European Genome-Phenome Archive

File Quality

File InformationEGAF00004190177

File Data

Base Coverage Distribution

This chart represents the base coverage distribution along the reference file. Y-axis represents the number of times a position in the reference file is covered. The x-axis represents the range of the values for the coverage.

Data is represented in a log scale to minimise the variability. A high peak in the beginning (low coverage) and a curve descending is expected.

391 656255 365141 783129 60099 22492 10579 27575 71667 84566 38961 41159 65856 05254 64952 78950 00348 64047 10845 56043 79741 22739 53937 88736 02234 63932 75531 06629 28726 80025 89924 09122 91220 86219 42617 57316 15014 89414 01312 56511 62210 4049 1228 2387 4576 4835 9445 4065 0884 7104 0693 6563 3693 0502 7772 5802 5272 1691 9391 9841 6731 7161 4921 4491 3941 3281 2891 2361 1971 3141 1141 0639711 01295095590980789286774469469671466462761769353661449648348743341239340940031532940339336635037032938030736630329027227826425322424427125529128028923825226127527822023121519523821121923624425925123420625425225023122019521319520619119916817112313213713215514712213512812810914488136941061078479811017775889660967166641027865669177838067757365955860586869687377676057645157634653605067696076455651643151455650815941625959574674394344305039362934353840363432372833283137292034212830243218352822363125212031332419313630233331302724261734244262244559293361352217231822222230192625253525374737293935234941162331223120182819202820232816111827281729161422162924202118151516222024131516192513241411161612241314272214161812272327181916162017161617241720171315201623221914221719181818252229313626323029363225311726213224293029302826181424261827231423212123172818222123251714221620212532242120201718239161422911129189101596872146511128546813134146107121019761510117121510148181415991110191010121710916181213161517152213171511161415141711131011151177141510105131510121716121610991013161814916252726343221323825251823261818221420172712201419111717131518181725191631201791822222720135221681820241724191517249117191413121118171216761213129812141081310161011128188101318141417141113169121412133125161091313124867757811936119367247568785557455721341149155384658765624121357353253546744355463385437556244373292221383554773145656233211232212215272243265245641241228132523111211342311112211122133221223513431122225321111321134413211141122324222211213111111121221311111111112121 800100200300400500600700800900>1000Coverage value1210201002001k2k10k20k100k200k# Bases

Base Quality

The base quality distribution shows the Phred quality scores describing the probability that a nucleotide has been incorrectly assigned; e.g. an error in the sequencing. Specifically, Q=-log10(P), where Q is the Phred score and P is the probability the nucleotide is wrong. The larger the score, the more confident we are in the base call. Depending on the sequencing technology, we can expect to see different distributions, but we expect to see a distribution skewed towards larger (more confident) scores; typically around 40.

7 83200000000000001 449 194824 7343 990 53900000000003 216 661249 8090844 4271 097 744208 683290 2342 999 1875 822 8093 182 5072 282 0955 136 79024 944 77854 125 37700510152025303540Phred quality score0M5M10M15M20M25M30M35M40M45M50M# Bases

Mapped Reads

Number of reads successfully mapped (singletons & both mates) to the reference genome in the sample. Genetic variation, in particular structural variants, ensure that every sequenced sample is genetically different from the reference genome it was aligned to. Small differences against the reference are accepted, but, for more significant variation, the read can fail to be placed. Therefore, it is not expected that the mapped reads rate will hit 100%, but it is supposed to be high (usually >90%). Calculations are made taking into account the proportion of mapped reads against the total number of reads (mapped/mapped+unmapped).

40.6 %449 36140.6 %59.4 %

Both Mates Mapped

When working with paired-end sequencing, each DNA fragment is sequenced from both ends, creating two mates for each pair. This chart shows the fraction of reads in pairs where both of the mates successfully map to the reference genome. .

Notice that reads not mapped to the expected distance are also included as occurs with the proper pairs chart.

37.9 %419 57437.9 %62.1 %

Singletons

When working with paired-end sequencing, each DNA fragment is sequenced from both ends, creating two mates for each pair. If one mate in the pair successfully maps to the reference genome, but the other is unmapped, the mapped mate is a singleton. One way in which a singleton could occur would be if the sample has a large insertion compared with the reference genome; one mate can fall in sequence flanking the insertion and will be mapped, but the other falls in the inserted sequence and so cannot map to the reference genome. There are unlikely to many such structural variants in the sample, or sequencing errors that would cause a read not to be able to map. Consequently, the singleton rate is expected to be very low (<1%).

6.6 %29 7876.6 %93.4 %

Forward Strand

Fraction of reads mapped to the forward DNA strand. The general expectation is that the DNA library preparation step will generate DNA from the forward and reverse strands in equal amounts so after mapping the reads to the reference genome, approximately 50% of them will consequently map to the forward strand. Deviations from the 50%, may be due to problems with the library preparation step.

50 %553 36750 %50 %

Proper Pairs

A fragment consisting of two mates is called a proper pair if both mates map to the reference genome at the expected distance according to the reference genome. In particular, if the DNA library consists of fragments ~500 base pairs in length, and 100 base pair reads are sequenced from either end, the expectation would be that the two reads map to the reference genome separated by ~300 base pairs. If the sequenced sample contains large structural variants, e.g. a large insertion, where we expect the reads mapping with a large separation would be a signal for this variant, and the reads would not be considered as proper pairs. Based on the sequencing technology, there is also an expectation of the orientation of each read in the fragment.

The rate of proper pairs is expected to be well over 90%; even if the mapping rate itself is low as a result of bacterial contamination, for example.

24.5 %271 44624.5 %75.5 %

Duplicates

PCR duplicates are two (or more) reads that originate from the same DNA fragment. When sequencing data is analyzed, it is assumed that each observation (i.e. each read) is independent; an assumption that fails in the presence of duplicate reads. Typically, algorithms look for reads that map to the same genomic coordinate, and whose mates also map to identical genomic coordinates. It is important to note that as the sequencing depth increases, more reads are sampled from the DNA library, and consequently it is increasingly likely that duplicate reads will be sampled. As a result, the true duplicate rate is not independent of the depth, and they should both be considered when looking at the duplicate rate. Additionally, as the sequencing depth in increases, it is also increasingly likely that reads will map to the same location and be marked as duplicates, even when they are not. As such, as the sequencing depth approaches and surpasses the read length, the duplicate rate starts to become less indicative of problems.

2.8 %31 4582.8 %97.2 %

Mapping Quality Distribution

The mapping quality distribution shows the Phred quality scores describing the probability that a read does not map to the location that it has been assigned to (specifically, Q=-log10(P), where Q is the Phred score and P is the probability the read is in the wrong location). So the larger the score, the higher the quality of the mapping. Some scores have a specific meaning, e.g. a score of 0 means that the read could map equally to multiple places in the reference genome. The majority of reads should be well mapped, and so we expect to see this distribution heavily skewed to a significant value (typically around 60). It is not unusual to see some scores around zero. Reads originating from repetitive elements in the genome will plausibly map to multiple locations.

705 06646527135524512148441 0631891 3021 228781402 8802071 8579251783 0083472310 2833 420845 999122121420141 378144 05442645648225482 6526541416024422264164614177 071051015202530354045505560Phred quality score50k100k150k200k250k300k350k400k450k500k550k600k650k700k# Reads

Mapped vs Unmapped

Stacked column chart for both mapped and unmapped reads along all chromosomes in the reference file. It is a similar representation as shown in the Mapped reads chart but for each chromosome. Although sequenced sample may be a female, it is possible to get reads in the Y chromosome as there are common regions in both chromosomes called pseudoautosomal regions (PAR1, PAR2).

Unmapped reads belonging to each chromosome are determined when the one mate/pair is aligned and the other is not. The unmapped read should have chromosome and POS identical to its mate. It could also be due when aligning is performed with bwa as it concatenates all the reference sequences together, so if a read hangs off of one reference onto another, it will be given the right chromosome and position, but it also be classified as unmapped.

93.55%93.07%94.65%95.13%92.52%93.64%94.03%93.37%93.77%95.25%94.51%92.71%92.23%92.34%91.53%92.89%93.52%94.71%93.31%93.52%93.88%94.43%97.61%97.79%6.45%6.93%5.35%4.87%7.48%6.36%5.97%6.63%6.23%4.75%5.49%7.29%7.77%7.66%8.47%7.11%6.48%5.29%6.69%6.48%6.12%5.57%2.39%2.21%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped