EPI2ME Labs 23.01.01 Release

By Stephen Rudd
Published in Software Releases
January 25, 2023
1 min read
EPI2ME Labs 23.01.01 Release

Dear Nanopore Community,

  • The EPI2ME Labs application has been updated to revision v4.1.1. This update includes a number of fundamental back-end changes that will facilitate the implementation of new functionality over the coming months. The update also fixes bugs that may have led to application crashes when e.g. docker is not installed. Improved crash reporting should now also simplify the resolution of issues. The settings dialogs have been updated and a new option has been introduced to adjust local CPU resources limits when launching workflows. The updated software can be downloaded from the EPI2ME download page.
  • wf-alignment is now available with revision v0.3.0. The workflow code has been simplified. It is also more performant, uses bamstats (part of the fastcat software), and produces a  less cluttered report. Please see the CHANGELOG for further information on these updates.
  • wf-cnv, our Nextflow workflow for copy number variation analysis, has been updated to revision v0.0.8. The workflow now supports mapped BAM files as sequence input. Users are no longer required to specify the genome build information - this is now detected from either the BAM input or reference FASTA information provided. Additional information may be found at the workflow CHANGELOG.
  • modbam2bed has been updated to revision v0.7.0. This update handles both explicit and implicit forms of modified base tags as defined in the updated SAM tag specifications. Users working with basecalling models that work only in CpG contexts will notice minor differences in call frequencies. A new column has been added to the extended BED output which now enumerates base counts where no modified base call was attempted (previously such sites were implied to be the canonical base). Please see the CHANGELOG for further information.
  • Release of a Drosophila melaogaster FAST5 dataset to ont-open-data. This dataset, produced by Bernard Kim from Stanford University, is described in a blogpost and is the first community contributed dataset to be hosted at s3://ont-open-data. The basecalled data is available through NCBI accession PRJNA914057. Please read our ont-open-data tutorial for more information on accessing the Amazon S3 bucket and read the blogpost for information on contributing your datasets.

We look forwards to your feedback.




Stephen Rudd

Stephen Rudd

Director, Bioinformatics Product

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