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YAMP Resources with Human Genome indexed bbmap 38.76

<p>Resource dataset for <strong>YAMP</strong> (https://github.com/alesssia/YAMP) with Human Genome indexed using <strong>&nbsp;bbmap 38.76</strong></p> <p><em>Original dataset:</em></p> <p>Alessia. (2017). Data for YAMP (https://github.com/alesssia/YAMP) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1068229</p> <p><em>From the original dataset :</em></p> <p>This dataset includes all the databases/datasets queried by YAMP (https://github.com/alesssia/YAMP). This&nbsp;dataset has been created to help the&nbsp;users to get started with YAMP, and&nbsp;to save them from the hassle of collecting and downloading the data from different sources.</p> <p>More in details, this dataset contains:</p> <ul> <li>a FASTA file listing the adapter sequences to remove in the trimming step. This file is usually&nbsp;provided within the&nbsp;BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68).&nbsp;</li> <li>two FASTA files describing synthetic contaminants (sequencing_artifacts.fa.gz&nbsp;and&nbsp;phix174_ill.ref.fa.gz). These files are usually provided within the&nbsp;BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68).&nbsp;</li> <li>a FASTA file provided by Brian Bushnell for removing human contamination (described here:&nbsp;http://seqanswers.com/forums/showthread.php?t=42552).&nbsp;&nbsp;Please note that this file should be indexed beforehand. This can be done using BBMap, using the following command: `bbmap.sh -Xmx24G ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz`.&nbsp;</li> <li>the BowTie2 database file for MetaPhlAn2. This file is usually provided within the MetaPhlAn2 installation (version 2.6.0)</li> <li>the ChocoPhlAn and UniRef (Uniref50, Uniref90)&nbsp;databases, downloaded directly by HUMAnN2 (version 0.9.9), as explained here:&nbsp;https://bitbucket.org/biobakery/humann2/wiki/Home#markdown-header-5-download-the-databases</li> </ul>

ShareScore

28/100

Overall dataset sharing score

Score breakdown

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

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
0
Engagement
4