European Genome-Phenome Archive

File Quality

File InformationEGAF00000773560

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.

355 422 119112 191 36633 740 18314 938 9087 619 6325 243 2544 008 4853 387 8462 971 9972 695 4862 475 7992 311 7262 175 7832 070 0451 980 7281 894 9171 818 6271 757 8421 702 0351 652 8941 605 6791 560 4081 518 0941 479 9841 436 8541 401 0351 368 8601 334 5771 301 5431 270 3051 240 7381 210 0341 183 8361 155 0521 128 6161 100 5941 077 8401 054 3781 029 7501 005 708983 598957 377935 882912 405891 305875 701855 728835 643816 460798 165779 964759 124744 653724 284708 275689 858673 495656 068639 251622 556607 932591 654576 004559 228542 905528 566513 179499 293483 064471 311454 834441 942427 599414 170400 440386 941372 548360 148348 146335 913323 301310 389301 662289 910279 389268 561258 267249 989239 666230 158222 338212 265204 973194 869187 256179 070171 791165 441158 345151 871146 216140 150133 794128 173122 159116 320111 088105 763101 67997 06892 79788 12184 34280 89776 97773 68869 91466 27763 11060 32357 48654 09451 57349 16446 98344 40041 85739 20437 38435 55333 92232 11530 49728 92127 67925 46024 74723 43622 09420 61919 59518 47317 59516 62315 88514 84713 91913 62912 45711 58211 22410 5969 9649 3418 6808 1967 6307 2386 9016 4346 1845 8625 4335 2554 9704 6354 3424 1163 8253 6953 5443 4393 2343 0192 8022 8002 5482 4272 3202 0892 1642 0121 9431 8541 7691 7371 6261 5401 4801 4991 4211 4631 3481 2991 1881 1281 1411 0891 049988990998944915920864842858821805790831781726739647674667678655610606601588605583581517595534525523493460463493525466425443431432418398417421390375329376350360328336328303292286300289242274259261288248285255263221267255259252219230194216221211211177199204203186156185169186185174149155154158133155148110130125110111119143136118125130831028611110312080107961009610380871029771978282819093859182598479756863747559735877534657555637505249504947314052726045565232514059393836403336414255413837353838424437324238303432424953313936403541322731303233333626292218201917251829251812252423192516231619202122202018202312242522142315152728282416162422171113141428181819171312121819131120181512181915111715915161515211411171718121724131411161516121418576141278127710756871279115138119117118266669588496614971110716122075101115151276119765986757510101289518129658648495779118610869139771210710118612755541036344626668758555453108725286456284328543422332424336341675114566866238345612464553433561154521112534447231462374462636347352413142543221411311212321514321143224831532221223724341611142411142312221131211221111113433111232122221122131144123213222222114444513121153342411244235421211311111311121122112332111111132111134211211221232435100200300400500600700800900>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.

437 93300005 757 1684 168 54218 538 34815 954 9434 993 3457 876 6823 620 0293 859 4247 296 9862 765 0648 180 8576 266 8507 324 90712 644 5186 590 64611 724 9477 201 23312 856 12118 329 93118 850 06621 572 06935 040 13533 270 64131 993 49633 906 68073 686 461113 191 19372 276 101121 190 081219 201 045308 574 983160 719 644405 390 968296 478 534481 717 289565 593 0261 053 488 46400510152025303540Phred quality score0G0.1G0.2G0.3G0.4G0.5G0.6G0.7G0.8G0.9G1G# 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).

99.7 %55 972 37999.7 %0.3 %

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.

99.5 %55 889 68099.5 %0.5 %

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 %82 6990.1 %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 %28 083 52950 %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.

98.9 %55 567 29098.9 %1.1 %

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.

12.2 %6 864 63112.2 %87.8 %

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.

1 376 2103 1571 9236 0702 2413 3993 7545 0354 69826 30711 71811 61526 9217 6386 409111 9058 48940 66619 6518 47349 6992 23972 251209 8952 03012 4071 7861 5811 3661 344 5042 1991 5181 6622 0401 8862 470143 287700 2805 3322 45211 2947 0142 92422 9804 9686 980103 35810 38812 04413 38222 9749 51626 53420 25828 54047 750120 84651 448 145051015202530354045505560Phred quality score5M10M15M20M25M30M35M40M45M50M# 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