How to run SKA (split k-mer analysis) for microbial samples

Introduction

Split k-mer analysis is a powerful bioinformatics technique that compares genetic sequences without requiring a reference genome or complete assemblies. It's effective for analyzing closely related samples, allowing identification of variations like single nucleotide polymorphisms (SNPs) and structural differences. This method is especially valuable for phylogenetic analysis, strain typing, and understanding evolutionary relationships between organisms. SKA is commonly used in microbial genomics, pathogen surveillance, and population genetics.

How to perform split kmer analysis

Option 1: using SKA on the command line

Split kmer analysis can be performed using the SKA (Split Kmer Analysis) tool by Simon Harris, a command-line utility designed for comparing sequences and identifying genetic variations. Below is a step-by-step guide to using SKA:

Prerequisites:

Basic knowledge of the command line (Linux/MacOS).
Python, conda, GNU make, and a version of g++ that supports C++11.
Sequencing reads or assembled genomes in FASTA or FASTQ format.

Installation:

1. Install SKA using conda:

conda install -c bioconda ska

2. Alternatively, download the latest version from the SKA GitHub repository and follow the instructions to make it executable.

Running SKA:

1. Prepare your input files (e.g., sample1.fasta, sample2.fasta).

2. Run SKA to generate split kmers:

ska fasta -o output_directory sample1.fasta sample2.fasta

3. You can now analyze the output files with SKA commands to review them, compute SNP distances, and construct alignments.

Examples

Compare split kmers:‍

ska compare -q sample1.skf sample2.skf

‍Calculate SNP distance and cluster samples based on a 20 SNP cutoff and 95% minimum identity:

ska merge -o merged sample1.skf sample2.skf
ska distance -s 20 -i 0.95 sample1.skf sample2.skf

For more usage and documentation, read the SKA subcommands wiki on GitHub.

Option 2: automate with Solu

For researchers seeking a faster, more user-friendly solution, the Solu Platform simplifies split kmer analysis with just a few clicks. Solu eliminates the need for manual setup and ensures accurate, reproducible results.

Upload data: Upload your sequencing reads or assembled genomes in FASTA or FASTQ format.
Automated analysis: Solu automatically runs SKA, constructs a phylogeny, and calculates SNP distances.
Export results: Download results in various formats, including visualizations and raw data for further analysis.

By automating the process, Solu saves time, reduces errors, and makes advanced bioinformatics accessible to all researchers.

Conclusion

Split kmer analysis is a vital tool in bioinformatics for understanding genetic variations and evolutionary relationships. While performing this analysis manually using tools like SKA is possible, it can be technically challenging and resource-intensive. Solu Platform offers a seamless, automated solution, enabling researchers to focus on their scientific goals rather than computational complexities. Explore the platform today and see how it can streamline your research workflow.

FAQs

Q: Can I use this method for large datasets?

A: Yes, but for extremely large datasets, consider using tools like SKA2 or cloud-based solutions for better performance. Solu Platform is optimized to handle large datasets efficiently.

Q: What if my files are in a different format?

A: Both SKA and Solu platform work for genomic data in FASTA or FASTQ format. Solu Platform also automatically standardizes the files to the correct format to reduce errors.

Q: Is Solu Platform suitable for beginners?

A: Absolutely! Solu has an intuitive interface and requires no installation or configuration, making it ideal for researchers at all skill levels.

Q: Can I customize the analysis parameters on Solu?

A: No, Solu is is designed as a zero-configuration tool which ensures result reproduction and validated against real outbreak scenarios.

Q: How secure is my data on Solu Platform?

A: Solu prioritizes data security and complies with industry standards (HIPAA, ISO 27001) to ensure your data is protected at all times.

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