European Genome-Phenome Archive

File Quality

File InformationEGAF00002485546

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.

1 861 738494 560271 705225 946199 062182 016165 980157 251146 224135 677126 751120 389115 202110 066103 87798 69793 89091 76684 53783 02077 74275 39072 36867 87364 83761 39057 67755 05251 92648 24845 89143 49940 94338 70336 84034 85532 88232 24929 49527 26826 22524 71623 61622 46821 12720 13118 64118 22716 62316 14714 94914 16712 96712 40711 76711 41110 61310 1889 5499 2448 8767 9857 8417 2256 4606 2866 1605 8945 4555 1235 2714 7424 6634 1824 1294 0593 5893 5533 4713 2763 1113 1403 0862 7202 6262 4852 2762 1762 3462 0932 0111 9692 0131 8321 7351 6381 8411 5381 5071 4081 4951 5701 4981 3641 3181 3791 3311 3351 2691 2491 1591 2201 1591 0631 0521 0161 057946902999927838789839777774790716812711692695659620619570593585566557549491428512489438469480426466439401446493408378406468432477501416361410414382389314329313356329403338358330386377354307285317245284300292286283282238327244248245242263245230238256215217220217216238249232246165186249192178212249169189180216199175215197188207191180167213189186163190185179167196192187174168187189177201174196166151157194129211126123139151150141155136128136132196125138119131139145123120116120118104110105128109109101949812212210811112471901147689829897105102881091159597729086106798411271798194869278998310685871069415710010281110751059482829077779179699476748377626872686958598274716863717866768183744973717462607163787774917976927271635762635867616074496356654856515569664950435343594496464757365040564846455950453651374134444362565538454932484348404348755740455054485862614954616269578361656358575641706165385469606658525756525651686264634847605242474548403252433055396048435051444943364847342931323129314937474131404031283848373633363738523248422543404030383936555257364036563746525644374346464744464440584953443338472754414435353334472934412329303036353531193230243142283236262129263529287027383433393134293329332622242225232822282022232326282121362621202219211722201713171421252012171720211317152311211827199231515121584101614131213141413101611131114148181216131512149109847121412111014108201074718712121481091051110311456614210121412431013678662266566387115788832571458136453374842564161755466443455542412335272253414122241234525442434214122222111314135622225635642311152342321212234442212122112233136341111231512415422211311332312441222124262232213332411221412 417100200300400500600700800900>1000Coverage value1101001k10k100k1M# 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.

3 27700000000000005 288 8990000000357 44900005 285 9030000013 113 55500097 336 16700000510152025303540Phred quality score0M10M20M30M40M50M60M70M80M90M# 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).

87.1 %1 409 26487.1 %12.9 %

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.

86.8 %1 405 42286.8 %13.2 %

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%).

0.3 %3 8420.3 %99.7 %

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 %809 23550 %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.

70.6 %1 141 99470.6 %29.4 %

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.

9 %145 7369 %91 %

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.

353 3831 9254953 3768388411 9172 2967264 7141 6191 3214 8991 7058262 8446851 0081 4421 4883793 2931 4882 2833 6155 15085215 6701 0653 9061 6371 4118473 9544371 0731 3421 8868474 81940 6321 7551 3621 6763 9232 5131 7652 1342 3704 3083 8104 17016 3559743 7092 5937867 8822 9275551 104 186051015202530354045505560Phred quality score0.1M0.2M0.3M0.4M0.5M0.6M0.7M0.8M0.9M1M1.1M# 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.

99.73%99.73%99.79%99.61%99.63%99.82%99.8%99.69%99.72%99.77%99.83%99.8%99.73%99.85%99.8%99.68%99.71%99.69%99.77%99.77%99.54%99.48%98.12%99.92%0.27%0.27%0.21%0.39%0.37%0.18%0.2%0.31%0.28%0.23%0.17%0.2%0.27%0.15%0.2%0.32%0.29%0.31%0.23%0.23%0.46%0.52%1.88%0.08%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped