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The theoretical mean sequencing depth is the number of sequenced bases divided by the length of the genome or target region.
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Formula
N is a count · L̄ and G in bp · coverage C is a dimensionless fold depth such as 30×
| Symbol | Meaning | Unit |
|---|---|---|
| Theoretical mean read depth across the target | times | |
| Number of reads of a common mean length or read pairs, according to the stated counting convention | read(s) | |
| L̄ | Mean length of each counted read, or combined mate lengths per pair | bp |
| Length of the genome or target region | bp | |
| Length of one mapped read in the summed-base expression | bp |
C=\frac{N\bar L}{G}=\frac{\sum_i L_i}{G}C=ΣL_i/G estimates theoretical mean depth, not the fraction of the target actually covered. For paired-end data, state whether N counts individual reads or pairs and whether L̄ includes both mates. Raw reads overestimate usable depth when adapters, low quality, mismapping or duplicates remain. Actual coverage varies by position; breadth of coverage is a different metric.
Read lengths, target size and counting convention for reads or pairs are stated.
Total sequenced bases are divided by the length of the target considered.
Rearrange for read count
Only if reads have comparable length and N follows the paired-end convention.
12 million single-end reads of 150 bp across 3.0 Gbp. Find C.
C=(12·106·150)/(3·109)=0.60× theoretical mean depth.
Confusing theoretical depth with breadth covered at least once.
C is mean base depth; breadth counts target positions above a stated minimum depth.
Formula cluster
greater mean depth means more base observations on average, not guaranteed uniformity.
Twelve million single-end reads averaging 150 bp yield 1.8×109 sequenced bases. For a haploid 3.0×109 bp target genome, C=1.8/3.0=0.60×. Check: base-pair units cancel; low mean depth does not say what fraction of the genome was covered at least once.
Estimate sequencing yield and planned depth for genome or targeted-region projects
Here is how to work through a typical Mean Sequencing Coverage (C = Reads × mittlere Länge / Zielgröße) task step by step:
Task
12 million single-end reads of 150 bp across 3.0 Gbp. Find C.
Solution path
C=(12·106·150)/(3·109)=0.60× theoretical mean depth.
the theoretical mean number of sequence reads per target base. Its output is a fold value such as 30× from total bases and target size.. greater mean depth means more base observations on average, not guaranteed uniformity. The quantity answers a specific biological or statistical question, not automatically the full cause of an observed pattern. Tie it to the population, sample or model level from which inputs came. A number without that reference can be misleading across studies. Keep description separate from explanation: the metric summarizes data but does not by itself establish the underlying mechanism.
Use N counts reads under the stated rule, L̄ is mean length and G target size. The units are reads are counts; bp cancel between sequenced bases and genome length.. Check that numerator and denominator refer to the same time interval, target population and scale. Proportions entered as percentages usually need conversion to values between zero and one. Write units and definitions beside the observations. This helps catch swapped groups, mismatched age intervals or a wrong denominator before they distort the result. Also document measurement definitions and preprocessing, because studies can operationalize the same symbols differently.
The central assumption is: uniform or correctly averaged reads map to the same reference genome. adapters, poor reads and duplicates are handled consistently in usable base totals. If mapping bias or coverage islands make the mean misleading for individual genes., interpretation changes or the formula no longer fits. State sampling, measurement method and biological scale. A model is useful because it simplifies a question, but its limits belong in the report. Do not automatically transfer parameters to different species, populations, laboratories or sampling conditions. Report these assumptions with the result so readers can judge which populations and conditions the conclusion can cover.
In the example, 12 million 150 bp reads across 3 billion bp give 0.60×. The check is: 1.8 billion sequenced bp divided by 3 billion target bp gives 0.60. Substitute calculated values back into the original relation and check whether ranges and units are biologically plausible. Rounded data can create a small discrepancy, but should not change direction or order of magnitude. Also test a simple limiting case, such as zero, complete detection or equal groups. This separates arithmetic errors from model limitations. Round intermediate values only at the end, and check the order of magnitude against the biological question.
A nearby quantity is breadth of coverage, the fraction of bases reached by at least one or a chosen number of reads. The substantive distinction is that depth averages reads per base; breadth measures the target fraction above a threshold. Both can appear in the same biological analysis, but they use different inputs and answer different questions. Choose by the requested output and verify the data definition before comparing values. A related name does not imply identical units or interchangeable interpretation. This matters when a descriptive statistic is reported alongside a model parameter. Before comparing values, align definitions, populations, observation periods and analysis rules; otherwise the difference may be methodological.
Quanta exam set
Curated exam set for "Mean Sequencing Coverage":
Question (front)
Which formula describes Mean Sequencing Coverage?
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Question (front)
How do you rearrange Mean Sequencing Coverage for Rearrange for read count?
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Question (front)
Which common mistake happens with Mean Sequencing Coverage?
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+ 8 more cards: units, variables, derivation, example, exam task
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This list names subject references and further reading. A page or section number appears when it is part of the reference; these are not verbatim quotations.
Create a curated FSRS exam set for C = Reads × mittlere Länge / Zielgröße: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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