remove redundant text

README.rst

@@ -34,7 +34,7 @@ Overview and Use of RGI
 =======================
 
 * `Help Menu and Usage </docs/rgi_help.rst>`_
-* `RGI Databases </docs/rgi_load.rst>`_
+* `RGI Reference Databases </docs/rgi_load.rst>`_
 * `Analyzing Genomes, Genome Assemblies, Metagenomic Contigs, or Proteomes </docs/rgi_main.rst>`_ (a.k.a. RGI main)
 * `Analyzing Metagenomic Reads </docs/rgi_bwt.rst>`_ (a.k.a. RGI bwt)
 * `K-mer Prediction of Pathogen-of-Origin for AMR Genes </docs/rgi_kmer.rst>`_ (beta-testing)

docs/rgi_bwt.rst

@@ -1,7 +1,5 @@
-Analyzing Metagenomic Reads (a.k.a. RGI bwt)
---------------------------------------------
-
- >  The text below provides an overview of analysis of FASTQ sequencing reads. For command line examples see `Running RGI bwt with FASTQ files <#running-rgi-bwt-with-fastq-files>`_.
+Analyzing Metagenomic Reads
+---------------------------
 
 RGI can align short DNA sequences in FASTQ format using `Bowtie2 <http://bowtie-bio.sourceforge.net/bowtie2/index.shtml>`_ , `BWA <http://bio-bwa.sourceforge.net>`_ , or `KMA <https://bitbucket.org/genomicepidemiology/kma/src/master>`_ against CARD's `protein homolog models <https://card.mcmaster.ca/ontology/40292>`_. The default and recommended read aligner is `KMA <https://bitbucket.org/genomicepidemiology/kma/src/master>`_ due to its documented `better performance for redundant databases <https://pubmed.ncbi.nlm.nih.gov/30157759/>`_ such as CARD. While CARD is not truly redundant, i.e. there are no identical reference sequences, CARD does reflect the `AMR alelle network problem <https://pubmed.ncbi.nlm.nih.gov/29335005/>`_ in that many sequences are very similar. For example, the nucleotide sequences of TEM-1 and TEM-2 are `99% similar with no alignment gaps </images/TEM-alignment.jpg>`_. A sample generating short reads from a legitimate TEM-1 gene may result in reads aligned among TEM-1, TEM-2, or other TEM beta-lactamases depending upon the alignment algorithm chosen. The `KMA publication <https://pubmed.ncbi.nlm.nih.gov/30157759/>`_ and our own simulations find KMA best resolves this issue:
 
@@ -20,10 +18,8 @@ CARD's `Resistomes & Variants <https://card.mcmaster.ca/genomes>`_ and `Prevalen
 
 **Note**: As RGI bwt makes no assumptions about pre-processing of metagenomics data, we suggest prior quality/adaptor trimming of reads with `skewer <https://github.com/relipmoc/skewer>`_ and deduplication of reads using `dedupe.sh <https://sourceforge.net/projects/bbmap/>`_. If needed, down-sampling of FASTQ data can be performed using `seqtk <https://github.com/lh3/seqtk>`_. Thanks to Allison Guitor of McMaster University for these suggestions.
 
-Using RGI bwt (Metagenomic Short Reads, Genomic Short Reads)
-------------------------------------------------------------
-
-**UPDATED RGI version 6.0.0 onward: In earlier versions of RGI, by default RGI bwt aligned reads to reference sequences from CARD's protein homolog models, protein variant models, rRNA mutation models, and protein over-expression models. However, the latter three model types require comparison to CARD's curated lists of mutations known to confer phenotypic antibiotic resistance to differentiate alleles conferring resistance from antibiotic susceptible alleles, e.g. a wild-type gyrase susceptible to fluoroquinolones. As such, earlier versions of RGI were over-reporting antibiotic resistance genes by not checking for these curated mutations. For example, while the KMA algorithm reports SNPs relative to reference, RGI was not screening these SNPs against CARD. Read alignments against the protein variant model, rRNA mutation model, and protein over-expression model reference sequences can now only be listed by use of the new --include_other_models parameter, but at this time these results still do not include comparison to CARD's curated lists of mutations. As such, these often spurious results are no longer included in default RGI bwt output. Support for mutation screening models will be added to future versions of RGI bwt.**
+Using RGI bwt
+-------------
 
 .. code-block:: sh
 

docs/rgi_kmer.rst

@@ -1,14 +1,12 @@
-K-mer Prediction of Pathogen-of-Origin for AMR Genes (beta-testing)
---------------------------------------------------------------------------
-
- > The text below provides an overview of k-mer prediction of pathogen-of-origin. For command line examples see `Using RGI kmer_query <#using-rgi-kmer-query-k-mer-taxonomic-classification>`_.
+K-mer Prediction of Pathogen-of-Origin for AMR Genes
+----------------------------------------------------
 
 CARD's `Resistomes & Variants <https://card.mcmaster.ca/genomes>`_ and `Prevalence Data <https://card.mcmaster.ca/prevalence>`_ (see above) provides a data set of AMR alleles and their distribution among pathogens and plasmids. CARD's k-mer classifiers sub-sample these sequences to identify k-mers (default length 61 bp) that are uniquely found within AMR alleles of individual pathogen species, pathogen genera, pathogen-restricted plasmids, or promiscuous plasmids. CARD's k-mer classifiers can then be used to predict pathogen-of-origin for matches found by RGI for genomes, genome assemblies, metagenomic contigs, or metagenomic reads.
 
 **CARD's k-mer classifiers assume the data submitted for analysis has been predicted to encode AMR genes, via RGI or another AMR bioinformatic tool. The k-mer data set was generated from and is intended exclusively for AMR sequence space.** As above, the reported results are entirely dependant upon the curated AMR detection models in CARD, the algorithms available in RGI, and the pathogens & sequences sampled during generation of CARD's `Resistomes & Variants <https://card.mcmaster.ca/genomes>`_ and `Prevalence Data <https://card.mcmaster.ca/prevalence>`_.
 
-Using RGI kmer_query (K-mer Taxonomic Classification)
------------------------------------------------------
+Using RGI kmer_query
+--------------------
 
 **This is an unpublished algorithm undergoing beta-testing.**
 

docs/rgi_main.rst

@@ -1,7 +1,5 @@
-Analyzing Genomes, Genome Assemblies, Metagenomic Contigs, or Proteomes (a.k.a. RGI main)
------------------------------------------------------------------------------------------
-
- > The text below provides an overview of analysis of FASTA sequences (contigs, genomes, etc.). For command line examples see `Running RGI main with Genome or Assembly DNA Sequences <#running-rgi-main-with-genome-or-assembly-dna-sequences>`_.
+Analyzing Genomes, Genome Assemblies, Metagenomic Contigs, or Proteomes
+-----------------------------------------------------------------------
 
 If DNA FASTA sequences are submitted, RGI first predicts complete open reading frames (ORFs) using `Prodigal <https://github.com/hyattpd/Prodigal>`_ (ignoring those less than 30 bp) and analyzes the predicted protein sequences. This includes a secondary correction by RGI if Prodigal undercalls the correct start codon to ensure complete AMR genes are predicted. However, if Prodigal fails to predict an AMR ORF, RGI will produce a false negative result.
 
@@ -74,10 +72,8 @@ Lastly, analyzing metagenomic assemblies or merged metagenomic reads using RGI m
 
  > `What RGI settings are best for a Metagenome-Assembled Genome (MAG)? <https://github.com/arpcard/FAQ#rgi-faqs>`_
 
-Using RGI main (Genomes, Genome Assemblies, Metagenomic Contigs, or Proteomes)
--------------------------------------------------------------------------------
-
-**UPDATED RGI version 6.0.0 onward: In earlier versions of RGI, by default all Loose matches of 95% identity or better were automatically listed as Strict, regardless of alignment length. At that time, this behaviour could only be suppressed by using the --exclude_nudge parameter. This default behaviour and the --exclude_nudge parameter have been discontinued. Loose matches of 95% identity or better can now only be listed (i.e, nudged) as Strict matches, regardless of alignment length, by use of the new --include_nudge parameter. As such, these often spurious results are no longer included in default RGI main output.**
+Using RGI main
+--------------
 
 .. code-block:: sh