Complete documentation on how to run SuperCRUNCH is available at: https://github.com/dportik/SuperCRUNCH/wiki The following generalized commands were used to run the analysis. Please note that the `/Path/To/` should be changed to the actual file locations. These are the multiple sequence alignment commands. **MACSE** MACSE was used to align all nuclear and mtDNA markers (except TTN, which produced odd results). Because MACSE runs rather slowly, I broke the markers into 5 sets for parallel analysis. To run MACSE, you will need to download it separately from SuperCRUNCH (and use v2.06!). ``` python Align.py -i /Path/To/9-Alignment/macse-nuc-other1/input -o /Path/To/9-Alignment/macse-nuc-other1/output -a macse --mpath /Path/To/macse_v2.06.jar --table standard --mem 5 --pass_fail ``` ``` python Align.py -i /Path/To/9-Alignment/macse-nuc-other2/input -o /Path/To/9-Alignment/macse-nuc-other2/output -a macse --mpath /Path/To/macse_v2.06.jar --table standard --mem 5 --pass_fail ``` ``` python Align.py -i /Path/To/9-Alignment/macse-nuc-rag1-tyr/input -o /Path/To/9-Alignment/macse-nuc-rag1-tyr/output -a macse --mpath /Path/To/macse_v2.06.jar --table standard --mem 5 --pass_fail ``` ``` python Align.py -i /Path/To/9-Alignment/macse-mtdna-coi_p1-cytb/input -o /Path/To/9-Alignment/macse-mtdna-coi_p1-cytb/output -a macse --mpath /Path/To/macse_v2.06.jar --table vertmtdna --mem 10 --pass_fail ``` ``` python Align.py -i /Path/To/9-Alignment/macse-mtdna-coi_p2-nd1-nd2-nd4/input -o /Path/To/9-Alignment/macse-mtdna-coi_p2-nd1-nd2-nd4/output -a macse --mpath /Path/To/macse_v2.06.jar --table vertmtdna --mem 10 --pass_fail ``` **MAFFT** MAFFT was used to align TTN, which behaved strangely with MACSE. ``` python Align.py -i /Path/To/9-Alignment/mafft/input -o /Path/To/9-Alignment/mafft/output -a mafft --threads 6 ``` **TaxonomyAlign** I developed a new strategy called TaxonomyAlign (https://github.com/dportik/TaxonomyAlign) and used it to align 12S and 16S. The process for this was a little different, as it is a standalone tool I developed for usage outside of SuperCRUNCH. The unaligned sequences needed to be relabeled to obtain genus_species labels. Note that this particular step is not shown in the module folder contents. ``` python Fasta_Relabel_Seqs.py -i /Path/To/9-Alignment/Taxonomy-Align/input/1-to-relabel -o /Path/To/9-Alignment/Taxonomy-Align/input/2-relabeled-by-species -r species ``` Once the sequences are relabeled, we can run the analysis for each marker independently. TaxonomyAlign requires a map file, which is used to guide groups for the subalignments. This file is provided (TaxonomyAlign-map-file-Anura.txt). For more instructions please refer to the TaxonomyAlign github. 12S ``` python TaxonomyAlign.py -f /Path/To/9-Alignment/Taxonomy-Align/input/MTDNA_12S_extracted_contfiltered_oneseq_Adjusted_relabeled.fasta -m /Path/To/9-Alignment/Taxonomy-Align/input/TaxonomyAlign-map-file-Anura.txt -o /Path/To/9-Alignment/Taxonomy-Align/output-12S -a E-INS-i -t 10 ``` 16S ``` python TaxonomyAlign.py -f /Path/To/9-Alignment/Taxonomy-Align/input/MTDNA_16S_extracted_contfiltered_oneseq_Adjusted_relabeled.fasta -m /Path/To/9-Alignment/Taxonomy-Align/input/TaxonomyAlign-map-file-Anura.txt -o /Path/To/9-Alignment/Taxonomy-Align/output-16S -a E-INS-i -t 10 ```