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Dongwan Don Kang

Publications and source records attributed to Dongwan Don Kang.

2 recordsLinked to original sources

Widespread polycistronic transcripts in mushroom-forming fungi revealed by single-molecule long-read mRNA sequencing

Genes in prokaryotic genomes are often arranged into clusters and co-transcribed into polycistronic RNAs. Isolated examples of polycistronic RNAs were also reported in some eukaryotes but their presence was generally considered rare. Here we developed a long-read sequencing strategy to identify polycistronic transcripts in several mushroom forming fungal species including Plicaturopsis crispa, Phanerochaete chrysosporium, Trametes versicolor and Gloeophyllum trabeum1. We found genome-wide prevalence of polycistronic transcription in these Agaricomycetes, and it involves up to 8% of the transcribed genes. Unlike polycistronic mRNAs in prokaryotes, these co-transcribed genes are also independently transcribed, and upstream transcription may interfere downstream transcription. Further comparative genomic analysis indicates that polycistronic transcription is likely a feature unique to these fungi. In addition, we also systematically demonstrated that short-read assembly is insufficient for mRNA isoform discovery, especially for isoform-rich loci. In summary, our study revealed, for the first time, the genome prevalence of polycistronic transcription in a subset of fungi. Futhermore, our long-read sequencing approach combined with bioinformatics pipeline is a generic powerful tool for precise characterization of complex transcriptomes.

Genomics

A robust statistical framework for reconstructing genomes from metagenomic data

We present software that reconstructs genomes from shotgun metagenomic sequences using a reference-independent approach. This method permits the identification of OTUs in large complex communities where many species are unknown. Binning reduces the complexity of a metagenomic dataset enabling many downstream analyses previously unavailable. In this study we developed MetaBAT, a robust statistical framework that integrates probabilistic distances of genome abundance with sequence composition for automatic binning. Applying MetaBAT to a human gut microbiome dataset identified 173 highly specific genomes bins including many representing previously unidentified species.

Bioinformatics