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shRNAI

AI-aided Design of shRNAmir and shRNA 

(http://big2.hanyang.ac.kr/shRNAI)

Park et al., bioRxiv, 2024.

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Fly and Human α-arrestin interactomes

 

AP/MS-based interactome maps of twelve fly and six human α-arrestins and their related Github resource.

(http://big.hanyang.ac.kr/alphaArrestin_PPIN)

https://www.ebi.ac.uk/intact/search?query=pubid:38270169

Lee et al., eLife, 2024.

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Clustering with UVG

 

UVG: Clustering malignant cell states using universally variable genes(UVGs), which unbiased gene signatures in population-scale single-cells

(https://github.com/sangho1130/UVG)

Yoon et al., Briefings in Bioinformatics, 2023.

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CINCIA

 

CINCIA is a meta doublet caller to infer heterotypic cell-cell interactions from single-cell RNA-seq data, which is implemented by R. You can download all source codes from

https://github.com/jwnam/CINCIA

Kim et al., In prep.

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FlyscRNA

 

FlyscRNA is an interactive Sophia web-program (deployed from Seurat) to visualize scRNA-seq data from fly lymph gland in three larvae stages.

http://big.hanyang.ac.kr/flyscrna

Cho et al., Nature Comm., 11:4483, 2020

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DGD

 

DGD is a new DL method for the optimal design of single guide RNAs (sgRNAs) by taking account of the secondary structure of sgRNAs. DGD is outperforming the current state-of-art AI methods.

https://github.com/GuideDesigner/DGD-Cas9

Vipin et al, in prep.

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ETCHING

ETCHING is the fastest somatic SV and fusion gene caller that utilizes pan-genome k-mer set

http://big.hanyang.ac.kr/ETCHING

Sohn, Choi, and Yi et al., Nature Biomed. Eng. 7, 853–866, 2023.

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CGD

 

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.

http://big.hanyang.ac.kr:2195/CGD

Menon et al., CSBJ, 18:814-820, 2020.

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CINDEL

 

A regression-based model for CRISPR AsCpf1 Indel score, trained with hundreds of in-vitro datasets that Prof. Hyungbum Kim's lab generated.

http://big.hanyang.ac.kr/cindel/

Kim et al., Nature Methods, 14:153–159, 2017.

CANDL

 

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.

(http://big.hanyang.ac.kr/CANDL/)

TERIUS

 

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.

(https://bigterius.wordpress.com/)

Choi et al., BMC Bioinformatics, 19(Suppl 1):41, 2018.

CAFE

 

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.

CASOL

 

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

 

LINDEL is the Integrated Design system for LincRNA deletion (LINDEL) , which was implemented on an interactive web genome-browser.  (http://big.hanyang.ac.kr/LINDEL)

 

Lee et al., NAR, 47:8, 3875–3887, 2019.

GETUTR

 

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.

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