CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure.

Autor: Varabyou A; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. ales.varabyou@jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ales.varabyou@jhu.edu.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA. ales.varabyou@jhu.edu., Sommer MJ; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Erdogdu B; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Shinder I; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Cross Disciplinary Graduate Program in Biomedical Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA., Minkin I; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Chao KH; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Park S; School of Biological Sciences, Seoul National University, Seoul, South Korea.; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea., Heinz J; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Pockrandt C; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Shumate A; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Rincon N; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Puiu D; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA., Steinegger M; School of Biological Sciences, Seoul National University, Seoul, South Korea.; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.; Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea., Salzberg SL; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. salzberg@jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. salzberg@jhu.edu.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA. salzberg@jhu.edu.; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. salzberg@jhu.edu.; Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA. salzberg@jhu.edu., Pertea M; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. mpertea@jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. mpertea@jhu.edu.; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA. mpertea@jhu.edu.; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. mpertea@jhu.edu.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2023 Oct 30; Vol. 24 (1), pp. 249. Date of Electronic Publication: 2023 Oct 30.
DOI: 10.1186/s13059-023-03088-4
Abstrakt: CHESS 3 represents an improved human gene catalog based on nearly 10,000 RNA-seq experiments across 54 body sites. It significantly improves current genome annotation by integrating the latest reference data and algorithms, machine learning techniques for noise filtering, and new protein structure prediction methods. CHESS 3 contains 41,356 genes, including 19,839 protein-coding genes and 158,377 transcripts, with 14,863 protein-coding transcripts not in other catalogs. It includes all MANE transcripts and at least one transcript for most RefSeq and GENCODE genes. On the CHM13 human genome, the CHESS 3 catalog contains an additional 129 protein-coding genes. CHESS 3 is available at http://ccb.jhu.edu/chess .
(© 2023. The Author(s).)
Databáze: MEDLINE
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