Seqs = (skbio.DNA(e, metadata=) for e in feat_lumns) ![]() The first solution to get around the error is either to use the sequences as the IDs for the sequences: I would argue, however, that sequences should not need IDs as they will always be unique, and the solutions for dealing with this are not ideal. If this should in fact fail a more useful error message should be returned letting the user know that IDs are required. masked_alignment)ĬalledProcessError: Command '' returned non - zero exit status 1 mafft( a)įiltered_aligned_seqs = alignment. import_data( FeatureData, dna_iter)Īligned_seqs = alignment. I understand these limited architectures are usually a concern in bioinformatics, but I also heard of cases where architectures like armhf (which is 32 bits) were in use in various educational contexts. Please note I did not know whether 32 bits support was under your radar or not, so in doubt I just leave that merge request for your convenience. TypeError: NumericMetadataColumn 'foo' does not support a pandas.Series object with dtype int64 Num_md = metadata.NumericMetadataColumn(value)įile "/tmp/autopkgtest-lxc.shmdvuw3/downtmp/autopkgtest_tmp/qiime2/metadata/metadata.py", line 875, in _init_ Nature Biotechnology 37:852–857.ĮRROR: test_decode_numeric_value (.test_primitive.TestMetadataColumn)įile "/tmp/autopkgtest-lxc.shmdvuw3/downtmp/autopkgtest_tmp/qiime2/core/type/tests/test_primitive.py", line 66, in test_decode_numeric_value Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. ![]() If you use QIIME 2 for any published research, please include the followingīolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, and Caporaso JG. ![]() Information on contributions, documentation links, and more. Just have a question? Please ask it in our ![]() With a focus onĭata and analysis transparency, QIIME 2 enables researchers to start anĪnalysis with raw DNA sequence data and finish with publication-quality figuresĭetailed instructions are available in theĬore concepts, tutorials, and other resources. Platform that is free, open source, and community developed. QIIME 2™ is a powerful, extensible, and decentralized microbiome bioinformatics Source code repository for the QIIME 2 framework.
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