Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results

Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results

Background: 16S rRNA-gene sequencing is a valuable approach to characterize the taxonomic content of the whole bacterial population inhabiting a metabolic and spatial niche, providing an important opportunity to study bacteria and their role in many health and environmental mechanisms. The analysis...

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Bibliographic Details
Main Authors: Baruzzo, G. (Author), Di Camillo, B. (Author), Patuzzi, I. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
16s rDNA
16s rDNA-seq
16S rDNA-Seq
Bacteria
bacterium
Benchmarking
Count data
Count datum
Count preprocessing
Count simulation
data analysis
Data Analysis
DNA sequence
Genes
Genes, rRNA
genetics
high throughput sequencing
High-Throughput Nucleotide Sequencing
Information analysis
Normalisation
Normalization
Pipelines
Pre-processing step
Recovery
RNA 16S
RNA gene
RNA, Ribosomal, 16S
Sequence Analysis, DNA
Sparsity
Work-flows
Zero-imputation
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