Data Files and Other Resources

Education

• Gut Check: Exploring Your Microbiome (Coursera)
• An Introduction To Applied Bioinformatics
• Guide to statistical analysis in microbial ecology: GUSTA ME

Data Files

• Greengenes OTUs (16S)

  13_8 (most recent)

  13_5

  12_10

  February 4th, 2011

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• SILVA OTUs (16S/18S)

  QIIME-compatible SILVA releases as well as the licensing information for commercial and non-commercial use.

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• UNITE OTUs (ITS)

  All releases, including the latest, are available for download from the UNITE website here

  12_11 (alpha release*)
  

  *These reference sequence sets represent de-replicated (clustered) versions (at 99% and 97% sequence similarity) of all fungal rDNA ITS sequences in the current UNITE+INSD (International Nucleotide Sequence Databases: NCBI, EMBL, DDBJ) release of circa 300,000 sequences. The taxonomy mapping files provided here were created from the Index Fungorum ranked classification schema provided by UNITE in association with each sequence in their database. Efforts have been made by UNITE to improve the taxonomic information associated with some of the sequences in their database. The QIIME reference sequence sets linked here have not been subject to any other form of curation (manual or automated) and certainly include incorrectly identified sequences, chimeras, and other problematic sequences. The data provide here are a fungal ITS reference alpha version. Responsibility lies with the user to verify the accuracy of associated taxonomic information as well as quality of sequences in these datasets. Improved fungal rDNA ITS reference sets based on the semi-curated centroids for sequence clusters in the UNITE Global Key Annotations module will soon be available here.

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• IMG/QIIME reference protein sequences

  img-qiime-25oct2012 (alpha release; most recent)

Latest Amazon Web Services (AWS) virtual machine identifier

• QIIME 1.9.1 AMI: ami-1918ff72

Older Amazon Web Services (AWS) virtual machine identifiers

• QIIME 1.9.0 AMI: ami-ea2a7682
• QIIME 1.8.0 AMI: ami-f60b7c9e (formerly ami-e788a08e)
• QIIME 1.7.0 AMI: ami-920a7dfa (formerly ami-9bc9a7f2)
• QIIME 1.6.0 AMI: ami-76097e1e (formerly ami-69c34c00)
• QIIME 1.5.0 AMI: ami-3e097e56 (formerly ami-e4bf1b8d)
• QIIME 1.4.0 AMI: ami-3265125a (formerly ami-458d5b2c)
• QIIME 1.3.0 AMI: ami-48700720 (formerly ami-dce51eb5)

NOTE: QIIME AMIs can be found by searching Community AMIs from the AWS console. You must be in the US East region when logged into the AWS console.

NOTE: Our AWS virtual machine identifiers (AMIs) changed on 5 January 2015, but no changes were made to the underlying machine images. The changes to the AMIs were necessary to move the virtual machine images to a different AWS account. The former AMIs are listed above for reference.

Latest QIIME Virtual Box image

• QIIME 1.9.1 64-bit (MD5: 0c30d069dadaa4e8f3acda11d7df54eb)

Older QIIME Virtual Box images

• QIIME 1.9.0 64-bit (MD5: 2e5750a45bc68a218faaee6d5eef4a46)
• QIIME 1.8.0 64-bit (MD5: c85b64c93afb2927144dc2a2452ab06d)
• QIIME 1.7.0 64-bit (MD5: 0928165ac70ff004b0c04b7cb857f65d)
• QIIME 1.6.0 64-bit (MD5: ac1e037318b7505deb10b7d708f4a037)
• QIIME 1.5.0 64-bit (MD5: 0f798b33ab131898ed8541ed37efde39)
• QIIME 1.4.0 64-bit (MD5: 16ce01944a5b1746215c7c841b183d1c)
• QIIME 1.3.0 64-bit (MD5: 7ed9074fd50870e26ee563cfac54d46c)
• QIIME 1.2.1 64-bit (MD5: d9e129548837eec8341b68dfdad6bfae)
• QIIME 1.2.0 64-bit (MD5: 01423310a4cc5f0024f8c8892b628b40)
• QIIME 1.1.0 64-bit (MD5: f0091e56db74381ab081a0efde60cc0c)
• QIIME 1.1.0 32-bit (MD5: da013ac50c74a98b78347ebe708e3021)
• QIIME 1.0.0 32-bit (MD5: 53dba86b9be0c47488ec5dc1a74ebcef)
• QIIME 0.92 32-bit (MD5: e0445213b4d6bb97244ce614bcd9c35d)

Protocols

• Primers and amplification protocols for 454 FLX+ long read 16S and 18S amplicons. (version 13_4)
• 16S Illumina HiSeq/MiSeq Protocols: Caporaso et al, 2011 and EMP protocols page
• 16S Illumina GAIIx Protocols (these are different from those for the HiSeq/MiSeq!): Caporaso et al, 2010

Download mock community datasets from mockrobiota

Several of our benchmarking studies make use of mock communities (artifical communities constructed by pooling isolated microorganisms together in known abundances). These studies include Bokulich et al., 2013, Caporaso et al., 2011, and Bokulich et al., 2015 (pre-print). The data used in these studies can be downloaded from mockrobiota, a resource compiling mock microbiome data sets (Bokulich et al., (2016)).