comparison blockclust.xml @ 4:49e600128a73 draft

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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 modes: 
1) Pre-processing - converts given mapped reads (BAM) into BED file of tags
2) Clustering and classification - of given input block groups (from blockbuster tool) as explained in the original paper.
3) Post-processing - extracts distribution of clusters searched against Rfam database and plots hierarchical clustering made out of centroids of each BlockClust predicted cluster.

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

**Inputs**

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

**Output**
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:
Distribution of clusters with annotations searched against Rfam database
hierarchical clustering made out of centroids of each BlockClust predicted cluster

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**References** 

4:49e600128a73

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 - extracts distribution of clusters searched against Rfam database and plots hierarchical clustering made out of centroids of each BlockClust predicted cluster.

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

**Inputs**

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

**Outputs**

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:
    * Distribution of clusters with annotations searched against Rfam database
    * Hierarchical clustering made out of centroids of each BlockClust predicted cluster

------

**References**