miso, short for microbiome software is a collection of helpers that we use to analyze microbiome data. It makes it easier to run some common analyses and is pretty opinionated towards our own experiences.

General philosophy

miso is simply a one stop location for smaller analysis helper and visualizations we often use in the lab. It is supposed to remove some common pain points we encountered or to implement custom (mostly genomic) analysis steps.

At this point complex workflows in the lab have been ported to nextflow and are no longer included here. See our pipelines for this.

Analysis

For misos an analysis step is based on input data and a configuration, thus having the function signature step(object, config). Most steps can be chained with the pipe operator. For instance, the following is possible with miso:

library(miso)

config <- list(
    demultiplex = config_demultiplex(barcodes = c("ACGTA", "AGCTT")),
    preprocess = config_preprocess(truncLen = 200),
    denoise = config_denoise()
)

output <- find_read_files("raw") %>%
          demultiplex(config$demultiplex) %>%
          quality_control() %>%
          preprocess(config$preprocess) %>%
          denoise(config$denoise)

This clearly logs the used workflow and the configuration. The configuration can also be saved and read in many formats, for instance yaml.

config.yaml:

preprocess:
  threads: yes
  out_dir: preprocessed
  trimLeft: 10.0
  truncLen: 200.0
  maxEE: 2.0
denoise:
  threads: yes
  nbases: 2.5e+08
  pool: no
  bootstrap_confidence: 0.5
  taxa_db: https://zenodo.org/record/1172783/files/silva_nr_v132_train_set.fa.gz?download=1
  species_db: https://zenodo.org/record/1172783/files/silva_species_assignment_v132.fa.gz?download=1
  hash: yes

This can now be reused by someone else:

config <- read_yaml("config.yml")

output <- find_read_files("raw") %>%
          quality_control() %>%
          preprocess(config$preprocess) %>%
          denoise(config$denoise)

Other functions

All other functions are usually functions that are meant to be inside more complex code or functions that produce plots and endpoints of an analysis. Most of them act on phyloseq objects and some on tidy data tables. Some are general lab helpers, for instance to make plate layouts.