Importance of Proper Pairs vs Aligned Reads for RNASeq data I have stranded, paired end RNASeq reads that I have aligned using STAR. I plan do conduct a differential expression analysis with DESeq2. After running quality control checks, a good portion of my reads are aligned but I'm a little concerned about the proportion of properly paired reads



Here is the inner distance plot for the samples



For human RNA sequencing, does anyone have a rule of thumb that they'd consider is an appropriate proportion of properly paired reads? Are there any good papers that talk about this issue? As MultiQC marks the column as yellow, I'm concerned the proper pair percentage may not be satisfactory. 

How important are properly paired reads for differential expression analyses?
If you check the RSeQC docs you can get an explanation of how the inner distance stats work. By the look of your inner-distance MultiQC plot I guess that your sequencing is 2x100bp reads. This plot shows that your insert sizes on the pairs is pretty short, meaning that a lot of the paired reads are overlapping significantly. This will be affecting the alignments a little, resulting in the low proportion of properly paired reads.

The good news is that for differential expression, this probably doesn't really matter too much. What is important is getting fragment counts, so as long as at least one of the pair mates is mapping properly, then you will will be getting sensible counts - even if read 2 is not mapping properly. You should be able to have confidence in your results from multiple replicates if in doubt - you have more than just -rep1 right? ;)

Note that the yellow background in the MultiQC column is not passing judgement with categories like pass/warn/fail in FastQC - it just happens that the colour scheme ended up there for your samples. It's a continuous gradient from red to yellow to green for those values (0-100%).