Merge pull request #76 from platforma-open/20251114-update-block-metadata

20251114 update block metadata

Author: aiturbidemil

.changeset/weak-candies-hope.md

@@ -0,0 +1,5 @@
+---
+'@platforma-open/milaboratories.clonotype-clustering': patch
+---
+
+Block metadata updated.

block/package.json

@@ -26,9 +26,9 @@
       "title": "Clonotype clustering",
       "logo": "file:../logos/block-logo.png",
       "url": "https://github.com/platforma-open/clonotype-clustering",
-      "docs": "https://github.com/platforma-open/clonotype-clustering/docs",
+      "docs": "https://docs.platforma.bio/guides/antibody-discovery/antibody-clustering/",
       "support": "mailto:support@milaboratories.com",
-      "description": "Fast, sequence-based clonotype clustering.",
+      "description": "Groups similar clonotypes into clusters based on sequence similarity using MMseqs2.",
       "longDescription": "file:../docs/description.md",
       "changelog": "file:../CHANGELOG.md",
       "tags": [

docs/description.md

@@ -1,7 +1,9 @@
 # Overview
 
-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.
+
+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:
 
-> 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)