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Platform

Covary releases

Explore the latest versions and updates of Covary, from the new Covary-V3 web platform to earlier Colab-based releases.

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Current release  ·  Web platform

Covary-V3

Covary-V3 introduces the hosted Covary web application for browser-based sequence analysis, managed job submission, dataset-backed result storage, and public sharing of completed experiments.

  • Hostinger web interface connected to a cloud compute backend
  • Durable result storage through the cloud, including raw embedding, distance, and linkage TSV outputs
  • Public shared-result pages with experiment context and metadata
  • Shared experiments directory for browsing reusable Covary runs
  • Authenticated user dashboard with dedicated storage and institutional support
Beta release

Covary v3.0.1

This beta release improves Covary's performance in the following areas:

  • 3-mer representation
  • Mega- to Giga-scale genetic encoding
  • Chromosome-level long-range DNA representation

All improvements are executable in low-tier computational resource (at least 8GB RAM)*.

*tested on Google Colab (free-tier)

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Public release  ·  Stable

Covary v2.1

This release adds user-defined plot customization and introduces filtering of sequence entries containing invalid characters.

Open v2.1 on Colab →
Public release

Covary v2.0

This release introduces codon normalization, resolving inconsistencies in the relationship between input sequences and open reading frames or protein translations within unaligned datasets. This version is depracated – use the most recent, supported version(s).

Open v2.0 on Colab →
Pre-release

Covary v1.3

Optimizes the perplexity parameter for t-SNE, enabling reliable analysis of datasets with limited sample sizes.

Pre-release

Covary v1.2

Reduces sequence ambiguity, improving support for datasets containing unprocessed or ambiguous genetic sequences.

Pre-release

Covary v1.1

Enhances memory efficiency to prevent session crashes caused by RAM overload.

Pre-release

Covary v1.0

Initial release. No longer recommended for experiments with large datasets.