Logging started at 09:12:45 on 21 Apr 2016 QIIME version: 1.9.0 qiime_config values: blastmat_dir /usr/share/ncbi/data pick_otus_reference_seqs_fp /usr/lib/python2.7/dist-packages/qiime_default_reference/gg_13_8_otus/rep_set/97_otus.fasta jobs_to_start 1 qiime_scripts_dir /usr/lib/qiime/bin/ working_dir . pynast_template_alignment_fp /usr/share/qiime/data/core_set_aligned.fasta.imputed python_exe_fp python temp_dir /tmp assign_taxonomy_reference_seqs_fp # /usr/share/qiime/data/gg_13_8_otus/rep_set/97_otus.fasta blastall_fp blastall seconds_to_sleep 60 assign_taxonomy_id_to_taxonomy_fp # /usr/share/qiime/data/gg_13_8_otus/taxonomy/97_otu_taxonomy.txt parameter file values: parallel:jobs_to_start 1 Input file md5 sums: combined_seqs.fna: 7d7659239d1555de30890d74dbbc7742 /home/luz/analisis/Geengenes_Database/gg_13_8_otus/rep_set/97_otus.fasta: 50b2269712b3738afb41892bed936c29 Forcing --suppress_new_clusters as this is reference-based OTU picking. Executing commands. # Pick Reference OTUs command pick_otus.py -i combined_seqs.fna -o Tercera_vencida/step1_otus -r /home/luz/analisis/Geengenes_Database/gg_13_8_otus/rep_set/97_otus.fasta -m uclust_ref --suppress_new_clusters Stdout: Stderr: # Generate full failures fasta file command filter_fasta.py -f combined_seqs.fna -s Tercera_vencida/step1_otus/combined_seqs_failures.txt -o Tercera_vencida/step1_otus/failures.fasta Stdout: Stderr: Executing commands. # Pick rep set command pick_rep_set.py -i Tercera_vencida/step1_otus/combined_seqs_otus.txt -o Tercera_vencida/step1_otus/step1_rep_set.fna -f combined_seqs.fna Stdout: Stderr: # Subsample the failures fasta file using API python -c "import qiime; qiime.util.subsample_fasta('/home/luz/analisis/combined_fasta/Tercera_vencida/step1_otus/failures.fasta', '/home/luz/analisis/combined_fasta/Tercera_vencida/step2_otus/subsampled_failures.fasta', '0.010000') "Forcing --suppress_new_clusters as this is reference-based OTU picking. Executing commands. # Pick de novo OTUs for new clusters command pick_otus.py -i Tercera_vencida/step2_otus//subsampled_failures.fasta -o Tercera_vencida/step2_otus/ -m uclust --denovo_otu_id_prefix New.ReferenceOTU Stdout: Stderr: # Pick representative set for subsampled failures command pick_rep_set.py -i Tercera_vencida/step2_otus//subsampled_failures_otus.txt -o Tercera_vencida/step2_otus//step2_rep_set.fna -f Tercera_vencida/step2_otus//subsampled_failures.fasta Stdout: Stderr: # Pick reference OTUs using de novo rep set command pick_otus.py -i Tercera_vencida/step1_otus/failures.fasta -o Tercera_vencida/step3_otus/ -r Tercera_vencida/step2_otus//step2_rep_set.fna -m uclust_ref --suppress_new_clusters Stdout: Stderr: # Create fasta file of step3 failures command filter_fasta.py -f Tercera_vencida/step1_otus/failures.fasta -s Tercera_vencida/step3_otus//failures_failures.txt -o Tercera_vencida/step3_otus//failures_failures.fasta Stdout: Stderr: # Pick de novo OTUs on step3 failures command pick_otus.py -i Tercera_vencida/step3_otus//failures_failures.fasta -o Tercera_vencida/step4_otus/ -m uclust --denovo_otu_id_prefix New.CleanUp.ReferenceOTU Stdout: Stderr: # Merge OTU maps command cat Tercera_vencida/step1_otus/combined_seqs_otus.txt Tercera_vencida/step3_otus//failures_otus.txt Tercera_vencida/step4_otus//failures_failures_otus.txt > Tercera_vencida/final_otu_map.txt Stdout: Stderr: # Pick representative set for subsampled failures command pick_rep_set.py -i Tercera_vencida/step4_otus//failures_failures_otus.txt -o Tercera_vencida/step4_otus//step4_rep_set.fna -f Tercera_vencida/step3_otus//failures_failures.fasta Stdout: Stderr: # Filter singletons from the otu map using API python -c "import qiime; qiime.filter.filter_otus_from_otu_map('/home/luz/analisis/combined_fasta/Tercera_vencida/final_otu_map.txt', '/home/luz/analisis/combined_fasta/Tercera_vencida/final_otu_map_mc2.txt', '2')" # Write non-singleton otus representative sequences from step1 to the final rep set file: Tercera_vencida/rep_set.fna # Copy the full input refseqs file to the new refseq file cp /home/luz/analisis/Geengenes_Database/gg_13_8_otus/rep_set/97_otus.fasta Tercera_vencida/new_refseqs.fna # Write non-singleton otus representative sequences from step 2 and step 4 to the final representative set and the new reference set (Tercera_vencida/rep_set.fna and Tercera_vencida/new_refseqs.fna respectively) Executing commands. # Make the otu table command make_otu_table.py -i Tercera_vencida/final_otu_map_mc2.txt -o Tercera_vencida/otu_table_mc2.biom Stdout: Stderr: Executing commands. # Assign taxonomy command assign_taxonomy.py -o Tercera_vencida/uclust_assigned_taxonomy -i Tercera_vencida/rep_set.fna *** ERROR RAISED DURING STEP: Assign taxonomy Command run was: assign_taxonomy.py -o Tercera_vencida/uclust_assigned_taxonomy -i Tercera_vencida/rep_set.fna Command returned exit status: 2 Stdout: Stderr Usage: assign_taxonomy.py [options] {-i/--input_fasta_fp INPUT_FASTA_FP} [] indicates optional input (order unimportant) {} indicates required input (order unimportant) Contains code for assigning taxonomy, using several techniques. Given a set of sequences, assign_taxonomy.py attempts to assign the taxonomy of each sequence. Currently the methods implemented are assignment with BLAST, the RDP classifier, RTAX, mothur, and uclust. The output of this step is an observation metadata mapping file of input sequence identifiers (1st column of output file) to taxonomy (2nd column) and quality score (3rd column). There may be method-specific information in subsequent columns. Reference data sets and id-to-taxonomy maps for 16S rRNA sequences can be found in the Greengenes reference OTU builds. To get the latest build of the Greengenes OTUs (and other marker gene OTU collections), follow the "Resources" link from http://qiime.org. After downloading and unzipping you can use the following files as -r and -t, where is the name of the new directory after unzipping the reference OTUs tgz file. -r /rep_set/97_otus.fasta -t /taxonomy/97_otu_taxonomy.txt Example usage: Print help message and exit assign_taxonomy.py -h Assign taxonomy with the uclust consensus taxonomy assigner (default): Perform database search with uclust to retrive up to uclust_max_accepts hits for each query sequence. Then assign the most specific taxonomic label that is associated with at least min_consensus_fraction of the hits. assign_taxonomy.py -i repr_set_seqs.fasta -r ref_seq_set.fna -t id_to_taxonomy.txt Assignment with SortMeRNA: Perform database search with sortmerna to retrieve up to sortmerna_best_N_alignments hits for each query sequence. Then assign the most specific taxonomic label that is associated with at least min_consensus_fraction of the hits. assign_taxonomy.py -i repr_set_seqs.fasta -r ref_seq_set.fna -t id_to_taxonomy.txt -m sortmerna Assignment with BLAST: Taxonomy assignments are made by searching input sequences against a blast database of pre-assigned reference sequences. If a satisfactory match is found, the reference assignment is given to the input sequence. This method does not take the hierarchical structure of the taxonomy into account, but it is very fast and flexible. If a file of reference sequences is provided, a temporary blast database is built on-the-fly. The quality scores assigned by the BLAST taxonomy assigner are e-values. To assign the sequences to the representative sequence set, using a reference set of sequences and a taxonomy to id assignment text file, where the results are output to default directory "blast_assigned_taxonomy", you can run the following command assign_taxonomy.py -i repr_set_seqs.fasta -r ref_seq_set.fna -t id_to_taxonomy.txt -m blast Optionally, the user could changed the E-value ("-e"), using the following command assign_taxonomy.py -i repr_set_seqs.fasta -r ref_seq_set.fna -t id_to_taxonomy.txt -e 0.01 -m blast Assignment with the RDP Classifier: The RDP Classifier (Wang, Garrity, Tiedje, & Cole, 2007) assigns taxonomies using Naive Bayes classification. By default, the classifier is retrained using the values provided for --id_to_taxonomy_fp and --reference_seqs_fp. assign_taxonomy.py -i repr_set_seqs.fasta -m rdp Assignment with the RDP Classifier using an alternative minimum confidence score by passing -c assign_taxonomy.py -i repr_set_seqs.fasta -m rdp -c 0.80 Assignment with RTAX: Taxonomy assignments are made by searching input sequences against a fasta database of pre-assigned reference sequences. All matches are collected which match the query within 0.5% identity of the best match. A taxonomy assignment is made to the lowest rank at which more than half of these hits agree. Note that both unclustered read fasta files are required as inputs in addition to the representative sequence file. To make taxonomic classifications of the representative sequences, using a reference set of sequences and a taxonomy to id assignment text file, where the results are output to default directory "rtax_assigned_taxonomy", you can run the following command assign_taxonomy.py -i rtax_repr_set_seqs.fasta -m rtax --read_1_seqs_fp read_1.seqs.fna --read_2_seqs_fp read_2.seqs.fna -r rtax_ref_seq_set.fna -t rtax_id_to_taxonomy.txt Assignment with Mothur: The Mothur software provides a naive bayes classifier similar to the RDP Classifier.A set of training sequences and id-to-taxonomy assignments must be provided. Unlike the RDP Classifier, sequences in the training set may be assigned at any level of the taxonomy. To make taxonomic classifications of the representative sequences, where the results are output to default directory "mothur_assigned_taxonomy", you can run the following command assign_taxonomy.py -i mothur_repr_set_seqs.fasta -m mothur -r mothur_ref_seq_set.fna -t mothur_id_to_taxonomy.txt assign_taxonomy.py: error: --id_to_taxonomy_fp is required when assigning with uclust. Logging stopped at 09:21:43 on 21 Apr 2016