Use MUSCLE v5 MSA Generation

Official Neurosnap webserver for accessing MUSCLE v5 MSA Generation online.

Overview

MUSCLE v5 is a novel multiple sequence alignment algorithm that generates an ensemble of high-accuracy MSAs with diverse biases. It achieves this by perturbing a hidden Markov model (HMM) and permuting the guide tree used in the progressive alignment.

Neurosnap Overview

The MUSCLE v5 MSA Generation online webserver allows anybody with a Neurosnap account to run and access MUSCLE v5 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 service and privacy policy.

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Features

  • Generates high-quality multiple sequence alignments for biological sequences, with demonstrated accuracy improvements over popular tools like Clustal-Omega and MAFFT.
  • Constructs an ensemble of MSA replicates by introducing controlled variations in the HMM parameters and guide tree, enabling assessment of alignment uncertainty and bias.
  • Designed to scale to alignments of tens of thousands of sequences using a divide-and-conquer strategy.

Statistics

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API Request

Access MUSCLE v5 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.

Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

The amino acid sequences corresponding to the proteins you want to generate an MSA for. This will not generate a seperate MSA for each provided protein but will instead generate a single MSA using all provided sequences.

The algorithm used to generate the MSA. The Muscle v5 utilizes two main algorithms for multiple sequence alignment - Parallel Perturbed Probcons (PPP) and the Super5 algorithm. PPP is a parallelized and perturbed implementation of the Probcons algorithm, enabling alignment of datasets up to a few hundred sequences. For larger datasets, Super5 applies a divide-and-conquer strategy, first identifying and removing redundant sequences, then dividing the remaining representatives into smaller clusters that can be tractably aligned by PPP. The consensus sequences from these cluster alignments are then combined using progressive alignment guided by a UPGMA tree. This hybrid approach in super5 allows Muscle v5 to effectively scale to large datasets, while maintaining high alignment accuracy compared to other state-of-the-art methods.

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