Avantika Lal

The paper designs Müller glia-specific cis-regulatory elements using deep learning and validates their function in vivo.

The paper introduces Decima, a model that predicts cell type- and condition-specific gene expression from DNA sequence using single-cell RNA sequencing data.

This paper introduces CompBioBench, a benchmark for evaluating agentic systems in computational biology, showing strong performance across diverse tasks.

Nona is a multimodal masking framework that unifies sequence-to-function models, language models, and generative models for functional genomics, improving predictions and revealing privacy vulnerabilities in ATAC-seq data.

GAME provides a standardized framework for benchmarking genomics models across tasks, enabling consistent evaluation and community contributions.

Polygraph assesses synthetic regulatory DNA elements using features like diversity and motif composition to improve gene therapy design.

gReLU provides a framework for DNA sequence modeling and design, including tools for training, evaluating, and applying pre-trained models to predict and create regulatory elements.

This paper introduces regLM, a framework using autoregressive language models to design synthetic regulatory DNA elements with desired properties, showing they match biological features of validated CREs.

Bench-ML provides a web interface for benchmarking machine learning models in genomics, enabling end-to-end analysis, visualization, and comparison of model performance.

The paper develops SMEAGOL, a tool to analyze RBP binding motif enrichment in ssRNA viral genomes, finding most are enriched or depleted in human RBP motifs, suggesting selection pressure on these interactions.

The paper demonstrates the use of GPU computing with RAPIDS to accelerate single-cell genomic analysis workflows and provides open-source examples.

GEPSi simulates GWAS phenotype data with biological complexity to benchmark genetic analysis methods.

SparseSignatures is a framework for discovering mutational signatures in tumor genomes, outperforming existing methods and identifying signatures linked to known mutagenic mechanisms in pancreatic and breast tumors.

This paper trains deep learning models to reduce sequencing errors in PacBio HiFi reads, achieving 25-40% error reduction in human and E. coli genomes.

The paper uses bioinformatic analyses of RNA sequencing data to identify host and viral factors involved in SARS-CoV-2 pathogenesis, including differentially expressed genes and viral sequence variants linked to disease severity.

AtacWorks denoises and identifies accessible chromatin regions from low-quality ATAC-seq data, achieving high-resolution results with minimal input.

This study uses RNA-Seq to show Rsd and 6S RNA regulate sigma factor activity and gene expression in E. coli across growth phases, revealing novel interactions and a mathematical model of transcriptional control.