European Genome-Phenome Archive

File Quality

File InformationEGAF00005114737

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.

265 184 48770 136 28619 076 7675 225 6571 483 305458 134163 18278 20545 64933 86026 97223 24619 12817 42715 06513 76911 31210 5029 5749 4269 3158 1327 6746 9196 9896 4675 6165 2555 6534 7154 8894 4974 1684 5083 9633 8313 8383 0073 0822 9382 8762 5522 9762 7222 6272 3572 4472 4352 2972 3672 2392 0682 1211 8731 7961 8691 8001 7691 5961 5781 5301 5881 3311 2961 5701 4511 3521 2251 2781 2891 0531 4241 1801 0491 0741 1511 1191 1059859058547867848157597307707468177957688039157506076796996546106776885345756056385697195014656246395275787075504874444255075255086274794093894525364004814564594634614494524274324474694544805645005274074384514474655404304753874714325384014105075295304294223553704934225214434204274175164223594524603864104983934384064124043883803993694484123833303813962903233153242632832152112102392172162122471821971971581451301422471242031161161462311381221051151401351041061111011581401371261401391059395115154101999891107659073951531471291145788109765988658058775567696075736772788710214868806373677686799273475952363755647757507239606273525657606153415169525151518134395754387536637053505750575839685351614236483947485357415335558551384948464142526353414231594034454157504644505280633458405962303754405342513657694634376036563028372562232427324340356143883419202835231418232221272630312219244326282122151923223019363229393149342216212130172016201117201816717151213191936151516232110141614131015161616111116172038272411121422181416171718159161819152827917154446231412121413171417201115177141221161513218122725299101071067108101416102115149892128981071417101515151116101510111711917813961099696692014111011171420152114714121617122010119111371114116123812111591091714141311151016211416161511841641149111010871828166123251247468371191774710124561413121068129157121921141291381610109158211611141111151087136118712941051581110201181410121518159211124248746118657124810792855998141391051610599356523310786665296485476568276555396247987558361036321356108824343775107555243384237386822757545181441047478764676767448510109611101413513135106106914117118610116811148712171613137761720141563118461310107161821129111015171615141181171281261414261618132061097812471012898161024119109179101 725100200300400500600700800900>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.

0021 802 41300000000017 109 084000000000000021 372 6480000000501 622 62600000000510152025303540Phred quality score0M50M100M150M200M250M300M350M400M450M500M# 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).

87.2 %5 233 52187.2 %12.8 %

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 %5 233 521100 %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 %5 998 466100 %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.

19.7 %1 180 40019.7 %80.3 %

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.

1 157 52111 23212 78211 91612 11715 12413 91318 12619 09014 97037 6054 0566 4563 3763 3826 1972 7713 2903 6943 9719 6564 5515 14010 1277 96929 74216 8583 5297 4653 4802 3898 8633 5369 0443 9593 3877 9376 71129 6123 9212 3898 1503 7789 6614 0522 51610 0132 7366 6277 8701 73922 8502 8745 7306 0191 28010 9621 7081 16212 1394 356 045051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M3.5M4M# 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