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Here we introduce experimental design, quality control and \u201cdenoising\u201d approaches for sequencing low biomass specimens.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus\/Pathogen Mini Kit\u00ae (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore\u00ae Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass\u00a0bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (<jats:italic>r<\/jats:italic>\u2009=\u20090.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by<jats:underline>&lt;<\/jats:underline>\u2009500 16S rRNA gene copies\/\u03bcl) were primarily collected at<jats:underline>&lt;<\/jats:underline>\u200914\u2009days of age. We found that low biomass technical repeats also produced higher alpha diversities (<jats:italic>r<\/jats:italic>\u2009=\u2009\u2212\u20090.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the<jats:italic>decontam<\/jats:italic>package in R, provides better representations of indigenous bacteria following decontamination.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We provide insight into experimental design, quality control steps and \u201cdenoising\u201d approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12866-020-01795-7","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T17:02:37Z","timestamp":1589302957000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Optimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-3776","authenticated-orcid":false,"given":"Shantelle","family":"Claassen-Weitz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sugnet","family":"Gardner-Lubbe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kilaza S.","family":"Mwaikono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elloise","family":"du Toit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heather J.","family":"Zar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark P.","family":"Nicol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,12]]},"reference":[{"key":"1795_CR1","doi-asserted-by":"publisher","first-page":"441","DOI":"10.3343\/alm.2016.36.5.441","volume":"36","author":"KH Hong","year":"2016","unstructured":"Hong KH, Hong SK, Cho SI, Ra E, Han KH, Kang SB, et al. 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