European Genome-Phenome Archive

File Quality

File InformationEGAF00001767592

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.

445 753 194131 063 87836 069 33614 854 3827 316 4905 094 4813 979 4043 431 8373 062 3082 799 7922 605 8392 440 1362 304 8442 190 7652 096 6662 003 8951 931 8801 857 6561 794 3231 732 9871 678 5201 628 4811 583 5721 535 3371 487 3091 445 4401 400 7181 358 0101 320 2351 275 9491 238 3781 202 7751 168 2631 132 6801 097 8771 068 9761 037 1941 007 269977 176946 371917 774894 187864 134836 106810 609788 741763 732740 482716 555693 478673 662655 395636 016616 343599 300578 046560 112542 137526 691510 326491 722474 742459 906443 661429 658413 129401 646388 530375 552363 235352 695338 310327 729314 928304 425294 555284 087273 160265 219255 621246 512238 173229 790220 455212 888204 683196 477187 528180 918173 713167 628161 438155 621149 897143 038138 149132 306127 027122 404118 051113 023107 643102 59698 94794 74090 49386 90583 59780 55476 48373 00570 27167 29564 02561 24358 56856 61753 46651 74849 35247 38645 56143 93941 28740 14638 25936 31234 77433 45331 98630 13228 85027 42225 90125 00723 76922 81721 77521 31920 02919 12418 16317 26616 74715 83715 44214 66614 11713 30912 69312 09611 35910 94010 2219 8639 3798 8528 6318 2747 8807 4097 2316 7266 4086 2636 0465 7445 4945 2164 9294 5504 5644 1804 1323 9873 8053 4663 4073 3902 9932 9512 7492 5962 5352 4182 3212 1652 1102 0982 0071 9811 8481 8061 7541 6841 5091 4651 4451 4821 3571 3661 2931 2291 1321 0851 1721 0941 0831 0421 0019428538548207897707207297087126966006046035365625765885334895304934444694244474294194733934384063483603743653342902942963152972972682813032772382572262462052172572112302362012011821851861701881491941831691761571831791521611641801621561601801661771851971481551681571611671541491641681581491601631491401731471441671561651451481381491411081311611111351361331111511311301391261541361281361291321481171329010912310312011112411413115011913012212912911512011415010510213912415813312515012511914513513012511612811211611910010510812112111211712911412211712413711712714011015612112012211511113411212612711213511410912288122102901211171001069692101869084104105112931161121201221121221141031071059880771051038580887911189999589737881707972817479698183809777927776756049586756676647526753485762535750545958606560665464455455425550655361405559626348525367654634474143494347404539485338363732433035422531273838353235253726383322303327273232372925353140203018243531303330262822313640313023311716201713211815171710151316231291213101612111313199201915121718201511141419101011138108181815141399197716191924212519241611202124222811232212131411181813161716131120171315181213171519158156108771515141614813131213111011109156131213132010131312712117812151910810614191316159151959121481511138131613119791011131311811951965149612976141389174128127101291371314119971812947117128866255742595457771111133510561371099784613578710587743774468645793428102574235146326553136636316645566357324556744764535655535237731176444621167646364566135512724215252121455721224441023410255626114426224143421 201100200300400500600700800900>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.

418 58500002 869 7202 233 58411 041 81210 383 8893 094 1845 447 3572 276 8822 411 9985 293 7691 804 2455 198 7434 684 8195 296 5318 354 4924 526 2867 456 8964 780 6649 177 39612 772 05312 849 78215 909 16724 913 85324 369 85924 440 42724 279 00855 775 05691 315 30955 517 98497 948 985190 043 946283 873 740137 125 146379 509 018240 779 325456 378 758508 960 5721 264 536 96000510152025303540Phred quality score0G0.1G0.2G0.3G0.4G0.5G0.6G0.7G0.8G0.9G1G1.1G1.2G# 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).

99.3 %52 916 27299.3 %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.

99.1 %52 825 60899.1 %0.9 %

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%).

0.2 %90 6640.2 %99.8 %

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 %26 653 67250 %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 %52 765 51899 %1 %

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.

10.4 %5 534 97710.4 %89.6 %

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.

1 486 9863 9702 5396 6232 5434 0783 4305 2734 65929 86214 24010 70328 9317 5957 571119 3948 46352 22418 5226 68141 8642 16369 765203 5831 40912 5771 1901 1551 1961 285 3132 2791 4701 5242 0621 7382 408137 334435 8215 0682 4449 7406 5383 01622 1845 4846 880103 73411 11412 60614 14221 9589 61827 24220 09228 51245 914116 58448 805 336051015202530354045505560Phred quality score5M10M15M20M25M30M35M40M45M# 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