Welcome to Laboratory for BioInformatics & Genomics (BIG)

Computational cancer genomics
WGS/WES analysis
  Studies of regulatory ncRNAs
microRNA / lncRNA

Computational biology and Bioinformatics
Laboratory for BioInformatics & Genomics (BIG) takes advantage of both computational and experimental techniques to identify novel cell-type- and disease-specific factors that alter gene expression or protein function in abnormal conditions.  Among such factors, we mainly focus on regulatory non-coding RNAs (ncRNAs) - such as microRNAs and lincRNAs, in diverse conditions as well as mRNA 3' UTR dynamics and genomic variations between diverse developmental stages, cell-types, and normal vs abnormal cells (e.g., cancer cells).
For instance, abnormal expression of miRNAs (e.g., miR-17~92 cluster) and lincRNAs (e.g., malat-1), shortening or lengthening of mRNA 3'UTRs,
and mutation of regulatory elements (cMyc) generally perturb target gene expression at both the transcriptional and post-transcriptional levels,
and thus can cause severe diseases, such as cancer development and metastasis.
For systematic approaches of the studies, our laboratory has equipped high-throughput sequencing platforms (e.g., Illumina HiSeq2000, high performance computing with network attached storage (NAS)) and is routinely performing whole genome sequencings, whole exome sequencing, RNA-Seq, Chip-Seq, and small-RNA sequencing. We are also developing new protocols (called 3P-Seq and CAGE-Seq) to profile 3'UTR and transcription start sites (TSSs).

We thus aim to address the following important problems:

Aim 1. Identification of cancer/cell-type specific regulatory ncRNAs.
We aim to sequence primary cancer transcriptomes using RNA-seq and 3P-seq and to identify regulatory ncRNAs, including miRNAs and lincRNAs,
that are misregulated in cancer. We also propose to study how alternatively spliced and polyadenylated isoforms specific to cancer sub-types cause
differential miRNA targeting.

Aim 2. Identification of molecular mechanism of Alternative cleavage and PolyAdenylation (APA) regulation.
Using a candidate-based approach, we aim to identify factors involved in shortening or lengthening of 3′UTRs in cancer.
For this, we will use over-expression and knock-down approaches for components of polyadenylation complexes and their binding partners
(i.e., CPSFs, PAP, CFI and II, CstF2 and 3, PABPs, FIP1L1, and SLBP) and assay the effect on APA by using 3P-seq.
In the long term, we like to investigate how 3′UTR isoform usage are altered during cancer development, development, differentiation,
and de-differentiation
by taking advantage of NGS techniques like 3P-seq.

Aim 3. Identification of cancer-driver genes in Korean cancer genomes.
We aim to develop computational methods to identify Korean-specific genomic variants (germline and somatic mutations,
and structural variations) that cause formation of cancer and to discover cancer subtype-specific variants relating to specific biological pathways.