This is, I'm afraid, one of those open-ended questions whereby you'll get 2 different answers if you ask 2 different people.

From just looking at the VCF, it's next-to-impossible to relate quality to the entire sequencing run because a VCF may or may not be heavily filtered along the way. If you're lucky, and standard tools were used, then at least all filtering applied to the 'raw' VCF will be recorded in the VCF header, but even a 'raw' VCF may have been generated from a heavily filtered BAM/SAM and thus conceal much information about the overall run and quality of data.

For example, I've been working from assumption like:

• If the BAM file or even the VCF/BCF files have a very low, or a very broad curve for the DEPTH...the entire data file may be invalid and throwing it out should be considered.
• If the INDEL lengths, QUAL, or GQ do not fall in a relatively normal bell curve, the data may be invalid.

'Very broad curve' is vague but I guess that you mean generally uneven depth of coverage? It would be incorrect to automatically assume that a sample was poor quality by just looking at this. The depth of coverage profile can be influenced by one or more of the following:

• target depth of coverage (obvious)
• difficulty in priming due to high GC content
• sequence similarity [to other regions of the genome]
• outdated reagents
• degraded DNA
• delays in the wet-laboratory processing of the sample
• et cetera.

Thus, there are many 'parameters' that go into the depth of coverage 'equation', and I believe that variations in depth of coverage are expected. You haven't elaborated on whether what you're observing is extreme variations in the profile or not(?).

I'm not sure that the indel profile should necessarily fall into a bell curve profile, and neither that of the QUAL nor GQ. There's a lot that indirectly goes into the calculation of these (QUAL and GQ), and sometimes the assigned values don't even make sense.

The sense of 'quality' for a sample and run is more a human feeling that should come from looking at a whole host of parameters. In order to make an honest decision on whether a run failed or not, I would love to see:

Wet lab

• DNA concentration

• Gel electrophoresis of DNA

• Length of time DNA was in transit

• Date the reagent kit was produced (and expiry date)

Sequencing

• Sequencer type and maintenance records

• Target depth of coverage

• Passed filter reads

• % Q10 bases

• % Q20 bases

• % Q30 bases

Bioinformatics

• All programs and versions used to process the data, including the base-caller in the sequencer

• Total reads

• Min/Max/Mean/Median/Upper-/Lower-quartile read length

• Any QC or trimming applied to reads

• Alignment % to reference genome

• Genome version used for alignment

• Mate-pairs mapped together

• Reads aligned to >1 location

• Singletons / Lone mates

Bioinformatics coverage and other QC

• Number of reads off target (targeted sequencing only)

• Min/Max/Mean/Median/Upper-/Lower-quartile read-depth per chromosome and genome-wide

• Plot of depth of coverage profile in bins (e.g. 50,000 bp) per chromosome and genome-wide

• Bases with 0, <5, et cetera read-depth (and then summarised into regions that have same read-depth at each level)

• Overall % genome covered at read depth 1, 2, 3, 4, 5, 10, 18, 20, 30, et cetera.

Variant calling (to produce a VCF)

• MAPQ filters

• MAPQ bias

• Phred-scaled base quality filters

• Base quality bias

• Strand bias

• Downsampling performed (to what level?)

• Min number of variant bases required to make a variant call

• Min allelic fraction at which to call heterozygous/homozygous variant

• Read-end bias

• Min total (ref+alt) read depth at which a variant is even reported in the VCF

This is just off the top of my head. The list is exhaustive...

Kevin