Bioinformatics Lab

Modern-day biology is a data-driven field, thanks largely to the advances in high-throughput sequencing technologies. The Bioinformatics Lab brings together faculty members and students interested in deploying the arsenal of computer science and computer engineering to solve problems in computational biology. We work on topics such as: algorithms and software for high-throughput sequencing data analysis, bioinformatics for diseases and epidemics, computer architecture for bioinformatics algorithms. Given the interdisciplinary nature of our research, we collaborate with a variety of research groups including: the Practical Genomics Lab (DLSU), Systems and Computational Biology Unit (DLSU), and Translational Medicine  Unit (DLSU) and research groups in the University of Tokyo, the International Rice Research Institute, National Institute of Health UP Manila, Southeast Asian Fisheries Development Center.

For recent updates, visit the lab’s website at www.bioinfodlsu.com.

Research Team

Research Programs

Bioinformatics for combatting antimicrobial resistance

Antimicrobial resistance (AMR) occurs when pathogens evolve to resist medicines used to treat their infection. WHO has declared it as one of the top health threats to humanity. Our lab, along with our collaborators in SComB and the e-Asia JRP ATTACK-AMR project, is using metagenomics to understand the abundance and diversity of AMR-related genes in hospital wastewater. Another angle of attack on AMR is phage therapy, an alternative to conventional antibiotics, the heavy and loosely regulate used of which has been exacerbating the AMR crisis. Given the lab experiments to study phages can be costly and tedious, our lab is investigating state-of-the-art machine learning techniques to aid in phage characterization.

Computational interpretation of genomic regions implicated by genome-wide association studies in rice

Rice feeds half of humanity. The production of rice needs to match human population growth while being environmentally sustainable and climate change-resilient. These challenges have motivated the identification of genetic factors behind agronomically important traits, often using genome-scale techniques such as QTL analysis or genome-wide association studies (GWAS). These studies report regions in the genome that are statistically significant, but they remain short of explaining the biological significance. Our lab has been working on software solutions to gain biological insights on statistically significant genomic sites.

Differential gene expression analysis for non-model organisms

RNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. We propose a shortcut that avoids the computationally demanding assembly process and instead obtains counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation.

Bioinformatics for HIV surveillance

The landscape of molecular surveillance for HIV is undergoing a significant transformation, shifting from the conventional Sanger sequencing approach towards the utilization of modern high-throughput sequencers. This shift has necessitated the construction of bioinformatics pipelines for analyzing the big data output of these sequencers. Our lab is working towards a pipeline that improves accuracy by taking into account the high mutation and recombination rates seen in HIV.

Bioinformatics for cancer genomics

In collaboration with DLSU’s Translational Research and Medicine Unit and St. Luke’s Medical Center, our lab is applying modern multinomics technologies on commercial cell lines as well as real patient tissues, to investigate regulated cell deaths in the context of colorectal cancer.