Your browser doesn't support javascript.
Phytest: quality control for phylogenetic analyses.
Wirth, Wytamma; Mutch, Simon; Turnbull, Robert; Duchene, Sebastian.
  • Wirth W; Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne 3010, Australia.
  • Mutch S; Melbourne Data Analytics Platform, University of Melbourne, Melbourne 3010, Australia.
  • Turnbull R; Melbourne Data Analytics Platform, University of Melbourne, Melbourne 3010, Australia.
  • Duchene S; Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne 3010, Australia.
Bioinformatics ; 38(22): 5124-5125, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2062861
ABSTRACT
MOTIVATION The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses.

RESULTS:

Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. AVAILABILITY AND IMPLEMENTATION Phytest source code is available on GitHub (https//github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https//phytest-devs.github.io/phytest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Randomized controlled trials Limits: Humans Language: English Journal: Bioinformatics Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bioinformatics

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Randomized controlled trials Limits: Humans Language: English Journal: Bioinformatics Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bioinformatics