In ruy204/FiTnEss: FiTnEss: Finding TnSeq Essential Genes

Abstract

Transposon insertion sequencing (Tn-Seq) is a high-throughput technique to generate and culture a large random collection of genetically perturbed bacteria, followed by sequencing to measure the frequency of each perturbation in the culture. Tn-Seq is used for the identification of essential genes by utilizing the fact that mutants carrying insertions in such genes are expected to be greatly depleted in the growth culture.

Here we present FiTnEss (Finding Tn-Seq Essential Genes) - a novel statistical method to identify gene essentiality using Tn-Seq data. The method contains only two global parameters that are estimated from the data. It is robust and has greater power to detect essential genes than most current existing methods.

Introduction

General Introduction

In this package, we present a major function called FiTnEss_Run conducting one-step analysis from raw tally files to final essential gene calls. This major function consists of three analysis steps including

• raw tally file processing
• model fitting
• essential gene identifications

Quick Start

#Main function
FiTnEss_Run(strain,
            tally_file,
            non_permissive_file,
            homologous_file,
            gene_file,
            results_save,
            repeat_time)

#Example
FiTnEss_Run("UCBPP",
            "/home/TnSeq/data/test_data/PA14_M9_rep1_tally.txt",
            "/home/TnSeq/data/test_data/nonpermissive_TA_sites.txt",
            "/home/TnSeq/data/test_data/homologous_TA_sites.txt",
            "/home/TnSeq/data/test_data/PA14_gene_file.txt",
            "/home/TnSeq/test_result/test_results.xlsx",
            repeat_time = 3)

Pipeline Workflow

Step 1. install dependent packages

install.packages(c("devtools","dplyr","fBasics","goftest","openxlsx","scales","stats","tidyr"))

Step 2. install FiTnEss package from github

devtools::install_github("ruy204/FiTnEss")

Step 3. load FiTnEss and dependent packages

Packages <- c("devtools","dplyr","fBasics","goftest","openxlsx","scales","stats","tidyr")
lapply(Packages, library, character.only = TRUE)

require(FiTnEss)

Step 4. run FiTnEss

FiTnEss_Run("UCBPP",
            "/home/TnSeq/data/test_data/PA14_M9_rep1_tally.txt",
            "/home/TnSeq/data/test_data/nonpermissive_TA_sites.txt",
            "/home/TnSeq/data/test_data/homologous_TA_sites.txt",
            "/home/TnSeq/data/test_data/PA14_gene_file.txt",
            "/home/TnSeq/test_result/test_results.xlsx",
            repeat_time = 3)

Data format

Raw tally file format

Raw tally file is in .txt format, and usually contains 6 columns:

chromosome, TA start position, TA stop position, gene name, reads on "+" strand, reads on "-" strand

For example:

rawtally<-read.delim("/home/unix/ruiyang/TnSeq/data/test_data/PA14_M9_rep1_tally.txt")

head(rawtally)

Output results

library(openxlsx)
results<-read.xlsx("/home/unix/ruiyang/TnSeq/data/test_data/test_result/test_results.xlsx")

head(results)

The results are automatically saved into .xlsx format with each tab contains results from one replicate.

ruy204/FiTnEss documentation built on Jan. 23, 2021, 2:52 a.m.