The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences

Riehle, Kevin; Coarfa, Cristian; Jackson, Andrew; Ma, Jun; Tandon, Arpit; Paithankar, Sameer; Raghuraman, Sriram; Mistretta, Toni Ann; Saulnier, Delphine M.; Raza, Sabeen; Díaz, M.A. Valdovinos; Shulman, Robert J.; Aagaard, Kjersti M.; Versalovic, James; Milosavljevic, Aleksandar

doi:10.1186/1471-2105-13-s13-s11

Cited by 41 publications

(49 citation statements)

References 23 publications

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“…Numerous bioinformatics suites, such as Quantitative Insights into Microbial Ecology (QIIME), Mothur, and Genboree, have been developed specifically to handle the taxonomic information gathered from 16S studies (Schloss et al 2009; Caporaso et al 2010; Riehle et al 2012). These tools streamline the process of quality filtering, operation taxonomic unit (OTU) picking, chimeric sequence removal and taxonomic assignment.…”

Section: Metagenomic Science: Methods Applications and Challenges Imentioning

confidence: 99%

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

Prince

Chu

Seferovic

et al. 2015

Cold Spring Harbor Perspectives in Medicine

118

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The human microbiome, the collective genome of the microbial community that is on and within us, has recently been mapped. The initial characterization of healthy subjects has provided investigators with a reference population for interrogating the microbiome in metabolic, intestinal, and reproductive health and disease states. Although it is known that bacteria can colonize the vagina, recent metagenomic studies have shown that the vaginal microbiome varies among reproductive age women. Similarly, the richness and diversity of intestinal microbiota also naturally fluctuate among gravidae in both human and nonhuman primates, as well as mice. Moreover, recent evidence suggests that microbiome niches in pregnancy are not limited to maternal body sites, as the placenta appears to harbor a low biomass microbiome that is presumptively established in early pregnancy and varies in association with a remote history of maternal antenatal infection as well as preterm birth. In this article, we will provide a brief overview on metagenomics science as a means to investigate the microbiome, observations pertaining to both variation and the presumptive potential role of a varied microbiome during pregnancy, and how future studies of the microbiome in pregnancy may lend to a better understanding of human biology, reproductive health, and parturition.

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Section: Metagenomic Science: Methods Applications and Challenges Imentioning

confidence: 99%

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

Prince

Chu

Seferovic

et al. 2015

Cold Spring Harbor Perspectives in Medicine

118

View full text Add to dashboard Cite

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“…Several studies have demonstrated the utility of the Boruta algorithm as an efficient technique for feature selection [25], [39], [40] and it has been applied in a number of studies including document indexing [43], microbial metagenomic analyses [44], [45], microarray gene expression studies [46], and forest biodiversity modeling [47]. Reference [47] employed Boruta to determine relevant predictive features for modeling forest species richness.…”

Section: Introductionmentioning

confidence: 99%

Using Boruta-Selected Spectroscopic Wavebands for the Asymptomatic Detection of Fusarium Circinatum Stress

Poona

Ismail

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High spectral resolution multitemporal data were used to model asymptomatic stress caused by Fusarium circinatum in 3-month old Pinus radiata seedlings. The objectives of the study were: 1) to identify an optimal subset of wavebands that could model asymptomatic stress in P. radiata seedlings and 2) to develop a robust classification model for discriminating healthy and stressed seedlings. To achieve these objectives, spectral data were collected for healthy, infected, and damaged seedlings using a hand-held field spectroradiometer. The data were analyzed, first for combined classes and then for class pairs using the Boruta algorithm. Results indicated that the best discrimination was possible at week three for all classes, with a KHAT value of 0.79 and an out of bag error of 14.00% ( ), using a subset of 107 wavebands. A closer examination of the class pairs, namely healthy-infected (H-I) and infected-damaged (I-D), showed improved discrimination with KHAT values of 0.82 and 0.84, respectively. The H-I class pair was classified using a subset of just 38 wavebands, whereas the I-D class pair was classified using a subset of just 40 wavebands. Overall, this study demonstrated that it is more difficult to discriminate asymptomatic stress when additional stress related classes are present. Nonetheless, the methodology developed in this study has the potential to be operationalized within a nursery environment for the early detection of F. circinatum-induced stress in P. radiata seedlings.

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“…41 Sequences that had lengths shorter than 200 bp, had average quality scores <20, harbored ambiguous base calls, or had mismatches to their barcode or sequencing primer were excluded from downstream analysis. The quality-filtered sequences were assigned to operational taxonomic units (OTUs, sequences that share ⩾97% similarity) using Cd-Hit.…”

Section: 37mentioning

confidence: 99%