European Genome-Phenome Archive

File Quality

File InformationEGAF00001173838

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.

320 279 43369 025 62618 001 7039 098 2586 117 2725 000 9254 360 4303 938 2413 621 6363 363 1873 155 7022 957 5622 797 6632 649 8552 506 9542 378 7192 265 9702 160 4462 060 2751 958 9231 865 7991 777 8341 694 9101 612 7281 533 3311 455 5581 378 3541 311 3231 237 8311 174 2121 105 0791 043 216981 503928 293871 617817 796764 936716 935666 451619 792575 988533 159495 664458 348422 380388 608359 024330 074303 418279 599256 412235 508216 356197 041179 662163 713148 126134 284122 934112 794102 37292 68483 53675 36767 07361 09255 15649 84045 56540 50636 58833 09430 15127 03024 22921 79119 74117 70915 90614 47613 38812 50511 1139 7188 9998 1977 4076 6306 2705 8605 2404 8734 6494 1124 0083 6443 3783 2863 0602 7942 6612 4132 1772 2232 1762 0331 8591 7941 6541 6451 5721 5161 3031 3721 2771 1981 1691 1311 0801 0401 0249579138408387868057026746476355975665235224664564494084283874203943623162982982712662832682552362111671821941741711851731881841591251561341161291101111059111599981051039711610482951039193948111077788193807383808066767579736751496856625466706659626261544841485743435246474539444552323837364439364133313928443443344432332117213426272335313325272624253130263323252633322732272919252030202623272720141413141715891597121512671012131310915712119161323121414191610162217201914176211716111320122216181761212129122116121514151891617211115121719171615131912918122415212017181618181713111292014131320172022192418222411171418141217141468111616141215102015141315117874654467710814249989817344765494869737372326643524311472513447563693611657173755810676675481046134723448244441441534424623163677637333143633313232716225312153344154964544426641322543361123132234391045310383541634241331333834431153313241132753335243325413555213827527413293526323634532552424244834585765754443114433313344329462510345510109846753584743117144974322142532111313142324444411224222143222211122122121122413423112233342236253113434222432135333135212433212121111111108100200300400500600700800900>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.

298 711000000000000060 634 77900000003 207 521000057 079 47200000220 952 4200001 808 278 09700000510152025303540Phred quality score0G0.2G0.4G0.6G0.8G1G1.2G1.4G1.6G1.8G# 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).

100 %28 670 460100 %0 %

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.

100 %28 668 438100 %0 %

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 0220 %100 %

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 %14 336 34050 %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.

99.4 %28 505 26699.4 %0.6 %

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.

5.4 %1 542 5935.4 %94.6 %

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.

754 6338 4344 00914 2284 4704 3339 0417 6024 26313 0735 2754 3149 7115 7613 38111 5374 4034 3815 1807 1466 00412 09010 7379 86213 15136 5952 189147 8032 7585 3716 6694 2021 97511 9551 9172 3443 0685 0111 78213 979264 5168 5156 64814 44010 98417 12614 77215 57215 14732 55439 41829 272163 9273 58329 05210 9695 50950 1723 3031 83126 867 211051015202530354045505560Phred quality score2M4M6M8M10M12M14M16M18M20M22M24M26M# 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.

99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%99.99%100%99.99%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0.01%0%0.01%123456789101112131415161718192021XYM0%10%20%30%40%50%60%70%80%90%100%mappedunmapped