Your browser doesn't support javascript.
loading
Transcriptome assembly from long-read RNA-seq alignments with StringTie2.
Kovaka, Sam; Zimin, Aleksey V; Pertea, Geo M; Razaghi, Roham; Salzberg, Steven L; Pertea, Mihaela.
Afiliação
  • Kovaka S; Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Zimin AV; Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Pertea GM; Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Razaghi R; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Salzberg SL; Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Pertea M; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Genome Biol ; 20(1): 278, 2019 12 16.
Article em En | MEDLINE | ID: mdl-31842956
ABSTRACT
RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new methods to handle the high error rate of long reads and offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of short-read assemblies. StringTie2 is more accurate and faster and uses less memory than all comparable short-read and long-read analysis tools.
Assuntos
Palavras-chave

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Técnicas Genéticas / Genômica / Transcriptoma Tipo de estudo: Evaluation_studies Limite: Animals / Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Técnicas Genéticas / Genômica / Transcriptoma Tipo de estudo: Evaluation_studies Limite: Animals / Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos