European Genome-Phenome Archive

File Quality

File InformationEGAF00002444974

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.

351 103157 39572 62857 95844 26439 31333 55031 27227 61726 15024 42023 14322 46121 58119 83718 66418 10918 47817 24617 47016 94715 78716 23314 93215 38915 03914 50314 11513 76313 99713 62013 51513 37713 31112 60112 91912 49612 02612 19211 65211 63512 08911 34811 20111 17610 77410 34610 1939 6909 5639 2869 4169 2008 9108 9559 0498 4488 4028 6108 5218 2858 7758 2727 8238 0408 0017 7667 1907 4497 3486 9517 0856 7046 6536 2786 1895 8725 8096 0355 7525 4975 5725 3585 3075 0614 9844 8554 8254 8754 8124 4044 4824 2834 0053 8913 8883 7013 6893 4763 4963 3163 2363 0102 9012 9772 9472 8992 8372 6862 6682 5532 4862 5022 1852 1542 2072 1462 1791 9202 0341 8451 9181 7861 7101 7341 4791 4801 5901 5771 6361 4121 4641 3431 3271 2761 2691 1411 2831 0599971 0301 0438901 0269249358019178707497357556727417036666336395905876255655176055574795334634704984925004564243894253883503303983754213183343283203493532893162543092792902562253392492392271932522151981741821852002072351821631761601831601651551521741671541492312211431591561361391591521349615614613614612916413714812414714314513695163110127142126125951111441261381311211531381251431221221871061031301121689210012511410912110011696929884100841321111109091818711586921191178993978885891638492669575911178188686884476052596365501396171545463456152565338525151424140596340615047495652126355033374443454141384244324552332731342835323527363137202927244546334139264830173536273633483924222717252521272042403728183326203638233271151922169302814251978201825662321181920212511172322131123191419132420169262122232115172114111717131222171616161013141423131222171819202117212021242130232811162225171725181414161982014141919202222182117151116121421222218261820192616242118122417151213151418119101081420201417172015121118141415201071417951514101191721131615161151210101511132113111415131419151011101012414515129161313201513161615132016181710161110981316991216615141295777129101379121251089511126101241251041310516567634664611896151379711121345734129988115969684471310446497767105858106564974510117267958333106363453378669965512846666625427723635235345836345323417368125134367761646363416383899679336248841167461265108376829526638444347556435518865444510588694376499564659711468554599797108464633437645157995614335222656559654983459104448588837463765273411652 703100200300400500600700800900>1000Coverage value1210201002001k2k10k20k100k200k# 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.

2 08800000000000002 526 2730000000134 60000002 555 381000008 211 81800071 491 44000000510152025303540Phred quality score0M10M20M30M40M50M60M70M# 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).

80.8 %915 42480.8 %19.2 %

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.

80.7 %913 49080.7 %19.3 %

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 %1 9340.2 %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 %566 14450 %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.

66.6 %754 40466.6 %33.4 %

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.

7.8 %88 1927.8 %92.2 %

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.

279 6471 4623452 7567936121 3281 8394583 3491 2079333 7391 3925332 0923336988651 2102842 3247081 5082 8175 51052017 3755723 0448999564723 0612586327691 2205243 53029 3699707801 2012 2891 9141 3591 3471 3802 8112 4042 97614 8545702 5411 5974965 9751 654294722 478051015202530354045505560Phred quality score100k200k300k400k500k600k700k# 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.

99.8%99.76%99.83%99.79%99.57%99.89%99.84%99.81%99.7%99.82%99.89%99.9%99.79%99.92%99.87%99.76%99.81%99.83%99.76%99.73%99.11%99.49%98.44%99.98%0.2%0.24%0.17%0.21%0.43%0.11%0.16%0.19%0.3%0.18%0.11%0.1%0.21%0.08%0.13%0.24%0.19%0.17%0.24%0.27%0.89%0.51%1.56%0.02%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped