European Genome-Phenome Archive

File Quality

File InformationEGAF00005115048

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.

192 507 39639 431 6928 732 1512 038 439516 737161 40868 48540 11429 18322 86819 44115 47814 86112 59510 88710 0108 9718 5008 0487 3088 0156 2836 0795 6955 0434 3354 8314 5484 4664 1563 6663 4003 3753 3442 6792 9382 5532 4252 7282 2612 2852 2572 2832 0531 8111 8011 6571 6411 6661 7291 5091 6221 6891 6571 6141 5781 4531 5201 6281 4521 4951 3911 4641 4141 3371 2561 2011 3751 2681 1281 0481 0211 1519401 1429888289107298316086816955916966986376135485594965605544574133553704174513994743803884083753733753313782992893543483495203252982973023323483022632882542512322482863292773132161992632041432381781852071352193042171832041341501911231391151741221551571061331341141781722191101481431201841641641301181471401521609010012310011511492104109961029872899683848792801089764767771825274129476958586270668156638577647911299947254127108546358716177528995618096103869453684149553840403043363024352931233430383761335435502243313564253355353020324066276029518221202819221220291922311541332833283046223228151828534824302719381614243233221719193016181419212515222925201931720301912131712191224171913191413109141415171712202522252121262736381110391611221116101313181818232524141422202513151818144034211691017121520810202313151212181715181516151313151714101527162411211411141322191725131415201710914151713222023921171721316192215131912181712201417211118231815241616171210261025121418191218142218151612111612131113108191581514205121711141517151210221091072107761085898128699610171111162166136117111013861211206101512171812129911101331789111247999715131717109816413412777948871313131085846869241119131145969959101359571111111822277101214156712491110101014151014111013111776111010147871499127151154677777611754256458678567253492225334513235435431543135521026375247934569346635853124123322346426442543214554441045824373223241123322111145234125411231111111325222132123651354223432211411112111323211141126321144635722231233112124121211153673522112311234437100200300400500600700800900>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.

0036 173 33300000000011 108 389000000000000013 684 0740000000312 367 85000000000510152025303540Phred quality score0M20M40M60M80M100M120M140M160M180M200M220M240M260M280M300M# 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).

74.2 %3 257 87674.2 %25.8 %

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.

0 %00 %100 %

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%).

100 %3 257 876100 %0 %

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.

100 %4 389 304100 %0 %

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.

0 %00 %100 %

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.

13.6 %598 33913.6 %86.4 %

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.

1 374 2236 9997 8997 4717 4939 3338 56511 48611 5619 34923 2562 5794 0042 2232 1323 8841 8772 1272 1392 4536 1102 9303 2046 3035 00218 19210 1842 2704 6492 2271 5425 5102 1895 6042 4662 1744 8744 34818 3962 4001 5605 1422 4215 8102 4951 6106 4231 7654 0714 8581 09314 4201 8523 4603 7988336 9031 1107407 5672 713 211051015202530354045505560Phred quality score0.2M0.4M0.6M0.8M1M1.2M1.4M1.6M1.8M2M2.2M2.4M2.6M# 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