European Genome-Phenome Archive

File Quality

File InformationEGAF00005114840

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.

302 115 70754 816 66010 869 4612 828 1851 059 880508 976270 758152 18092 62759 71941 61828 50423 94518 40616 63813 88711 26811 4329 8258 9088 7677 1086 8856 3956 5136 0845 0375 2054 5624 7424 3433 8743 3493 5013 4103 0553 3112 8692 8492 7792 9202 7832 5912 4542 1082 2632 2032 3931 9391 6252 1371 9981 8551 5281 6351 6841 7031 8151 6541 5581 2911 2701 3801 3441 4601 2471 0581 0631 1101 0911 0618929641 0049781 1869821 0321 07982099089881185781676179790571985380670783766582868372967675174068175878967570483258270065576868370067467763360768658977357554654257155856251455145848449953745052651742945743439140138937637938242438038241741638233233930629234126223538621522024724119727522919916422118821420217016516817521718522414419518624213120817410697991091061301101428089128123106155144951461911191091181341238810110212514114712985971179493879792849993106109781139280928791979411783726163889069985957515180679768917266796691867596928361595743645394576461425341487260616358413531304237324036333767304164876139312936464343686542405240523856503865664844579835454246253037272940432249665424442925403628352650363251302745453733222116132323222320288423404830202848313421914242223211615171021141314102118201318191414131424202481929242312233119272220243024272018172731231943192524312161113171110131114133819131291013121714101414131215202120912121116141812151725302011241822242032331931812145961591114568566548810445141415157141114141171191013138969811761715951499121415151297101517132379111114191516781951518131713131181610159151310171299101416101191913161012139982043181712138131413181411131512181022121891122131313720799619610677410106138421212121161110108981210713591310511851318161510818251061088118514512878121514101015138132111269141171597810891047141112172416111313211439151915161118121217859131281389181671313151071511141181515161110106164101212117156101198513591012512771081261614123514101512136951481067141110111515791014267103523151968578334614136152377453513633233324424143642437343424312521341232324434126124253256213111133215255352322212253274425114224181282854146112133111313113113131241 134100200300400500600700800900>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.

0066 157 33300000000015 134 293000000000000019 217 3060000000462 490 28800000000510152025303540Phred quality score0M50M100M150M200M250M300M350M400M450M# 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).

71.8 %4 910 84871.8 %28.2 %

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.

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 %4 910 848100 %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 %6 840 421100 %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.

10.8 %740 13410.8 %89.2 %

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.

2 285 20910 10511 34110 58210 62513 67412 20816 53516 91913 41735 0643 7166 1113 2352 9845 5322 5263 0113 1953 4438 7764 0514 2769 1927 27228 19715 7923 3147 2013 2692 2338 4893 2008 4283 5983 0007 5756 27228 1283 7412 4238 0663 6439 3453 7892 3869 9142 6156 6327 5331 72322 1202 8685 7916 0821 29410 9321 8081 21811 6154 106 875051015202530354045505560Phred quality score0.5M1M1.5M2M2.5M3M3.5M4M# 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