├── .DS_Store ├── QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md ├── README.md ├── _config.yml ├── bash_lesson.sh ├── images ├── example-mapping-file-2018.png └── qiime2-demux.png ├── presentations ├── .DS_Store ├── AdelaideRhodes-Tanaids-19Jan2018.pdf ├── AlbertodeJesúsNavarrete-Nematoda-16Jan2018.pdf ├── AlessandroPrantoni-Oligochaeta-18Jan2018.pdf ├── FabioMoretzsohn-Bivalves-and-Gastropods-15Jan2018.pdf ├── FrancescaLeasi-BiologicalAssays-15Jan2018.pdf ├── KelleyThomas-Introduction-15Jan2018.pdf ├── MartinSorensen-Gnathostomulida-and-Kinorhyncha-15Jan2018.pdf ├── MichaelReuscher-Polychaete-18Jan2018.pdf ├── TiagoPereira-Nematoda-16Jan2018.pdf └── WonchoelLee-Copepoda-17Jan2018.pdf ├── qiime-downloads ├── alpha-rarefaction.qzv ├── core-metrics-results-metazoa │ ├── bray_curtis_distance_matrix.qza │ ├── bray_curtis_emperor.qzv │ ├── bray_curtis_pcoa_results.qza │ ├── evenness-group-significance.qzv │ ├── evenness_vector.qza │ ├── faith-pd-correlation.qzv │ ├── faith-pd-group-significance.qzv │ ├── faith_pd_vector.qza │ ├── jaccard_distance_matrix.qza │ ├── jaccard_emperor.qzv │ ├── jaccard_pcoa_results.qza │ ├── observed_otus_vector.qza │ ├── rarefied_table.qza │ ├── shannon_vector.qza │ ├── unweighted-unifrac-Matrix-group-significance.qzv │ ├── unweighted_unifrac_distance_matrix.qza │ ├── unweighted_unifrac_emperor.qzv │ ├── unweighted_unifrac_pcoa_results.qza │ ├── weighted_unifrac_distance_matrix.qza │ ├── weighted_unifrac_emperor.qzv │ └── weighted_unifrac_pcoa_results.qza ├── core-metrics-results-unfiltered │ ├── bray_curtis-emperor-Depths.qzv │ ├── bray_curtis_distance_matrix.qza │ ├── bray_curtis_emperor.qzv │ ├── bray_curtis_pcoa_results.qza │ ├── evenness-correlation.qzv │ ├── evenness-group-significance.qzv │ ├── evenness_vector.qza │ ├── faith-pd-correlation.qzv │ ├── faith-pd-group-significance.qzv │ ├── faith_pd_vector.qza │ ├── jaccard_distance_matrix.qza │ ├── jaccard_emperor.qzv │ ├── jaccard_pcoa_results.qza │ ├── observed_otus_vector.qza │ ├── rarefied_table.qza │ ├── shannon_vector.qza │ ├── unweighted-unifrac-Matrix-group-significance.qzv │ ├── unweighted-unifrac-emperor-Depths.qzv │ ├── unweighted-unifrac-emperor.qzv │ ├── unweighted_unifrac_distance_matrix.qza │ ├── unweighted_unifrac_emperor.qzv │ ├── unweighted_unifrac_pcoa_results.qza │ ├── weighted_unifrac_distance_matrix.qza │ ├── weighted_unifrac_emperor.qzv │ └── weighted_unifrac_pcoa_results.qza ├── demux.qzv ├── metazoa-only-table.qzv ├── taxa-bar-plots-metazoa.qzv ├── taxa-bar-plots-unfiltered.qzv ├── taxonomy.qzv └── unfiltered-table.qzv └── qiime_walkthrough.sh /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BikLab/BITMaB2-Tutorials/ea800b7d5cd3dbf4947a2153a72e17c0f3622662/.DS_Store -------------------------------------------------------------------------------- /QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md: -------------------------------------------------------------------------------- 1 | ## QIIME 2 workflow for metabarcoding analysis (18S/16S rRNA) with already-demultiplexed fastq files. 2 | 3 | Here, we will utilize a pipeline called QIIME (v2) to analyze and visualize microbial diversity using raw DNA sequences in fastq files. In contrast to QIIME 1, QIIME 2 features several new ways of analyzing NGS data and has been modified significantly bioinformatically, but NOT biologically. 4 | 5 | Below is a list of a few terms you should know when utilizing QIIME 2. 6 | 7 | 1. **Action** - a generic term for a method or visualizer. 8 | 2. **Artifact** - zipped input or output data for QIIME actions. Artifacts have file extension `.qza`. 9 | 3. **Parameter** - an input to an action. For instance, `--p-trim-left` is a parameter that takes in an integer value for any number of bases that the user wishes to trim off from the start of the sequence. 10 | 4. **Plugin** - a general term for an external tool that is built around QIIME 2. 11 | 5. **Visualization** - output data from a QIIME visualizer that have file extension `.qzv`. These can be viewed [online](https://view.qiime2.org/). 12 | 13 | 14 | Please use this pipeline if your **fastq files are already demultiplexed** - meaning each fastq file pairs (R1 and R2) represent sequences from ONE sample type. 15 | 16 | Please also keep in mind that QIIME 2 is a work in progress; so some features may not yet be available. But rest assured that QIIME 2 team is working hard and should you have any uber specific questions we cannot answer, please sign-up and post them on the [QIIME 2 forum](https://forum.qiime2.org/). 17 | 18 | [Link to the main QIIME 2 website](https://docs.qiime2.org/2017.12/) (for more tutorials and detailed documentation of the pipeline). 19 | 20 | --- 21 | 22 | ## What is QIIME 2? 23 | QIIME 2 is a microbiome analysis pipeline, and it is significantly different from the previous version QIIME 1. Instead of directly using data files such as FASTQ and FASTA files, QIIME 2 utilizes artifacts. See definition above. 24 | 25 | Here is a list of files you must have in order to run the QIIME 2 pipeline. 26 | 27 | 1. **A mapping file** 28 | * This is a tab-delimited file containing all the sequencing info. You can create this file in excel but it should be saved as a text version. Below is an example mapping file. The required columns are shown in **bold**. 29 | 30 | ![Example mapping file](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/images/example-mapping-file-2018.png?raw=1) 31 | 32 | 2. **R1 fastq** 33 | * This file contains reads returned by the sequencer first. 34 | 35 | 3. **R2 fastq** 36 | * This file contains reads returned by the sequencer second. 37 | 38 | 39 | QIIME 2 supports various data formats for sequences files and BIOM tables, however the descriptions of these formats are still being developed. Some common data formats are described in the [Importing Data tutorial](https://docs.qiime2.org/2017.12/tutorials/importing/). 40 | 41 | 42 | ## Activating QIIME 2 and copying over data files 43 | 44 | `source activate qiime2-2017.12` 45 | 46 | `source tab-qiime` 47 | 48 | `mkdir q2-tutorial` 49 | 50 | `cd q2-tutorial` 51 | 52 | `cp -r /data/share/BITMaB-2018/18S_metabarcoding_Project_FranPanama/* .` 53 | 54 | 55 | ## Pipeline Overview 56 | 57 | Here is an overview of the general steps of the QIIME pipeline for already demultiplexed reads that we will carry out during the BITMaB workshop (click links to jump to detailed instructions for each step): 58 | 59 | #### [Step 1](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-1---importing-data-summarize-the-results-and-examing-quality-of-the-reads): Importing data, summarize the results, and examing quality of the reads 60 | 61 | #### [Step 2](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-2---quality-controlling-sequences-and-building-feature-table-and-feature-data): Quality controlling sequences and building Feature Table and Feature Data 62 | 63 | #### [Step 3](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-3---assigning-taxonomy): Assigning Taxonomy 64 | 65 | #### [Step 4](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-4---summarizing-feature-table-and-feature-data): Summarizing Feature Table and Feature Data 66 | 67 | #### [Step 5](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-5---generating-a-phylogenetic-tree): Generating a phylogenetic tree 68 | 69 | #### [Step 6](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md#step-6---analyzing-alpha-and-beta-diversities): Analyzing Alpha and Beta diversities 70 | 71 | 72 | --- 73 | 74 | * NOTE: For the purposes of this tutorial, we are running all the analysis in a single directory and using non-descriptive names when assigning output files. 75 | 76 | ## Step 1 - Importing data, summarize the results, and examing quality of the reads 77 | 78 | ### A. Import data files as Qiime Zipped Artifacts (.qza) 79 | 80 | In order to work with your data within QIIME 2, we first must import the FASTQ files as a QIIME artifact. The action to import files is `qiime tools import`. 81 | 82 | Let's start by pulling the help menu for the `qiime tools` action first. To do this for any QIIME 2 action, you can run that particular action followed by `--help` as shown below. 83 | 84 | ``` 85 | qiime tools --help 86 | ``` 87 | > ### What are some commands you can run with the `qiime tools` action? 88 | 89 | And, to get more info on the commands associated with an action, run the action along with the desired command as shown below. 90 | 91 | ``` 92 | qiime tools import --help 93 | ``` 94 | > ### What are the *required* options we must specify when importing FASTQ files as QIIME artifact? 95 | 96 | And and, to get more info on the options associated with a command associated with an action, run the option along with the desired command and action as shown below. This may not work for all the options fyi. 97 | 98 | ``` 99 | qiime tools import --show-importable-formats --help 100 | ``` 101 | 102 | Now, we can use the `import` command to import our files as QIIME artifact. The data format used here is called `CasavaOneEightSingleLanePerSampleDirFmt`. In this format, there are two `fastq.gz` files for each sample. The forward and reverse read file names for a single sample might look like `L2S357_15_L001_R1_001.fastq.gz` and `L2S357_15_L001_R2_001.fastq.gz`, respectively. The underscore-separated fields in this file name are the sample identifier, the barcode sequence or a barcode identifier, the lane number, the read number, and the set number. 103 | 104 | ``` 105 | qiime tools import \ 106 | --type 'SampleData[PairedEndSequencesWithQuality]' \ 107 | --input-path raw_reads_paired/ \ 108 | --source-format CasavaOneEightSingleLanePerSampleDirFmt \ 109 | --output-path demux-paired-end.qza 110 | ``` 111 | --- 112 | --- 113 | * NOTE: In case your paired-end data are multiplexed, you may use the following command, after importing the multiplexed files as QIIME artifact, for separating/demultiplexing your read files based on sample names. 114 | 115 | ``` 116 | qiime demux emp-paired \ 117 | --m-barcodes-file sample-metadata.tsv \ 118 | --m-barcodes-category BarcodeSequence \ 119 | --i-seqs emp-paired-end-sequences.qza \ 120 | --o-per-sample-sequences demux \ 121 | --p-rev-comp-mapping-barcodes 122 | 123 | ``` 124 | --- 125 | 126 | 127 | ### B. Summarize and visualize the qza 128 | 129 | ``` 130 | qiime demux summarize \ 131 | --i-data demux-paired-end.qza \ 132 | --o-visualization demux.qzv 133 | ``` 134 | * Here, you must copy over the `.qzv` output to your computer, and open `demux.qzv` in [www.view.qiime2.org](https://view.qiime2.org/) 135 | 136 | ![qiime2 demux summary](images/qiime2-demux.png?raw=1) 137 | 138 | 139 | ## Step 2 - Quality controlling sequences and building Feature Table and Feature Data 140 | 141 | * QIIME 2 has plugins for various quality control methods such as [DADA2](https://benjjneb.github.io/dada2/tutorial.html) and [Deblur](https://github.com/biocore/deblur). The result of both of these methods will be a `FeatureTable[Frequency]` QIIME 2 artifact containing counts (frequencies) of each unique sequence in each sample in the dataset, and a `FeatureData[Sequence]` QIIME 2 artifact, which maps feature identifiers in the FeatureTable to the sequences they represent. We will use DADA2 in this tutorial. The `FeatureTable[Frequency]` and `FeatureData[Sequence]` are analogous to QIIME 1's Biom table and rep_set fasta file, respectively. 142 | 143 | * The `dada2 denoise-paired` requires four parameters: `--p-trim-left-f`, `--p-trim-left-r`, `--p-trunc-len-f`, and `--p-trunc-len-r`. The `--p-trim-left m` trims off the first `m` bases of each sequence, and `--p-trunc-len n` truncates each sequence at position `n`. The `f` and `r` in each parameter stand for forward and reverse read, correspondingly. 144 | 145 | * Please consider the question below before you quality trim the sequences. 146 | 147 | > ### Based on the plots you see in `demuz.qzv`, what values would you choose for `--p-trim-left-f`, `--p-trim-left-r`, `--p-trunc-len-f`, and `--p-trunc-len-r` in this case? 148 | 149 | ``` 150 | qiime dada2 denoise-paired \ 151 | --i-demultiplexed-seqs demux-paired-end.qza \ 152 | --p-trim-left-f VALUE \ 153 | --p-trim-left-r VALUE \ 154 | --p-trunc-len-f VALUE \ 155 | --p-trunc-len-r VALUE \ 156 | --p-n-threads 12 \ 157 | --o-representative-sequences rep-seqs.qza \ 158 | --o-table table.qza 159 | 160 | ``` 161 | 162 | If this step completed correctly, your command line prompt should notify you with the following information: 163 | 164 | ``` 165 | Saved FeatureTable[Frequency] to: table.qza 166 | Saved FeatureData[Sequence] to: rep-seqs.qza 167 | ``` 168 | 169 | 170 | ## Step 3 - Assigning Taxonomy 171 | 172 | The default QIIME2 workflow does not include a typical OTU picking step - the developers now reccomend working with "Amplicon Sequence Variants", whereby you go directly into taxonomy assignment after using dada2/deblur to quality filter your dataset. 173 | 174 | Here, we are comparing our metabarcoding sequences to the SILVA reference database to assign taxonomy based on pairwise identity of rRNA seqeunces. 175 | 176 | ### A. Import reference data files as Qiime Zipped Artifacts (.qza) 177 | 178 | We are using the manually curated SILVA database to assign taxonomy to unkonwn (eukaryotic) 18S rRNA sequences. 179 | 180 | The databases have been pre-downloaded onto the server from the the ARB-SILVA website: https://www.arb-silva.de/download/archive/qiime 181 | 182 | ``` 183 | qiime tools import \ 184 | --type FeatureData[Sequence] \ 185 | --input-path /usr/local/share/SILVA_databases/SILVA_128_QIIME_release/rep_set/rep_set_18S_only/99/99_otus_18S.fasta \ 186 | --output-path 99_otus_18S 187 | 188 | ``` 189 | 190 | ``` 191 | qiime tools import \ 192 | --type FeatureData[Taxonomy] \ 193 | --input-path /usr/local/share/SILVA_databases/SILVA_128_QIIME_release/taxonomy/18S_only/99/majority_taxonomy_7_levels.txt \ 194 | --source-format HeaderlessTSVTaxonomyFormat \ 195 | --output-path majority_taxonomy_7_levels 196 | 197 | ``` 198 | 199 | Taxonomy assignment can be done using either SILVA's "consensus" or "majority" taxonomy mapping files - we STRONGLY reccomend you read the SILVA release notes to understand the differences in how these have been constructed: https://www.arb-silva.de/fileadmin/silva_databases/qiime/Silva_128_notes.txt 200 | 201 | For eukaryotic 18S data - especially for meiofaunal groups where the databases are pretty sparse - we recommend using the `majority_taxonomy_7_levels.txt` taxonomy mapping file, since it does a better job of incorporating "environmental" rRNA sequences and the seven levels have been manually curated to better reflect the known phylogenetic classifications of diverse eukarytoic groups. 202 | 203 | Here is complete explanation of the taxonomy differences from the SILVA database curators: 204 | 205 | ``` 206 | Taxonomy strings that are either consensus (all taxa strings must match for every read that fell into the cluster) or majority (greater than or equal to 90% of the taxonomy strings for a given cluster). If a taxonomy string fails to be consensus or majority, then it becomes ambiguous, moving up the levels of taxonomy until consensus/majority taxonomy strings are met. 207 | 208 | For example, if a cluster had two reads, and one taxonomy string was: 209 | D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__Methanobacteriaceae;D_5__Methanobrevibacter;D_6__Methanobrevibacter sp. HW3 210 | and the second taxonomy string was: 211 | D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__Methanobacteriaceae;D_5__Methanobrevibacter;D_6__Methanobrevibacter smithii 212 | 213 | Then for either consensus or majority strings, the level 7 (0 is the first level, the domain) data would become ambiguous, as the species levels do not match. The above string for the representative sequence taxonomy mapping file becomes: 214 | D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__Methanobacteriaceae;D_5__Methanobrevibacter;Ambiguous_taxa 215 | 216 | Because the taxonomy strings are not perfectly matched in terms of names/depths across all of the SILVA data, this can lead to some taxonomies being more ambiguous with my approach (exact string matches) than they actually are, particularly for the eukaryotes. There are over 1.5 million taxonomy strings in the non-redundant SILVA 119 release (even more in later releases), so I can't fault the maintainers of SILVA for these taxonomy strings being imperfect from a parsing/bioinformatics perspective. 217 | ``` 218 | 219 | 220 | ### B. Classify query sequences using Blast 221 | 222 | Here we are using BLAST to assign taxonomy to environmental rRNA sequences, using a 90% pairwise identity cutoff against the curated SILVA database (so any rRNA OTUs with <90% identity will come back with a taxonomic string as "unassigned"). 223 | 224 | 225 | 226 | ``` 227 | qiime feature-classifier classify-consensus-blast \ 228 | --i-query rep-seqs.qza \ 229 | --i-reference-taxonomy majority_taxonomy_7_levels.qza \ 230 | --i-reference-reads 99_otus_18S.qza \ 231 | --o-classification taxonomy \ 232 | --p-perc-identity 0.90 \ 233 | --p-maxaccepts 1 234 | 235 | ``` 236 | Change the filename on your table to "unfiltered" so we can keep track of the original qiime output. 237 | 238 | ``` 239 | mv table.qza unfiltered-table.qza 240 | 241 | ``` 242 | 243 | QIIME2 has a number of different options for classifying your sequences. For simplicity (and familiarity) we are using BLAST, but other options offer more sophisticated algorithmic methods for taxonomy assignment: 244 | 245 | ``` 246 | classify-consensus-blast BLAST+ consensus taxonomy classifier 247 | classify-consensus-vsearch VSEARCH consensus taxonomy classifier 248 | classify-sklearn Pre-fitted sklearn-based taxonomy classifier 249 | extract-reads Extract reads from reference 250 | fit-classifier-naive-bayes Train the naive_bayes classifier 251 | fit-classifier-sklearn Train an almost arbitrary scikit-learn 252 | classifier 253 | ``` 254 | 255 | ### C. Filter the Feature Table to contain only metazoa OTUs 256 | 257 | ``` 258 | qiime taxa filter-table \ 259 | --i-table unfiltered-table.qza \ 260 | --i-taxonomy taxonomy.qza \ 261 | --p-include metazoa \ 262 | --o-filtered-table table.qza 263 | 264 | ``` 265 | 266 | 267 | ## Step 4 - Summarizing Feature Table and Feature Data 268 | 269 | ``` 270 | qiime feature-table summarize \ 271 | --i-table table.qza \ 272 | --o-visualization table.qzv \ 273 | --m-sample-metadata-file mapping_file_panama_MAY_2017.tsv 274 | 275 | ``` 276 | 277 | 278 | ``` 279 | qiime feature-table tabulate-seqs \ 280 | --i-data rep-seqs.qza \ 281 | --o-visualization rep-seqs.qzv 282 | 283 | ``` 284 | 285 | * Here, you must copy over the `.qzv` outputs to your computer, and open `table.qzv` and `rep-seqs.qzv` in [www.view.qiime2.org](https://view.qiime2.org/) 286 | 287 | 288 | ## Step 5 - Generating a phylogenetic tree 289 | 290 | ``` 291 | qiime alignment mafft --i-sequences rep-seqs.qza --o-alignment aligned-rep-seqs.qza 292 | ``` 293 | ``` 294 | qiime alignment mask --i-alignment aligned-rep-seqs.qza --o-masked-alignment masked-aligned-rep-seqs.qza 295 | ``` 296 | ``` 297 | qiime phylogeny fasttree --i-alignment masked-aligned-rep-seqs.qza --o-tree unrooted-tree.qza 298 | ``` 299 | ``` 300 | qiime phylogeny midpoint-root --i-tree unrooted-tree.qza --o-rooted-tree rooted-tree.qza 301 | ``` 302 | 303 | 304 | ## Step 6 - Analyzing Alpha and Beta diversities 305 | 306 | #### A. Assess alpha rarefaction 307 | 308 | Here you must make a decision about the rarefaction values you will use to carry out ecological diversity analyses on your dataset - this is dependent on the sequencing depth you observe across all your samples (e.g. the minimum value will throw out any samples with a sequencing depth below that threshold). 309 | 310 | > ### View the `table.qzv` QIIME 2 artifact, and in particular the Interactive Sample Detail tab in that visualization. What value would you choose to pass for `--p-sampling-depth` below? How many samples will be excluded from your analysis based on this choice? How many total sequences will you be analyzing in the core-metrics-phylogenetic command? 311 | 312 | In the below script, replace `MINIMUM` and `MAXIMUM` with the values you choose to use for rarefaction. 313 | 314 | ``` 315 | qiime diversity alpha-rarefaction \ 316 | --i-table table.qza \ 317 | --i-phylogeny rooted-tree.qza \ 318 | --p-min-depth MINIMUM \ 319 | --p-max-depth MAXIMUM \ 320 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 321 | --o-visualization alpha-rarefaction.qzv 322 | ``` 323 | 324 | * Here, you must copy over the `.qzv` output to your computer, and open it in [www.view.qiime2.org](https://view.qiime2.org/) 325 | 326 | 327 | #### B. Compute several alpha and beta diversity metrics and plot PCoAs using Emperor 328 | 329 | Script to generate taxonomy bar charts: 330 | 331 | First do this for the unfiltered data, and view the `.qzv` output in the QIIME2 viewer [www.view.qiime2.org](https://view.qiime2.org/) 332 | 333 | ``` 334 | qiime taxa barplot \ 335 | --i-table unfiltered-table.qza \ 336 | --i-taxonomy taxonomy.qza \ 337 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 338 | --o-visualization taxa-bar-plots.qzv 339 | ``` 340 | 341 | Now generate the same taxonmy plots for the filtered (Metazoa-only) 18S dataset, and visualize this file as well: 342 | 343 | ``` 344 | qiime taxa barplot \ 345 | --i-table table.qza \ 346 | --i-taxonomy taxonomy.qza \ 347 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 348 | --o-visualization taxa-bar-plots.qzv 349 | ``` 350 | 351 | > ### What are the differences in alpha diversity between the filtered and untiltered Feature Tables? 352 | 353 | 354 | Beta Diversity Core Analyses (runs a whole bunch of metrics at once): 355 | 356 | 357 | ``` 358 | qiime diversity core-metrics-phylogenetic \ 359 | --i-phylogeny rooted-tree.qza \ 360 | --i-table table.qza \ 361 | --p-sampling-depth VALUE \ 362 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 363 | --output-dir core-metrics-results 364 | ``` 365 | 366 | ``` 367 | qiime diversity alpha-group-significance \ 368 | --i-alpha-diversity core-metrics-results/faith_pd_vector.qza \ 369 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 370 | --o-visualization core-metrics-results/faith-pd-group-significance.qzv 371 | ``` 372 | 373 | ``` 374 | qiime diversity alpha-group-significance \ 375 | --i-alpha-diversity core-metrics-results/evenness_vector.qza \ 376 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 377 | --o-visualization core-metrics-results/evenness-group-significance.qzv 378 | ``` 379 | 380 | * View the `.qzv` outputs in [www.view.qiime2.org](https://view.qiime2.org/) and answer the following questions. 381 | 382 | > ### What discrete sample metadata categories are most strongly associated with the differences in microbial community richness? Are these differences statistically significant? 383 | 384 | 385 | ``` 386 | qiime diversity alpha-correlation \ 387 | --i-alpha-diversity core-metrics-results/faith_pd_vector.qza \ 388 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 389 | --o-visualization core-metrics-results/faith-pd-correlation.qzv 390 | ``` 391 | 392 | ``` 393 | qiime diversity alpha-correlation \ 394 | --i-alpha-diversity core-metrics-results/evenness_vector.qza \ 395 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 396 | --o-visualization core-metrics-results/evenness-correlation.qzv 397 | ``` 398 | 399 | ``` 400 | qiime diversity beta-group-significance \ 401 | --i-distance-matrix core-metrics-results/unweighted_unifrac_distance_matrix.qza \ 402 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 403 | --m-metadata-category Matrix \ 404 | --o-visualization core-metrics-results/unweighted-unifrac-Matrix-group-significance.qzv \ 405 | --p-pairwise 406 | ``` 407 | 408 | ``` 409 | qiime emperor plot \ 410 | --i-pcoa core-metrics-results/unweighted_unifrac_pcoa_results.qza \ 411 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 412 | --p-custom-axis Depths \ 413 | --o-visualization core-metrics-results/unweighted-unifrac-emperor-Depths.qzv 414 | ``` 415 | 416 | ``` 417 | qiime emperor plot \ 418 | --i-pcoa core-metrics-results/bray_curtis_pcoa_results.qza \ 419 | --m-metadata-file mapping_file_panama_MAY_2017.tsv \ 420 | --p-custom-axis Depths \ 421 | --o-visualization core-metrics-results/bray-curtisc-emperor-Depths.qzv 422 | ``` 423 | 424 | ``` 425 | qiime metadata tabulate \ 426 | --m-input-file taxonomy.qza \ 427 | --o-visualization taxonomy.qzv 428 | ``` 429 | 430 | * View the `.qzv` outputs in [www.view.qiime2.org](https://view.qiime2.org/). 431 | 432 | 433 | --- 434 | 435 | ### Other QIIME Tutorials and resources 436 | 437 | [Loading a phylogentic tree into iTOL](https://forum.qiime2.org/t/loading-greengenes-tree-in-itol/2319/3) - instructions and and guide on how to load taxonomy metadata onto a phylogeny and view it in iTOL 438 | 439 | Follow the instructions on [this QIIME 2 forum post](https://forum.qiime2.org/t/is-there-any-way-to-summarize-taxa-plot-by-category/446) to convert your Feature Table into a tsv file. 440 | 441 | 442 | --- 443 | 444 | ### Other Options for downstream visualization and community analysis 445 | 446 | #### [Phinch](http://phinch.org) 447 | An interactive browser-based data visualization framework (for exploring QIIME outputs) - use your OTU table with taxonomy and mapping file embedded - instructions are here: [https://github.com/PitchInteractiveInc/Phinch/wiki/Quick-Start](https://github.com/PitchInteractiveInc/Phinch/wiki/Quick-Start) - **NOTE: Phinch only works with BIOM 1.0 files, which are no longer the default output in QIIME 1.9 and higher - see file conversion instructions on the link above. 448 | 449 | #### [LEfSE - Linear Discriminate Analasis (LDA) Effectd Size](https://huttenhower.sph.harvard.edu/galaxy/) 450 | Statistical test that looks for enrichment or depletion of OTUs across sample metadata categories (e.g. Prespill vs. Postspill samples). You can run this analysis on the Huttenhower Lab's online Galaxy server (above link) - you will need to convert your OTU table into tab-delimited format and add metadata headings before you can run LEfSE 451 | 452 | #### [Phyloseq](https://github.com/joey711/phyloseq/wiki) - An R package for visualizing QIIME outputs 453 | Offers sleeker visualizaitons and more flexibility than the visualizations offered within QIIME. You can produce heatmaps, box plots, and trim down your OTU table to only look at community patterns within certain OTUs or taxonomic groups. Great for generating publication-ready figures, but requires quite a bit of R knowledge and tweaking to get working. 454 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Welcome to the BITMaB-2 workshop - Corpus Christi, TX (January 15-19, 2018) 2 | 3 | This workshop provides training on how to describe the taxonomic diversity in a marine benthic community, using both morphological taxonomy and environmental sequencing approaches (genome assembly, metabarcoding amplicons, transcriptomics using RNA-seq, and other -Omics methods). 4 | 5 | **Workshop Schedule** can be found [here](https://docs.google.com/document/d/1fztNYDuBBz93ZuKGAw-SGH066xMJ2lJx2_3fi6LDk6I/edit) 6 | 7 | **Computer Preparation Instructions** for this workshop are available [online](https://docs.google.com/document/d/1VdYD8CFJvCIh1KDAz_9e09-Ipju8YJRfQ4RNdWkhI-U/edit#) 8 | 9 | **Participant List** can be found [here](https://docs.google.com/spreadsheets/d/15G_CikjmffEvd0ASti5UzmHY9tem1iWmEbH3h1lDiAs/edit#gid=0) 10 | 11 | Workshop Organizers: 12 | 13 | * [W. Kelley Thomas](https://hcgs.unh.edu/) (PI), University of New Hampshire 14 | * [Holly Bik](https://biklab.github.io) (Co-PI), University of California Riverside 15 | * [Paul Montagna](https://www.harteresearchinstitute.org/people/paul-montagna) (Co-PI), Texas A&M University Corpus Christi 16 | * Francesca Leasi, University of New Hampshire 17 | * Krystalynne Morris, University of New Hampshire 18 | * Michael Reuscher, University of New Hampshire 19 | 20 | Invited Taxnomists: 21 | 22 | * Richard Heard, University of Southern Mississippi 23 | * Alberto De Jésus Navarrete, ECOSUR-Chetumal 24 | * Wonchoel Lee, Hanyang University 25 | * Jesús Ángel de León González, Autonomous University of Nuevo León 26 | * Fabio Moretzsohn, Texas A&M University Corpus Christi 27 | * Tiago Pereira, University of California Riverside 28 | * Alessandro Prantoni, Federal University of Paraná 29 | * Adelaide Rhodes, University of Vermont 30 | * Melissa Rohal, Texas A&M University Corpus Christi 31 | * Martin Sørensen, Natural History Museum of Denmark 32 | 33 | Bioinformatics team: 34 | 35 | * Taruna Schuelke, University of California Riverside 36 | * Joseph Sevigny, University of New Hampshire 37 | * Devin Thomas, University of New Hampshire 38 | 39 | Fieldwork and Lab Technicians: 40 | 41 | * Noe Barrera, Texas A&M University Corpus Christi 42 | * Larry Hyde, Texas A&M University Corpus Christi 43 | * Rick Kalke, Texas A&M University Corpus Christi 44 | * Elani Morgan, Texas A&M University Corpus Christi 45 | * Terry Palmer, Texas A&M University Corpus Christi 46 | * Jamie Smith, Texas A&M University Corpus Christi 47 | 48 | --- 49 | # Workshop Lectures and Tutorials 50 | 51 | **Monday January 15, 2018** 52 | 53 | * [Introduction to Genomics, Bioinformatics, and Sequencing Technologies](https://github.com/BikLab/BITMaB2-Tutorials/tree/master/presentations/KelleyThomas-Introduction-15Jan2018.pdf) (Kelley Thomas) 54 | * Introduction to the command line environment and the world of BASH (Devin Thomas and Joe Sevigny) 55 | * [BASH intro commands](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/bash_lesson.sh) - first part of printout 56 | * [QIIME2 common commands](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/bash_lesson.sh) - second part of printout 57 | * BASH command cheatsheet - third part of printout 58 | * Advanced command line tricks 59 | * **Taxonomy Lecture**: [Goals of the workshop and how it fits into the context of the research project “Genomic Responses to the Deepwater Horizon event and development of high-throughput biological assays for oil spills”](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/FrancescaLeasi-BiologicalAssays-15Jan2018.pdf) (Dr. Francesca Leasi) 60 | * **Taxonomy Lecture**: [Bivalvia and Gastropoda of the GoM](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/FabioMoretzsohn-Bivalves-and-Gastropods-15Jan2018.pdf) (Dr. Fabio Moretzsohn) 61 | 62 | **Tuesday January 16, 2018** 63 | 64 | * [Genome assembly and annotation](https://github.com/BikLab/BITMaB2-Tutorials/tree/master/presentations/KelleyThomas-Introduction-15Jan2018.pdf) (Kelley Thomas) 65 | * **Taxonomy Lecture**: [Nematoda - ecology and species diversity](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/AlbertodeJes%C3%BAsNavarrete-Nematoda-16Jan2018.pdf) (Dr. Alberto de Jesús Navarrete) 66 | * **Taxonomy Lecture**: [Nematoda - molecular phylogeny and metabarcoding](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/TiagoPereira-Nematoda-16Jan2018.pdf) (Dr. Tiago Pereira) 67 | 68 | **Wednesday January 17, 2018** 69 | 70 | * Introduction to the QIIME2 pipeline for microbial ecology and metabarcoding data analysis (Dr. Holly Bik) 71 | * [QIIME 2 Overview Slides](http://bit.ly/q2-vegas-2017) (from June 2017 QIIME2 workshop) 72 | * [QIIME 1 to QIIME 2 script conversion cheatsheet](https://docs.google.com/spreadsheets/d/1AOyA84lGPBk5C2Uflg_pILEL75a6_gdY6SK3ltOt44E/edit#gid=0) 73 | * [QIIME 2 workflow for metabarcoding analysis (18S/16S rRNA) with already-demultiplexed fastq files](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/QIIME2-metabarcoding-tutorial-already-demultiplexed-fastqs.md) 74 | * **Taxonomy Lecture**: [Harpacticoida](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/WonchoelLee-Copepoda-17Jan2018.pdf) (Dr. Wonchoel Lee) 75 | * **Taxonomy Lecture**: [Kinorhyncha & Gnathostomulida](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/MartinSorensen-Gnathostomulida-and-Kinorhyncha-15Jan2018.pdf) (Dr. Martin Sørensen) 76 | 77 | **Thursday January 18, 2018** 78 | 79 | * Data analysis and visualization in QIIME 2, continued (Dr. Holly Bik) 80 | * Discussion and Q&A session about genome assembly results and data analysis 81 | * **Taxonomy Lecture**: [Polychaeta](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/MichaelReuscher-Polychaete-18Jan2018.pdf) (Dr. Jesús Ángel de León González or Dr. Michael Reuscher) 82 | * **Taxonomy Lecture**: [Oligochaeta](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/AlessandroPrantoni-Oligochaeta-18Jan2018.pdf) (Dr. Alessandro Prantoni) 83 | 84 | **Friday January 19, 2018** 85 | 86 | * Mapping DNA sequence reads to reference genomes to investigate genetic diversity or profiling gene expression (Dr. Kelley Thomas, Devin Thomas, and Joe Sevigny) 87 | * Genome visualizations using the UCSC Genome Browser (Dr. Kelley Thomas, Devin Thomas, and Joe Sevigny) 88 | * Introduction to Whole Metagenome Shotgun Analysis - principles, data sets, methods, and introduction to PALADIN (Drs. Kelley Thomas and Holly Bik) 89 | * **Taxonomy Lecture**: [Cumacea and Tanaidacea, with emphasis on shallow water]() (Dr. Richard Heard) 90 | * **Taxonomy Lecture**: [Cumacea and Tanaidacea, with emphasis on deep-sea](https://github.com/BikLab/BITMaB2-Tutorials/blob/master/presentations/AdelaideRhodes-Tanaids-19Jan2018.pdf) (Dr. Adelaide Rhodes) 91 | 92 | --- 93 | 94 | # Some General Notes and Suggestions on Computational Training: 95 | 96 | The [Software Carpentry](https://software-carpentry.org/) and Data Carpentry organizations are a wonderful resource for learning general programming skills. They run in person bioinformatics workshps at institutes worldwide, and maintain open source lesson plans of all workshop materials. These lessons are continually updated as bioinformatics and software tools evolve (which happens frequently!) 97 | 98 | We suggest starting with these keystone Carpentry lessons: 99 | 100 | * [The Unix Shell](http://swcarpentry.github.io/shell-novice/) 101 | * [Version Control with Git](http://swcarpentry.github.io/git-novice/) 102 | * [R for Reproducible Scientific Analysis - Software Carpentry](http://swcarpentry.github.io/r-novice-gapminder/) 103 | * [Programming with R](http://swcarpentry.github.io/r-novice-inflammation/) 104 | * [Programming with Python - Software Carpentry](http://swcarpentry.github.io/python-novice-inflammation/) 105 | * [Data Carpentry Ecology Curriculum](http://www.datacarpentry.org/lessons/#ecology-workshop) 106 | * [Data Carpentry Genomics Curriculum](http://www.datacarpentry.org/lessons/#genomics-workshop) 107 | 108 | Also, try to find a good way to take notes (bioinformatics commands and comments about what they are doing and what parameters you should think about). We reccomend doing this using simple text files (e.g. with Markdown notation) instead of proprieary software such as Microsoft word that does not play nice with copy/paste because of hidden characters. 109 | 110 | Some reccommendations for note taking tools: 111 | 112 | * [Mou](http://25.io/mou/) (markdown text editor) 113 | * [Sublime](https://www.sublimetext.com/) (markdown / programming text editor) 114 | * [TextWrangler](https://www.barebones.com/products/textwrangler/) (basic text editor with programming features) 115 | * [Jupyter Notebooks](http://jupyter.org/) (interactive browswer-based programming notebooks with built in visualzation capabilities) 116 | 117 | 118 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /bash_lesson.sh: -------------------------------------------------------------------------------- 1 | #! /bin/bash 2 | 3 | ## Bash is learning a new language 4 | ## What is a computer? 5 | ## What differentiates a server from your laptop? 6 | ## What are computers good at? 7 | ## What are they not good at? 8 | ## How does that play into the ubiquity of command line tools? 9 | 10 | ## Log into the server: 11 | ## If you were making a program to log people onto a remote server 12 | ## what would you need to know to do that? 13 | 14 | ssh username@ron.sr.unh.edu 15 | 16 | ## Anatomy of a command 17 | ## command - ssh 18 | ## argument - username@ron.sr.unh.edu 19 | 20 | ## Sometimes commands don't need arguments. 21 | cal 22 | pwd 23 | echo 24 | ## And most of the time the arguments are optional 25 | echo Hello World! 26 | 27 | ## What does echo do? 28 | ## How many arguments? 29 | ## what will the output of: 30 | echo Hello World! 31 | ## look like? Why? 32 | 33 | cal 34 | cal -3j 35 | cal -j3 36 | 37 | #What is weird about 1752? How can you tell? 38 | man cal 39 | whatis cal 40 | ## google "bash cal" 41 | 42 | ## Let's talk about paths! 43 | ## Folders and files 44 | ## What is the structure like? Tree analogy 45 | ## root, relative vs absolute paths 46 | ## When should you use which? 47 | tree 48 | ## Where are we? 49 | ## . .. ~ - 50 | pwd 51 | 52 | cd 53 | cd example_assembly 54 | ## Where are you know? 55 | ## What are some ways you could get back to your home directory? 56 | ## How about to the example reads directory? 57 | 58 | ls -lah 59 | ## what are the options? What does ls do? 60 | 61 | ## Move copy and delete: 62 | cd 63 | mkdir sandbox 64 | cd sandbox 65 | cp -r ~/example_assembly . 66 | cd example_assembly 67 | ls -lh 68 | mv quast_report/report.txt . 69 | less report.txt 70 | ## That's lame, let's spruce those results up a bit! 71 | nano report.txt 72 | head report.txt 73 | ## What's the opposite of head? 74 | tail report.txt 75 | tail -20 report.txt 76 | ## What advantages does less have over editors like nano? 77 | grep l report.txt 78 | grep -i l report.txt 79 | grep -v l report.txt 80 | cd ../prokka_report 81 | grep -v hypothetical *.faa 82 | grep -v hypothetical *.faa | grep ">" | less ## quotes are very important!!! 83 | ## anyone got a favorite protein? 84 | 85 | ## Rant about scripting and reproducibility of results... 86 | 87 | ## That was generic and "relatively" unchanged since the dawn of time (1980s) 88 | ## The rest is not, and will probably change lots. 89 | 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tools export demux-paired-end.qza --output-dir . 19 | 20 | qiime demux summarize 21 | --i-data demux-paired-end.qza 22 | --o-visualization demux.qzv 23 | 24 | # pwd 25 | # scp ron.sr.unh.edu:~/q2-tutorial/*.qzv . ## on your own computer!! Don't forget your username, uname@ron... 26 | ## in your browser view.qiime2.org and open demux.qzv 27 | 28 | qiime dada2 denoise-paired\ 29 | --i-demultiplexed-seqs demux-paired-end.qza\ 30 | --p-trim-left-f 6 --p-trim-left-r 6\ 31 | --p-trunc-len-f 205 --p-trunc-len-r 160\ 32 | --p-n-threads 36\ 33 | --o-representative-sequences rep-seqs\ 34 | --o-table table ## ~5 min to run solo 35 | 36 | qiime feature-table summarize\ 37 | --i-table table.qza\ 38 | --o-visualization unfiltered-table.qzv\ 39 | --m-sample-metadata-file mapping_file_panama_MAY_2017.tsv 40 | 41 | qiime tools import\ 42 | --type FeatureData[Sequence]\ 43 | --input-path /usr/local/share/SILVA_databases/SILVA_128_QIIME_release/rep_set/rep_set_18S_only/99/99_otus_18S.fasta\ 44 | --output-path 99_otus_18S 45 | 46 | qiime tools import\ 47 | --type FeatureData[Taxonomy]\ 48 | --input-path /usr/local/share/SILVA_databases/SILVA_128_QIIME_release/taxonomy/18S_only/99/majority_taxonomy_all_levels.txt\ 49 | --source-format HeaderlessTSVTaxonomyFormat\ 50 | --output-path majority_taxonomy_all_levels 51 | 52 | qiime feature-classifier classify-consensus-blast\ 53 | --i-query rep-seqs.qza\ 54 | --i-reference-taxonomy majority_taxonomy_all_levels.qza\ 55 | --i-reference-reads 99_otus_18S.qza\ 56 | --o-classification taxonomy\ 57 | --p-perc-identity 0.97\ 58 | --p-maxaccepts 1 59 | 60 | mv table.qza unfiltered-table.qza 61 | 62 | qiime taxa filter-table \ 63 | --i-table unfiltered-table.qza \ 64 | --i-taxonomy taxonomy.qza \ 65 | --p-include metazoa \ 66 | --o-filtered-table table.qza 67 | 68 | qiime feature-table summarize\ 69 | --i-table table.qza\ 70 | --o-visualization table.qzv\ 71 | --m-sample-metadata-file mapping_file_panama_MAY_2017.tsv 72 | 73 | qiime feature-table tabulate-seqs\ 74 | --i-data rep-seqs.qza\ 75 | --o-visualization rep-seqs.qzv 76 | 77 | # scp ron.sr.unh.edu:~/q2-tutorial/*.qzv . ## on your own computer!! Don't forget your username, uname@ron... 78 | ## in your browser view.qiime2.org and open table.qzv and rep-seqs.qzv 79 | 80 | qiime alignment mafft\ 81 | --i-sequences rep-seqs.qza\ 82 | --o-alignment aligned-rep-seqs.qza 83 | 84 | qiime alignment mask\ 85 | --i-alignment aligned-rep-seqs.qza\ 86 | --o-masked-alignment masked-aligned-rep-seqs.qza 87 | 88 | qiime phylogeny fasttree\ 89 | --i-alignment masked-aligned-rep-seqs.qza\ 90 | --o-tree unrooted-tree.qza 91 | 92 | qiime phylogeny midpoint-root\ 93 | --i-tree unrooted-tree.qza\ 94 | --o-rooted-tree rooted-tree.qza 95 | 96 | qiime diversity alpha-rarefaction\ 97 | --i-table table.qza\ 98 | --i-phylogeny rooted-tree.qza\ 99 | --p-min-depth 500\ 100 | --p-max-depth 6018\ 101 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 102 | --o-visualization alpha-rarefaction.qzv 103 | 104 | qiime diversity core-metrics-phylogenetic\ 105 | --i-phylogeny rooted-tree.qza\ 106 | --i-table table.qza\ 107 | --p-sampling-depth 2999\ 108 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 109 | --output-dir core-metrics-results 110 | 111 | qiime diversity alpha-group-significance\ 112 | --i-alpha-diversity core-metrics-results/faith_pd_vector.qza\ 113 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 114 | --o-visualization core-metrics-results/faith-pd-group-significance.qzv 115 | 116 | qiime diversity alpha-group-significance\ 117 | --i-alpha-diversity core-metrics-results/evenness_vector.qza\ 118 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 119 | --o-visualization core-metrics-results/evenness-group-significance.qzv 120 | 121 | qiime diversity alpha-correlation\ 122 | --i-alpha-diversity core-metrics-results/faith_pd_vector.qza\ 123 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 124 | --o-visualization core-metrics-results/faith-pd-correlation.qzv 125 | 126 | qiime diversity alpha-correlation\ 127 | --i-alpha-diversity core-metrics-results/evenness_vector.qza\ 128 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 129 | --o-visualization core-metrics-results/evenness-correlation.qzv 130 | 131 | qiime diversity beta-group-significance\ 132 | --i-distance-matrix core-metrics-results/unweighted_unifrac_distance_matrix.qza\ 133 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 134 | --m-metadata-category Matrix\ 135 | --o-visualization core-metrics-results/unweighted-unifrac-Matrix-group-significance.qzv\ 136 | --p-pairwise 137 | 138 | qiime emperor plot\ 139 | --i-pcoa core-metrics-results/unweighted_unifrac_pcoa_results.qza\ 140 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 141 | --p-custom-axis Depths\ 142 | --o-visualization core-metrics-results/unweighted-unifrac-emperor-Depths.qzv 143 | 144 | qiime emperor plot\ 145 | --i-pcoa core-metrics-results/bray_curtis_pcoa_results.qza\ 146 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 147 | --p-custom-axis Depths\ 148 | --o-visualization core-metrics-results/bray-curtisc-emperor-Depths.qzv 149 | 150 | qiime metadata tabulate\ 151 | --m-input-file taxonomy.qza\ 152 | --o-visualization taxonomy 153 | 154 | qiime taxa barplot\ 155 | --i-table table.qza\ 156 | --i-taxonomy taxonomy.qza\ 157 | --m-metadata-file mapping_file_panama_MAY_2017.tsv\ 158 | --o-visualization taxa-bar-plots 159 | --------------------------------------------------------------------------------