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What it does

BlockClust is an efficient approach to detect transcripts with similar processing patterns. We propose a novel way to encode expression profiles in compact discrete structures, which can then be processed using fast graph-kernel techniques. BlockClust allows both clustering and classification of small non-coding RNAs.

BlockClust runs in three operating modes:

  1. Pre-processing - converts given mapped reads (BAM) into BED file of tags

  2. Clustering and classification - of given input blockgroups (output of blockbuster tool) as explained in the original paper.

  3. Post-processing - plots for overview of predicted clusters.

For a thorough analysis of your data, we suggest you to use complete blockclust workflow, which contains all three modes of operation.


BlockClust input files are dependent on the mode of operation:

  1. Pre-processing mode:

    • Binary Sequence Alignment Map (BAM) file
  2. Clustering and classification:

    • A blockgroups file generated by blockbuster tool
    • Select reference genome
  3. Post-processing:

    • Output of cmsearch, searched clusters generated by BlockClust against Rfam
    • BED file containing clusters generated by BlockClust
    • Pairwise similarities of blockgroups generated by BlockClust


  1. Pre-processing mode:

    • BED file of tags with expressions
  2. Clustering and classification:

    • Hierarchical clustering plot of all input blockgroups by their similarity
    • Pairwise similarities of all input blockgroups
    • BED file containing predicted clusters
    • BED file containing prediction of blockgroups by pre-compiled SVM binary classification model.
  3. Post-processing:

    • Plot of distribution of ncRNA families per predicted cluster (overview of cluster precissions). The annotation of ncRNA families are retrieved by searching cluster instances against Rfam database.
    • Hierarchical clustering made out of centroids of each BlockClust predicted cluster
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