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@SaberHQ SaberHQ released this 17 Aug 04:42
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v3.2.0
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NanoSim Version 3.2.0 Release Notes

Overview

This release includes changes from April 2022 to August 2024. It adds a new feature to NanoSim and provides a new pre-trained model (R10 Chemistry and Dorado Basecaller) for users to choose from. It also contains several bugfixes. Details of all updates are outlined below:

New Feature

  • This release incorporates the calculation and analyses related to homopolymer length and base quality into the characterization stage, removing the dependency on hard-coded metrics as discussed in #212 (pull request #217). Thanks, @theottlo for this.

Enhancements

  • Uploaded a new pre-trained model: NA24385 - hg002 AshkenazimTrio - Son, sequenced by Kitv14 (R10 chemistry) and basecalled by dorado. Thanks @lcoombe for this. The model trained on 1M subset reads is uploaded to NanoSim Github and the model with the whole dataset is available through Zenodo. (79e5f92) - by @SaberHQ
  • Relaxed package requirements by @kmnip in #177

Bug Fixes

  • Fixed a bug related to read headers in metagenome simulation by @LokiLuciferase in #167
  • Fixed a bug related to potential infinite loops in metagenome mode by @kmnip in #189 (Addresses #184 and #185)
  • Fixed an infinite loop bug for very short references by @kmnip in #199 (fixes the issue reported in #130 (comment))

Documentation Updates

We made changes to NanoSim’s documentation, so that it is more clear.

  • Consolidated citation format, clarified installation details, and updated old content: #192
  • Added more clarification regarding the pre-trained models and included information about the newly trained model on NA24385 - hg002 with R10 chemistry and basecalled with dorado. (ef56977) - by @SaberHQ
  • Added information on how to avoid package incompatibility issues and also problems with conda installations (f7b6cea) - by @SaberHQ
  • Fixed typo by @xinehc in #169
  • Added information about cs tag in BAM files (adfd7c6)

Known Issues

We acknowledge that there is still package dependency issue when using the old pre-trained models with the newest version of some python packages such as sci-kit-learn. We highly recommend everyone to take a look at dependencies section of readme file for more information. That being said, if you want to train your own models (which is super easy and straight forward), NanoSim should work just fine. However, if you prefer to use older pre-trained models, then you should pay attention to the package versions installed on your environment and use the same versions indicated here.

Full Changelog: v3.1.0...v3.2.0

Contributors

kmnip, lcoombe, and 4 other contributors
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