<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/37502?offset=530 https://bioinformaticsonline.com/bookmarks/view/43867/genomeqc-a-quality-assessment-tool-for-genome-assemblies-and-gene-structure-annotations Thu, 19 May 2022 04:29:05 -0500 https://bioinformaticsonline.com/bookmarks/view/43867/genomeqc-a-quality-assessment-tool-for-genome-assemblies-and-gene-structure-annotations <![CDATA[GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations]]> The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2

Address of the bookmark: https://github.com/HuffordLab/GenomeQC

]]> Neel https://bioinformaticsonline.com/bookmarks/view/44501/minda-a-tool-for-evaluating-structural-variant-sv-callers Sun, 31 Mar 2024 02:43:50 -0500 https://bioinformaticsonline.com/bookmarks/view/44501/minda-a-tool-for-evaluating-structural-variant-sv-callers <![CDATA[Minda: a tool for evaluating structural variant (SV) callers]]> Minda is a tool for evaluating structural variant (SV) callers that

  • standardizes VCF records for compatibility with both germline and somatic SV callers,
  • benchmarks against a single VCF input file, or
  • benchmarks against an ensemble call set created from multiple VCF input files.

Address of the bookmark: https://github.com/KolmogorovLab/minda

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences Sun, 31 Aug 2025 06:30:16 -0500 https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences <![CDATA[Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences]]> Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

Address of the bookmark: https://github.com/MGXlab/Jaeger

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