CSBL::Computational & Synthetic Biology Laboratory at KU symbol

Research Topics Publications Members Tools & Softwares Journal Club

Deep Learning provides innovative tools for understanding BioData. It is a Digital Biology Approach.

Genome analysis begins with annotation.Precise and accurate annotation is essential for genomic, transcriptomic, functional analysis. We developed a light-weight portable annotation pipeline that can be maintained by small-scale individual laboratory.

Mycoatlas

AI-predicted catalog of 1.4 million novel short coding sequences across 609 Basidiomycota genomes — including conserved micro-proteins, RiPP candidates, and cross-kingdom homologs.

MaaD

Every known PfCSP epitope, its cognate antibody, and the structure that proves it.131 monoclonal antibodies across 138 antigen-antibody complexes. Epitope footprints are computed from the deposited coordinates at a 4.5 A heavy-atom cutoff, not transcribed from figure legends.

FunGap (Fungal Genome Annotation Pipeline)

FunGAP performs gene prediction on given genome assembly and RNA-seq reads. At the moment, RNA-seq data is mandatory for gene prediction. GitHub Download “FunGAP: Fungal Genome Annotation Pipeline using evidence-based gene model evaluation”. In: BIOINFORMATICS (OXFORD, ENGLAND) 33.18 (2017), pp. 2936-2937. DOI: 10.1093/bioinformatics/btx353. PMID: 28582481

p-CAPS (prokaryotic-Contig Annotation Pipeline Server)

p-CAPS is a portable web server that can annotate any chunk of DNA assembly from contigs to scaffolds to genome. GitHub Download. “Prokaryotic Contig Annotation Pipeline Server: Web Application for a Prokaryotic Genome Annotation Pipeline Based on the Shiny App Package”. In: JOURNAL OF COMPUTATIONAL BIOLOGY : A JOURNAL OF COMPUTATIONAL MOLECULAR CELL BIOLOGY 24.9 (2017), pp. 917-922. DOI: 10.1089/cmb.2017.0066. PMID: 28632399

pyGCAP: a python Gene Cluster Annotation & Profiling

pyGCAP is a python package for Probe-based Gene Cluster Finding in Large Microbial Genome Database. It is developed as a URAP (undergraduate research apprentice project) during 2023-2024 Winter by Jisu. The manuscript is prepared for submitting a peer-reviewed journal. Github Download

pyCDM4F: a python Chill Day Model for Flowering date

pyCDM4F is a python package for predicting cherry blooming/flowering date based on the Chill-Day Model. Model prediction accuracy for Korean local areas is best among those previously published models. It is developed as a URAP (undergraduate research apprentice project) during 2024 Summer by Songwon.