European Genome-Phenome Archive

File Quality

File InformationEGAF00002484919

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.

409 613210 145149 288119 877105 42795 07089 83583 42877 02771 59268 69465 45262 94058 46056 57653 72451 73549 17546 68643 43640 57138 25436 36034 61431 82529 36927 89626 62724 44422 63520 89019 12618 13516 31115 38314 28513 09011 70110 6909 8698 9658 3457 6077 1526 5385 7505 1784 6844 1493 8683 6953 3983 1062 6072 5892 3192 1862 0302 0591 9061 8261 6461 5111 5241 4591 2891 2621 1061 02596194587991481680278778881674877573869076267561967160758056552449453855442949545945137141338643235434636036531434430835931634231925626232827726726328425022924925726424024826325224626121222921622323822024322423629026322023121621718618218016717915316217116216816020619116416016719718918218114814116415416616717515714911219512014815515114915814615513315611213815112915012717516615115614415212614115415413915014616215114414715815414313914414312013911913614115513313611611812013814211194124123123110102941041061091128681114114112112898310083878294981057979768673848161738778735873727070647158606459695143596155575734514445314342405145374055474141463749492837344040364645323635403524273830353129222126192312182320161324232820211717101317819162018232216191615141211911811115710111010591065911171216131056101364126876577558820185910916924131151384127512101291110811125129131489919151367398118124854789115818116109141088118852697313592841051361011107510976634411818756810410863693510859594410889934744556412455657637723464383822642242532441321354114124523132342543142213234342216415513112341233122222421112112123211123132132311122232324829433321114332222241112332144621213323212154324242132423356353221241313232222412221121433112344136541111221221111132212123211121111315324112222223312111112111231213113143113112112321224234314121241222431221111211422496100200300400500600700800900>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.

3 1180000000000000215 708204 4781 153 68700000000001 015 99249 0500450 498639 515155 281211 4151 457 7313 833 1381 692 5601 268 1233 021 15013 983 65232 131 85400510152025303540Phred quality score0M5M10M15M20M25M30M# 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).

67.2 %551 02967.2 %32.8 %

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.

63.2 %518 04263.2 %36.8 %

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%).

6 %32 9876 %94 %

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 %409 91350 %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.

56.9 %466 20056.9 %43.1 %

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.

4.7 %38 5204.7 %95.3 %

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.

336 0311 3908027834193111691042 2693902 9011 1961233644 0963432 1181 2403042 308351 0637 917282 1238105057 452288181020462 77575 013108342401884662638802 68274140112258642122662547001 916309 035051015202530354045505560Phred quality score50k100k150k200k250k300k# 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.

91.14%95.62%96.16%94.52%95.13%95.34%94.11%94.93%93.36%93.97%94.99%92.67%94.09%94.88%93.48%92.46%94.95%97.28%94.57%92.9%93.47%93.77%97.35%98.21%8.86%4.38%3.84%5.48%4.87%4.66%5.89%5.07%6.64%6.03%5.01%7.33%5.91%5.12%6.52%7.54%5.05%2.72%5.43%7.1%6.53%6.23%2.65%1.79%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped