European Genome-Phenome Archive

File Quality

File InformationEGAF00000488328

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.

124 846 00930 545 2926 215 8482 059 729637 276317 374196 474151 982128 957113 704102 48194 29487 72182 90977 32673 15470 27766 74464 50661 28659 16457 74556 51254 82653 15951 02849 49746 61345 67244 19142 93541 85740 64739 20437 42936 30035 17934 17733 00031 93331 07030 65229 42028 53027 94527 37226 35225 13724 13023 02221 90821 33020 96320 56119 87919 05418 53317 75617 20016 38116 00815 28314 65614 40213 37413 04412 33612 10011 81310 89610 90810 28510 0729 9449 5409 2948 9888 9458 6598 3977 8017 3487 1256 8596 5816 4576 1636 0145 9455 5695 3245 1904 9634 8454 7234 5444 2724 1183 9603 8353 7693 5883 5003 3533 2483 2913 0923 2003 0102 9313 0252 8952 7502 7382 6292 5452 4542 3062 3252 1742 1401 9752 0021 8511 8941 8021 6681 6481 5861 5591 5241 4751 3431 3311 2991 3851 3191 1871 2371 2131 1121 0991 0661 0941 0131 0351 0041 069983990974972915931864852861855728803728708757725687653652641640622600596553582513518495507463463486410453436411379402395379371373399375396416361344338328306305300298292251243247249241267219232259244230221204199184193180199185217178191192181180171163161163162170141152154116139120141130148130101104109110112120101109999710294868495849410010710090908883799476778384717161736165606176706878767756496061605459494755574735243934524740444046444237354238353033353532282428292221171515231714242426228161914142019251021181298131312161014912916141411181216191413111410105131269579128111310151287131612813128141311151312131061215107891051713147141386981065129745106566981189139119551164728486517552442153223511211111211222111221212111231233312611351234111111223112222435312232112141322312112113122213121211121221211112111121151421150100150200250300350400450500550600650Coverage 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 4740000664 878228 3261 501 584748 483158 990558 102198 157231 937289 621122 464493 626244 090381 489662 062280 047758 575352 450584 122927 214847 846994 7971 666 3581 462 9711 352 3721 726 7843 403 1435 745 5083 587 8117 360 69517 283 01827 431 16210 762 11133 973 94916 689 23340 721 15040 763 282111 212 06900510152025303540Phred quality score0M10M20M30M40M50M60M70M80M90M100M110M# 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).

98.3 %4 410 62798.3 %1.7 %

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.

97.6 %4 378 98897.6 %2.4 %

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%).

0.7 %31 6390.7 %99.3 %

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 %2 242 61350 %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.

95.6 %4 286 09695.6 %4.4 %

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.

5 %222 5665 %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.

247 3301 0686442 0981 0171 7781 4722 0502 20012 2096 0814 1929 8545 1122 34321 3733 82010 8727 0571 87513 2151 3636 12731 1941 2946 3481 0719041 046266 0651 2451 1301 1321 5481 0821 54425 945160 3722 7561 5383 7803 1941 9887 6883 7004 70816 3367 8547 3589 80814 3626 57216 98611 81816 00823 50840 4583 417 736051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M# 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