Ecosyste.ms: Papers
An open API service providing mapping between scientific papers and software projects that are mentioned in them.
All mentions data is based on the CZI Software Mentions dataset.
Projects: bioconductor: GenomeInfoDb
https://packages.ecosyste.ms/registries/bioconductor.org/packages/GenomeInfoDb
Utilities for manipulating chromosome names, including modifying them to follow a particular naming style
2 versions
Latest release: about 1 year ago
323 dependent packages
6,146,238 downloads total
Papers Mentioning GenomeInfoDb 7
10.1186/1471-2156-15-81
Copy number polymorphisms near SLC2A9 are associated with serum uric acid concentrationsCited by: 16
Author(s): Robert B. Scharpf, Lynn Mireles, Qiong Yang, Anna Köttgen, Ingo Ruczinski, Katalin Suszták, Eitan Halper-Stromberg, Adrienne Tin, Stephen Cristiano, Aravinda Chakravarti, Eric Boerwinkle, Caroline S. Fox, Josef Coresh, W. H. Linda Kao
Software Mentions: 56
Published: almost 11 years ago
10.12688/f1000research.17824.2
Enhancing gene set enrichment using networksCited by: 2
Author(s): Michael Prummer
Software Mentions: 51
Published: over 5 years ago
10.12688/f1000research.22259.1
Fluent genomics with plyranges and tximetaCited by: 2
Author(s): Stuart Lee, Michael C. Lawrence, Michael I. Love
Software Mentions: 35
Published: almost 5 years ago
10.3389/fimmu.2021.701085
A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 PatientsCited by: 29
Author(s): Florian Bieberich, Rodrigo Vazquez-Lombardi, Alexander Yermanos, Roy Ehling, Derek M Mason, Bastian Wagner, Edo Kapetanovic, Raphaël Brisset Di Roberto, Cédric R. Weber, Miodrag Savic, Fabian Rudolf, Sai T. Reddy
Software Mentions: 23
Published: over 3 years ago
10.1186/s12859-015-0683-0
diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C dataCited by: 172
Author(s): Aaron T. L. Lun, Gordon K. Smyth
Software Mentions: 14
Published: over 9 years ago
10.3390/cancers13174348
Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression DataCited by: 4
Author(s): Péter Gargya, Bálint László Bálint
Software Mentions: 14
Published: about 3 years ago
10.7554/eLife.37072
Changes in the genetic requirements for microbial interactions with increasing community complexityCited by: 60
Author(s): Manon Morin, Emily C Pierce, Rachel J Dutton
Software Mentions: 5
Published: about 6 years ago