biotindeficiency

Scripts used in biotin deficiency project.

• Raw FastQ files can be found in NCBI with accession PRJNA904819
• Please cite: -MDPI and ACS Style Yang, J.C.; Jacobs, J.P.; Hwang, M.; Sabui, S.; Liang, F.; Said, H.M.; Skupsky, J. Biotin Deficiency Induces Intestinal Dysbiosis Associated with an Inflammatory Bowel Disease-like Phenotype. Nutrients 2023, 15, 264. https://doi.org/10.3390/nu15020264 -AMA Style Yang JC, Jacobs JP, Hwang M, Sabui S, Liang F, Said HM, Skupsky J. Biotin Deficiency Induces Intestinal Dysbiosis Associated with an Inflammatory Bowel Disease-like Phenotype. Nutrients. 2023; 15(2):264. https://doi.org/10.3390/nu15020264

Three datasets used in this project

The institution refers to the institution that created the sequencing libraries.

• UCI "Host deficiency"(2020)
  • Contains data from WT and Biotin Receptor KO mice at Day 0 and Day 7
• Zymo "Biotin-deficient diets and supplementation" (2019)
  • Contains data from mice on biotin deficient diet (BD), control diet (CD), and biotin-deficient diet with biotin supplementation (BD_supp)
• UCLA "Biotin-deficient diet and the microbiome at different sites"
  • Contains cross-sectional data from mice on biotin deficient diet and control diet,

Preprocessing Details

UCI "Host Deficiency"

• Used UCI's ASV table (preprocessed with DADA2 in QIIME) - lacked raw FASTQ files
• Used Silva v132 trained on 16S for species assignment

Zymo 2.0 "Biotin-deficient diets and supplementation"

• Used Zymo's ASV table (preprocessed with DADA2 in QIIME) - did not know error modeling parameters
• v3v4 sequencing was done so could not use Silva classifier

UCLA "Biotin-deficient diet and the microbiome at different sites"

• Truncation 220,150 forward, reverse
• DADA2 in R library(dada2)
• Used Silva v132 trained on 16S for species assignment

Analysis Details for all datasets

• Datasets were rarefied and used for alpha diversity
• Datasets split into subsets of each sample type
  • These subsets were utilized as is for collapsing to higher order phylogeny
• Subsets were prevalence filtered to number of samples matching 3 mice
• RPCA performed on prevalence filtered subsets
• L6 count table used as input for fitting linear models with Maaslin2 on log-transformed, TSS- normalized data. -Genera were first prevalence filtered to number of samples matching 3 mice (built into Maaslin2 with min_prevalence) -Only Genera which are q<0.05 shown in dotplots. -Relative abundance is relative to the data subset.