions, which may not be apparent in pan-cancer studies. However, assessing the genetic burden of CaP in Africa is challenging due to limited data availability and inaccurate cancer incidence measures.

Xie, Manke Kate Greg Gibson Predicting Crohn's Disease Recurrence in Post-Operative Patients through RNA Splicing Profiling and Splicing QTL Analysis

Introduction:
Crohn’s disease (CD) is an inflammatory bowel disease that affects the gastrointestinal tract. Most patients with Crohn’s disease take drugs such as anti-TNF agents and thiopurines to alleviate their symptoms. Patients who are less responsive to their initial treatment or with bowel obstruction may undergo surgery with careful medical assessment. However, surgery does not provide a cure for Crohn’s Disease and the risk of postoperative recurrence is 44-55% after 10 years1.

Aberrant RNA splicing contributes to various diseases such as spinal muscular atrophy, cancers, and Hutchinson–Gilford Progeria Syndrome2,3. The genetic variants affecting the RNA splicing event is called splicing quantitative loci (sQTL). Recent studies predicted that sQTL correlates with certain diseases and might be a major contributor to diseases like Parkinson’s disease4,5. Previous research from Gibson lab shows that CD individuals with distinct transcript profile and altered exon usage might be caused by altered splicing events, which is also called spliceopathy6.
In this project, our aim is to investigate the splicing events in post-operative patients. By comparing the splicing profile of recurrence patients with non-recurrence patients, we intend to find differential isoform usage or differential exon usage that is associated with CD recurrence. Combining alternative splicing analysis, splicing QTL, and genome-wide association study (GWAS), I will address the following questions:

1) Does alternative splicing contribute to the risk of postoperative recurrence?
2) Do male and female show the same pattern of alternative splicing in CD recurrence?
3) Which genetic loci control the differential splicing between the recurrence and non-recurrence patients?

The main objective of this project is to predict the recurrence of CD from genotype and expression data. The integration of sQTL into GWAS and RNA splicing analyses provides insights into the mechanism behind post-operative disease recurrence.

Project Year: Computational Biology Faculty Research Awards, Spring 2023
Student Name Faculty Mentor Title
Aggarwal, Bhavay Saurabh Sinha Machine Learning methods for analysis of morphological and spatial lipidomics data for cell manufacturing
Bhatia, Neha Greg Gibson Uncovering Primary Immunodeficiency Disease Genes In A Healthy Cohort
Boysen, Joanne Yunan Luo Protein Fitness Prediction with Language Model-Based Deep Neural Network and Data Distribution-Smoothing
Cara, Brendon Matt Torres Validation of SAPH-irr TFx Model's Accuracy Over Time
Chea, Andy Matt Torres Broadening the SAPH-ire Functional PTM Training Set with New Data Streams
Chhatlani, Kirti Francesca Storici Mutation and Ribonucleotide Incorporation Analysis in Distal and Tumor Liver Tissue Biopsy Samples
Choi, Jiyeong Lachance Lab Understanding Natural Selection in African Populations
Govindarajan, Sandhya Peng Qiu scRNA -seq Data Analysis to determine key cell types interacting in the Gut Inflammatory disease IBD
Groves, Daniel Mark Borodovsky Developing GeneMark-ETP Gene Finder for Fungal Genomes and Genomes with Frequent Transcript Overlaps
Guruprasad, Jyothi Rishi Kamaleswaran (Emory) Genome-wide association studies for sepsis mortality
Karve, Dhruv Patrick McGrath Automated Markerless Pose Estimation to study Ethology in Lake Malawi Cichlids
Lagwankar, Asmita Kishor Patrick McGrath Employing DeepLabCut for improved Object Tracking and Quantification of Cichlid Behavior
Mehta, Pushti Dhananjay Matt Torres Rectifying the Misclassification of Functional PTMs by the SAPH-ire Model
Morin, Kathryn Nicole Melissa Kemp Flux Balance Analysis of Multiple Myeloma Patient and Cell Line Transcriptomes to Determine the Impact of Metabolic Fluxes on CD38 Expression and Dimerization
Mudge, Zack Melissa Kemp Integrating scRNA-seq Data into an Automated Flux Balance Analysis Pipeline
Mudradi, Harini Patrick McGrath Quantifying behavior in lake Malawi Cichlids using Computer vision techniques
Pellebon, Jasmyn King Jordan Identifying Genetic and Socioenvironmental Contributions to Mental Health Disparities
Pham, Michael Yunan Luo Evaluating protein-protein interactions with Rate-distortion techniques
Pryor, Chloe King Jordan Health outcomes of Latino expatriates in the UK Biobank
Rajasekar, Shreya May Wang Development of Multi-Modal Deep Learning Models for Early Detection of Alzheimer’s Disease
Ranbhor, Siddhi Vivekkumar Todd Streelman Single cell RNA sequencing to reveal markers for Tooth Regeneration after induced damage in Lake Malawi Cichlids
Royer, Charlotte Kostas Konstantinidis Characterization of gut microbiome profiles in pathogenic diarrheal disease
Sankar Ramalaxmi, Gautham Krishna Amirali Aghazadeh Investigating the utility of 3D structure in Prokaryotic Gene Prediction using Deep Learning-assisted Hidden Markov Models
Singh, Akshita Pamela Peralta-Yahya Performing computational modelling in Gαi coupled GPCRs for better integration into the yeast GPCR machinery for sensor development
Sinha, Agniruudrra Lynn Kamerlin Exploring Natural Language Processing as a Tool for Artificial Enzyme Design
Srinivasan, Varsha King Jordan Integrated Risk Prediction Tool for Colorectal Cancer
Suri, Anirudh Rishi Kamaleswaran (Emory) Decoding Sepsis via a Medical Informatics Approach and Understanding Age Governed Pathways
Tambe, Saanika Joe Lachance Exploring Heterogeneity in the Genetic Architecture of Sub-Saharan Africans by Reconstructing Regional Allele Ages and Recombination Maps
Vaz, Joel Markus Joe Lachance Decoding genetic mechanisms of prostate cancer in African populations
Venkatesh, Likitha Kristal Maner-Smith (Emory) The Multi-omics Integration Analysis of Human Brain in Alzheimer’s and Parkinson’s Disease
Verma, Ishika Francesca Storici Use of Ribonucleotide Incorporation Features to Predict the Origin of Yeast DNA Replication
Xie, Manke (Kate) Greg Gibson Cell type-specific Transcriptional Risk Score for Ulcerative Colitis and Its Power to Predict Disease Progression
Yerradoddi, Geetha King Jordan Apportionment of Human Genomic Variation: from Lewontin’s fallacy to Edward’s paradox
Zhang, Jiahong Mark Borodovsky Development of the prokaryotic and metagenomic gene finders with protein data support and more accurate models for genomes with low and high GC
Project Year: Computational Biology Faculty Research Awards, Fall 2022
Student Name Faculty Mentor Title
Aggarwal, Palak Rishi Kamaleswaran (Emory) Clinical Translation from Animal to Human Model Prediction of Sepsis Onset Time Using Regression
Bangaru, Arvind Yuhong Fan Signal quantifier pipeline for ATAC-Seq and CUT&RUN peaks
Bhat, Varsha Greg Gibson Development of Single-cell Atlases of Erythrocytes and Platelets in Healthy and Disease States
Bhatia, Neha Greg Gibson Longitudinal -Omics for Precision Health
Calder, Rachel Joshua Weitz Characterizing the dominant ecological functions of Spartina alterniflora root virome in Georgia saltmarshes
Chopra, Akul Deqiang Qiu (Emory) Developing a Deep Learning Medical Image Segmentation Technique to distinguish Epilepsy Lesions in MRI images
Gogate, Ashlesha Francesca Storici HUMAN EXON MICROARRAY DATA ANALYSIS
Gupta, Shreyash King Jordan / Rob Meller (Morehouse School of Medicine) Genetic Ancestry Interference of COVIRT RNA-Seq Samples
Harris, Adrian Joe Lachance The Impact of Different Genetic Architectures on Trait Prediction and Portability across Human Populations
Koundinya, Nidhi John McDonald Identifying cancer driver genes in thyroid cancer through analyzing coexpression networks
Krishnan, Upaasana Manoj Bhasin (Emory) Single cell transcriptomics analysis of genetic heterogeneity in pediatric Acute Myeloid Leukemia
Mahale, Aishwarya May Wang Robust Identification of Long-Term COVID Effects Using Weakly Supervised Learning on Free-Text Clinical Notes
Mandal, Amartya Patrick McGrath Deep Learning methods for object tracking and path trajectories in Cichlids
Mudge, Zachary Melissa Kemp Integrating scRNAseq Data into an Automated Flux Balance Analysis Pipeline
Narendrula, Saideep Rishi Kamaleswaran (Emory) Using machine learning for the prediction of ARDS onset by utilizing clinical EMR and continuous waveform features
Nawaz, Mariam Manoj Bhasin (Emory) Development of an interactive web resource and analytical tools for Pediatric Cancers single-cell data
Pryor, Chloe King Jordan Health outcomes of Latino expatriates in the UK Biobank
Qiu, Xu Yifei Wang Cystic Fibrosis Sputum Sample Contamination Study and Identifying Oral and Lung Microbiomes' Ecological Coupling
Ramesh, Ashika Joe Lachance Detecting signals of positive selection in African genomes using imputed data
Ravindra Raju, Dinesh Eric Ortland (Emory) Defining the Molecular Mechanism of Antibody Recognition and Investigating the Effect of Mutations on SARS-Cov-2 mAbs in Indian Patients
Sensarma, Nilavrah John McDonald Improving Precision Medicine for Cancer Patients using Machine Learning and Multi-omics data
Srikrishnan, Sreenath Peng Qiu Deep-Learning Based Cancer Survival and Subtype Prediction Using Integrated Multi-Omics Data
Sun, Zhaoyu Yifei Wang Full-stack Development for the Georgia Cystic Fibrosis (CF) Data Warehouse and Construction of Data Processing Pipelines for CF Related Research
Upadhyaya, Kunjur Manasa Matt Torres Expanding the Protein Feature Database (PFD) and associating the data with overlapping site-specific data from ELM and PTM cosmos cancer databases
Wang, Zun Deqiang Qiu (Emory) Optimization of Deep Learning Neural Network for Diffusion MRI Model Reconstruction to Find White Matter Microstructure Changes in Alzheimer’s Disease Early Stage
Project Year: Computational Biology Faculty Research Awards, Summer 2022
Student Name Faculty Mentor Title
Bangaru, Arvind Yuhong Fan Signal quantifier pipeline for ATAC-Seq and CUT&RUN peaks
Krishnan, Upaasana Manoj Bhasin (Emory) Single cell study of pediatric Acute Myeloid Leukemia to identify tumor-specific biomarkers and genotype associated characteristics
Wang, Zun Deqiang Qiu (Emory) Optimization of Diffusion MRI Model Reconstruction for Finding White Matter Microstructure Changes in Alzheimer’s Disease Early Stage with Less Input Data using Convolutional Neural Networks
Project Year: Computational Biology Faculty Research Awards, Spring 2022
Student Name Faculty Mentor Title
Bhattaram, Swethasree Rishi Kamaleswaran (Emory) Natural Language Processing (NLP) applied to radiology interpretations identify patients with Sepsis-related ARDS
Bhola, Mannan Deqiang Qiu (Emory) Deep Learning for Reconstruction of Multi-Band MRI Images
Chhatlani, Kirti Francesca Storici Mutation and Ribonucleotide Incorporation Analysis in Distal and Tumor Liver Tissue Biopsy Samples
Choudhury, Rakin Brian Hammer Evolution of Resistance in E. coli Against T6SS V. cholerae
DuBose, James "Gabe" Kostas Konstantinidis A multi-omics approach for comparing the physiological differences between slow and fast-growing bacteria
Eum, Jinyoung Sam Brown Developing Automated Pipeline for Microbiome Data Analysis
Gloster, Logan May Wang Integrative Deep Learning for Predicting Neurodevelopmental Consequences of Congenital Heart Disease
Karve, Dhruv Patrick McGrath Using Pose Estimation to study Ethology in Lake Malawi Cichlids
Koundinya, Nidhi John McDonald Identifying cancer driver genes in thyroid cancer through analyzing coexpression networks
Krishnan, Upaasana Manoj Bhasin (Emory) Examining the tumor microenvironment at the single cell level in T-ALL using existing and novel approaches
Mandal, Amartya Patrick McGrath Implementing Multiple Object Tracking for Path Trajectories in Cichlids
Meade, Caeden Loren Williams Programmatic Support for Visualization and Data Manipulation of RNA Secondary Structures
Mudge, Zachary Melissa Kemp Integrating scRNAseq Data into an Automated Flux Balance Analysis Pipeline
Narendrula, Saideep Rishi Kamaleswaran (Emory) Estimation of Respiratory Rate from Photoplethysmogram Using Emory University Hospital Patient Data
Nishiura, Kenji Kostas Konstantinidis Updates to MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences
Ravindra Raju, Dinesh Eric Ortland (Emory) Defining the Molecular Mechanism for Antibody Recognition and Neutralization of SARS-CoV-2 in Indian Patients
Sensarma, Nilavrah John McDonald Search for improved machine learning approaches to the prediction of responses to cancer drug therapy
Song, Haojun Sam Brown Understanding community-level microbial interaction using synthetic data
Srikrishnan, Sreenath Peng Qiu Deep-Learning Based Cancer Survival and Subtype Prediction Using Multi-Omics Data Integration
Sun, Zhaoyu Yifei Wang Production Full-stack Development for the Georgia Cystic Fibrosis (CF) Data Warehouse
Tran, Huy King Jordan Cardiometabolic disease burden in Latinos in the United Kingdom
Upadhyaya, Kunjur Manasa Matt Torres Application and Development of Protein Informatics Pipelines for Post-Translationally Modified Peptides
Varsha Bhat Greg Gibson Influence of Ancestry on Pain Susceptibility in Sickle Cell Disease
Wang, Zun Deqiang Qiu (Emory) Identification of Biomarkers in Asymptomatic Alzheimer's Disease using Diffusion MRI, Machine Learning, and Deep Learning
Xu, Zheying Greg Gibson Individual Genetic-phenotypic Network Analysis Based on Clinical Features of Inflammatory Bowel Disease
Yerradoddi, Geetha King Jordan Apportionment of Human Genomic Variation: from Lewontin’s fallacy to Edward’s paradox
Project Year: Computational Biology Faculty Research Awards, Fall 2021
Student Name Faculty Mentor Title
Adepu, Harini Greg Gibson Single-Cell Characteristics in Patients with Crohn’s Disease
Augenbroe, Anneke King Jordan Population-Specific Differences in Tier 1 Genomics Disease Burden
Bharadwaj, Ajay Joe Lachance Understanding the Evolution of Heritable Phenotypes Using Polygenic Risk Scores and Ancient DNA
Bhukar, Rohan King Jordan Clinical Utility of Integrated Polygenic Risk Scores (iPRS)
Chelsea, Fnu Joel Kostka Comparison of the Root Microbiome of Terrestrial Plants vs Wetland Plants Using Existing 16S rRNA Gene Amplicon Sequence Datasets.
Damle, Gargi Joe Lachance Applying Confidence Intervals to Polygenic Risk Scores for Ancient DNA
Emmel, Grace Peng Qiu Construction of Cell Type-Specific Gene Regulatory Networks from Single-Cell RNA-Seq Data
Eum, Jinyoung Eliver Ghosn Validating and Optimizing Cell Trajectory Algorithms for Single Cell RNA-Sequencing Datasets
Gregory, Mia Candace Fleischer Assessing Pain Processing in Temporomandibular Disorder using Functional Magnetic Resonance Imaging, Quantitative Sensory Testing, and Pain Metrics
Hoyt, Taylor Soojin Yi Evaluating the Extent of Enhancer Duplication in Human Evolution
Kintzle, Jennifer Greg Gibson Combined Single-Cell ATAC and RNA Sequencing of Peripheral Blood Mononuclear Cells in African Americans with Crohn’s Disease
Liang, Yifan Greg Gibson Combined Single-Cell ATAC and RNA Sequencing of Peripheral Blood Mononuclear Cells in African Americans with Crohn’s Disease
Meade, Caeden Anton Petrov/Loren Williams Computational Analysis and Visualization of Multiple-Sequence Alignments of Protein Homologs
Natu, Aditya Michael Goodisman Gene Duplication and the Evolution of Sociality in Insects
Shantaraman, Anantharaman King Jordan The Effect of Genetic Ancestry on Clinical Genomic Diagnoses in Colombia
Toporek, Aaron Deqiang Qiu A Pupil Activation Network as a Predictor of Alzheimer’s Disease Risk
Valancius, Marcus Yifei Wang (Sam Brown Lab) Georgia Cystic Fibrosis (CF) Data Warehouse: Collaboration between Emory, Children’s, Georgia Tech, and Augusta University
Project Year: Computational Biology Faculty Research Awards, Summer 2021
Student Name Faculty Mentor Title
Gregory, Mia Candace Fleischer Assessing Pain Processing in Temporomandibular Disorder using Functional Magnetic Resonance Imaging and 1H-MRS
Shantaraman, Anantharaman King Jordan The Effect of Genetic Ancestry on Clinical Genomic Diagnoses in Colombia
Project Year: Computational Biology Faculty Research Awards, Spring 2021
Student Name Faculty Mentor Title
Bharadwaj, Ajay Joe Lachance Effects of Natural selection on Prostate cancer risk using Ancient European DNA
Bhukar, Rohan King Jordan Genome-wide modelling of polygenic risk scores using Ancestry-specific and Trans-ancestry meta analysis
Chelsea, Fnu Joel Kostka Metagenome-enabled elucidation of the Spartina alterniflora root microbiome
Emmel, Grace Peng Qiu Imputation of Single-Cell RNA-Seq Data for Robust Gene-Gene Correlations
Gregory, Mia Candace Fleischer Assessing Pain Processing in Temporomandibular Disorder using Functional Magnetic Resonance Imaging
Hoyt, Taylor Soojin Yi Evaluating the Extent of Enhancer Duplication in Human Evolution
Kintzle, Jennifer Greg Gibson Combined Single Cell Assay of Transposase Accessible Chromatin and RNA Sequencing of Peripheral Blood Mononuclear Cells in African Americans with Crohn's Disease
Kollapaneni, Sai Suraj Deqiang Qiu Investigation of CSF Ferritin as a Predictive Alzheimer’s Disease Exacerbation Factor
Liang, Yifan Greg Gibson The Use of Single-Cell RNA-seq for Transcriptional Risk Scores in Crohn’s Disease
Narayanan, Priya Patrick McGrath Implementing Multiple Object Tracking for Real-Time Detection of Lake Malawi Cichlids During Bower Building
Natu, Aditya Daniel Cornforth Determining the impact of normalization method and laboratory on model accuracy evaluation for Pseudomonas aeruginosa infection of the Cystic Fibrosis lung
Shantaraman, Anantharaman King Jordan The Effect of Genetic Ancestry on Clinical Genomic Diagnoses in Colombia
Vegesna, Manasa Matt Torres FEATURE EXTRACTION FOR ENHANCING THE PREDICTIVE POWER OF THE SAPH-ire MODEL
Project Year: Computational Biology Faculty Research Awards, Fall 2020
Student Name Faculty Mentor Title
Cleland, Sara Ainsley Nicholson Population Structure and Genome Analysis of Elizabethkingia Anophelis
Gan, Shuheng Sam Brown Machine Learning Method to Identify Microbial Biomarkers in COPD Sputum Samples
Gupta, Sonali King Jordan Rare Variant Analysis of Health Disparities
Hazra, Ujani John McDonald Studying the Role of Antisense RNA in Mediating Aberrant Alternative Splicing in Cancer
Kesar, Devishi King Jordan Exploring the Genetic Diversity in Hispanic/Latino Populations
Lin, Shuting Peng Qiu Pan-Cancer Analysis of Correlation Patterns Between DNA Methylation and Alternative Splicing in TCGA
Mora, Laura Deqiang Qiu Neuroimaging as a Predictor of Cognitive Decline in Alzheimer’s Disease
Rozanski, Allison John Varga Identification of Virulence Factors in Burkholderia Cenocepacia Through Large Scale Comparative Genomics
Sharma, Rhiya Patrick McGrath Employing Fast R-CNN to Obtain Cichlid Position Before and After Sand Manipulation Events During Bower-Building
Sharma, Shivam John McDonald Analyzing the Role of Aberrant Alternative Splicing Leading to Loss of Micro-RNA Binding Sites in Cancer
Singu, Swetha Yuhong Fan Single Cell RNA-seq Identifies Expression Signatures Associated with Histone Hyperacetylation in Embryonic Stem Cells
Xiao, Yiqiong Sam Brown Cystic Fibrosis Data Integration and Front-End Database Development
Project Year: Computational Biology Faculty Research Awards, Summer 2020
Student Name Faculty Mentor Title
Ahmad, Maria King Jordan Genetic susceptibility and risk prediction for SARS-CoV-2 infection and COVID-19 progression
Gan, Shuheng Sam Brown Random Forest to Predict COPD Exacerbations with Microbiome Data
Lin, Shuting Peng Qiu Identifying potential biomarkers that are predictive of patient drug response to cancer therapies
Pfennig, Aaron Mark Borodovsky Identification of a type of genetic code in an anonymous, prokaryotic DNA sequence
Sharma, Rhiya Patrick McGrath Employing Neural Network Models to Obtain Accurate Positional Coordinates of Malawi Cichlids
Project Year: Computational Biology Faculty Research Awards, Spring 2020
Student Name Faculty Mentor Title
Gupta, Sonali King Jordan Web-Based Platform for Approximation of Pulse Field Gel Electrophoresis Pattern Identifiers from Whole Genome Sequences
Hazra, Ujani John McDonald A Strategy for Identification of Cryptic Promoters in Human Genome Based on the Presence of Transcription Factor Binding Motifs
Kesar, Devishi King Jordan Exploring the Genetic Diversity in Hispanic/Latino Populations
Kumar, Sachin Eliver Ghosn Validation and Optimization of Multiple Batch Integration Methods for Single Cell Sequencing
Lacek, Kristine Greg Gibson Generation and Comparison of Polygenic Risk Scores for Medical Conditions in the North and South of England—Continuation: PRS, PXS, and Ancestry
Mora, Laura Deqiang Qiu Locus Coeruleus and Its Relationship to Alzheimer’s Disease
Parekh, Paarth Francesca Storici Determining the Firing Time of Autonomous Replication Site (ARS) Based on Ribo-Nucleotide Incorporation Using Machine Learning
Rozanski, Allison John Varga Identification of Virulence Factors in Burkholderia cenocepacia Through Large Scale Comparative Genomics
Sharma, Rhiya Patrick McGrath Improving the Accuracy of DeepLabCut to Obtain Accurate Positional Coordinates of Malawi Cichlids
Sharma, Shivam John McDonald Understanding the Role of Aberrant Alternative Splicing in Loss of Regulatory Control of Gene Expression by Micro-RNAs in Cancer
Sharma, Siddhartha Joe Lachance Development and Testing of STRUCTUREpainter: A Local Ancestry Inference Algorithm
Singu, Swetha Yuhong Fan Single Cell RNA-seq Identifies Expression Signatures Associated with Histone Hyperacetylation in Embryonic Stem Cells
Temples, Danielle Tim Cope Encoding of Information of Limb Position from Distinctive Muscles to Successfully Form a Movement
Vegesna, Manasa Matthew Torres Feature Extraction for Enhancing the Predictive Power of the SAPH-ire Model
Xiao, Yiqiong Sam Brown Cystic Fibrosis Data Integration and Front-End Database Development
Yang, Ruize Le Song Design Lipid Nanoparticles Using Machine Learning
Project Year: Computational Biology Faculty Research Awards, Fall 2019
Student Name Faculty Mentor Title
Aljapur, Vineeth Patrick McGrath Understanding Natural Social Behaviors of Cichlids using Convolutional Neural Networks and Depth Sensing
Arrojwala, Manu Tej Sharma Patrick McGrath Automated Analysis of Sand Topology and Image Data to Study Natural Courtship Behavior in Malawi Cichlids
Bharanikumar, Ramit John McDonald Optimization and Comparison of Machine Learning Approaches for Ovarian Cancer Diagnosis
Feldman, Jacob James Dahlman A Nanoinformatics Approach to Predicting Lipid Nanoparticle Formation
Fisher, Margaret Greg Gibson Quantitative Comparisons Between Fine-Mapping Methods and Defining Credible Intervals with Multi-Objective Optimization
Gerald, Nishant Joe Lachance Leveraging Local Ancestry of the MADCaP Pilot Dataset to Identify Strong Selection Signals
Gruenhagen, George Todd Streelman RNA-Seq Analysis of Stem Cells Involved in Tooth Repair
Jain, Mani King Jordan Approximation of PulseNet PFGE Banding Pattern Identifiers from Whole Genome Sequences using Machine Learning
Jain, Prerna Sam Brown Machine Learning Approaches to Predict Antibiotic Resistance in Pseudomonas Aeruginosa
Kothandaraman, Sachin Eliver Ghosn A Gene-Set Library for Immune Cell Type Classification from Expression Analysis of Single-Cell RNA-Sequence Data
Lacek, Kristine Greg Gibson Generation and Comparison of Polygenic Risk Scores for Medical Conditions in the North and South of England
Leventhal-Douglas, Gabriel Yuhong Fan ChIP-seq Analysis Pipeline for Quantifying Histone Modifications
Li, Tianci Todd Streelman Variants in Conserved Region Between Rock-Sand Dwelling and Different Tooth Shape of Cichlids in Lake Malawi
Lu, Yinquan Concettina Guerra Development of Computational Methods for Gene Prioritization via Rank Aggregation
Ma, Jialin Joe Lachance Quantify Natural Selection Strength of Human SNPs with Ancestral and Primate Allele
Neary, Bridget Peng Qiu Identifying Molecular Signatures that are Predictive of Patient Response to Cancer Therapies
Pan, Hanying Sam Brown Cystic Fibrosis Related Clinical and Experimental Data Cleaning and Integration
Raut, Priyam Raquel Lieberman Identification of Biological Substrates for Signal Peptide Peptidase in Archaea and Elucidating their Substrate Specificity
Sharma, Shrinkhla John McDonald Prediction of Personalized Chemotherapies for Cancer Patients Based on Genomic Profiles
Yang, Junkai Eliver Ghosn Implementation of a New Clustering Algorithm (Exhaustive Projection Pursuit) to Automatically Identify Immune Cell Subsets in Multi-Omics Single-Cell Sequencing Datasets
Project Year: Computational Biology Faculty Research Awards, Summer 2019
Student Name Faculty Mentor Title
Cao, Mingming Raquel Lieberman An In Silico Phylogenetic and Structural Analysis of Olfactomedins and Comparison with Existing Protein Subfamilies
Fisher, Margaret Greg Gibson A Novel Conditional Analysis Approach to Fine Map eQTLs Associated with Autoimmune Diseases
Gupta, Mansi Brian Hammer Identifying Novel Type VI Secretion System Genomic Loci in Diverse Bacterial Species
Hutwagner, Will Fredrik Vannberg and Joe Lachance From Genotype to Phenotype: Analyzing Breed and Physical Characteristics of the Domestic Dog
Li, Tianci Todd Streelman Variant Calling for 198 Cichlid Species in Lake Malawi and Conservation Scores for Cichlids
Neary, Bridget Peng Qiu Identifying Molecular Signatures that are Predictive of Patient Response to Cancer Therapies
Raut, Priyam Raquel Lieberman Identification of Biological Substrates for Signal Peptide Peptidase in Archaea.
Project Year: Computational Biology Faculty Research Awards, Spring 2019
Student Name Faculty Mentor Title
Aljapur, Vineeth Patrick McGrath Using Neural Networks to study Natural Social Behaviors of Cichlids
Arrojwala, Manu Tej Sharma Patrick McGrath Automated approach to study the courtship behavior in Malawi Cichlids
Bharanikumar, Ramit John McDonald A Machine Learning approach towards diagnosing Ovarian Cancer
Duan, Jiachen Alberto Stolfi Identifying Progenitors of CNS Cells in the Tunicate Ciona Robusta by scRNAseq Technology
Feldman, Jacob James Dahlman A Nanoinformatics Approach to Predicting Lipid Nanoparticle Formation
Fisher, Margaret Greg Gibson Fine Mapping eQTLs and Causal Variants
Gerald, Nishant Joe Lachance Benchmarking & Optimizing STRUCTUREpainter - A Pipeline for Local Ancestry Inference
Gowrishankar, Preethi Joe Lachance Differences in polygenic risk scores between populations
Gruenhagen, George Todd Streelman Genetic Basis of Tooth Shape in Lake Malaŵi Cichlids
Gupta, Sinjini John McDonald Analysis of the Impact of P-53 Loss-of-Function in an Ovarian Cancer Cell Line
Huang, Tzu-Chuan King Jordan Evaluating the effects of alternative splice isoforms regulated by transposable elements in cancer
Hutwagner, Will Fredrik Vannberg From Genotype to Phenotype: Analyzing Breed and Physical Characteristics of the Domestic Dog
Jain, Mani King Jordan Prediction of PulseNet PFGE Banding Pattern Identifiers from core-genome Multi-Locus Sequence Type allelic profiles using Random Forest classification
Jain, Prerna Sam Brown Prediction of Antibiotic Resistance Development in Pseudomonas aeruginosa
Kothandaraman, Sachin Sarath Yadav Fredrik Vannberg An Automated Software Pipeline for Multi-dimensional Analysis of Patient Cancer Data
Leventhal-Douglas, Gabriel Yuhong Fan ChIP-seq Analysis Pipeline for Quantifying Histone Ubiquitination
Li, Tianci Todd Streelman Bower behavior in Cichlid fish, pit-digging and castle-building F1 hybrids Allele-specific expression in the specific brain region
Ma, Jialin Joe Lachance Large scale ancestral allele inference and quantifying the strength of purifying selection
Neary, Bridget Peng Qiu Identifying Molecular Biomarkers that are Predictive of Patient Response to Cancer Therapies
Pan, Hanying Sam Brown Cystic Fibrosis Related Clinical and Experimental Data Cleaning and Integration
Raut, Priyam Raquel Lieberman Identification of biological substrates for Signal Peptide Peptidase in Archaea
Zhang, Linglin Matt Torres Analysis and mapping of PTM hotspots occurring near biological motifs of protein families
Project Year: Computational Biology Faculty Research Awards, Fall 2018
Student Name Faculty Mentor Title
Ban, Dongjo John McDonald Assessing the Impact of Aberrant Epigenetic Changes on Allele-Specific Expression in Cancer Cells and the Therapeutic Value of Altering its Mechanisms
Brandt, Genevieve Kostas Konstantinidis Metagenomic Analysis of the Response of Coastal Microbial Communities to Extreme Weather Pertubations
Gulati, Saurabh King Jordan Evaluating the Oncogenic Properties of a Novel Histone H2B Modification that Regulates Transposable Element Expression
Harvey, William Kostas Konstantinidis Transcriptome and Proteome Insights into the Physiology of Dehalococcoides sp. BAV1 During Active Dehalogenation and Differences Compared to Model Organisms
Jin, Rong Joshua Weitz Examination of Phages as a Biocontrol Approach for Citrus HLB Disease
Li, Junyu Patrick McGrath Machine Learning on Sand-Manipulating Behaviors of Cichlids
Long, Jiani Matt Torres Analysis of Post-Translational Modification Hotspots in Domain Families
Mathur, Shrey May Wang Predicting Underlying Cause of Death Using Electronic Death Records
Merritt, Brian Lily Cheung Balancer Chromosome and Viral Immunity Design Using CRISPR/Cas9
Nagpal, Sini Greg Gibson Pervasive Modulation of Obesity Risk by the Environmental and Genomic Background
Ou, Yihao Fredrik Vannberg The Epidemiological Dynamics of Highly Mutable Viruses
Prabhu, Prachiti Yuhong Fan Differential Allelic Expression Regulated by Linker Histone Variants in Development
Raghuram, Vishnu Brian Hammer Understanding VgrG-Effector Pairs: A Bioinformatics Approach
Saldana Farias, Beatriz King Jordan Comparing the Effects of Ancestry Versus Admixture on Autozygosity Associated Disease Burden
Semwal, Ayush Soojin Yi Variation in X Chromosome Inactivation: Can it be Directly Linked to Variation in DNA Methylation?
Shah, Nirav King Jordan Effects of Ancestry on Prostate Cancer
Sharma, Sarthak Alberto Stolfi Software for Custom Analysis of Non-Model Organism Single-Cell RNA Sequencing Data from the ddSeq Platform
Thakur, Mohit Joe Lachance STRUCTUREpainter: A Local Ancestry Inference Algorithm
Yue, Qinyu Sam Brown C.elegans Tracking and Image Processing with Deep Learning
Zhuang, Qinwei Peng Qiu Pipeline Construction for Optimal Experimental Design in Tackling Problems of Limited Available Experimental Data in a Multiple-Substgrates-Multiple-Proteases System
Project Year: Computational Biology Faculty Research Awards, Summer 2018
Student Name Faculty Mentor Title
Li, Junyu Patrick McGrath Machine learning on sand-manipulating behaviors of cichlids
Long, Jiani Matt Torres Analysis of Post-Translational Modification Hotspots in Domain Families
Raghuram, Vishnu Brian Hammer Understanding VgrG - effector pairs: A bioinformatics approach
Saldana Farias, Beatriz King Jordan Genetic risk and disease burden in admixed American populations; The effect of ancestry on the risk of disease
Zhuang, Qinwei Peng Qiu Pipeline Construction for Optimal Experimental Design in Tackling Problems of Limited Available Experimental Data in a Multiple-Substrates-Multiple-Proteases System
Project Year: Computational Biology Faculty Research Awards, Spring 2018
Student Name Faculty Mentor Title
Ban, Dongjo King Jordan A Computational Method for Predicting Down-regulated Metabolites in Cancer Which May Constitute Potential Therapeutic Targets
Brandt, Genevieve Kostas Konstantinidis A Comparison of the Tara Oceans Metagenome Assembled Genomes
Gulati, Saurabh King Jordan Evaluating the oncogenic properties of a novel histone H2B modification that regulates transposable element expression
Jin, Rong Joshua Weitz Examination of marine virus and microbial host abundance correlation with metagenomics data
Long, Jiani Matt Torres Analysis of 3-Dimensional Structural Features as Potential Predictors of PTM Function
Mathur, Shrey May Wang Predicting Underlying Cause of Death Using Electronic Death Records
Mavura, Yusuph John McDonald Analysis of Mutations in Cancer Driver Genes in Primary, Metastatic, and recurrent Ovarian Cancer While-Exome Sequecing (WES) Samples
Merritt, Brian Lily Cheung Engineering the First Balancer Chromosomes in Arabidopsis thaliana
Nagpal, Sini Greg Gibson Gene Environment Interaction Study for BMI to study the Association of FTO gene with Risk of Obesity
Ou, Yihao Fredrik Vannberg The Epidemiological Dynamics of Highly Mutable Viruses
Prabhu, Prachiti Yuhong Fan Differential Allelci Expression Regulated by Linker Histone Variants in Development
Raghuram, Vishnu Brian Hammer Understanding VgrG – Effector Pairs: A Bioinformatics Approach
Ramakrishnan, Ajay Patrick McGrath Detecting Subpopulations in Mouse Brain Cells Using Single Cell RNA Sequencing Data
Saldana Farias, Beatriz King Jordan Genetic Risk and Disease Burden in Genetically Diverse Admixed American Populations
Shah, Nirav James Dahlman Studying Nanoparticle-Based Changes in Heterochromatin to Improve In Vivo Gene Editing
Sharma, Sarthak Alberto Stolfi Identification of Ciona Larval Brain Cell Subpopulations Using Single-Cell RNA-Seq
Thakur, Mohit Joe Lachance Consequences of Admixture in Modern Humans
Wist, Stephen John McDonald Genotyping Primary, Metastatic, and Recurrent Ovarian Tumors to Inform Treatment
Yue, Qinyu Sam Brown Using Quantitative Measurement of Fluorescence Intensity to Estimate the Pathogen Load in the Intestine of C. elegans
Zhuang, Qinwei Peng Qiu Optimal Experimental Design in Tackling Problems of Limited Available Experimental Data in a Multiple-Substrates-Multiple-Proteases System
Project Year: Computational Biology Faculty Research Awards, Fall 2017
Student Name Faculty Mentor Title
Adam Dabrowski King Jordan Genetics of Sex Difference in Human Mate Preference
Aditi Paranjpe Kostas Konstantinidis Understanding the Underlying Mechanisms of Genome-Wide Selective Sweeps in Natural Microbial Populations Using Time-Series Metagenomic Analysis
Chris Monaco James Dahlman Technology Development for High Throughput Analysis of the Nanoparticles
Hannah Hatchell Soojin Yi CpG Island Identification in Mus musculus Using Whole Genome Bisulfite Sequencing Data from Multiple Tissues
Jacob Boswell Frank Rosenzweig Phylogenomic Analysis of Multiple Recent Origins of Cellular Differentiation
Juichang Lu Sam Brown The Dynamics of Death
Junke Wang King Jordan A Genome-Wide Screen for Genetic Modifiers of Human Transposable Element Expression
Kalyani Patankar Fredrik Vannberg Microarray Based Gene Expression Analysis to Predict Cancer Tissue of Origin
Karan Kapuria Sam Brown "Inferring Interactions in the Cystic Fibrosis Microbiome with Dynamic Modeling and Machine Learning"
Meixue Duan Greg Gibson Single-Cell RNA Sequencing Data Analysis and Pipeline Construction
Michael Finlayson Greg Gibson An RNA-Seq Based Characterization of Immune Reconstitution in Bone Marrow Transplants
Mrunal Dehankar Yuhong Fan Computational Modeling of Neural Stem Cell Differentiation
Ramya Madupuri Matt Torres Analysis of 3-Dimensional Structural Features of Eukaryotic Phosphorylation Sites
Rushika Pandya Matt Torres Computational Analysis of PTMs in Domain Families
Shareef Khalid John McDonald Analyzing Allele Specific Expression (ASE) in paired Tumor Normal Tissue Samples
Shashwat D. Nagar King Jordan Exploring Ancestry-Specific Differences in Human Mate Preference
Su Jun Zhao Concettina Guerra A Distributed Alignment-Free Algorithm to Determine Sequence Similarity Based on Bounded Hamming Distance
Venna Wang Joe Lachance Ancestral State Reconstruction
Xinrui Zhou Concettina Guerra Development of Computational Methods for Classifying Permutations with Applications to Rank Aggregation Based Gene Prioritization
Project Year: Computational Biology Faculty Research Awards, Summer 2017
Student Name Faculty Mentor Title
Brandon Smith Soojin Yi Comparative Analysis of DNA Methylation from Whole Genome Bisulfite Sequencing Maps of Hymenopteran Insects
Camila Medrano Trochez King Jordan HLA-omics of Admixed LatinAmerican Populations and Its Impact on Health and Disease
Chris Monaco James Dahlman An Analytical Pipeline for High Throughput Nanoparticle Screening
Meixue Duan Greg Gibson Longitudinal Evaluation of Human Physiological Conditions Based on Geographical Information System (GIS) and Clinical Outcomes
Rohini Mopuri Patrick McGrath Study of Single Nucleotide Polymorphisms Arising in East African Lakes
Rushika Pandya Matt Torres Computational Analysis of PTMs in Domain Families
Xinrui Zhou Concettina Guerra Development of Computational Methods for Classifying Permutations with Applications to Rank Aggregation Based Gene Prioritization
Project Year: Computational Biology Faculty Research Awards, Spring 2017
Student Name Faculty Mentor Title
Adam Dabrowski King Jordan Ancestry-Specific Assortative Mating in Latin America
Aditi Paranjpe Kostas Konstantinidis Quantifying the Role of Recombination in Maintaining Diversity Within Natural Microbial Populations with Metagenomics
Ajay Ramakrishnan Varadarajan Patrick McGrath The Role of Transposable Elements in Gene Regulation in the Sand and Rock Dwelling Cichlids of Lake Malawi
Bowen Yang Lee Cooper Genomic and Histology Analysis of Diffuse Lower-Grade Gliomas
Camila Medrano Trochez King Jordan HLA-omics of Admixed Latin American Populations and Its Impact on Health and Disease
Hannah Hatchell Soojin Yi Relating Tissue-Specific Gene Expression and Drug Killing Indices
Juichang Lu Sam Brown The Dynamic of Death
Junke Wang King Jordan Genetic Canalization and Sex-Specific Protective Model for Diseases
Karan Kapuria Sam Brown Network Analysis of Microbial Communities in Cystic Fibrosis Lung
Krithika Ravindran Naidu Fredrik Vannberg Drug Response Prediction on Cancer Patients Using Support Vector Machines
Meixue Duan Greg Gibson Investigating Personal Differences During Complicated Malaria Development
Michael Finlayson Greg Gibson The Effect of Bone Marrow Transplantation on Peripheral Blood Expression Profiles
Mrunal Dehankar Yuhong Fan Computational Modeling of Neural Stem Cell Differentiation
Ramyasree Madupuri Matt Torres Analysis of 3-Dimensional Structural Features of Eukaryotic Phosphorylation Sites
Rushika Pandya Matt Torres Computational Analysis of PTMs in Domain Families
Shareef Khalid John McDonald Analyzing Allele Specific Expression in Tumor Tissue Samples
Shashwat Deepali Nagar King Jordan Detecting Polygenic Adaptive Introgression
Tian Jin Fredrik Vannberg De novo Genome Assembly Post-assembly Processing
Tyrone Lee Patrick McGrath A System for High Throughput Behavioral Analysis of Lake Malawi Cichlid Fishes using Computer Vision techniques
Venna Wang Joe Lachance Is the Rate of Human Evolution Accelerating?
Project Year: Computational Biology Faculty Research Awards, Fall 2016
Student Name Faculty Mentor Title
Andrew Teng Joe Lachance From Genetic Risk Scores to Clinical Disease Risks
Chen Guo Greg Gibson Evaluating Contribution of Rare Variants to Gene Expression in Humans
Devika Singh Joshua Weitz A New Method of Modeling Phage Therapy
Hari Prasanna Subramanian Greg Gibson RNA-Sequencing as a diagnostic tool for Neuromuscular Dystrophy
Harshmi Shah John McDonald Developing an Algorithm to Predict Drug Therapies from Tumor Sample Analysis
James Moore Fredrik Vannberg Forecasting Tuberculosis Emergence Using Agent-based Modeling with Partial Data
Min Yi Fredrik Vannberg Processing, Storing, and Analytics of Electronic Health Records
Niveda Sundararaman Matthew Torres Developing a Scoring and Ranking Scheme for PTM Hotspots
Petar Penev Eric Gaucher The Aroma of Fluorescence
Tannishtha Som King Jordan Suffix Tree Based Approach to Bacterial Typing
Tyrone Lee Patrick McGrath A System for High Throughput Behavioral Analysis of Lake Malawi Cichlid Fishes Using Computer Vision Techniques
Walker Gussler Joshua Weitz Modeling Infectious Disease Spread Over Complex Networks Outside of the SIS Framework
Project Year: Computational Biology Faculty Research Awards, Summer 2016
Student Name Faculty Mentor Title
Aroon Chande King Jordan Developing a Typing Scheme for Nontypeable Haemophilus influenzae
Harshmi Shah John McDonald Developing an Algorithm to Predict Drug Therapies from Tumor Sample Analysis
Namrata Kalsi Patrick McGrath Prediction of Chemical Ligands Using Molecular Dynamic Simulations
Niveda Sundararaman Matt Torres Analysis and Comparison of PTM Hotpots between Experimental and Putative PTMs
Peijue Zhang Eva Lee Models for Choosing the Drug-of-Choice in Response to a Biochemical Terrorist Attack
Project Year: Computational Biology Faculty Research Awards, Spring 2016
Student Name Faculty Mentor Title
Alicia Francis Fredrik Vannberg De Novo RNA-SEQ Ovarian Cancer Profile Analysis
Andrew Teng Joe Lachance From Genetic Risk Scores to Clinical Disease Risks
Devika Singh Joshua Weitz A New Method of Modeling Phage Therapy
Hari Prasanna Subramanian Greg Gibson Comparative analysis of Conventional RNA-seq and Targeted RNA-seq in Neuromuscular Diseases
Harshmi Shah John McDonald Developing an Algorithm to Predict Drug Therapies from Tumor Sample Analysis
Min Yi Fredrik Vannberg Drug Prediction on Ovarian Cancer Patients
Namrata Kalsi Patrick McGrath Prediction of Chemical Ligands Using Molecular Dynamic Simulations
Niveda Sundararaman Matt Torres SAPH-ire: Structural Analysis of PTM Hotspots
Tannishtha Som King Jordan Machine Learning Based Method for Reporting Antimicrobial Resistance of Fungal Infections in Colombia
Tyrone Lee Patrick McGrath Genomics of Complex Behavior in Lake Malawi Cichlid Fishes
Project Year: Computational Biology Faculty Research Awards, Fall 2015
Student Name Faculty Mentor Title
Bhanu Gandham King Jordan Colombian Admixture Studies And Visualization Tools for Population Genomic Health Profiles
Binbin Huang Joe Lachance Dating an ancient African population bottleneck using whole genome sequences from the Simons Genome Diversity Project
Menghan Li Chong Shin Multi-Cellular Crosstalk During Liver Regeneration After Injury
Raghavendra Padmanabhan Patrick McGrath Analysis of Chromosomal Inversions in Cichlids using Next Gneration Sequencing (NGS) data
Roopa Reddy Nagilla Greg Gibson The 'CorRelate' package for understanding correlation between high-throughput genomic data
Shashidhar Ravishankar Fredrik Vannberg Alignment free quantification of gene expression from RNA-Seq experiments
Vaishnavi Venkat Patrick McGrath Prediction of Chemical Ligands Using Molecular Dynamic Simulations
Xin Wu Soojin Yi Non-coding RNA Methylation and Expression during Human Brain Development and Evolution
Yasvanth Kulasekarapandian Fredrik Vannberg Diagnostic Primer Design Tool: Sparrow
Project Year: Computational Biology Faculty Research Awards, Summer 2015
Student Name Faculty Mentor Title
Binbin Huang Joe Lachance A Journey of African Genetic Variation
Menghan Li Chong Shin Multi-Cellular Crosstalk During Liver Regeneration After Injury
Roopa Reddy Nagilla Fredrik Vannberg Universal data structures for manipulating clustered k-mers
Project Year: Computational Biology Faculty Research Awards, Spring 2015
Student Name Faculty Mentor Title
Anuj Gupta King Jordan A Genome Based Sequence Typing Tool for Bacterial Pathogens
Bhanu Gandham King Jordan Detection of Causal Mutation for Mucopolysaccharidosis by Trio Exome Analysis
Menghan Li Chong Shin Multi-cellular Crosstalk During Liver Regeneration After Injury
Nandida Damaraju Constantine Dovrolis Structural Analysis of Metabolic Networks
Rachel Kutner Greg Gibson Integrative -omic Study of Malaria Virulence and Disease Progression Over Time
Raghavendra Padmanabhan Patrick McGrath A Computational Model to Predict Phenotype from Genotype in Caenorhabditis elegans
Shashidhar Ravishankar Fredrik Vannberg Alignment free quantification of gene expression from RNA-Seq experiments
Taylor Griswold Brian Hammer Identifying Mutations in Novel Genetic Factors that Prevent Natural Competence in the Human Pathogen Vibrio cholerae
Tian Mi Steve Harvey Evolution of Ribosome at Insertion Points
Vaishnavi Venkat Patrick McGrath Prediction of Chemical Ligands Using Molecular Dynamics Simulation
Xin Wu Soojin Yi Investigating the Roles of Non-CpG Methylation of Duplicate Genes during Human Brain Development
Yasvanth Kulasekarapandian Fredrik Vannberg K-mer based solutions for highly dynamic viral geomes
Project Year: Computational Biology Faculty Research Awards, Fall 2014
Student Name Faculty Mentor Title
Ali Pirani King Jordan Bordetella Genome Informatics Platform - BGIP
Dhruviben Patel Fredrik Vannberg Comparative Analysis of Plasmodium Parasites P. malariae and P. brasilinum
Emily Norris King Jordan The Genomic Basis of Capsule Switching and its Relationship to Disease in Neisseria meningitidis
Karthikeyan Murugesan Greg Gibson Gene Expression Profiling Workflow for Bioconductor With an Emphasis on SNM
Krutika Satish Gaonkar Eric Gaucher An Experimental Method to Benchmark Ancestral Sequence Reconstruction Methods
Sanjeev Sariya Patrick McGrath Predicting Residues Involved for Ligand Binding in C. elegans Chemoreceptors
Taylor Griswold Brian Hammer Identifying Novel Genetic Factors that Prevent Natural Competence in the Human Pathogen Vibrio cholerae
Ying Sha King Jordan Dynamic Analysis of Nucleosomes of Human Stem Cell
Project Year: Computational Biology Faculty Research Awards, Summer 2014
Student Name Faculty Mentor Title
Ali Pirani King Jordan Genome Rearrangement Analysis to Characterize the Emergence of Bordetella pertussis
Dhruviben Patel Fredrik Vannberg Comparative Analysis of Plasmodium Parasites P. malariae and P. brasilinum
Emily Norris Steve Harvey Unusual RNA Secondary Structures
Gayathri Kurup Francesca Storici Genome Wide Mapping and Detailed Analysis of rNMP Incorporation in Yeast
Parimala Devi William Ratcliff Determining the Molecular Basis of Multicellular Complexity in Experimentally-Evolved Saccharomyces cerevisiae and Chlamydomonas reinhardtii
Project Year: Computational Biology Faculty Research Awards, Spring 2014
Student Name Faculty Mentor Title
Ali Pirani King Jordan Genome-Based MLST Scheme for Bordetella pertussis
Emily Norris Steve Harvey Unusual RNA Secondary Structures
Karthikeyan Murugesan Greg Gibson Gene Expression as a Predictor of Complex Disease Traits
Krutika Satish Gaonkar Eric Gaucher An Experimental Method to Benchmark Ancestral Sequence Reconstruction Methods
Sanjeev Sariya Patrick McGrath Predicting Residues Involved for Ligand Binding in C. elegans Chemoreceptors
Ying Sha King Jordan Senescence of Human Stem Cell
Project Year: Computational Biology Faculty Research Awards, Fall 2013
Student Name Faculty Mentor Title
Cai Huang Fredrik Vannberg Alignment Free Sequence Analysis
Dan Sun Soojin Yi A Global Analysis of DNA Methylation and Aging in Human Brain
Deepak Unni Eric Gaucher To Understand the Interactions Between Elongation Factor - Tu and Other Proteins Within the tufA Network by Using Techniques in Structural Bioinformatics and to Understand How Mutations in These Proteins Affect Protein-Protein Interactions
Esha Jain Yuhong Fan Repeat Analysis of Individual H1 Variants Enriched Regions
Kelley Bullard Yuhong Fan An Investigation of the Relationship Between Linker Histone H1 and the Insulator Binding Protein CTCF
Pramod Mayigowda King Jordan Integration of Bacterial Isolate Genome Sequence database to Meningococcus Genome Informatics Platform
Raghu Chandramohan May Wang Identifying the Detection Threshold and Systematic Assessment of RNA-seq Expression Profiling Using Simulated Data
Siddharth Biswal John McDonald Missing title
Smruthy Sivakumar King Jordan Antagonistic Pleiotropy and Cancer
Vartika Agrawal Greg Gibson Analysis of allele specific expression using DNA and RNA-Sequencing
Vivek Sagar Radhakrishna Fredrik Vannberg Developing a Generic Web-Based Framework for to Analyze and Visualize Global Distribution Patterns Pathogen
Project Year: Computational Biology Faculty Research Awards, Summer 2013
Student Name Faculty Mentor Title
Cai Huang Fredrik Vannberg K-Mer Based Algorithm for Virus Identification
Dan Sun Soojin Yi A Study of Epigenetic Divergence and Genome Evolution Using Primates
Siddharth Biswal John McDonald Development of a Drug Recommendation Pipeline for Personalized Cancer Treatment
Project Year: Computational Biology Faculty Research Awards, Spring 2013
Student Name Faculty Mentor Title
Deepak Unni Eric Gaucher To Use Experimental Systems to Understand Evolution of Proteins in a Protein-Protein Interaction Network (PPIN) and to Use Computer Simulations to Aid Future Laboratory Experiments
Gaurav Sureka Steve Harvey To build a relational database and find unusual features in the secondary structure of RNA or find outliers by comparing various properties
Peter Audano Fredrik Vannberg Rapid Detection of Pathogens in Metagenomic Sequence Data
Pramod Mayigowda King Jordan Meningococcus Genome Informatics Platform
Raghu Chandramohan May Wang Integrated Prediction Modeling for Neuroblastoma Treatment Outcome Using RNA-Seq Data
Siddharth Biswal John McDonald Development of an Integrated Genomic Pipeline for Personalized Cancer Treatment
Vartika Agrawal Greg Gibson Exome-Wide Study of Histiocytoid Cardiomyopathy in a Family-Based Cohort
Vivek Sagar Radhakrishna Fredrick Vannberg R-Tree Based Qualitative K-Merised Metagenomic Analysis and Data Visualization
Xiao Dong Yuhong Fan Bioinformatics Analysis of Linker Histone H1 and DNA Methylation
Project Year: Computational Biology Faculty Research Awards, Fall 2012
Student Name Faculty Mentor Title
Ambily Sivadas Greg Gibson The WHOLE Study – An Approach to Personalized Medicine through Integrative Genomic Profiling
Amit Rupani Fredrik Vannberg Expression-based genome-wide association study to identify susceptibility loci linked to HCV infection in liver tissue
Ashwath Kumar Yuhong Fan Analysis of Histone H1 and H3 modifications from CHIP-Seq data
Deepak Purushotham King Jordan Transposable element derived structural variation in human prostate cancer
Haozheng Tian Greg Gibson Methylome in the Role of Regulation of Transcriptome in WHOLE Study
Hema Nagrajan Soojin Yi Genome wide characterization of CpG Islands by Epigenetic properties
Lu Wang King Jordan Transcriptional profiling of non-coding RNA expression in human adult stem cells during aging
Piyush Ranjan Steve Harvey Searching for unusual features in RNA secondary structure
Shengyun Peng Fredrik Vannberg Clinical Metagenomics Using K-Mer Arrays
Shimantika Sharma Joshua Weitz G-DES: an efficient software for microbial gene-level diversity estimation
Project Year: Computational Biology Faculty Research Awards, Summer 2012
Student Name Faculty Mentor Title
Hema Nagrajan Soojin Yi Genome-wide characterization of CpG islands by epigenetic properties
Lu Wang King Jordan The relationship between DNA damage and non-coding RNA expression in adult stem cells
Piyush Ranjan Steve Harvey Incorporating Kinetic Information into RNA Secondary Structure Prediction
Project Year: Computational Biology Faculty Research Awards, Spring 2012
Student Name Faculty Mentor Title
Ambily Sivadas Greg Gibson Genome Wide Association Study (GWAS) of Human Metabolic profiles to identify common and rare variants-associated metabolic phenotypes
Amit Rupani Fredrik Vannberg Differential RNA dynamics study of exosomes post vaccination: A systems biology approach
Ashwath Kumar Yuhong Fan Analysis of Histone H1 and H3 modifications from CHIP-Seq data
Deepak Purushotham King Jordan Transposable element derived structural variation in human prostate cancer
Hema Nagrajan Soojin Yi Identifying Determinants of Mammalian Non-synonymous and Synonymous Substitution Rates Using Novel Multivariate Principal Component Regression Method
Keerti Surapaneni Greg Gibson Extending functionality of GeneVar to perform eQTL analysis on metabolite data & developing an open source visualization tool for the Fly Database Project
Piyush Ranjan Steve Harvey Incorporating Kinetic Information into RNA Secondary Structure Prediction
Shengyun Peng Fredrik Vannberg Multivariate Analysis of Exosomes Pre- and Post-Stimulation
Shimantika Sharma Joshua Weitz G-DES: an efficient software for microbial gene-level diversity estimation
Project Year: Computational Biology Faculty Research Awards, Fall 2011
Student Name Faculty Mentor Title
Kristen Knipe Joshua Weitz DynBio: an Educational Tool to Aid in the Understanding of Modeling Dyamical Systems and to Reduce the Fear of Mathematical Biology
Neha Varghese King Jordan & Nael McCarty Correlation of site-specific evolutionary parameters with mutational effects in Cystic Fibrosis Transmembrane Regulator
Paul Cooper Yuhong Fan Analysis of Histone H1 binding to DNA
Robert Petit Timothy Read & King Jordan Bacterial Pathogen Database and Browser for Clinical Diagnostics
Shaopu "Peter" Qin Greg Gibson & Nicoleta Serban Comparison of different normalization method and application of variable selection methods in microarray data analysis
Shrutii Sarda Soojin Yi Understanding evolution of gene-body methylation in diverse animals using experimentally determined methylation levels
Vani Rajan Mark Borodovsky & Brian Hammer Computational methods to identify genes involved in control of horizontal gene transfer in the bacterial pathogen Vibrio cholerae
Project Year: Computational Biology Faculty Research Awards, Summer 2011
Student Name Faculty Mentor Title
Kristen Knipe Joshua Weitz PhageLab: a Matlab-based Java Web Application to Aid in the Understanding of Phage Dynamics
Robert Arthur John McDonald Study and Stent Usage and Restenosis in Sus scrofa
Robert Petit King Jordan and Tim Read Bacterial Pathogen Database and Browser for Clinical Diagnostics
Shaopu Qin Greg Gibson & Nicoleta Serban Application of Variable Selection Methods in Microarray Data Analysis
Project Year: Computational Biology Faculty Research Awards, Spring 2011
Student Name Faculty Mentor Title
Kristen Knipe Joshua Weitz PhageLab: a suite of GUIs to aid in the understanding of phage dynamics
Neha Varghese King Jordan & Nael McCarty Comparison of site-specific evolutionary parameters to mutational effects in Cystic Fibrosis Transmembrane Regulator
Paul Cooper Yuhong Fan Analysis of Histone H1 binding to DNA
Racchit Thapliyal Eric Gaucher Benchmarking of ancestral sequence reconstruction tools
Robert Adam Arthur John McDonald Gene expression in coronary arteries exposed to stents eluting synthetic rapamycin derivatives
Robert Petit Timothy Read & King Jordan Bacterial pathogen database and browser for clinical diagnostics
Shaopu Qin Greg Gibson & Nicoleta Serban Gene clustering analysis from human leukocyte gene expression profiles
Shrutii Sarda Soojin Yi Understanding evolution of gene-body methylation in diverse animals using experimentally determined methylation levels
Project Year: Computational Biology Faculty Research Awards, Fall 2010
Student Name Faculty Mentor Title
Aarthi Talla Greg Gibson Gene Network Analysis of Human Gene Expression Data
Chandni Desai Brian Hammer Comparative Sequence Analysis to Predict Interspecies Horizontal Gene Transfer among Bacterial Pathogens
Eishita Tyagi King Jordan Epigenetic regulation of cell-type specific gene expression by transposable element derived enhancers
Jay Humphrey King Jordan Assembly and Annotation Pipeline and Scalable Genome Browser
Nadeem Bulsara Yuhong Fan Structural and Functional Analysis of Linker Histone H1
Neha Gupta Greg Gibson Use of Next-Gen Sequencing & Association Based Mapping for dissecting laboratory-based adaptation in Drosophila melanogaster
Phillip Lee Melissa Kemp Mathematical modeling of cellular thioredoxin redox states and cellular signaling
Sandeep Namburi King Jordan InGen: A Suite of Tools to Aid in the Epidemiology of Influenza
Project Year: Computational Biology Faculty Research Awards, Summer 2010
Student Name Faculty Mentor Title
Eishita Tyagi King Jordan Epigenetic regulation of cell-type specific gene expression by transposable-element derived enhancers
Jay Humphries King Jordan Scalable multiple genome browser and pipeline
Namburi Sandeep King Jordan InGen: a suite of tools to aid in the epidemiology of influenza
Phillip Lee Melissa Kemp Mathematical modeling of cellular thioredoxin redox states and cellular signaling
Project Year: Computational Biology Faculty Research Awards, Spring 2010
Student Name Faculty Mentor Title
Aarthi Talla Greg Gibson Microarray analysis of human gene expression data
Eishita Tyagi King Jordan Epigenetic regulation of cell-type specific gene expression by transposable-element derived enhancers
Kanika Arora Eric Gaucher & Stephen Harvey Heterotachy in ribosomal RNA
Nadeem Bulsara Yuhong Fan Structural and functional analysis of linker histone H1
Neha Gupta Greg Gibson BSA-SFP linkage mapping analysis of Drosophila melanogaster for cardiac dysfunctions
Nitya Sharma King Jordan Investigating the role of IS elements in capsule switching in Neisseria meningitidis
Vinay Vyas Stephen Harvey A study of the structural basis of ribosomal fidelity using MD (Molecular Dynamic) simulations
Project Year: Computational Biology Faculty Research Awards, Fall 2009
Student Name Faculty Mentor Title
Abhiram Das Joshua Weitz Duplicate gene fate finder
Jasreet Hundal Yuhong Fan I. Genome-wide DNA methylation analysis II. Histone H1 structure modeling
Jeffrey Martin Mark Borodovsky Machine learning algorithms for inferring bacterial genome function from RNA-Seq data
Kanika Arora Stephen Harvey Dependence of 3D size of single-stranged RNA molecules on sequence length, secondary structure and presence of condensing agents
Khanjan Ghandi Timothy Read, Emory University Design of database of bacterial pathogen genomes for PCR primer design and ongoing validation
Nitya Sharma King Jordan Using SNPs to discriminate virulent and carriage strains in Neisseria meningitidis
Viswateja Nelakuditi King Jordan Neisseria Base: a comparative genomics database and genome browser for species of the Neisseria genus
Project Year:
Student Name Faculty Mentor Title
Leung, Ho Yeung "Ozi" Peng Qiu In silico CITE-seq model for protein prediction in the human blood sample and IMMUNO-age predicting pipeline
Venkatesh, Likitha Kristal Maner-Smith The Identification Of Diurnal Lipids Oscillations Across Tissues