ATAqC

Sample Information

Summary

Note that all these read counts are determined using 'samtools view' - as such,
these are all reads found in the file, whether one end of a pair or a single 
end read. In other words, if your file is paired end, then you should divide 
these counts by two. Each step follows the previous step; for example, the 
duplicate reads were removed after reads were removed for low mapping quality.

This bar chart also shows the filtering process and where the reads were lost
over the process. Note that each step is sequential - as such, there may
have been more mitochondrial reads which were already filtered because of
high duplication or low mapping quality. Note that all these read counts are 
determined using 'samtools view' - as such, these are all reads found in 
the file, whether one end of a pair or a single end read. In other words, 
if your file is paired end, then you should divide these counts by two.

Alignment statistics

Bowtie alignment log

546760 reads; of these:
  546760 (100.00%) were paired; of these:
    15671 (2.87%) aligned concordantly 0 times
    351852 (64.35%) aligned concordantly exactly 1 time
    179237 (32.78%) aligned concordantly >1 times
    ----
    15671 pairs aligned concordantly 0 times; of these:
      620 (3.96%) aligned discordantly 1 time
    ----
    15051 pairs aligned 0 times concordantly or discordantly; of these:
      30102 mates make up the pairs; of these:
        24975 (82.97%) aligned 0 times
        3256 (10.82%) aligned exactly 1 time
        1871 (6.22%) aligned >1 times
97.72% overall alignment rate

  

Samtools flagstat

1093520 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 duplicates
1068545 + 0 mapped (97.72%:-nan%)
1093520 + 0 paired in sequencing
546760 + 0 read1
546760 + 0 read2
1062178 + 0 properly paired (97.13%:-nan%)
1064134 + 0 with itself and mate mapped
4411 + 0 singletons (0.40%:-nan%)
548 + 0 with mate mapped to a different chr
226 + 0 with mate mapped to a different chr (mapQ>=5)

  

Filtering statistics

Mapping quality refers to the quality of the read being aligned to that 
particular location in the genome. A standard quality score is > 30. 
Duplications are often due to PCR duplication rather than two unique reads
mapping to the same location. High duplication is an indication of poor 
libraries. Mitochondrial reads are often high in chromatin accessibility
assays because the mitochondrial genome is very open. A high mitochondrial
fraction is an indication of poor libraries. Based on prior experience, a
final read fraction above 0.70 is a good library.
  

Library complexity statistics

ENCODE library complexity metrics

The non-redundant fraction (NRF) is the fraction of non-redundant mapped reads 
in a dataset; it is the ratio between the number of positions in the genome 
that uniquely mapped reads map to and the total number of uniquely mappable 
reads. The NRF should be > 0.8. The PBC1 is the ratio of genomic locations
with EXACTLY one read pair over the genomic locations with AT LEAST one read 
pair. PBC1 is the primary measure, and the PBC1 should be close to 1. 
Provisionally 0-0.5 is severe bottlenecking, 0.5-0.8 is moderate bottlenecking, 
0.8-0.9 is mild bottlenecking, and 0.9-1.0 is no bottlenecking. The PBC2 is 
the ratio of genomic locations with EXACTLY one read pair over the genomic 
locations with EXACTLY two read pairs. The PBC2 should be significantly 
greater than 1.

Picard EstimateLibraryComplexity

Yield prediction

Preseq performs a yield prediction by subsampling the reads, calculating the
number of distinct reads, and then extrapolating out to see where the 
expected number of distinct reads no longer increases. The confidence interval
gives a gauge as to the validity of the yield predictions.

Fragment length statistics

Open chromatin assays show distinct fragment length enrichments, as the cut 
sites are only in open chromatin and not in nucleosomes. As such, peaks 
representing different n-nucleosomal (ex mono-nucleosomal, di-nucleosomal) 
fragment lengths will arise. Good libraries will show these peaks in a 
fragment length distribution and will show specific peak ratios.

Sequence quality metrics

GC bias

Open chromatin assays are known to have significant GC bias. Please take this
into consideration as necessary.

Annotation-based quality metrics

Enrichment plots (TSS)

Open chromatin assays should show enrichment in open chromatin sites, such as
TSS's. An average TSS enrichment is above 6-7. A strong TSS enrichment is 
above 10.
  

Annotated genomic region enrichments

Signal to noise can be assessed by considering whether reads are falling into
known open regions (such as DHS regions) or not. A high fraction of reads 
should fall into the universal (across cell type) DHS set. A small fraction
should fall into the blacklist regions. A high set (though not all) should
fall into the promoter regions. A high set (though not all) should fall into 
the enhancer regions. The promoter regions should not take up all reads, as
it is known that there is a bias for promoters in open chromatin assays.

Comparison to Roadmap DNase

This bar chart shows the correlation between the Roadmap DNase samples to
your sample, when the signal in the universal DNase peak region sets are 
compared. The closer the sample is in signal distribution in the regions
to your sample, the higher the correlation.