European Genome-Phenome Archive

File Quality

File InformationEGAF00000488337

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.

127 049 33532 620 9326 170 2622 195 274645 381320 362190 740146 897119 953103 30994 24786 90580 13574 50569 38567 09663 37461 37558 65956 25854 29853 35951 75350 23849 10048 23948 19346 73946 89345 19344 92844 51243 63643 21542 39942 17440 88740 78539 81239 61438 10537 40536 02036 02935 34734 57333 58032 81831 52331 19630 49729 67529 11228 01027 12725 95525 22324 43523 78322 92921 88020 85120 40519 77919 10818 67818 29817 42716 58715 90015 32214 54613 81713 25512 70912 17011 56110 95710 3709 9109 3119 1838 8768 3088 0247 4107 2266 8946 5986 3015 9615 7235 4385 1804 8984 6844 4604 0703 9063 6713 4953 3103 1482 9152 8792 7082 6392 6562 5312 3992 2462 1401 9952 0781 8951 6871 5541 5021 4271 3691 2591 2261 1491 1491 1021 05695793493190080375672266165656153852354443744343138039434839337033536032531232032029127621524923423427226824522922017317815217716316114613615315312212410012410912312311111710410610510696868260646556485172455250376248304439434137343345333240323437494144323625352924302227364431162339303130244139313838332024152023213134333120171622161718121212131063141487941289716121211129148515102214141911751078845587411612661128544326242710754544876129129137116127181312586874232121111111225121211112322221111222312111111331213211221131122211122134114211211121456115352231221425532435433214111223213121133221133241211111131411314150100150200250300350400450500550Coverage 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.

21 7710000606 001207 6881 443 916727 383168 912538 760186 780210 429289 192115 514440 643234 786353 068619 110278 155652 192320 099547 171825 761789 969939 6681 547 0991 369 5911 317 4001 611 5513 240 3825 572 7363 423 2176 718 71517 171 34723 701 83010 678 30934 599 49617 519 15442 382 93343 880 938121 827 78400510152025303540Phred quality score0M10M20M30M40M50M60M70M80M90M100M110M120M# 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).

98.2 %4 543 29098.2 %1.8 %

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.

97.4 %4 508 66497.4 %2.6 %

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%).

0.8 %34 6260.8 %99.2 %

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 %2 313 86350 %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.

94.5 %4 375 10294.5 %5.5 %

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.

5 %231 4305 %95 %

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.

263 3971 3637022 5321 0861 6341 8752 2912 30212 5556 4855 09510 2745 2472 76621 8603 94912 3537 3161 91913 0861 5746 37830 8871 3727 7461 162942930274 5411 1911 0981 1341 4861 1741 52424 025179 3172 6821 3523 7443 2341 7266 9583 3144 17415 9267 2326 8548 69813 3905 97015 90810 83015 02221 86437 6763 524 604051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M3.5M# 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