mmseqs2 MSA Generation
Rapidly generate diverse and quality MSAs with support for various pairing modes.
Overview
Generate MSAs using the mmseqs2, a robust algorithm designed for efficient multiple sequence alignment (MSA) generation, optimized to process large datasets with high speed and accuracy. Utilizing state-of-the-art alignment algorithms, mmseqs2 generates deep MSAs to support downstream analysis in protein structure prediction, evolutionary studies, and function annotation.
Run mmseqs2 MSA Generation on Neurosnap
The mmseqs2 MSA Generation online webserver allows anybody with a Neurosnap account to run and access mmseqs2 MSA Generation, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Generates high-quality multiple sequence alignments for protein sequences.
- Supports various input sequences for pairing related modes.
- Enables deep MSAs, aiding in accurate protein structure prediction and functional analysis.
- Ideal for applications in evolutionary studies, protein annotation, and bioinformatics research.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access mmseqs2 MSA Generation using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using mmseqs2 MSA Generation in publications or research outputs.
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Steinegger, M., Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988 |
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Mirdita, M., Schütze, K., Moriwaki, Y. et al. ColabFold: making protein folding accessible to all. Nat Methods 19, 679–682 (2022). https://doi.org/10.1038/s41592-022-01488-1 |
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Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger S J, and Söding J (2019) HH-suite3 for fast remote homology detection and deep protein annotation, BMC Bioinformatics, 473. doi: 10.1186/s12859-019-3019-7 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |