European Genome-Phenome Archive

File Quality

File InformationEGAF00007928037

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.

541 453 839153 166 24041 899 62011 504 5383 314 0341 061 585408 317197 780118 68583 63163 46550 18942 51836 46531 50327 42524 72421 53420 12018 19117 07215 58214 91814 18813 37812 62211 84311 76011 25710 71110 40010 0799 6389 4949 1318 6188 3778 3127 6087 5417 4117 0936 6616 4096 1346 1075 9325 5055 3115 1784 7444 8144 3094 1903 9453 6743 6503 4373 2713 1412 9022 7982 7392 4472 2752 3482 3352 3342 0232 0971 9981 9362 0921 8661 8121 6931 6381 6251 6371 4691 5181 4671 5721 3681 2951 1901 2211 2651 1511 1501 0991 1651 0191 0029911 0269709378779348638737908148198136977497426876965636316075395625456175224914444524675014594794154584454534194473974073773923524193233183183633733082722792692843172542522932632462462353143302042582102332392182092551972272151822161921891841831591861862161631601911451281661571411481401501811661501381631561541531481491061181511851141111189611310614114311713012511311012312113114211211910696104109135119129130139131135135130124137156132139167146126130114100100128116116132136115110118120100951199089102117110961171207311793848082101111101896979859366718976971109269667078726366788057618583817460736050701078376517048556453415245345640425546473543365352464240393838344630362438387456574836343457524743413738343336343840394744524857424235322030242424182532364533433434283620293527303526303228394430273641482524242521262631453636293227344622254957353425333929282224293322342831283623272527252533573832293622302928372029341723183230172929920231122242327223827433731263523283223163136241837353439302735193534264526552321464127342330292920391618191323191516101513191815142315151411172028221623192225192571718151518237201114121619111410147131618181321221310191313141521152414171192310151913151012141310592013111512181881211513191172718132832412149671128710646498310765476788765588910366433357533561734276587665858957611552685251127268445110910699865242414521611731232654345332635133133558353555931453452966624643465332331136428313284433426273424595557441571865111234141552512233434112113344114152512433235211321141334337352744312371187869103235714261413522114476721337534583141832102613343512236211121112221131521 699100200300400500600700800900>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.

255 8520000000000019 771 80000000000012 399 552000026 426 729000082 593 4440000160 144 632000791 040 04100510152025303540Phred quality score0M100M200M300M400M500M600M700M# 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).

99.4 %21 722 31399.4 %0.6 %

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 %21 722 313100 %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 %21 852 641100 %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.

13.9 %3 028 70013.9 %86.1 %

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.

3 078 72945 46232 94829 71646 29133 89441 68334 03862 63937 91253 64567 52758 536107 10816 151206 74321 67313 58657 4949 83822 6074 25666 7002 3634 9265 06611 5658 119111 47012 4886 9886 907106 44725 3845 918280 73614 2875 3394 278129 3794 2804 337140 8533 0736 8607 690144 2883 2929 13420 085135 1937 84018 050204 6369 2854 68120 557148 7673 35822 24916 044 062051015202530354045505560Phred quality score2M4M6M8M10M12M14M16M# 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