This page highlights software and research projects that reflect the practical side of my work in computational genomics, bioinformatics infrastructure, and scientific software.

lncRNAexp

This project explores whether the expression of protein-coding genes can be predicted from lncRNA signals using deep learning. The broader goal is to use predictive structure to investigate lncRNA function and uncover hidden regulatory relationships.

Status: active research direction.

GenomKit

GenomKit is a Python toolkit for handling common genomic file formats and analysis tasks. It is designed to make routine bioinformatics work more coherent by exposing reusable abstractions across different data types and biological representations.

Use it when you need a compact programming layer for genomic intervals, annotation-related tasks, or file-driven analysis workflows.

GPM

GPM, short for Genomic Project Manager, is a command-line framework for organizing and automating bioinformatics projects from raw input to reporting and archive steps.

Key capabilities include:

• End-to-End Workflow: Seamlessly manage demultiplexing, processing, analysis, interactive reporting, and archiving tasks.
• Customizable: Easily adapt GPM to your environment, computer, web service, institute, or specific author requirements.
• Comprehensive NGS Application Coverage: Covering a wide range of Next-Generation Sequencing (NGS) applications, including RNAseq, tRNAseq, mRNAseq, ChIPseq, ATACseq, CLIPseq, ampliseq, scRNAseq, scVDJseq, scATACseq, miRNAseq, BWGS, WES, 16S, MAG, and more.
• Ideal for Bioinformatics Services: A powerful tool for individuals providing bioinformatic services.

Seqmon: A Lightweight Sequencing Monitor

Seqmon is a lightweight web application built with Plotly Dash for monitoring sequencing runs across instruments and tracking demultiplexing statistics in an operational setting.

It is aimed at facilities and teams that need a simple interface for keeping an eye on sequencing progress and output quality.

GenomicAI

GenomicAI is the umbrella label for work that applies AI methods to genomics problems, including representation learning, prediction tasks, and AI-assisted interfaces for analysis workflows.

This section will grow as more methods, prototypes, and applied use cases are published on the site.