20251114 update block metadata #76
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Summary of ChangesHello @aiturbidemil, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request focuses on enhancing the clarity and accessibility of the 'Clonotype clustering' block's metadata and documentation. It updates external links, refines the block's brief description, and significantly expands its detailed overview to better explain its functionality, underlying technology, and potential downstream uses within the platform. These changes aim to provide users with more accurate and comprehensive information about the block. Highlights
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Code Review
This pull request updates the block's metadata, including the documentation URL and descriptions in 'package.json' and 'docs/description.md'. The changes significantly improve the clarity and detail of the block's purpose and usage. I've added a couple of suggestions to further enhance the readability of the markdown documentation by formatting block names and cleaning up a citation link.
| Groups similar clonotypes into clusters based on their sequences. It utilizes the MMseqs2 (`easy-cluster` command) software for fast and sensitive sequence searching and clustering, grouping sequences that meet a specified identity threshold. The results are visualized using bubble plot for easy interpretation. | ||
| Groups similar clonotypes into clusters based on their sequence similarity, enabling researchers to identify related clonotypes that may share functional properties or antigen specificities. The block utilizes MMseqs2's `easy-cluster` command for fast and sensitive sequence searching and clustering, grouping sequences that meet a specified identity threshold. The clustering is performed on amino acid or nucleotide sequences (configurable), and results include cluster assignments for each clonotype along with cluster-level statistics, visualized using bubble plots. | ||
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| The clustered clonotype data can be used in downstream analysis blocks such as Clonotype Enrichment to analyze enrichment patterns at the cluster level across selection rounds, or Antibody/TCR Lead Selection to identify top candidates based on cluster-level scoring metrics. |
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To improve readability and clearly distinguish the names of other analysis blocks, consider formatting them with bold markdown. This helps users quickly identify them as specific components or tools they can use.
| The clustered clonotype data can be used in downstream analysis blocks such as Clonotype Enrichment to analyze enrichment patterns at the cluster level across selection rounds, or Antibody/TCR Lead Selection to identify top candidates based on cluster-level scoring metrics. | |
| The clustered clonotype data can be used in downstream analysis blocks such as **Clonotype Enrichment** to analyze enrichment patterns at the cluster level across selection rounds, or **Antibody/TCR Lead Selection** to identify top candidates based on cluster-level scoring metrics. |
| MMseqs2 is developed by the Söding lab and Steinegger group. For more information, please see: [https://github.com/soedinglab/MMseqs2](https://github.com/soedinglab/MMseqs2) and cite the following publication if used in your research: | ||
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| > Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017). | ||
| > Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. _Nature Biotechnology_, doi: 10.1038/nbt.3988 (2017). [https://doi.org/10.1038/nbt.3988](https://doi.org/10.1038/nbt.3988) |
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The citation format can be improved for readability. The DOI is mentioned and then a separate link to it is added, which is redundant. It would be cleaner to make the DOI text itself a hyperlink, which also resolves the awkward punctuation.
| > Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. _Nature Biotechnology_, doi: 10.1038/nbt.3988 (2017). [https://doi.org/10.1038/nbt.3988](https://doi.org/10.1038/nbt.3988) | |
| > Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. _Nature Biotechnology_, [doi: 10.1038/nbt.3988](https://doi.org/10.1038/nbt.3988) (2017). |
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