Testing
OverviewA project for miscellaneous testing of Workflow Hub.
Space: General
SEEK ID: https://dev.workflowhub.eu/projects/9
Public web page: Not specified
Organisms: No Organisms specified
WorkflowHub PALs: No PALs for this Team
Team created: 6th Apr 2020
Related items
Teams: MGnify, nf-core, Workflow Hub Administration, Galaxy COVID-19, Testing, Defragmentation training school
Organizations: University of Manchester, Working from Home
Teams: Testing
Organizations: Swiss Institute of Bioinformatics
https://orcid.org/0000-0003-1361-7301
Teams: Testing
Organizations: Ghent University
Teams: Testing, Finn's test team, PNDB, LifeMonitorDev, iPC: individualizedPaediatricCure, test, Test Team, Australian BioCommons Dev, opscientia, V-pipe, UX study leave, DALiuGE workflows, Team1, UX study, My Fancy Team, Team UX Study11, My team, ux study team, UX study koehorst, Team Koehorst, UX Study (satra), Steve's UX Study Test Team, TestSaandra, KJ-workflow-test, NewTeam, team, UXStudy, A Team, UX study K, UXStudy99, Study team, Another new team, UX study2, Team 20220225, my new team, UX Study AU, Testing_proteomics, A test space (KKH), Pleiade, QSM4SENIOR, IGG-Bioinfo, Defragmentation training school, FWCC-D@HZDR, Submission Tutorial
Web page: Not specified
Abstract
Authors: E. Rohmann, M. Wellenbrock, S. Hoffmann
Date Published: 1st Sep 1979
Publication Type: Journal
Abstract (Expand)
Authors: M. R. Keighley, P. Asquith, J. A. Edwards, J. Alexander-Williams
Date Published: 1st Oct 1975
Publication Type: Journal
Abstract
Authors: Newcomer M, Hubbard S
Date Published: No date defined
Publication Type: Journal
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Creator: Finn Bacall
Submitter: Finn Bacall
This workflow take as input a collection of paired fastq. It will remove bad quality and adapters with cutadapt. Map with Bowtie2 end-to-end. Will remove reads on MT and unconcordant pairs and pairs with mapping quality below 30 and PCR duplicates. Will compute the pile-up on 5' +- 100bp. Will call peaks and count the number of reads falling in the 1kb region centered on the summit. Will plot the number of reads for each fragment length.
K-means clustering is a method of cluster analysis that aims to partition ''n'' points into ''k'' clusters in which each point belongs to the cluster with the nearest mean. It follows an iterative refinement strategy to find the centers of natural clusters in the data.
Hypermatrix size 2x2 blocks, block size 2x2 elements
Hypermatrix size 2x2 blocks, block size 2x2 elements
Hypermatrix size 2x2 blocks, block size 2x2 elements
