<metaproperty="og:description"content="Use this function to determine a valid microorganism ID (mo). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like "Staphylococcus aureus"), an abbreviated name (like "S. aureus"), an abbreviation known in the field (like "MRSA"), or just a genus. Please see Examples."/>
<p>Use this function to determine a valid microorganism ID (<code>mo</code>). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like <code>"Staphylococcus aureus"</code>), an abbreviated name (like <code>"S. aureus"</code>), an abbreviation known in the field (like <code>"MRSA"</code>), or just a genus. Please see <em>Examples</em>.</p>
<td><p>a logical to indicate whether <em>Staphylococci</em> should be categorised into coagulase-negative <em>Staphylococci</em> ("CoNS") and coagulase-positive <em>Staphylococci</em> ("CoPS") instead of their own species, according to Karsten Becker <em>et al.</em> (1,2). Note that this does not include species that were newly named after these publications, like <em>S. caeli</em>.</p>
<td><p>a logical to indicate whether beta-haemolytic <em>Streptococci</em> should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (3). These <em>Streptococci</em> will be categorised in their first group, e.g. <em>Streptococcus dysgalactiae</em> will be group C, although officially it was also categorised into groups G and L.</p>
<p>This excludes <em>Enterococci</em> at default (who are in group D), use <code>Lancefield = "all"</code> to also categorise all <em>Enterococci</em> as group D.</p></td>
<td><p>a number between <code>0</code> (or <code>"none"</code>) and <code>3</code> (or <code>"all"</code>), or <code>TRUE</code> (= <code>2</code>) or <code>FALSE</code> (= <code>0</code>) to indicate whether the input should be checked for less probable results, please see <em>Details</em></p></td>
<td><p>a <ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a> to be used for extra reference when translating <code>x</code> to a valid <code>mo</code>. See <code><ahref='mo_source.html'>set_mo_source()</a></code> and <code><ahref='mo_source.html'>get_mo_source()</a></code> to automate the usage of your own codes (e.g. used in your analysis or organisation).</p></td>
<td><p>a regular expression (case-insensitive) of which all matches in <code>x</code> must return <code>NA</code>. This can be convenient to exclude known non-relevant input and can also be set with the option <code>AMR_ignore_pattern</code>, e.g. <code><ahref='https://rdrr.io/r/base/options.html'>options(AMR_ignore_pattern = "(not reported|contaminated flora)")</a></code>.</p></td>
<td><p>language to translate text like "no growth", which defaults to the system language (see <code><ahref='translate.html'>get_locale()</a></code>)</p></td>
<p>A <ahref='https://rdrr.io/r/base/character.html'>character</a><ahref='https://rdrr.io/r/base/vector.html'>vector</a> with additional class <code>mo</code></p>
<p>The algorithm uses data from the Catalogue of Life (see below) and from one other source (see <ahref='microorganisms.html'>microorganisms</a>).</p>
<p>The <code>as.mo()</code> function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:</p><ol>
<p>This will lead to the effect that e.g. <code>"E. coli"</code> (a microorganism highly prevalent in humans) will return the microbial ID of <em>Escherichia coli</em> and not <em>Entamoeba coli</em> (a microorganism less prevalent in humans), although the latter would alphabetically come first.</p>
<li><p>Uncertainty level 0: no additional rules are applied;</p></li>
<li><p>Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;</p></li>
<li><p>Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;</p></li>
<li><p>Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.</p></li>
<p>The level of uncertainty can be set using the argument <code>allow_uncertain</code>. The default is <code>allow_uncertain = TRUE</code>, which is equal to uncertainty level 2. Using <code>allow_uncertain = FALSE</code> is equal to uncertainty level 0 and will skip all rules. You can also use e.g. <code>as.mo(..., allow_uncertain = 1)</code> to only allow up to level 1 uncertainty.</p>
<p>With the default setting (<code>allow_uncertain = TRUE</code>, level 2), below examples will lead to valid results:</p><ul>
<li><p><code>"Streptococcus group B (known as S. agalactiae)"</code>. The text between brackets will be removed and a warning will be thrown that the result <em>Streptococcus group B</em> (<code>B_STRPT_GRPB</code>) needs review.</p></li>
<li><p><code>"S. aureus - please mind: MRSA"</code>. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result <em>Staphylococcus aureus</em> (<code>B_STPHY_AURS</code>) needs review.</p></li>
<li><p><code>"Fluoroquinolone-resistant Neisseria gonorrhoeae"</code>. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result <em>Neisseria gonorrhoeae</em> (<code>B_NESSR_GNRR</code>) needs review.</p></li>
<li><p>Use <code>mo_uncertainties()</code> to get a <ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a> that prints in a pretty format with all taxonomic names that were guessed. The output contains the matching score for all matches (see <em>Background on matching score</em>).</p></li>
<li><p>Use <code>mo_failures()</code> to get a <ahref='https://rdrr.io/r/base/character.html'>character</a><ahref='https://rdrr.io/r/base/vector.html'>vector</a> with all values that could not be coerced to a valid value.</p></li>
<li><p>Use <code>mo_renamed()</code> to get a <ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a> with all values that could be coerced based on old, previously accepted taxonomic names.</p></li>
<h3>Microbial prevalence of pathogens in humans</h3>
<p>The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the <code>prevalence</code> columns in the <ahref='microorganisms.html'>microorganisms</a> and <ahref='microorganisms.old.html'>microorganisms.old</a> data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.</p>
<p>Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Klebsiella</em>, <em>Pseudomonas</em> and <em>Legionella</em>.</p>
<p>Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Aspergillus</em>, <em>Bacteroides</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Chryseobacterium</em>, <em>Cryptococcus</em>, <em>Elisabethkingia</em>, <em>Flavobacterium</em>, <em>Fusobacterium</em>, <em>Giardia</em>, <em>Leptotrichia</em>, <em>Mycoplasma</em>, <em>Prevotella</em>, <em>Rhodotorula</em>, <em>Treponema</em>, <em>Trichophyton</em> or <em>Ureaplasma</em>. This group consequently contains all less common and rare human pathogens.</p>
<p>Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.</p>
<li><p>Becker K <em>et al.</em><strong>Implications of identifying the recently defined members of the <em>S. aureus</em> complex, <em>S. argenteus</em> and <em>S. schweitzeri</em>: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).</strong> 2019. Clin Microbiol Infect. <ahref='https://doi.org/10.1016/j.cmi.2019.02.028'>https://doi.org/10.1016/j.cmi.2019.02.028</a></p></li>
<li><p>Lancefield RC <strong>A serological differentiation of human and other groups of hemolytic streptococci</strong>. 1933. J Exp Med. 57(4): 571–95. <ahref='https://dx.doi.org/10.1084/jem.57.4.571'>https://dx.doi.org/10.1084/jem.57.4.571</a></p></li>
<li><p>Catalogue of Life: Annual Checklist (public online taxonomic database), <ahref='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a> (check included annual version with <code><ahref='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p></li>
The <ahref='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2class="hasAnchor"id="matching-score-for-microorganisms"><aclass="anchor"href="#matching-score-for-microorganisms"></a>Matching score for microorganisms</h2>
<p>With ambiguous user input in <code>as.mo()</code> and all the <code><ahref='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code><ahref='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\), is calculated as:</p>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><ahref='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code>?as.mo</code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (<ahref='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a>). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.</p>
<p><ahref='catalogue_of_life.html'>Click here</a> for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with <code><ahref='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>.</p>
<h2class="hasAnchor"id="reference-data-publicly-available"><aclass="anchor"href="#reference-data-publicly-available"></a>Reference data publicly available</h2>
<p>All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find <ahref='https://msberends.github.io/AMR/articles/datasets.html'>all download links on our website</a>, which is automatically updated with every code change.</p>
<p>On our website <ahref='https://msberends.github.io/AMR'>https://msberends.github.io/AMR</a> you can find <ahref='https://msberends.github.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR analysis, the <ahref='https://msberends.github.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <ahref='https://msberends.github.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>. As we would like to better understand the backgrounds and needs of our users, please <ahref='https://msberends.github.io/AMR/survey.html'>participate in our survey</a>!</p>
<divclass='dont-index'><p><ahref='microorganisms.html'>microorganisms</a> for the <ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a> that is being used to determine ID's.</p>
<p>The <code><ahref='mo_property.html'>mo_property()</a></code> functions (like <code><ahref='mo_property.html'>mo_genus()</a></code>, <code><ahref='mo_property.html'>mo_gramstain()</a></code>) to get properties based on the returned code.</p></div>
<p>Developed by <ahref='https://www.rug.nl/staff/m.s.berends/'>Matthijs S. Berends</a>, <ahref='https://www.rug.nl/staff/c.f.luz/'>Christian F. Luz</a>, <ahref='https://www.rug.nl/staff/a.w.friedrich/'>Alexander W. Friedrich</a>, <ahref='https://www.rug.nl/staff/b.sinha/'>Bhanu N. M. Sinha</a>, <ahref='https://www.rug.nl/staff/c.j.albers/'>Casper J. Albers</a>, <ahref='https://www.rug.nl/staff/c.glasner/'>Corinna Glasner</a>.</p>
