European Genome-Phenome Archive

File Quality

File InformationEGAF00005114833

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.

267 055 54248 816 02810 875 5473 168 9081 175 735531 057277 756179 954116 43381 32360 22743 34834 28425 96520 42915 91813 69012 24510 74210 0549 3787 5927 1426 9536 4986 1285 0635 4354 5174 5874 2113 9474 0843 6193 2983 4353 6843 5853 3472 6902 5172 5182 6082 4362 4162 2382 1342 2142 1841 9971 9151 9761 7861 7292 0221 6801 7802 0622 0451 6421 8701 6971 6111 5211 4261 5511 5921 4841 5421 4541 3291 4151 2471 2071 2541 3401 0681 2111 0291 0341 0401 0018949299537638367697678198688447487665966416325615746715496335875036226005105824664994254824453724395225153994005174854254334833944844134323823783504004113994664154133633453694654173644153693193083032714033392863042763072723322963232962822692602172272642322812762331992182072032362392243281952991791752261441982201752001941471421462021891321391191371311391171351731491131171521181161261091271111101151531051001511231301241379099100131991101081051211241491141341119890113148138107921131091241041059712511410482108758570879571696976525979616771931019568908468597310195998162111456161665371654449536662848071764548675157455366675649465360606662637185566466696862665044614752564234515347385038482846455135303241294451283654392939484238372842593035413736383432353031445848405731533942483364302932262634393131243139313030342322243127332629362621252639273135336139533833457843607245323330322323242736462931323533333734293118313126252055243230151618212515272041151231123117182125161713517141516128127818102717211518815918161215131935211513981512412126181679111014101114141717171516121212181919221821111791914142012172731261188938710111391226891319202016213617522231319101461097111216141010173038409655105111813134101014105251179123867581214161238710118667895481111118279116127918121061213178105681611121313899161441412371118811715868427579104711710786714138975841016715181389681099821146131091311514111791913738811117101011612891171719785121269610812131213128879128871088976388443375778575694561071141010475964116145953107884556777336975624633766435598937761315101455692618385629639101296191110596715862117761615510712675745264535104438426351332215343433325151314174113313332141224746641561525233241 920100200300400500600700800900>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.

0031 184 24300000000014 274 135000000000000018 205 1600000000424 941 20800000000510152025303540Phred quality score0M50M100M150M200M250M300M350M400M# 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).

82.8 %4 519 35782.8 %17.2 %

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 %4 519 357100 %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 %5 458 209100 %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.

15.1 %823 05015.1 %84.9 %

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.

1 306 76310 44811 77010 77711 34814 31613 42917 14817 74713 61931 5803 9665 8423 1362 7805 3012 5322 9483 0633 5688 5484 1904 5058 6966 86725 51714 6093 2636 6232 9062 0647 8213 1357 7363 4552 9126 8995 78625 6743 3862 1267 1193 2048 2273 4512 1179 0302 4425 8766 6141 45020 0712 4605 0925 4371 12510 0931 40196410 7353 724 138051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M3.5M# 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