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diamond

Introduction

DIAMOND is a sequence aligner for protein and translated DNA searches, designed for high performance analysis of big sequence data. The key features are:

  • Pairwise alignment of proteins and translated DNA at 100x-10,000x speed of BLAST.
  • Protein clustering of up to tens of billions of proteins
  • Frameshift alignments for long read analysis.
  • Low resource requirements and suitable for running on standard desktops or laptops.
  • Various output formats, including BLAST pairwise, tabular and XML, as well as taxonomic classification.

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Documentation

The online documentation is located at the GitHub Wiki.

Support

Diamond is actively supported and developed software. Please use the issue tracker for malfunctions and the GitHub discussions for questions, comments, feature requests, etc.

About

DIAMOND is developed by Benjamin Buchfink. From 2019-2024, it was developed by Benjamin Buchfink at the Drost lab, Max Planck Institute for Biology Tübingen. From 2018-2019, its development was supported by the German Federal Ministry for Economic Affairs and Energy through an EXIST grant. From 2016-2018, it was developed by Benjamin Buchfink as an independent researcher. From 2013-2015, the initial version was developed by Benjamin Buchfink at the Huson lab, University of Tübingen, Germany.

[📧Email] [X] [LinkedIn] [Google Scholar] [Drost lab] [MPI-BIO]

When using the tool in published research, please cite:

  • Buchfink B, Reuter K, Drost HG, "Sensitive protein alignments at tree-of-life scale using DIAMOND", Nature Methods 18, 366–368 (2021). doi:10.1038/s41592-021-01101-x

For sequence clustering:

  • Buchfink B, Ashkenazy H, Reuter K, Kennedy JA, Drost HG, "Sensitive clustering of protein sequences at tree-of-life scale using DIAMOND DeepClust", bioRxiv 2023.01.24.525373; doi: https://doi.org/10.1101/2023.01.24.525373

Original publication to cite DIAMOND until v0.9.25:

  • Buchfink B, Xie C, Huson DH, "Fast and sensitive protein alignment using DIAMOND", Nature Methods 12, 59-60 (2015). doi:10.1038/nmeth.3176