European Genome-Phenome Archive

File Quality

File InformationEGAF00001579052

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.

236 840 51447 510 72212 672 0987 914 2275 913 3315 114 6474 587 5714 191 6473 877 0183 609 4353 361 5013 149 8832 950 8242 753 9162 577 9192 403 3712 238 9362 094 3881 944 2171 811 8621 684 5451 560 3411 445 5551 333 2131 230 9671 138 9671 044 437965 372887 426812 372743 113680 016618 320563 583512 056463 361417 393377 170339 449304 309275 176248 545222 368199 586178 164159 893141 689125 978112 18298 69687 21076 21867 02958 86552 17746 36240 56636 01731 67627 61124 13221 29218 95616 57914 22612 38410 9819 6898 6807 5756 6705 8185 0234 6754 1653 7273 4533 3192 8372 6832 5732 3252 0301 8991 8131 7621 5131 5011 4551 2781 2281 2001 1031 1181 098989946824832828838769715646651558598522541551531506506398409391341348312309319272272254256242235237208186163181170160144149158155140137155121126127100116114108106101888380917350706446384757585155574530373939564437342117282226162419242526212725212525291823162424181414989161161156101099119696156101112157645122548455732725325553334343324477133353134322544632163222283323122455433111311421422214172212313223115312112222212222511211114212112111111411111121211111112111111222111111114121222212123411231123111211112132111111111132124221211112421111121211111214211111121150100150200250300350400450500550600Coverage 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.

2 104 391000000000000086 156 61900000004 255 888000080 017 64900000198 317 0510001 207 475 40200000510152025303540Phred quality score0G0.1G0.2G0.3G0.4G0.5G0.6G0.7G0.8G0.9G1G1.1G1.2G# 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).

99.8 %21 000 25799.8 %0.2 %

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.

99.6 %20 958 00699.6 %0.4 %

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%).

0.2 %42 2510.2 %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 %10 522 18050 %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.

99.2 %20 875 58499.2 %0.8 %

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.

4.3 %895 5844.3 %95.7 %

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.

528 4553 9231 6158 3872 3072 1173 4004 5112 3999 2843 2172 9045 9124 0312 1468 4963 1023 1293 4274 8644 4209 1997 5446 7979 53226 3821 327123 3921 8013 7744 4592 9961 1229 2771 2831 5022 3673 5881 14411 289210 5206 0824 88311 1657 87713 48011 22211 19910 98724 64630 59025 389129 2152 81721 1028 6114 50339 6242 6561 73119 631 793051015202530354045505560Phred quality score2M4M6M8M10M12M14M16M18M# 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%99.81%99.81%99.81%99.81%99.81%99.8%99.81%99.79%99.8%99.79%99.8%99.81%99.8%99.8%99.78%99.8%99.81%99.78%99.8%99.79%99.8%99.78%99.83%0.2%0.19%0.19%0.19%0.19%0.19%0.2%0.19%0.21%0.2%0.21%0.2%0.19%0.2%0.2%0.22%0.2%0.19%0.22%0.2%0.21%0.2%0.22%0.17%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped