European Genome-Phenome Archive

File Quality

File InformationEGAF00001159748

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.

299 549 94072 912 75619 039 7289 539 8655 899 7454 668 4313 974 2143 558 9843 245 2553 016 7192 825 5742 665 1482 534 1802 417 0722 305 7242 197 9942 107 4692 021 1611 947 8821 871 1121 798 1681 733 1321 665 5651 602 2611 543 7101 490 3911 442 0461 389 3371 338 5641 292 9091 243 5451 198 6541 150 3131 105 8351 061 4821 015 403971 470930 854891 139851 268812 601774 657735 698699 252665 702629 546596 860564 305534 352505 519476 726448 529422 311394 892370 836348 721326 156305 946286 430269 048251 122234 427218 303203 931188 687176 816163 605152 646141 955131 289120 876112 051103 95895 79388 22481 84975 26668 66863 49258 28853 71549 24245 66541 77038 39634 84832 58629 91827 41825 43623 04121 23019 26617 86616 15514 80413 74312 34911 37310 4949 4369 0578 3097 7236 9896 4936 1375 6575 5255 0404 7624 2084 0753 6913 4533 3033 1482 8572 7852 6062 4282 2942 1992 1491 9401 8421 7381 6191 5601 5201 4411 3441 3231 2661 2271 2031 1821 1981 1911 1061 1901 1231 04296987491688283480875274969068767466160856558756457854454150148444941737637139434430931531330229129829727627524423223819318118919718419917621121716418015916814914613013511511411911013712510888919375827590758565699076708367736876556068655251573851565258545153546449463748514363334852495258446258494861527452365241303544293226312526322029291928242427272829294128312939362632303124251324262015231120201725182711151813202310211116131622161318191315261425131525272019171321181122119161918814131389221515815161851491416121911152216867121679111012109182011161815151491918208141316162422131014121716201110111312131111141212111810169121912182818212116122181219112122151318181515191017201071313131388913615128147491261264510789143581049810117725511065910457432143311331311132421231462342442411313522423383643643858742231211523222233415742215432213241223425242121223233342342235135246221224531153123641133393426123523432322323755334332437763655575343453647441353284455564183343246154647781312222331313111222513213321313341411223321333212431234122121231321135121221453233231443222543113321432413111431111131241113242122111211215113135132211112222123222221123112121411111212122121221312215132822100200300400500600700800900>1000Coverage value1101001k10k100k1M10M100M# 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.

1 735 155000000000000086 583 73500000004 318 322000077 665 10200000269 893 5490002 121 378 73700000510152025303540Phred quality score0G0.2G0.4G0.6G0.8G1G1.2G1.4G1.6G1.8G2G# 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).

99.9 %34 129 49399.9 %0.1 %

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.

99.9 %34 104 78499.9 %0.1 %

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%).

0.1 %24 7090.1 %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 %17 077 16450 %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.

99.1 %33 851 08899.1 %0.9 %

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.

7.6 %2 583 5017.6 %92.4 %

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.

922 57111 2295 13817 6495 6895 14812 3018 6984 96415 3156 1464 95812 2056 5664 11512 8074 5835 2425 9127 8096 67413 84911 48310 52614 20639 8512 549172 5532 9926 5417 0584 8182 28913 5122 1972 7733 6595 7112 10616 384296 3969 6667 50816 57212 66019 36216 79117 28616 96836 29845 56931 152195 1433 99533 72112 8325 91958 5804 4592 54532 059 984051015202530354045505560Phred quality score5M10M15M20M25M30M# 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.

99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.92%99.92%99.93%99.93%99.92%99.93%99.94%99.93%99.88%99.91%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.08%0.08%0.07%0.07%0.08%0.07%0.06%0.07%0.12%0.09%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped