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Metaclusters by DPCfam clustering of UniRef50 v 2017_07

<p>Metaclusters obtained from the DPCfam clustering of UniRef50, v.&nbsp;2017_07.<br> Metaclusters represent putative protein families automatically derived using the DPCfam method, as described in <em>Unsupervised protein family classification by Density Peak clustering, Russo ET, 2020, PhD Thesis <a href="http://hdl.handle.net/20.500.11767/116345">http://hdl.handle.net/20.500.11767/116345</a> . Supervisors: Alessandro Laio, Marco Punta.</em></p> <p>Visit also&nbsp;<a href="https://dpcfam.areasciencepark.it/">https://dpcfam.areasciencepark.it/</a>&nbsp; to easily navigate the data.</p> <p><strong>VERSION 1.1 changes:</strong></p> <ul> <li>Added DPCfamB database, including all small metaclusters with&nbsp; 25&lt;=N&lt;50 seed sequences. DPCdamB files are named with the prefix B_</li> <li>Added Alphafold representative based on AlphaFoldDB for each MC</li> </ul> <p><strong>FILES DESCRIPTION:</strong></p> <p><strong>1) Standard DPCfam database</strong></p> <ul> <li><strong>metaclusters_xml.tar.gz </strong>Metaclusters&#39; seeds, unaligned in an xml table.&nbsp;Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 a.a.s&nbsp;are reported. Metaclusters entries include also some statistical information about each MC (such as size, average length, low complexity fraction etc, ) and Pfam comparison (Dominant Architecture). A README file is included describing the data. A parser is included to transform XML data to space-separated tables. XML schema is included.</li> <li><strong>metaclusters_msas.tar.gz</strong> Metsclusters&#39; multiple sequence alignments, in fasta format. Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;.</li> <li><strong>metaclusters_hmms.tar.gz</strong> Metsclusters&#39; profile-hmms.&nbsp;A&nbsp;&quot;.hmm&quot; file for each metacluser. Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;.</li> <li><strong>all_metaclusters_hmm.tar.gz</strong> Collctive metaclusters&#39; profile-hmm.&nbsp; A single .hmm file collecting all MC&#39;s profile-hmm.&nbsp;. Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;</li> <li><strong>uniref50_annotated.xml.gz</strong> UniRef50 v.2017_07 database annotated with Pfam families and DPCfam metaclusters. A README file is included describing the data. A parser is included to transform XML data to space-separated tables. XML schema is included. XML schema is derived from uniprot&#39;s UniRef50 xml schema.</li> </ul> <p><strong>2) DPCfamB database</strong></p> <ul> <li><strong>B_metaclusters_xml.tar.gz </strong>Metaclusters&#39; seeds, unaligned in an xml table. All metaclusters are listed. Metaclusters entries include also some statistical information about each MC (such as size, average length, low complexity fraction etc, ) and Pfam comparison (Dominant Architecture). A README file is included describing the data. A parser is included to transform XML data to space-separated tables. XML schema is included.&nbsp;</li> <li><strong>B_metaclusters_msas.tar.gz</strong> Metsclusters&#39; multiple sequence alignments, in fasta format. Only MCs with seeds with 1)&nbsp;25&lt;=N&lt;50&nbsp;&nbsp;elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;.</li> <li><strong>B_metaclusters_hmms.tar.gz</strong> Metsclusters&#39; profile-hmms.&nbsp;A&nbsp;&quot;.hmm&quot; file for each metacluser. Only MCs with seeds with 1) 25&lt;=N&lt;50&nbsp;elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;.</li> <li><strong>B_ all_metaclusters_hmm.tar.gz</strong> Collctive metaclusters&#39; profile-hmm.&nbsp; A single .hmm file collecting all MC&#39;s profile-hmm.&nbsp;. Only MCs with seeds with 1)&nbsp;25&lt;=N&lt;50 elements and 2) average length larger than 50 a.a.s&nbsp;are reported&nbsp;</li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
8
Access
16
Reuse readiness
0
Engagement
4

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