European Genome-Phenome Archive

File Quality

File InformationEGAF00005115120

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.

243 910 54753 369 10811 905 3062 740 707676 767199 80981 92048 01933 88627 38422 42818 60817 50915 17713 32111 74210 7979 9299 2509 4077 8957 0837 3566 1646 0275 2555 3324 8294 5574 3884 2084 2393 9853 3953 5793 1592 7962 7492 5692 7372 5602 3882 4752 2792 4752 4782 2922 2732 1641 8441 9371 7411 9041 9681 7851 8271 8271 6001 7431 7311 7391 5341 5871 3881 5141 4561 3761 3941 1301 3921 2251 2381 2611 1401 1771 1371 2271 1541 1211 2171 2231 2091 2941 0391 1451 125977898891834775761898808655714609603671515631447507492446445373501421465449425449389506366385310316343401328407325344378304313278329308329290274362339325284262404295306315293305332229235267264216267280250228185237252272215247259169181187207162167196162158161132147111114111127296991312281331171531451721301551561111521391351501551151621151252101441131141751071141298711711912910290108939212912211311210293761148075971069382939572109746985829672526767797962546759776148647143546960627853461026963689074597167876670737179617463968475687947678833455544513749631085052534166791205451424350525643577453485044484247394246354047494232344749705561467361117685150474226262540263843262412242229243427312220305419512419261628182516152815142027242758182012241713232216232619261822212627351826184328151811917914131011236112011111828161717121926131491630262022211624342013172126172014171911201713161811182110131213711158178161115713202110131212201212812171617121117131617181491649101423131212251912825923169201430282915202418172219221115201210151317181362015161520191813191418118102020111392221151310913171171917161617181223202424111219157121714161197158149941416912209126613104111312131912106131315151213141011151218108999121110108612679369967129133157192814796111083475671012142012101091313189565119141775858101396116881075111410988865815778881197811833135913201210812138915151015108251618221723161013121591171251215149108812108119129111013647515741081710152213588811552346531163463711668777375215561356105524568893347594410557632754341251037287253922466627791055343535324134634426711658177475251536221324223413212122315121135312326114135231722511123341341228121 049100200300400500600700800900>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.

0032 723 01900000000013 182 451000000000000017 154 5600000000403 958 62000000000510152025303540Phred 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).

80.7 %4 242 75280.7 %19.3 %

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.

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 %4 242 752100 %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 %5 256 909100 %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.

14.7 %775 27114.7 %85.3 %

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.

1 333 8999 15810 2849 82910 02012 29211 58814 95915 27912 23829 3783 3455 2482 7622 7204 9042 4202 7272 9193 1607 8793 7123 9258 1716 45523 52513 1862 9416 0242 6681 9887 2342 7907 0563 1142 7746 5235 33023 3423 0771 9826 5983 0368 1983 2251 9888 2352 2705 3926 2601 35318 2252 2424 5434 9031 0248 9861 4628919 7113 536 108051015202530354045505560Phred 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