Hye-sik Jang

Summary SARS-CoV-2 is a betacoronavirus that is responsible for the COVID-19 pandemic. The genome of SARS-CoV-2 was reported recently, but its transcriptomic architecture is unknown. Utilizing two complementary sequencing techniques, we here present a high-resolution map of the SARS-CoV-2 transcriptome and epitranscriptome. DNA nanoball sequencing shows that the transcriptome is highly complex owing to numerous recombination events, both canonical and noncanonical. In addition to the genomic RNA

Abstract Small, compact genomes confer a selective advantage to viruses, yet human cytomegalovirus (HCMV) expresses the long non-coding RNAs (lncRNAs); RNA1.2, RNA2.7, RNA4.9, and RNA5.0. Little is known about the function of these lncRNAs in the virus life cycle. Here, we dissected the functional and molecular landscape of HCMV lncRNAs. We found that HCMV lncRNAs occupy ~ 30% and 50–60% of total and poly(A)+viral transcriptome, respectively, throughout virus life cycle. RNA1.2, RNA2.7, and RNA4

Tandem repeat expansion disorders can be difficult to diagnose when expansions exceed 200 repeats, as standard methods (for example, Southern blot and modified PCR) often fail. We present a Cas9-targeted nanopore sequencing workflow and an automated analysis pipeline, RepeatLab, for accurate repeat-length estimation, structure assessment, and high-resolution methylation profiling. Validated on 13 myotonic dystrophy type 1 samples, 4 healthy controls, and 4 cell lines, this approach demonstrates

Translational regulation in tissue environments during in vivo viral pathogenesis has rarely been studied due to the lack of translatomes from virus-infected tissues, although a series of translatome studies using in vitro cultured cells with viral infection have been reported. In this study, we exploited tissue-optimized ribosome profiling (Ribo-seq) and severe-COVID-19 model mice to establish the first temporal translation profiles of virus and host genes in the lungs during SARS-CoV-2 pathoge

Please visit the GitHub page to see more updated information. Changes in tailseeker 3.1.7 Fix the docker wrapper script to take up the environment variable TAILSEEKER_REFDBDIR correctly. Remove U3 and 7SL RNAs from contaminant database pipeline. Fix the compatibility issue with recent versions of snakemake. Add support for MiSeq v3 chemistry. Fix a crash issue in tailseq-dedup-perfect when it fails if more than 1024 alignments with less optimal TAIL-seq signals are followed after an alignment wi

Correction to: Experimental & Molecular Medicine https://doi.org/10.1038/s12276-023-01110-0 , published online 01 November 2023

This HDF5 file contains the processed data of primary poly(A) tail length analyses for the TAIL-seq runs used for Chang and Yeo et al. (2018; doi:10.1016/j.molcel.2018.03.004). The read count tables are stored under the two-level group structure of the run identifier as the first level and the sample identifier as the second level. A dataset at a leaf node is an unsigned integer array of the read count numbers by the length of poly(A) in rows and the length of U tails following after poly(A) in

mRNA vaccine efficacy depends on sequence optimization, but designing optimal sequences is challenging due to complex cellular RNA regulatory mechanisms. Here we present VaxLab, an open-source web platform that provides the complete design-to-synthesis workflow for mRNA vaccines in a unified interface. VaxLab incorporates four codon optimization algorithms based on distinct approaches: codon usage matching, secondary structure design and deep generative models. 5' and 3' untranslated regions can

Tailseeker 3.1 is a software suite for profiling RNA poly(A) tails with a high-throughput DNA sequencer. This pre-built resource package contains the genome indices, gene annotations, and gene association databases processed to be used with Tailseeker. The data were built with ENSEMBL JGIxl91 <em>(X. laevis)</em> as of Dec 16, 2016.

This toy dataset contains a subset of direct RNA sequencing data from Jung et al. (2025). It has been anonymized and reorganized specifically for the AI co-scientist competition. The full original dataset is available from K-BDS (https://kbds.re.kr/KAP241592).