update formatting because I am a github newb

README.md

@@ -45,21 +45,25 @@ The following will walk through the code for the analysis done as an initial tes
 
 First, a simulated mock community was created. FASTA files were downloaded for eight taxa selected from NCBI's BioSample for their extensive phenotypic antibiotic resistance. NCBI's tool ART was used to simulate FASTQs from a HiSeq 2500 these FASTA files at three coverage levels - 5, 50, and 100x coverage.
 
-`conda install -c bioconda/label/cf201901 art 
+```
+conda install -c bioconda/label/cf201901 art 
 
-art_illumina -ss HS25 -sam -i reference.fa -p -l 150 -f 5 -m 200 -s 10 -o paired_dat ## -f flag changed for 5,50,100x coverage`
+art_illumina -ss HS25 -sam -i reference.fa -p -l 150 -f 5 -m 200 -s 10 -o paired_dat ## -f flag changed for 5,50,100x coverage
+```
 
 Once this was done for each of the eight mock community members, the simulated fastqs (`paired_dat_[1|2].fq`), the eight fastqs were combined to create one fastq file with even coverage at each coverage levels.
 
 `cat 'ls -1 A*1* ... | sort -V ' > combo_1.fq  ## the ... represents each taxa being identified in alphabetical order for the cat`
 
 Once fastqs were combined, contigs were assembled using metaSPAdes. 
 
-`conda create -y -n spades-test -c conda-forge -c bioconda spades python=3.7.7 
+```
+conda create -y -n spades-test -c conda-forge -c bioconda spades python=3.7.7 
 
 conda activate spades-test 
 
-spades.py --meta  --pe1-1 combo_1.fq --pe1-2 combo_2.fq -o fasta/ `
+spades.py --meta  --pe1-1 combo_1.fq --pe1-2 combo_2.fq -o fasta/ 
+```
 
 With this, three mock communities were generated, again, for the three coverage levels (5x,50x, and 100x).
 
@@ -71,7 +75,8 @@ This section will walk through how we set up a conda environment for each of the
 
 shortBRED13 v0.9.3 uses a set of marker genes to search metagenomic data for protein families of interest. The bioBakery team published an AMR gene marker database built from 849 AR protein families derived from the ARDB20 v1.1 and independent curation alongside shortBRED, which is used in this study.
 
-`conda create –n shortbred 
+```
+conda create –n shortbred 
 
 conda activate shorbred
 
@@ -84,56 +89,62 @@ conda install python=3.7
 wget https://raw.githubusercontent.com/biobakery/shortbred/master/sample_markers/ShortBRED_ARDB_Evaluation_markers.faa ## get database  
 
 shortbred_quantify.py --markers /full/path/to/shortbred_database/ShortBRED_ARDB_Evaluation_markers.faa --wgs /full/path/to/fastas/contigs.fasta --results combo_shortbred.tsv --tmp combo_quant --usearch /full/path/to/usearch11.0.667_i86linux32 
-`
+```
 
 ### [fARGene](https://github.com/fannyhb/fargene)
 
 fARGene v.0.1 uses Hidden Markov Models to detect AMR genes from short metagenomic data or long read data. This is different from most other tools which compare the reads directly. fARGene has three pre-built models for detecting resistance to quinolone, tetracycline, and beta lactamases, which we test here.
 
-`
+```
 conda activate python2 
 
 conda install -c conda-forge -c bioconda fargene 
 
-fargene -i fastq/combo* --store-peptides --hmm-model class_a -o fargene_out_fa/class_a --meta --force `
+fargene -i fastq/combo* --store-peptides --hmm-model class_a -o fargene_out_fa/class_a --meta --force 
+```
 
 The following arguments were used with the `--hmm-model` flag to run all the pre-built fARGene models, with the output directory matching the name and spelling of the `--hmm-model` flag: `class_a`, `class_b_1_2`, `class_b_3`, `class_c`, `class_d_1`, `class_d_2`, qnr, `tet_efflux`, `tet_rpg`, and `tet_enzyme`.
 
 ### [AMRFinderPlus](https://github.com/ncbi/amr/wiki)
 
 AMR Finder Plus v.3.9.3 uses BLASTX translated searches and hierarchical tree of gene families to detect AMR genes. The database is derived from the Pathogen Detection Reference Gene Catalog and was compiled as part of the National Database of Antibiotic Resistant Organisms (NDARO).
 
-`
+```
 conda install -y -c bioconda ncbi-amrfinderplus 
 
-amrfinder -n fasta/contigs.fasta --plus --threads 3 -o AMRFinderPlus_out/combo_out `
+amrfinder -n fasta/contigs.fasta --plus --threads 3 -o AMRFinderPlus_out/combo_out 
+```
 
 ### [RGI](https://github.com/arpcard/rgi)
 
 RGI v5.1.1 uses protein homology and SNP models to predict ‘resistomes’. It uses CARD’s protein homolog models as a database. RGI predicts open reading frames (ORFs) using Prodigal, detects homologs with DIAMOND, and matches to CARD’s database and model cut off values. 
 
-`conda create --prefix=/home/ewissel/conda/rgi -c bioconda rgi=5.1.1 -c conda-forge -c defaults  ## note I needed to use the prefix flag for this to work
+```
+conda create --prefix=/home/ewissel/conda/rgi -c bioconda rgi=5.1.1 -c conda-forge -c defaults  ## note I needed to use the prefix flag for this to work
 
 conda activate /home/ewissel/conda/rgi 
 
-rgi -i fasta/contigs.fasta -o rgi_out -t contig `
+rgi -i fasta/contigs.fasta -o rgi_out -t contig 
+```
 
 ### [ResFinder](https://cge.cbs.dtu.dk/services/ResFinder/)
 
 ResFinder v4.0 is available both as a web based application or the command line. We use ResFinder 4 for this data, which was specifically designed for detecting genotypic resistance in phenotypically resistant samples. ResFinder aligns reads directly to its own curated database without need for assembly. 
 
 Please note that this was run while ResFinder was being updated to version 4.0 (and the docker version was catching up). I ran into issues with the docker version of ResFinder in May/June, and instead used the web based ResFinder results in the publication study; hyperlink to the web based ResFinder in the heading of this section. 
 
-`docker build -t resfinder . 
+```
+docker build -t resfinder . 
 
 docker run --rm -it -v $(pwd)/db_resfinder/:/usr/src/db_resfinder -v $(pwd)/results/:/usr/src/results resfinder -v $(pwd)/dat/combo_1.fq:/usr/src/combo_1.fq -ifq /usr/src/combo_1.fq  /usr/src/combo_2.fq -acq -db_res /usr/src/db_resfinder -o /usr/src/results 
-`
+```
 
 ### [ABRicate](https://github.com/tseemann/abricate)
 
 ABRIcate v.1.0.1 takes contig FASTA files as inputs and compared reads against a large database created by compiling several existing database, including NCBI AMRFinder Plus, CARD, ResFinder, ARG-ANNOT, MEGARES, EcOH, PlasmidFinder, VFDB, and Ecoli_VF. ABRIcate reports on acquired AMR genes and not mutational resistance.
 
-`conda activate python3.6 
+```
+conda activate python3.6 
 
 conda install -c conda-forge -c bioconda -c defaults abricate 
 
@@ -152,26 +163,29 @@ conda update abricate
 abricate --list 
 
 abricate fasta/contigs.fasta 
-\abricate assembly.fa.gz `
+\abricate assembly.fa.gz 
+```
 
 ### [sraX](https://github.com/lgpdevtools/sraX#srax)
 
 sraX v.1.5 is built as a one step tool; in a single command, sraX downloads a database and aligns contigs to this database with DIAMOND21. By default, sraX uses CARD, though other options can be specified. As we use default settings for all tools, only CARD is used in this study for sraX.
 
-`conda create –name sraX 
+```
+conda create –name sraX 
 
 conda activate sraX 
 
 sraX –v 
 
 sraX -i sim_dat/fasta/ -o sraX_out 
-`
+```
 
 ### [deepARG](https://github.com/gaarangoa/deeparg2.0)
 
 deepARG v.2.0 uses a supervised deep learning based approach for antibiotic resistance gene annotation of metagenomic sequences. It combines three databases—CARD, ARDB, and UNIPROT—and categorizes them into resistance categories. 
 
-`conda create -n deeparg_env python=2.7.18 
+```
+conda create -n deeparg_env python=2.7.18 
 
 conda activate deeparg_env 
 
@@ -182,17 +196,19 @@ conda install -c bioconda diamond==0.9.24
 deeparg download_data -o /path/to/local/directory/ 
 
 deeparg predict -i fasta/contigs.fasta -o deeparg_out_combo_fasta --model SS –d full/path/to/downloaded_data/deeparg_data 
- `
+ ```
 
  ### [StarAMR](https://github.com/phac-nml/staramr#staramr)
 
 starAMR v.0.7.2 uses blast+ to compare contigs against a combined database with data from ResFinder, PointFinder, and PlasmidFinder. 
 
-`conda install -c bioconda staramr==0.7.2 
+```
+conda install -c bioconda staramr==0.7.2 
 
 cpan Moo 
 
-staramr search fasta/contigs.fasta -o staramr_out `
+staramr search fasta/contigs.fasta -o staramr_out 
+```
 
 With this, all nine bioinformatic tools were run on the same mock community at three coverage levels and combined using [hAMRonization](https://github.com/pha4ge/hAMRonization). For shortBRED and fARGene, which are not a part of hAMRonization at the time of analysis, options were added to hAMRoaster to examine its output (as decribed above in the section on hAMRoaster flags). 
 
@@ -202,8 +218,7 @@ hAMRonization is a strong tool which creates a unified format for examining the
 
 At the time of analysis, hAMRonization was under development and documentation was not yet complete. As such, the database versions and analysis software versions, which are required flags for hAMRonization, reflect a minimally sufficient string for this and not the true version used in this analysis. This is not a recommended use of this tool or these flags, but we are including this detail for the sake of true replication.
 
-`conda install hAMRonization 
-`
+`conda install hAMRonization `
 
 To generate the hAMROnized format for each tool: