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Analysing high-throughput sequencing data in Python with HTSeq 2.0.
Putri, Givanna H; Anders, Simon; Pyl, Paul Theodor; Pimanda, John E; Zanini, Fabio.
Afiliação
  • Putri GH; School of Clinical Medicine, University of New South Wales, Sydney, NSW 2033, Australia.
  • Anders S; Adult Cancer Program, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW 2033, Australia.
  • Pyl PT; Bioquant Center, University of Heidelberg, 69120 Heidelberg, Germany.
  • Pimanda JE; Division of Surgery, Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
  • Zanini F; School of Clinical Medicine, University of New South Wales, Sydney, NSW 2033, Australia.
Bioinformatics ; 38(10): 2943-2945, 2022 05 13.
Article em En | MEDLINE | ID: mdl-35561197
SUMMARY: HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support. AVAILABILITY AND IMPLEMENTATION: HTSeq 2.0 is released as an open-source software under the GNU General Public License and is available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala Idioma: En Ano de publicação: 2022 Tipo de documento: Article