Those instructions are precisely what you would have to do starting from a fastq file: map it to a genome and compare the size of regions above 5x with the size of the genome.

By the way, if you end up mapping your fastq and obtaining a bam file, I would suggest a faster way to get the information you need through bedtools:

bedtools genomecov -bga -ibam file.bam | awk '{
 if ($4<5) {below+=$3-$2} else {above+=$3-$2}
} END {
 print "Total:", below+above;
 print "Above:", above, "(", 100*above/(below+above), "%)";
 print "Below:", below, "(", 100*below/(below+above), "%)";
}'

Qualimap bamqc is an option that you can explore: http://qualimap.conesalab.org/doc_html/command_line.html#cmdline-bamqc. It gives information about both depth and breadth of coverage. You can also explore qualimap RNA-seq QC: http://qualimap.conesalab.org/doc_html/analysis.html#rna-seq-qc

Just to add, you need to align your raw reads to a reference genome before you use bamqc.

If you have a fasta file, instead of fastq, there are tools availble that can help you add dummy quality scores to your fasta file. I am not sure which is the best tool for that, but a simple google can help you with that.

I haven't tried this, but since you are working with RNA-seq data, STAR allows you to align fasta sequences to a reference genome according to its manual and this is mentioned specifically in page 5 (https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf)