European Genome-Phenome Archive

File Quality

File InformationEGAF00001586590

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.

381 569 65097 157 14124 565 47411 171 7486 396 3924 916 1364 127 4173 675 8933 355 7163 106 0852 907 6242 746 9542 603 4422 486 1872 371 1692 273 0212 172 7462 085 7012 003 4471 924 1131 848 2861 779 1541 712 5411 646 3571 584 8891 524 4981 468 1411 413 9301 361 0511 311 4891 258 0281 210 8181 162 9201 117 4751 073 5271 027 554986 058945 194902 830864 404824 414786 175751 547715 838679 224649 542616 581585 053555 358525 506496 818470 341443 206419 324396 421371 907352 482332 150311 989291 947273 994255 760239 816223 227208 583196 646183 026170 301158 150147 577137 512127 738118 179110 312101 63194 71086 95280 89875 06169 26364 31259 14054 34550 25346 14642 76839 62136 67133 96631 42828 83026 22524 80523 04221 22219 57517 80316 51515 00514 13912 88911 76210 7629 9059 0618 2597 6237 0536 5465 8715 4914 9454 7734 2794 0033 6783 5583 4253 1783 0852 7702 6132 4402 3692 3012 1282 0731 9081 9621 8031 6911 6261 5361 3791 4481 3381 2691 1921 1761 1291 1341 0391 0631 030971951951843887902889845819836843785724727699714696621631562528554471506533474463413391378341348351342334274314259284237274249228233225219213206183154163177157163157164175149153153161165173127161140133111169134141164145156149144136148135153113108119107106114858511711685797469767871837563725952717176676361414442505043625739474743333226212034232019201721262723242631242939262331293832312936294232422438423528232620182318182021252520141122181617162019232112111114520889139681485623466544478586884104115637667346244453516421226435636152394823725331214341411532433327451411211412537153613221113341112211212221442274625543476233723754564344616332351522312331333221231122133371433331251431213213111314325232622134322111115111131122231332112111211213122211121211111211111111121111111111111111111111211121211111111211111111112111221111113123111111111121111111111111211111102100200300400500600700800900>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.

212 8440000000000000124 333 04900000006 246 3160000117 188 48100000335 858 2300002 223 464 18000000510152025303540Phred quality score0G0.2G0.4G0.6G0.8G1G1.2G1.4G1.6G1.8G2G2.2G# 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 %37 397 64699.9 %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.

99.8 %37 365 68299.8 %0.2 %

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 %31 9640.1 %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 %18 715 35450 %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.

99.4 %37 221 33699.4 %0.6 %

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.

6.9 %2 593 8076.9 %93.1 %

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.

946 5167 6323 61015 4224 7624 1606 4169 0044 96416 9036 4005 21411 0207 4374 38515 6826 1556 0096 6809 6828 74717 42814 98313 55518 51649 1882 831216 8203 4026 8998 5165 5612 40216 4342 3283 0224 3666 8982 36020 123386 54211 3908 68219 72414 32124 16720 12721 00919 75944 40054 80742 274233 0004 84639 14315 3067 15471 0764 8972 83934 877 231051015202530354045505560Phred 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.

99.91%99.92%99.92%99.93%99.92%99.92%99.91%99.92%99.91%99.92%99.91%99.92%99.93%99.91%99.91%99.9%99.9%99.92%99.89%99.9%99.92%99.92%99.92%99.88%0.09%0.08%0.08%0.07%0.08%0.08%0.09%0.08%0.09%0.08%0.09%0.08%0.07%0.09%0.09%0.1%0.1%0.08%0.11%0.1%0.08%0.08%0.08%0.12%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped