European Genome-Phenome Archive

File Quality

File InformationEGAF00001159746

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.

330 279 36978 687 92020 317 1829 941 1456 127 6964 875 6374 144 3033 717 2643 415 8993 165 6342 965 0262 801 0562 664 0592 534 3552 414 8832 306 4522 206 7942 109 8872 029 6251 942 6421 862 3791 786 9121 715 4591 652 5591 582 4381 521 8581 461 3201 405 3801 347 1391 294 1661 240 9181 193 3961 141 8461 094 3471 046 6691 002 888953 436909 383867 356825 050783 673740 128699 660663 802628 171592 038558 443527 825498 459468 230440 176411 900385 930362 872339 518316 980295 452275 332256 193238 205222 116206 153191 396177 461163 812151 292141 245130 699120 941112 052103 03695 06687 52680 73274 57068 54662 97656 71752 21247 74443 31639 64335 62632 80729 93727 29725 00722 69220 55518 88117 26115 99014 45013 37912 28211 33410 1979 5438 4077 7437 0516 4185 8875 4405 0574 8054 3864 0773 5963 4373 1692 7862 6982 5452 4792 2372 2531 9951 9451 8651 7541 7111 5071 5301 3561 3611 3251 3121 2601 1531 0871 0971 0861 0351 0539809741 040961901870860775809761688661644626583540572550493478480420397384372349365352313276348284306273243267253250206234208211201188196160132136111111118130109129144117103119831081001241041079911693109969811281101102107979681837710895927490735759634972715957565251545361444354453744445042424436343136313944463837364040363842443844243049412030312927423330192131202828232624131618192230152423273722163216242211212419141813282130241811131911119151614111111106101181012121061191111131010158241691416131415121781035865667444835446143568856838462647774556766632313444733323367321166475383311105868945446324432616451564666774223532521027739661398563144454624635226610685161823233161135152512516622233634241524121451234434863110745685933867443433424865154566721522126366757234311412172152743433665337481367751342236313235466477792474611753544554622451231122111121253133123122111111212221315116236434517443321172102812814681056111310515878111211512913563622112331426211412111111111111211111111112111181100200300400500600700800900>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.

1 731 832000000000000081 888 20500000003 954 458000075 433 12200000271 082 0620002 123 076 12100000510152025303540Phred quality score0G0.2G0.4G0.6G0.8G1G1.2G1.4G1.6G1.8G2G# 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 %34 071 11599.9 %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.

99.9 %34 046 89099.9 %0.1 %

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%).

0.1 %24 2250.1 %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 %17 047 77250 %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.

99.2 %33 827 15299.2 %0.8 %

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.

7.2 %2 461 4407.2 %92.8 %

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.

916 90311 1745 19417 3655 9805 12611 9339 0695 18915 7206 5195 28011 9076 7164 30513 4265 2515 5046 3618 2457 57014 26812 48211 41115 41640 9412 608171 3243 2486 6397 8354 7242 34113 3832 1662 7643 6195 7102 09016 202307 1639 8777 83916 79413 17119 73116 96518 22717 57037 21546 52931 229195 5113 97734 40912 8185 79659 3884 1502 36531 971 397051015202530354045505560Phred 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.

99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.93%99.94%99.93%99.93%99.93%99.93%99.93%99.93%99.92%99.93%99.93%99.85%99.93%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.07%0.06%0.07%0.07%0.07%0.07%0.07%0.07%0.08%0.07%0.07%0.15%0.07%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped