European Genome-Phenome Archive

File Quality

File InformationEGAF00002444687

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.

957 230231 740144 881120 694102 03591 56781 14477 35674 53170 40666 80264 99461 20259 51556 95856 99753 85852 92751 67649 95849 66848 38048 62747 01345 52143 70043 51341 64741 01840 27139 24138 21437 33535 36935 85234 01033 75632 89931 78929 74128 88429 13627 34726 35725 16624 80023 49423 27321 66020 90520 16919 46819 09918 38117 38917 31116 11415 97815 50614 91914 48613 91713 21912 76712 57212 18911 34510 86510 34610 33010 0279 6159 0138 9598 2707 9467 7797 2497 0166 9656 4416 1656 0435 9275 6545 5985 3455 1145 0444 9344 7384 4894 4054 4314 1823 9533 9744 0063 6363 6723 3843 3293 1993 0042 8392 7542 8482 5782 5272 3322 4292 2372 4002 2212 0872 1431 9721 9762 0401 7611 7971 9021 8581 6651 7481 6521 5981 4941 4951 4151 3811 3641 3001 3501 4141 2601 2511 2481 1911 2231 1971 1781 1461 1151 1181 0589851 1191 0171 01697793790189989086391885086580276869968173274777871662663263660251262261260164156263858760952557751757350955653450546445341744243747149346643945639343340040240837639931031832632830232928223633027128829627227328428224524722522723422022324620123120619724221627319018018715217615918917018820313813413913914914015916413112812414213013912711513312895143147137146134132110133116121123100123109110147129131124931121221321178187110781039585727963777797808884991108071666493627976598098627874686985747471677956656176688474764463756276526399586879749943486554484549533245454154475849535151395843443734674735434040333933232429392933232936322832272421162620311620261515231816252318166231221614171823192520291217223818231916181118171818222119201927242121121918171713251414181082311121821121312201324152010182514163111714241826272519172518151581011201115161327172513191413181210667111110161091316104295585853761091399510101510757712131264126431151812141061520815711196107710919106728101214139102671312888710969111487971015161120818138128916819910121381213814671210151211111091349666129856657107136131111681081292178169510115107878617674554884851045636363779107545761058285115688107694568731656656234457855232634628535433544350610521326242555564431255223372135445132221321155112032222131214312214241313318322213241412111332423441331433232113111131115123344124132251111152272222222312314122216312111 379100200300400500600700800900>1000Coverage value1101001k10k100k# 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.

18 82200000000000002 431 9170000000119 85100002 366 125000009 610 10700092 887 67800000510152025303540Phred 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).

99.4 %1 424 41499.4 %0.6 %

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.

99.2 %1 420 62699.2 %0.8 %

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 7880.3 %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 %716 23050 %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.

78.3 %1 122 16878.3 %21.7 %

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.

22.3 %319 44222.3 %77.7 %

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.

122 0051 8756854 5549827588 3091 8526694 2271 1711 2709 2341 4031 0953 0875519185 0751 1864923 4049721 4725 5503 51187613 6399333 7055 0561 0969423 9785391 0154 4501 3758154 28327 4191 3724 4781 4473 8051 9361 4261 7506 4733 1352 5722 66314 7028757 0942 0516325 3192 6055151 234 350051015202530354045505560Phred quality score0.1M0.2M0.3M0.4M0.5M0.6M0.7M0.8M0.9M1M1.1M1.2M# 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.

99.68%99.66%99.65%99.72%99.82%99.84%99.54%99.84%99.82%99.87%99.92%99.85%99.1%99.92%99.74%99.13%99.93%99.73%99.93%99.87%99.88%99.86%98.06%99.97%0.32%0.34%0.35%0.28%0.18%0.16%0.46%0.16%0.18%0.13%0.08%0.15%0.9%0.08%0.26%0.87%0.07%0.27%0.07%0.13%0.12%0.14%1.94%0.03%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped