text: user4758: We currently use DADA2 for picking ASVs and the assignTaxonomy funciton for assingment to genera. Google does bring up various recent articles on species-level assignment with 16S. 
  Does anyone have an opinion on the current best-practice?
  More simply, are there any current candidates for reasonably accurate species-level assignment?
  
  user9088: I have been using dada2 and phyloseq (phangorn for trees) combination to analyze a V4 region 16s rRNA data set. I find their approach and explanations very understandable and usable since I prefer R.
  I was under the impression that even full-length 16s rRNA gene sequences were inadequate to classify up to species level since closely related species within a genus can have identical 16S rRNA gene sequences.
  But, turns out, intra-genomic variants of this gene within a species or even strain may allow their unambiguous identification according to this paper:
  Johnson, J.S., Spakowicz, D.J., Hong, B. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 10, 5029 (2019). https://doi.org/10.1038/s41467-019-13036-1) available at: https://rdcu.be/b5cgB
  Another relevant article regarding using black box ML algorithms (random forests) versus a straightforward logistic regression is discussed in this one:
  Topçuoğlu BD, Lesniak NA, Ruffin MT 4th, Wiens J, Schloss PD. A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems. mBio. 2020;11(3):e00434-20. Published 2020 Jun 9. doi:10.1128/mBio.00434-20
  Since the OP has probably already moved on long since the original query, I am writing this to document relevant current references for myself and anyone researching this topic on StackExchange Bioinformatics.
  
original_text: 4758: <p>We currently use DADA2 for picking ASVs and the <code>assignTaxonomy</code> funciton for assingment to genera. Google does bring up various recent articles on species-level assignment with 16S. </p>
  
  <p>Does anyone have an opinion on the current best-practice?</p>
  
  <p>More simply, are there any current candidates for reasonably accurate species-level assignment?</p>
   9088: <p>I have been using dada2 and phyloseq (phangorn for trees) combination to analyze a V4 region 16s rRNA data set. I find their approach and explanations very understandable and usable since I prefer R.</p>
  <p>I was under the impression that even full-length 16s rRNA gene sequences were inadequate to classify up to species level since <em>closely related species within a genus can have identical 16S rRNA gene sequences</em>.</p>
  <p>But, turns out, intra-genomic variants of this gene within a species or even strain may allow their unambiguous identification according to this paper:</p>
  <p>Johnson, J.S., Spakowicz, D.J., Hong, B. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 10, 5029 (2019). <a href="https://doi.org/10.1038/s41467-019-13036-1" rel="nofollow noreferrer">https://doi.org/10.1038/s41467-019-13036-1</a>) available at: <a href="https://rdcu.be/b5cgB" rel="nofollow noreferrer">https://rdcu.be/b5cgB</a></p>
  <p>Another relevant article regarding using black box ML algorithms (random forests) versus a straightforward logistic regression is discussed in this one:</p>
  <p>Topçuoğlu BD, Lesniak NA, Ruffin MT 4th, Wiens J, Schloss PD. A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems. mBio. 2020;11(3):e00434-20. Published 2020 Jun 9. doi:10.1128/mBio.00434-20</p>
  <p>Since the OP has probably already moved on long since the original query, I am writing this to document relevant current references for myself and anyone researching this topic on StackExchange Bioinformatics.</p>
  