European Genome-Phenome Archive

File Quality

File InformationEGAF00004189171

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.

1 711 8511 044 288574 581378 961254 647170 594115 19079 55354 04437 23127 91019 74515 10110 1367 9307 1065 5074 7564 2893 5533 0732 6412 3242 3921 9461 7931 5561 6081 3771 4591 1141 00392683693972461268860762953146840938740947239740434330427036431225427528626629728027724522016516116213313415713115111110594941019214380949589899752648561756270888865786357477675957044796473102735853725345756565484445524638615652684836354043423531282630374842453641433232322233383829262326504240314643264432313360352235524733333341335633424536464546373249383634475143515048374542233641383125263232223520182527111820283823181718219109152015148102416181413715111513118548438446624211063651187106881289105785753549612558111041027373764106235483227642421735354102229182433411213233221455414241323695135226545441213634772252741415214115651161075310758642532754366259673116131114393553234263222534262131118443279101151211448127611379712157141411167710517101597411551371057111477169171517101087810815910881055865874676785126148751289586697497493104811142111151812101581157886781079107679346468111162811559664101011114116374135141114910106101410711191518510664109108681312410105123117141288812412498365104612891397181113412615108111215105188811158121212109768121012811121410911219889121174117565446611107557343475445492673584315576254343253234663851315331111225222111222121121111111112111100200300400500600700800Coverage value1101001k10k100k1M# 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.

22 457000000000000094 45681 364449 0140000000000390 28724 6530187 708248 96552 14670 521607 3841 171 997739 168530 4521 228 7085 865 74411 790 77600510152025303540Phred quality score0M1M2M3M4M5M6M7M8M9M10M11M# 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).

74.1 %174 58874.1 %25.9 %

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.

70.6 %166 21270.6 %29.4 %

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%).

4.8 %8 3764.8 %95.2 %

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 %117 77950 %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.

64.3 %151 36064.3 %35.7 %

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.

18.1 %42 73618.1 %81.9 %

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.

84 791319254010226231154766784440322798913963123912568592881 72525932219 30511442263820 6471494301080614688620142610362248118238101 034051015202530354045505560Phred quality score10k20k30k40k50k60k70k80k90k100k# 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.

95.15%95.28%96.33%95.72%95.26%94.85%95.19%95.18%94.7%94.26%95.72%94.56%94.39%96.25%93.46%93.08%94.27%96.31%94.76%93.88%94.8%95.01%86.89%99.35%4.85%4.72%3.67%4.28%4.74%5.15%4.81%4.82%5.3%5.74%4.28%5.44%5.61%3.75%6.54%6.92%5.73%3.69%5.24%6.12%5.2%4.99%13.11%0.65%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped