Software
BOARDS (Blanket Overarching Antimicrobial-Resistance gene Database with Structural information)
Antimicrobial resistance (AMR) poses a significant threat to public health, and understanding and addressing appropriately to it is of great importance. In this context, the role of the BOARDS database, which comprehensibely collects AMR genes, becomes crucial. The BOARDS databae facilitates the monitoring of antibiotic resistance and serves as an essential tool for tracking the trends in antibiotic resistance patterns. The features described below enable BOARDS to provide a comprehensive understanding of AMR genes and their structures. Furthermore, the integration with an automated WGS (Whole Genome Sequencing) analysis pipeline allows for the rapid detection of AMR genes from WGS data. This integration enhances the efficiency and effectiveness of monitoring and analyzing antibiotic resistance patterns in various organisms.
The features of the BOARDS database can be summarized as follows:
BOARDS encompasses a comprehensive collection of AMR gene information and their corresponding predicted protein structures.
BOARDS is a web-based database server that allows users easy access to AMR genes and their predicted protein structures.
The predicted protein structures provided by BOARDS have been cross-validated through a reliable prediction pipeline.
The BOARDS database is open source, offering AMR gene data in an easily searchable and modifiable open source text format (FASTA format).
Citation information
Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure. Ko S#, Kim J#, Lim J, Lee SM, Park JY, Woo J, Scott-Nevros ZK, Kim JR, Yoon HJ, Kim D. mSystems. 2023 Dec 12.
DEOCSU ( DEep-learning Optimized ChIP-exo peak calling SUite )
Chromatin immunoprecipitation (ChIP) has been widely used to investigate DNA-binding proteins (e.g. transcription factors (TF) or transcriptional machinery) and their binding location at the genome-scale level. Although ChIP-exo increases the signal-to-noise ratio and allows researchers to identify high-resolution binding sites, a peak calling step for selecting bona fide peaks is time-consuming, and labor-intensive which is a major rate-limiting step of ChIP-exo data analysis.
Our newly developed DEOCSU has following characteristics
DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones.
Performance validation of the trained deep-learning model indicated its high accuracy, high precision, and high recall of over 95%.
DEOCSU can be executed on a cloud computing platform or the local environment.
With visualization software(https://github.com/SBML-Kimlab/MetaScope), adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users’ specific needs.
Citation information
Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling. Bang I#, Lee SM#, Park S#, Park JY, Nong LK, Gao Y, Palsson BO, Kim D. Brief Bioinform. 2023 Jan 25.
ChEAP ( ChIP-exo Analysis Pipeline )
ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results. The pipeline was implemented on the interactive web-based Python development environment – Jupyter Notebook, which is compatible with the Google Colab cloud platform to facilitate the sharing of codes and collaboration among researchers. Additionally, users could exploit the free GPU and CPU resources allocated by Colab to carry out computing tasks regardless of the performance of their local machines.
Citation information
ChEAP: ChIP-exo analysis pipeline and the investigation of Escherichia coli RpoN protein-DNA interactions. Bang I#, Nong LK#, Park JY, Le HT, Lee SM, Kim D. Comput Struct Biotechnol J. 2022 Dec 02.
MetaScope
MetaScope is a genome browser with integrative functions, highly flexible and interactive user interface, by which molecular biologists with minimal computational skills can visualizae their genome-scale datasets along with canonicalgenomic annotations, and analyze, curate and integrate with data operation functions of MetaScope. The datasets MetaScope can handle include tiling array data (ChIP-chip and expression profiling), calcualted peak data, transcription start site data, and genomic annotation in GFF format.
Use MetaScope in order to
Visualize datasets including ChIP-chip data, expression profiling data and transcription start site data
Analyze, validate, curate and integrate datasets by cross-referencing multiple -omic data
Build and share genome annotations
Analyze and compare multiple -omics data from two or more species
Citation information
Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling. Bang I#, Lee SM#, Park S#, Park JY, Nong LK, Gao Y, Palsson BO, Kim D. Brief Bioinform. 2023 Jan 25.