RNAseq DEG analysis protocol for non-human samples using ONT reads

python3 TrimmerLarge.py -f [filename] -i [inFolder] -o [outFolder] -w [windowSize] -l [minLen] -c [clipping] -g [fragments] -m [mode] -q [Qcutoff] -d [maxDataMB] -r [outMode]

-f, 	--file,	               filename:  The name of the file you want to trim, 
-i, 	--infolder, 	inFolder:The folderpath where your file to be trimmed is located (default = cwd)
-o, 	--outfolder,	outFolder: The folderpath where your want to save the trimmed file (default = cwd)
-w, 	--windowsize,	windowSize:  Change the size of the trimming window (default= 100bp)
-l, 	--minlen,	                 minLen:  Change the minimum acceptable numer of bases in a read (default=100)
-m, 	--trimmode,	mode:		Select trimming algorithm: S for static  or D for dynamic (default=S)
-q, 	--qscore,	Qcutoff:	Select the phred quality score trimming threshold (default=7)
-d, 	--datamax,	maxDataMB:	Select a maximum data subsample in MB (default=0, entire file)
-r, 	--outformat,	outMode:	Select output format of trimmed file (fastq or fasta) (default=.fastq)
-c, 	--clip,		clipping:	Select L to clip leading Ns, T to trim trialing Ns and LT to trim both (default=LT)
-g, 	--fragments,	fragments:	Select fragmentation mode (default=U0)


[filename] = filename, including file extention (string)
[inFolder] = source folder (string)
[outFolder] = output folder (string)
[windowSize] = number of bases in trimmer window (integer)
[minLen] = minimum number of bases in read. reads with fewer bases will be rejected
[trimSpecs] = trim specs: [X1]-[X2]-[X3] (string)	
   X1: "L", "T", "LT", or "". L clips leading Ns, T clips trailing Ns, LT clips both, "" clips neither. recommended mode is LT	
   X2: U0, F0, F1, F2... U0=unfragmented output, F0=fragmented output with all fragments, F[n]= fragmented output with n largest fragments	
   X3: S, D. S=static mode. D=dynamic mode
	
   example: "X1-X2-X3" -> "LT-U0-S"

[Qcutoff] = phred quality score threshold. (integer)
[seqsToAnalyze] = megabytes of data to trim. trimmer will read files in 1 MB chunks and stop when number of MB exceeds this number.
[outMode]: output file extension. saves trimmed data as either ".fasta" or ".fastq"

minimap2 [-x preset] -d target.mmi target.fa

Indexing options
-k INT Minimizer k-mer length [15]

-w INT Minimizer window size [2/3 of k-mer length]. A minimizer is the smallest k-mer in a window of w consecutive k-mers.

-H Use homopolymer-compressed (HPC) minimizers. An HPC sequence is constructed by contracting homopolymer runs to a single base. An HPC minimizer is a minimizer on the HPC sequence.

-I NUM Load at most NUM target bases into RAM for indexing [4G]. If there are more than NUMbases in target.fa, minimap2 needs to read query.fa multiple times to map it against each batch of target sequences. NUM may be ending with k/K/m/M/g/G. NB: mapping quality is incorrect given a multi-part index.

--idx-no-seq Don’t store target sequences in the index. It saves disk space and memory but the index generated with this option will not work with -a or -c. When base-level alignment is not requested, this option is automatically applied.

-d FILE Save the minimizer index of target.fa to FILE [no dump]. Minimap2 indexing is fast. It can index the human genome in a couple of minutes. If even shorter startup time is desired, use this option to save the index. Indexing options are fixed in the index file. When an index file is provided as the target sequences, options -H, -k, -w, -I will be effectively overridden by the options stored in the index file.

--alt FILE List of ALT contigs [null]

--alt-drop FLOAT Drop ALT hits by FLOAT fraction when ranking and computing mapping quality [0.15]

minimap2 -a -x map-ont target.mmi your_sequences.fasta > alignment.sam

samtools view -S -b alignment.sam > alignment.bam

salmon quant --ont --noLengthCorrection -p 8 -t {input.transcriptome} -l U -a {input.bam} -o {output

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("tximport")

library("tximport")

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DESeq2")

library("DESeq2")