European Genome-Phenome Archive

File Quality

File InformationEGAF00001586721

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.

353 182 91883 203 97120 905 9319 791 1176 006 5174 743 5074 056 6673 629 3413 323 3463 082 9842 902 9992 739 7622 611 1932 492 2832 390 1712 289 1782 189 2872 103 3752 022 3401 941 8541 864 9901 790 6141 716 9741 652 6721 585 9761 529 5911 469 8451 417 0421 357 7041 303 3741 252 6131 199 6671 152 8461 104 2881 060 3441 013 728968 866931 766888 371847 299807 410770 258732 610698 454665 036632 210595 514564 363533 874506 466480 413455 128429 330405 411382 565359 898340 047319 941298 489279 931262 171246 826230 811216 197201 314187 144176 649164 824152 926141 786131 763122 466113 578105 21397 48091 33784 96778 62072 50467 08062 02956 69952 27048 16344 53340 95437 89734 69531 78029 15626 84924 92623 06721 02719 23117 49616 20014 75913 44612 32811 42410 4039 5688 7318 1987 5166 9906 3065 8125 2974 8624 5734 3243 9633 7453 5253 2333 1122 8522 8352 6412 4512 1992 1072 0961 9461 8031 7991 7241 6551 6011 5481 4851 4091 3411 2821 1961 2151 1621 0961 0481 0711 03696995697594992988985787687184380285376872966668264463358960158654453150847748346145540239339237340141236337031633331730027226429928628729226025823124522022619318119818220819116916415113614716214915212714716014612910511812811213011212913310910597888098689592838374927483745188776067669172716873756466587359696345373228253432392926363735363132373236322624222925173325222231231911241815172423221933141918132319141921211713141913191315914151317151420111618151818131718191218141124181216141813913162110141114135121212716978161391017121717715171391283917914121718152010181314211015119231511161717101617913782196111714141818151518131518141410101010106209771388101716171118158813131399139101184109669347624263578453326343133311112113412323212421352133323113322213211114111242313421122212111422122222222222111111111112111121111441111211412212121212433423212121422322211132112113221321221441511121211221111213121111112111121111322112121211122111212231113311212112123111111121111112221211111121112211122211112121111111112112121133100200300400500600700800900>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.

3 694 9540000000000000113 529 64600000005 989 7580000106 995 87800000313 715 3890002 158 912 37500000510152025303540Phred 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).

99.8 %35 970 31999.8 %0.2 %

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.

99.6 %35 903 89899.6 %0.4 %

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 %66 4210.2 %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 %18 018 92050 %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 %35 753 32499.2 %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.

5.8 %2 101 1545.8 %94.2 %

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.

934 4907 1793 10115 1084 0503 7325 6348 1864 31316 2935 7934 91410 5446 7263 79514 9655 6125 6295 9538 4637 54916 22712 81112 08616 71447 4392 392214 4013 2996 9158 0285 2262 13616 4432 0012 8364 0416 4922 10419 589370 25410 7557 94119 14513 82523 10718 75419 76018 16341 68753 11438 462227 3874 70537 06714 6667 09469 1314 7242 76433 563 786051015202530354045505560Phred 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.

99.82%99.83%99.82%99.82%99.82%99.82%99.82%99.82%99.81%99.82%99.81%99.82%99.83%99.82%99.81%99.8%99.81%99.83%99.79%99.81%99.81%99.82%99.84%99.84%0.18%0.17%0.18%0.18%0.18%0.18%0.18%0.18%0.19%0.18%0.19%0.18%0.17%0.18%0.19%0.2%0.19%0.17%0.21%0.19%0.19%0.18%0.16%0.16%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped