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16S rRNA Seq

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Step 1: Upload your data

Upload Single Cell RNA-Seq Data File

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  • Your file can be in the following formats:h5ad
    • The h5ad format in scRNA-seq refers to the Hierarchical Data Format 5 (HDF5) Annotated Data format. It is commonly used to store single-cell gene expression data.
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Step 2: Set Parameters
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Step 3: Complete run profile

16s rRNA Amplicon Sequencing Pipeline. Analyzes high-throughput sequencing data, particularly for microbiome studies. It processes raw sequencing data, cleans and trims it, identifies and removes errors, and finally, performs taxonomic analysis and visualization.

Step-by-Step Description:

1. SRA Toolkit:
  • Function: Downloads raw sequencing data from the Sequence Read Archive (SRA), a repository of high-throughput sequencing data.
  • Technology: Uses command-line tools provided by NCBI to retrieve and convert sequencing data into a usable format.
2. cutadapt:
  • Function: Trims adapter sequences and filters low-quality reads from the raw sequencing data.
  • Technology: Python-based tool that removes unwanted sequences (e.g., adapters) from high-throughput sequencing reads.
3. DADA2:
  • Function: Error correction and chimera removal, and inference of amplicon sequence variants (ASVs).
  • Technology: R package that performs sample inference from raw sequencing data by modeling and correcting errors.
4. phyloseq:
  • Function: Integrates data and provides tools for statistical analysis and visualization of microbiome data.
  • Technology: R package that facilitates complex data analysis and visualization in microbiome research. Uses Silva reference database https://zenodo.org/records/4587955

Example Use Case:

A researcher studying the effects of diet on gut microbiota diversity in mice can use this pipeline to process and analyze sequencing data from fecal samples. Starting with raw data downloaded from SRA, the pipeline will trim and clean the sequences, correct errors, and produce high-quality data for taxonomic analysis and visualization.

Potential Limitations:

  • Computational Resources: High-throughput sequencing data requires significant computational power and storage. Processing may take some time.
  • Quality of Input Data: Poor quality sequencing data can affect the accuracy of downstream analysis.
  • Algorithm Sensitivity: Error correction and sequence inference algorithms may introduce biases or fail to capture rare variants.

Output Metrics:

  • Quality Metrics: Statistics on read quality before and after trimming (e.g., read length distribution, quality score distribution).
  • Error Rates: Estimated error rates before and after DADA2 processing.
  • ASVs: Number of unique amplicon sequence variants identified.
  • Taxonomic Assignments: Proportions of reads assigned to different taxonomic categories.
Citation:
Callahan, B., McMurdie, P., Rosen, M. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016). https://doi.org/10.1038/nmeth.3869 ; McMurdie and Holmes (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 8(4):e61217
Released:
May-22-2024
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