European Genome-Phenome Archive

File Quality

File InformationEGAF00000726365

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.

66 045 22622 928 0306 769 0513 020 6091 297 069843 077497 399359 201259 001195 971152 079123 849101 76484 97173 11662 59753 68447 73943 76439 38236 27333 64631 29027 97426 39524 92023 57522 42121 50820 45519 67618 86318 00417 57617 11416 58316 10715 61515 32114 84514 36814 04413 97813 62013 37513 07512 78112 52412 44211 88011 81611 90711 49311 41111 08510 93710 85610 60510 41610 32810 40810 27310 0499 9959 7539 5769 6959 4969 2669 3839 2229 1868 9668 9368 8128 6648 6078 3488 3608 2368 2098 1048 1187 9077 9808 0987 9208 0638 0078 0497 8987 7777 4897 4737 4037 3847 3477 1947 1927 1147 1167 0977 1707 0797 0376 8766 9477 0206 7716 7046 7286 5276 5286 4916 4996 6296 7026 6956 7556 6126 5696 4476 5306 5666 3796 3786 3576 4746 3416 3706 3086 2676 1846 0686 0465 9426 0116 0556 0926 0605 9916 0856 0796 0935 9486 0615 8295 8145 7665 7715 6345 6795 6055 6085 5255 6105 6725 5765 3725 4555 3285 5175 3935 3995 2045 2965 1675 1965 2265 0825 0525 0715 0425 0834 8325 0154 8484 7854 7124 7354 8414 7894 6714 6494 6604 7704 5394 6314 6234 4344 3624 2674 2544 2124 1254 1584 0674 0084 0544 0393 8923 9403 9063 7543 8553 7673 6923 6013 5223 4843 4093 4353 3173 1803 2803 2253 2213 1703 1923 1453 1233 1843 1633 0603 1213 1473 1003 0503 0762 9122 8492 8762 9572 7572 7852 7022 7532 7762 5942 5192 5612 5202 5202 4522 3712 2982 2102 2542 2122 2082 2702 2412 2282 2662 2062 1932 1892 0942 0902 0072 0351 9501 9611 8561 9401 8351 9121 7901 7881 7531 8361 7721 7511 6751 6691 6531 6981 6011 5891 6311 5511 5121 4841 4151 4091 4351 3891 3521 3571 2571 3631 2551 2911 2441 2041 2281 1731 1051 1621 0901 0631 012991903971877847849870848826773773772718703678683623575567577603583562514525439467453454433451397406397409398383412374313383367351312289280254257245223248229204210184223185224199183180190176158152170164137118141110135129113100971019983817584848678837080466560685465586254554754424849546352363842454340524458403232453933403351273534434431444534404128342936344750394039383646232122424137232642443949343235323137423426384133373429312831403635353028213534283230302429141823281825252523242617262926232021262223231522232718182324331920272627242315152125241525321713292122212721163415293121252316212920231922211415232316242321182424201618192120222220182312202418191825222314241914192221172416131721112010261588131722112012158171217616927141414171312151116151071481215714191717811109711891016815166161312152212131618132012121816132121131817141412121111101191591714111612122019913811813141411813121411795810796377798771011812129147101011969126667991176710116151211101212971195101389710584747144935412712259959676145789349746635313435552243445262422241132146522434452863414114735341335466455113623695643933766541746351144231633214221224351623123213113426322251114222544122221114124113323123122362660100200300400500600700800900>1000Coverage value1101001k10k100k1M10M# 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.

119 9080000550 442358 4401 857 2531 583 292604 672919 740357 785333 066662 530266 846753 223661 475717 0611 358 173795 8881 024 747707 4911 311 2811 782 6842 016 9222 234 7503 602 3223 641 8033 330 1513 592 7247 656 21911 708 4857 580 01411 780 34020 478 74726 699 45616 266 75437 500 55631 177 85243 937 17454 613 27585 186 30900510152025303540Phred quality score0M10M20M30M40M50M60M70M80M# 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).

99.1 %5 151 95299.1 %0.9 %

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.

98.9 %5 137 31698.9 %1.1 %

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%).

0.3 %14 6360.3 %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 %2 598 19950 %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.

98.6 %5 123 48098.6 %1.4 %

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.

21.2 %1 099 46221.2 %78.8 %

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.

222 5241 0024511 3917058197501 0841 1326 0444 3231 8667 6911 8231 28721 6902 3898 9367 2202 18012 10135810 69539 7583442 425233256373194 16569259059670260689823 77584 1642 6189422 9902 4206465 3801 2201 67619 5882 8663 1743 9126 6203 0628 9086 9509 40416 37431 5784 398 032051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M3.5M4M# 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.

100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%100%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped