Jellyfish

Jellyfish

Jellyfish is an open-source program for analyzing DNA sequencing reads. It can calculate statistics like k-mer counts and coverage depth for large datasets quickly and with low memory usage. Jellyfish is useful for preparing reads for genome or transcriptome assembly.
Science & Education Bioinformatics
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dna-sequencing kmer-counting coverage-depth genome-assembly transcriptome-assembly
Jellyfish alternatives ➤

Jellyfish: Open-Source DNA Sequencing Analysis Software

Analyze DNA sequencing reads with Jellyfish, a high-performance open-source program offering rapid calculation of k-mer counts and coverage depth for large datasets, ideal for genome or transcriptome assembly preparation.

What is Jellyfish?

Jellyfish is an open-source bioinformatics software tool developed specifically for counting k-mers (subsequences of length k) from large datasets of DNA sequencing reads. It is optimized to handle very large read sets of gigabase scale quickly and with relatively low memory usage.

Some key features of Jellyfish include:

  • Fast k-mer counting for large next-generation sequencing datasets
  • Multi-threading support for parallel processing on multiple CPU cores
  • Disk-based storage for limiting memory usage with very large data
  • Output of k-mer counts and coverage data
  • Support for most major Linux systems and Mac OSX

Jellyfish can process read files in FASTA or FASTQ format. It outputs k-mer counts and coverage information in a binary format that can then be parsed and analyzed as needed. The speed and memory efficiency make it well-suited for preparing sequencing data for downstream analysis like genome assembly, transcriptomics, metagenomics, and more. It is a flexible tool used in many bioinformatics pipelines.

Jellyfish Features

Features

  1. Fast k-mer counting
  2. Low memory usage
  3. Multi-threaded
  4. Dumps k-mer counts to disk
  5. Can load k-mer counts from disk
  6. Supports multiple hash array sizes

Pricing

  • Open Source

Pros

Very fast even for large datasets

Scales well with large amounts of threads/CPU cores

Low RAM usage allows analysis of big data on normal machines

Cons

Command line only, no GUI

Not as full-featured as some commercial competitors

Can be complex to set up and use for beginners

Official Links

Official Website
https://jellyfishlabs.io

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