The workflow of FastD program
There are two parts in the FastD program, FastD-TR (Fast Detection of
Target-site Resistance) to detect target-site insensitive mutations and
FastD-MR (Fast Detection of Metabolic Resistance) to detect
overexpressed detoxification genes.
The workflow of FastD-TR consists of five main steps: pre-processing,
mapping, mutation allele extraction, mutation allele frequency
calculation, and visualization (Figure 2 ). Raw reads from
RNA-Seq data should be processed by FastQC and trimmomatic (Bolger,
Lohse, & Usadel, 2014) to filter out reads with low sequencing quality.
The obtained clean reads are then mapped to the target gene sequence
using Bowtie2 (Langdon, 2015) with additional option, –no-unal
(filter out unaligned reads), to generate a Sequence Alignment/Map (SAM)
file (H. Li et al., 2009). The mapped reads which contain insertions or
deletions are deleted or marked with “N” respectively by parsing the
CIGAR string in the SAM file. According to the mutation position in
target gene and reads POS tag, mutation allele codons were extracted
from mapped reads by a Perl script. Then, all the mutation allele codons
are translated to amino acid residues. The reads containing the mutant
amino acid residues were treated as resistant reads. The mutation
frequency can be estimated according to a formula (Després et al., 2014;
D. Guo et al., 2017; Mackenzie-Impoinvil et al., 2019). An R script
called ggseqlogo (Wagih, 2017) was used to visualize the allele
distribution in all of the mutation loci.
\(Mutation\ frequency\ (\%)=\frac{\text{Number\ of\ resistant\ reads}}{\text{Number\ of\ all\ reads}\text{\ \ }\text{containing\ mutation\ loci}}\)×
100%
The workflow of FastD-MR consists of four main steps: pre-processing,
mapping, read count calculation, and differential gene expression
analysis (Figure 2 ). The pre-processing step of FastD-MR is the
same as what is used for FastD-TR. The obtained clean reads are then
mapped to the tested detoxification gene sequences using Bowtie2 with
additional parameter, –no-unal, to generate a SAM file. Read counts
per detoxification gene can be calculated by a Perl Script. To estimate
the expression fold change, read counts per gene from different samples
are processed by DESeq2 (Love, Huber, & Anders, 2014).