Curated, step-by-step analysis workflows and pipelines built for the Covary framework. Each protocol is validated against real datasets and designed for reproducibility.
This protocol describes the use of Covary for rapid, alignment-free analysis of short tandem repeat (STR) sequence variation using Y-chromosomal STR loci from the NCBI STRSeq BioProject (PRJNA380347). The workflow demonstrates the ability of Covary to (i) compare STR sequence similarity directly from raw sequence data, (ii) perform multi-locus STR analysis in a single run without locus-wise concatenation, and (iii) resolve locus-specific and inter-locus relationships using machine learning-derived vector representations. Traditional forensic STR analysis relies on length-based allele designation and locus-by-locus interpretation. In contrast, this protocol illustrates a sequence-level, machine learning approach that captures internal repeat structure, flanking variation, and compositional features across multiple STR loci simultaneously. Covary enables scalable STR sequence comparison without manual alignment, custom scripting, or local software installation. This protocol is optimized for execution in Google Colab and may be adapted for applications in forensic genomics, population genetics, and STR database exploration.
Read the protocol →This protocol describes the use of Covary for fast, large-scale, multi-species phylogenomic analysis using complete genome sequences. The workflow is optimized for comparative phylogenomic inference across diverse taxa and is demonstrated using datasets associated with genomes of outbreak-causing viruses (SARS-CoV-2, dengue virus, measles virus, and alphainfluenza virus). Covary enables alignment-free phylogenomic analysis by encoding genomic sequences into translation-aware vector representations and applying machine learning–based similarity inference. The protocol supports thousands-scale datasets, requires no coding experience, and is designed to run in a Google Colab environment without local software installation or maintenance. This protocol focuses on phylogenomic-scale analysis across multiple viral species. Covary is accessible at https://covary.chordexbio.com/ or on GitHub at https://github.com/mahvin92/Covary.
Read the protocol →This protocol describes the operational workflow of Covary, a machine learning-based framework for large-scale phylogenetic analysis and species identification. This protocol enables users to perform phylogenetic inference directly from sequence data without requiring coding experience or local software installation. The workflow is applicable to v2.1 of Covary and accepts multi-FASTA sequence files as input (or training data) and provides configurable parameters for encoding, neural network inference, and downstream analysis. Covary is designed to scale to thousands of sequences and produces interoperable outputs compatible with Matplotlib and other python-based libraries, R, and other downstream visualization and statistical tools. The protocol is optimized for execution in a Google Colab environment, eliminating software maintenance and platform dependency. This protocol focuses on the methodical operations of Covary for tree reconstruction and species identification. Covary is available at https://github.com/mahvin92/Covary.
Read the protocol →End-to-end workflow for phylogenomic comparison using multi-FASTA input. Covers preprocessing, embedding, dimensionality reduction, and dendrogram construction.
Rapid phylogenomic profiling workflow optimized for thousands of outbreak-causing viral genomes. Based on published benchmarks from Covary preprint (2025-12).
Workflow for mapping subclonal architecture using Mutagen-PX-generated FASTA outputs and downstream Covary phylogenetic analysis for cancer research.
Large-scale taxonomic profiling of mixed microbial communities. Designed for environmental and clinical metagenomic datasets without requiring MSA.
Identification workflow for forensic DNA samples — matching unknown sequences to reference clades using Covary's alignment-free identification engine.
Pre-analysis quality control using the Seed Aligner toolkit — removing invalid characters, whitespace, and standardizing sequence start positions for Covary input.
We can design and validate a Covary workflow specific to your organism, dataset, or research objective.
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