Publishing date: 2020-01-24
Published on: PLOS ONE
summary: None
authors: Chiwan Park, Ha-Myung Park, U. Kang
link to paper: 10.1371/journal.pone.0227032
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Publishing date: 2020-01-15
Published on: PLOS ONE
summary: None
authors: Hitesh Sapkota, Pradeep K. Murukannaiah, Yi Wang
link to paper: 10.1371/journal.pone.0226281
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Publishing date: 2020-01-15
Published on: PLOS ONE
summary: None
authors: Ivan Braga Campos, Todd J. Landers, Kate D. Lee, William George Lee, Megan R. Friesen, Anne C. Gaskett, Louis Ranjard
Publishing date: 2020-01-15
Published on: PLOS Genetics
summary: None
authors: Madison Caballero, Daniel N. Seidman, Ying Qiao, Jens Sannerud, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Shai Carmi, Amy L.
Publishing date: 2020-01-15
Published on: BMC Bioinformatics
summary: This software article presents “https://github.com/MathOnco/EvoFreq">EvoFreq<, a comprehensive tool to visualise the evolutionary and population frequency dynamics of clones at a single point in time or as population frequencies over time using a variety of informative methods.
Publishing date: 2020-01-15
Published on: PLOS ONE
summary: Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest such as disease state. A key challenge in this is the composition and dimensionality of microbiome data.
Publishing date: 2020-01-15
Published on: PLOS Computational Biology
summary: None
authors: Bowen Li, Denis Taniguchi, Jayathilake Pahala Gedara, Valentina Gogulancea, Rebeca Gonzalez-Cabaleiro, Jinju Chen, Andrew Stephen McGough, Irina Dana Ofiteru, Thomas P.
Publishing date: 2020-01-15
Published on: PLOS ONE
summary: Channel Editor Summary: PyLandStats is a fantastic example of an open source scientific package, with an open licence (GPL), clear documentation for both users and developers, and a source code repository set up to easily enable contributions and bug reports.
Publishing date: 2019-11-14
Published on: Giga Science
summary: The authors have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages.