FlyscRNA is an interactive Sophia web-program (deployed from Seurat) to visualize scRNA-seq data from fly lymph gland in three larvae stages.
Cho et al., Nature Comm., in press, 2020.
A comprehensive guide designer for CRISPR systems. The CGD is a predictive regression models that predict efficient gRNAs for CRISPRi, CRISPRa, Cas9, Cas12a in a comprehensive manner.
Menon et al., CSBJ, 18:814-820, 2020.
A regression-based model for CRISPR AsCpf1 Indel score, trained with hundreds of in-vitro datasets that Prof. Hyungbum Kim's lab generated.
Kim et al., Nature Methods, 14:153–159, 2017.
CANDL is a comprehensive liver cancer database built by BIG Lab. It currently integrates microarray, RNA-seq, sRNA-seq data from Korean and TCGA liver cancer samples. Users can easily get information about gene expression level changes and how each sample correlates with another. CANDL also provides a simple one-step analysis of DEG analysis between selected group of samples.
ERIUS provides a two-step filtration process to distinguish between bona fide and false lncRNAs. The first step successfully separates lncRNAs from protein-coding genes with low ribosome signals, showing enhanced sensitivity compared to other methods. To eliminate 3’UTR fragments, the second step takes advantage of the 3’UTR-specific association with regulator of nonsense transcripts 1, leading to refined lncRNA annotation.
Choi et al., BMC Bioinformatics, 19(Suppl 1):41, 2018.
description: We present a high-performing transcriptome assembly pipeline, called CAFE (Co-Assembly of stranded and unstranded RNA-seq data Followed by End-correction), that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites.
You et al., Genome Res. 27(6):1050-1062, 2017.
Comprehensive Annotation System Of mammalian LncRNAs, is a web program that (1) reconstructs transcriptomes from multi-modal transcriptomic data, such as RNA-seq, PolyA-seq, and DeepCAGE, and (2) classifies lncRNAs from protein-coding genes using orthologous classifiers: CPC, RPS, RRS, TE.
In this site, we have improved published lncRNA annotations and catalogue reference set of mammalian lncRNAs, identified from CASOL system.
LINDEL is the Integrated Design system for LincRNA deletion (LINDEL) , which was implemented on an interactive web genome-browser. Our LINDEL system makes possible the design of sgRNAs that i) are based on improved annotations of human and mouse lincRNA genes, ii) function as pairs for the deletion of both whole and segmental regions of these genes, again based on comprehensive functional annotations, and iii) efficiently generate mutations because of both sequence- and secondary structure-based features.
Lee et al., NAR, 47:8, 3875–3887, 2019.
GETUTR is global estimation of the 3’ UTR landscape based on RNA sequencing. The main purpose of GETUTR is to smooth fluctuating RNA-seq signals to estimate a monotonically decreasing landscape of 3’ UTRs. The three smoothing algorithms of GETUTR were incorpoated: Minfit, Maxfit, and PAVA. Using GETUTR, the dynamic changes of 3’ UTR usage can be quantified in any cell type, stage, and species for which RNA-seq data are available, thereby leading to a better understanding of 3’ UTR biology.
Kim et al., Methods, 83:111-117. 2015.
miTarget (under construction)
miTarget is a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features.
Kim et al., 7:411, BMC Bionformatics, 2006.
ProMiR2 (under construction)
ProMiR is a web-based service for the prediction of potential microRNAs(miRNAs) in a querysequence of 60–150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species.
Nam et al., Webserver issue Vol34, NAR, 2006.
Nam et al., 33:11 3570–3581, NAR, 2005.