ATAqC

Sample Information

Sample RTB29_S29_R1.trim.PE2SE
Genome hg19
Paired/Single-ended Paired-ended
Read length 75

Summary

Read count from sequencer 1,850,900
Read count successfully aligned 1,830,243
Read count after filtering for mapping quality 1,611,161
Read count after removing duplicate reads 1,604,529
Read count after removing mitochondrial reads (final read count) 1,588,702
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

925450 reads; of these:
  925450 (100.00%) were paired; of these:
    17647 (1.91%) aligned concordantly 0 times
    656278 (70.91%) aligned concordantly exactly 1 time
    251525 (27.18%) aligned concordantly >1 times
    ----
    17647 pairs aligned concordantly 0 times; of these:
      3445 (19.52%) aligned discordantly 1 time
    ----
    14202 pairs aligned 0 times concordantly or discordantly; of these:
      28404 mates make up the pairs; of these:
        20657 (72.73%) aligned 0 times
        2782 (9.79%) aligned exactly 1 time
        4965 (17.48%) aligned >1 times
98.88% overall alignment rate

  

Samtools flagstat

1850900 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
1830243 + 0 mapped (98.88%:-nan%)
1850900 + 0 paired in sequencing
925450 + 0 read1
925450 + 0 read2
1815606 + 0 properly paired (98.09%:-nan%)
1826850 + 0 with itself and mate mapped
3393 + 0 singletons (0.18%:-nan%)
3564 + 0 with mate mapped to a different chr
2422 + 0 with mate mapped to a different chr (mapQ>=5)

  

Filtering statistics

Mapping quality > q30 (out of total) 1,611,161 0.870
Duplicates (after filtering) 6,632.0 0.008
Mitochondrial reads (out of total) 106,586 0.058
Duplicates that are mitochondrial (out of all dups) 15,902 0.714
Final reads (after all filters) 1,588,702 0.858
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

Metric Result
NRF 0.997396 - OK
PBC1 0.997444 - OK
PBC2 396.923435 - OK
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

123,803,189

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

Metric Result
Fraction of reads in NFR 0.282678095241 out of range [0.4, inf]
NFR / mono-nuc reads 1.35831867772 out of range [2.5, inf]
Presence of NFR peak OK
Presence of Mono-Nuc peak OK
Presence of Di-Nuc peak OK
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.

Peak statistics

Metric Result
Raw peaks 57093 - OK
Naive overlap peaks 57093 - OK
IDR peaks 0 out of range [10000, inf]

Raw peak file statistics

Min size 150.0
25 percentile 244.0
50 percentile (median) 336.0
75 percentile 464.0
Max size 1461.0
Mean 372.363879985

Naive overlap peak file statistics

Min size 150.0
25 percentile 244.0
50 percentile (median) 336.0
75 percentile 464.0
Max size 1461.0
Mean 372.363879985

IDR peak file statistics

Min size 0
25 percentile 0
50 percentile (median) 0
75 percentile 0
Max size 0
Mean 0
Metric failed.
For a good ATAC-seq experiment in human, you expect to get 100k-200k peaks
for a specific cell type.

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

Fraction of reads in universal DHS regions 875,664 0.569
Fraction of reads in blacklist regions 6,357 0.004
Fraction of reads in promoter regions 352,160 0.229
Fraction of reads in enhancer regions 629,181 0.409
Fraction of reads in called peak regions 552,868 0.359
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.