European Genome-Phenome Archive

File Quality

File InformationEGAF00002444538

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.

353 222192 782123 306105 60994 73586 08679 86975 80770 11166 22563 70059 96157 32755 69851 40650 76548 28946 51243 42342 04241 81639 46836 95135 13833 39232 33130 42729 51028 17727 28126 43125 49523 28922 29420 96820 04619 50918 62817 58816 09515 70614 71613 95913 70812 55011 86411 87110 86710 1699 7079 1128 2938 1947 9617 2786 8676 7866 7496 0185 8685 8675 1914 9414 6484 4564 1363 9163 9133 5593 3583 1103 0192 8352 6382 3622 3132 3662 1801 9491 9131 9201 7941 6761 5021 6151 5391 4681 2751 2651 2091 2141 0559819581 04996499296281880972274567374164563957565864366258754854653256352848750645139944342638044145640841436134528033932727732726031327524028828426721218124524021325224924320922923620824420516026121023119723422219527520620722221424120817318625318019818119118119915515619515815816816416620115317818620219018313215412117913514213612710512499131127131125116139115116130941181241221131521261141181169013416011314013012311713813913211112614215015212414612515411411415613412112511113512711211810811299891111021019099961078789818410783108858381798069788062665262746982927874687676768168777579807374696257556065745246425167695146514564544852514346354936462845374544493842444544353552374438313133343443333729343122273029162227173023251638323613271536272025281718151718222219271622252120231418962118171633251618192722311920912419513191391513142113261425201891414151614251316209141413119181812101710141811161316151718111571320131214111911121511151217121110815158121614112212991311810528335465234453522555412412244327814743526610143435231331555123262641212434111221214222111342221321111321143625121133121121324224514313731214831224145313417317514211232113123112224311112113211211223113222112123115331133111111111311121122113112121311221113231211311111113111111112131114211411111111211131112211111121131113121311221212100200300400500600700800900>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.

16 12900000000000001 215 429000000056 62500001 270 279000004 414 70100047 085 63700000510152025303540Phred quality score0M5M10M15M20M25M30M35M40M45M# 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).

98.7 %711 32998.7 %1.3 %

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.

98.6 %710 46298.6 %1.4 %

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%).

0.1 %8670.1 %99.9 %

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 %360 39250 %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.

80.4 %579 67080.4 %19.6 %

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.

10.9 %78 74710.9 %89.1 %

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.

78 0001 0302452 2143753869581 3363462 3867036962 4481 0424181 7852646148129772371 8038311 3042 0973 6393908 2914741 9477917123862 0582535827348633702 66919 6648697439801 9641 1878561 2161 3542 1781 7962 0309 1945441 8721 2034043 0751 556281560 245051015202530354045505560Phred quality score50k100k150k200k250k300k350k400k450k500k550k# 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.

99.85%99.89%99.91%99.7%99.88%99.92%99.9%99.78%99.79%99.88%99.91%99.88%99.79%99.93%99.86%99.88%99.85%99.88%99.94%99.94%99.86%99.8%99.27%99.96%0.15%0.11%0.09%0.3%0.12%0.08%0.1%0.22%0.21%0.12%0.09%0.12%0.21%0.07%0.14%0.12%0.15%0.12%0.06%0.06%0.14%0.2%0.73%0.04%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped