How to cite the Bioconductor package decontam

Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2017). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. bioRxiv. https://doi.org/10.1101/221499

1.

Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data [Internet]. bioRxiv; 2017. Available from: http://dx.doi.org/10.1101/221499

@UNPUBLISHED{Davis2017-xt,
  title       = "Simple statistical identification and removal of contaminant
                 sequences in marker-gene and metagenomics data",
  author      = "Davis, Nicole M and Proctor, Diana M and Holmes, Susan P and
                 Relman, David A and Callahan, Benjamin J",
  abstract    = "AbstractBackgroundThe accuracy of microbial community surveys
                 based on marker-gene and metagenomic sequencing (MGS) suffers
                 from the presence of contaminants --- DNA sequences not truly
                 present in the sample. Contaminants come from various sources,
                 including reagents. Appropriate laboratory practices can
                 reduce contamination, but do not eliminate it. Here we
                 introduce decontam (https://github.com/benjjneb/decontam), an
                 open-source R package that implements a statistical
                 classification procedure that identifies contaminants in MGS
                 data based on two widely reproduced patterns: contaminants
                 appear at higher frequencies in low-concentration samples, and
                 are often found in negative controls.Resultsdecontam
                 classified amplicon sequence variants (ASVs) in a human oral
                 dataset consistently with prior microscopic observations of
                 the microbial taxa inhabiting that environment and previous
                 reports of contaminant taxa. In metagenomics and marker-gene
                 measurements of a dilution series, decontam substantially
                 reduced technical variation arising from different sequencing
                 protocols. The application of decontam to two recently
                 published datasets corroborated and extended their conclusions
                 that little evidence existed for an indigenous placenta
                 microbiome, and that some low-frequency taxa seemingly
                 associated with preterm birth were
                 contaminants.Conclusionsdecontam improves the quality of
                 metagenomic and marker-gene sequencing by identifying and
                 removing contaminant DNA sequences. decontam integrates easily
                 with existing MGS workflows, and allows researchers to
                 generate more accurate profiles of microbial communities at
                 little to no additional cost.",
  institution = "bioRxiv",
  month       =  nov,
  year        =  2017,
  url         = "http://dx.doi.org/10.1101/221499",
  doi         = "10.1101/221499"
}

TY  - INPR
AU  - Davis, Nicole M
AU  - Proctor, Diana M
AU  - Holmes, Susan P
AU  - Relman, David A
AU  - Callahan, Benjamin J
TI  - Simple statistical identification and removal of contaminant sequences in
      marker-gene and metagenomics data
PY  - 2017
DA  - 2017/11/17
AB  - AbstractBackgroundThe accuracy of microbial community surveys based on
      marker-gene and metagenomic sequencing (MGS) suffers from the presence of
      contaminants — DNA sequences not truly present in the sample. Contaminants
      come from various sources, including reagents. Appropriate laboratory
      practices can reduce contamination, but do not eliminate it. Here we
      introduce decontam (https://github.com/benjjneb/decontam), an open-source
      R package that implements a statistical classification procedure that
      identifies contaminants in MGS data based on two widely reproduced
      patterns: contaminants appear at higher frequencies in low-concentration
      samples, and are often found in negative controls.Resultsdecontam
      classified amplicon sequence variants (ASVs) in a human oral dataset
      consistently with prior microscopic observations of the microbial taxa
      inhabiting that environment and previous reports of contaminant taxa. In
      metagenomics and marker-gene measurements of a dilution series, decontam
      substantially reduced technical variation arising from different
      sequencing protocols. The application of decontam to two recently
      published datasets corroborated and extended their conclusions that little
      evidence existed for an indigenous placenta microbiome, and that some
      low-frequency taxa seemingly associated with preterm birth were
      contaminants.Conclusionsdecontam improves the quality of metagenomic and
      marker-gene sequencing by identifying and removing contaminant DNA
      sequences. decontam integrates easily with existing MGS workflows, and
      allows researchers to generate more accurate profiles of microbial
      communities at little to no additional cost.
DO  - 10.1101/221499
UR  - http://dx.doi.org/10.1101/221499
ER  -