European Genome-Phenome Archive

File Quality

File InformationEGAF00002444833

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.

525 437141 70390 64469 80960 04151 37747 11244 04040 80136 51534 44432 83031 85630 10128 20826 64826 31225 50824 21723 00621 82921 56720 12119 95219 19318 53118 02716 83316 09515 65915 23914 16014 19613 91612 78412 59811 83711 77210 88910 59210 0769 9719 5508 8868 5518 2968 0087 6717 3307 1997 0826 5816 4065 9315 9845 4705 0854 8374 8164 7414 6684 2323 9593 8463 6723 4853 4833 3123 1012 8612 9592 7862 6042 5962 4102 4362 3022 2441 9861 9611 9112 0541 8101 9651 6581 8161 7541 5771 4961 4461 4081 3851 3101 1161 2031 1021 2009911 0051 0381 1129688978748948467728959378007517926977187256866866676216366125615435915515065764955645334224824454554774944294224754454274083973763163243502713512703153113203113142522972473012492772212452272472592162201911742222842081911811951531681621361451491841131311281471741801471961731551351441301521591221131001172011131281131171361421221251151321201211191569291125133981101529315898731071491351021029371106957394837185816384968866846765685166617754794659594568604961656061729487745469577643563759556068547145554647535452354249443338403333513935385537452544473134483733453139325815436334524374446403839333025302340165029313232343332342640213032254122653412283240373331272740303828231925232819353721352522302421162419202314231318103019251425191420222317171323311521221619212522211317121521171622221920142420252527172226232124231227162591512181771221120122315171610162020626282015283026181720161618151133261815151220121416141417181220121813122521191512815111518201823121312101625151311138119161313101971413169101961413131187118161216811121412137611137121010116171071411981010118412118104265437544710678658201110957104266367686799931078910810378510121311392358448101285797786312781285101031110765859761257886697132864727810785412293114412671277845857222258317369925331235683476353242155217242411446673144242122748512726371131333232223512111112261111253131314521212221622532152112234335331415243133432431222224551321235211132112131142243131111152324233723344221112112143111132411422414131 284100200300400500600700800900>1000Coverage value1210201002001k2k10k20k100k200k# 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.

7 11400000000000001 533 168000000072 57300001 501 056000003 665 19400035 647 04500000510152025303540Phred quality score0M5M10M15M20M25M30M35M# 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).

99.3 %561 45699.3 %0.7 %

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.

98.9 %559 65698.9 %1.1 %

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 %1 8000.3 %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 %282 84150 %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.

84.2 %476 22884.2 %15.8 %

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.

25.2 %142 55425.2 %74.8 %

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.

63 8371 1192752 4405194194 5841 2794012 3507167924 3778925491 7812985442 9076632681 9216559983 8222 5335207 2445192 0282 4597054312 2002655272 3049094012 51616 7148102 1449701 8021 0389579693 6052 1341 8051 7438 7495453 9621 0353893 5111 319315450 308051015202530354045505560Phred quality score50k100k150k200k250k300k350k400k450k# 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.

99.53%99.63%99.59%99.6%99.72%99.77%99.58%99.8%99.68%99.73%99.91%99.87%99.4%99.88%99.77%99.53%99.69%99.68%99.91%99.65%99.8%99.72%97.48%99.98%0.47%0.37%0.41%0.4%0.28%0.23%0.42%0.2%0.32%0.27%0.09%0.13%0.6%0.12%0.23%0.47%0.31%0.32%0.09%0.35%0.2%0.28%2.52%0.02%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped