<p>Solexa Inc. sequenced NA12878 chrX to ~30x in early 2007, which later became part of <a href="https://www.ncbi.nlm.nih.gov/pubmed/18987734" rel="noreferrer">Bentley (2008)</a>. This, I believe, was the first time that 30x showed up. I don't recall they had a particular reason for that. Figure 5 in the published paper was just aftermath. It does not really explain why not 25x or 35x, given that the curves between 25x and 35x in that figure are about linear.</p>

<p>In the abstract of <a href="https://www.ncbi.nlm.nih.gov/pubmed/21771779" rel="noreferrer">Ajay et al (2011)</a>, the authors argued "the current recommendation of ~30x coverage is not adequate". Nonetheless, the discussion section seems to suggest 50–60x would be necessary with GAIIx, but 35x was adequate with HiSeq2000 plus better recent chemistry. Overall, this paper provides a more thorough analysis. The data quality at that time is also closer to data we produce today.</p>

<p>The required coverage is largely determined by two factors: read placement bias (e.g. GC bias) and base/mapping error rate. While GC bias has been reduced with the PCR-free protocol, base error rate has been going the downward since HiSeq2500. I guess 30x coverage would be necessary if you want to achieve the sensitivity with the older 30x data. Illumina, as a sequencing service provider, and our sequencing facility still insist on the 30x threshold.</p>

CC BY-SA 3.0
2017-08-07T08:46:27.547Z
Interestingly enough we also found in Sudbery et al, 2009 (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-10-r112), that the number of continuous regions without a break in coverage was more or less linear in the number of sequenced reads between 25x and 60x when sequencing the mouse genome.
Ian Sudbery
CC BY-SA 3.0
2017-08-06T03:27:17.950Z
2017-08-06T03:27:17.950Z
user172818
<p>30 times coverage is not unique to this problem, but number <strong>30</strong> has its empirical role in statistics:</p>

<blockquote>
  <p>In statistical analysis, the rule of three states that if a certain event did not occur in a sample with n subjects, the interval from 0 to 3/n is a 95% confidence interval for the rate of occurrences in the population. <strong>When n is greater than 30, this is a good approximation to results from more sensitive tests.</strong></p>
</blockquote>

<p>source: <a href="https://en.wikipedia.org/wiki/Rule_of_three_(statistics)" rel="noreferrer">Wikipedia: Rule of three (statistics)</a></p>

<p>Similarly, you can search for related questions like this one:</p>

<ul>
<li><a href="https://www.researchgate.net/post/What_is_the_rationale_behind_the_magic_number_30_in_statistics" rel="noreferrer">What is the rationale behind the magic number 30 in statistics?</a></li>
</ul>

<p>In line with this, I have seen data processing in other disciplines which required <strong>n ≥ 30</strong> for sufficient reliability of results.</p>

CC BY-SA 3.0
2018-04-18T16:27:37.587Z
In this type of community, I would expect some opponenture under the answer but I am getting only downvotes so far (curent score: +6/-3).
miroxlav
CC BY-SA 3.0
2017-08-04T20:51:38.387Z
2017-08-04T20:51:38.387Z
miroxlav
<p>The point I always miss in the discussion about coverage is, that no one tells how it was calculated. Were duplicates removed? How do you count overlapping paired-end reads? As 2 or 1? Just to point out two things that have influence on the coverage.</p>

CC BY-SA 3.0
2017-08-10T18:13:24.400Z
Anyone who counts overlapping paired-end reads as 2 is doing it wrong. There's never an excuse for that.
Devon Ryan
CC BY-SA 3.0
2017-08-10T19:51:50.863Z
@DevonRyan why? Overlapping parts of pair-end reads are two independent technical replicates of the same region. What is the difference to overlap of two reads that are not paired, should they also be counted as one?
Of course if you merge overlapping reads into longer sequences before an assembly, then you got to count it once, but otherwise I do not see a reason why they should be counted as one...
Kamil S Jaron
CC BY-SA 3.0
2017-08-10T19:53:50.757Z
@KamilSJaron They represent one sequenced fragment, counting them otherwise is lying.
Devon Ryan
CC BY-SA 3.0
2017-08-10T19:56:23.300Z
@DevonRyan Same fragment, but different sequencing and it is sequencing coverage, not a fragment coverage. I do not get your point.
Kamil S Jaron
CC BY-SA 3.0
2017-08-11T06:56:35.373Z
@KamilSJaron You're being overly literal. The point of the metric is to assess how much data you have per position. Overlapping PE reads don't represent different data points, that's why they're treated as a single unit in variant and peak calling.
Devon Ryan
CC BY-SA 3.0
2017-08-11T10:55:02.000Z
In my opinion the coverage should represent the number of different molecules. So counting overlapping paired reads and removing duplicates is mandatory. Otherwise the read counts is just a more or less useless number.
finswimmer
CC BY-SA 3.0
2017-10-08T18:13:46.353Z
@KamilSJaron, if you want to utilize that information, there are algorithms that collapse the overlapping segments of such reads in an intelligent way (e.g. for each overlapping base, use the one with the highest quality and discard the other). Devon is right that they absolutely should not be treated as independent events for variant discovery, depth calc, etc.
chrisamiller
CC BY-SA 3.0
2017-08-10T16:58:43.040Z
2017-08-10T18:12:40.087Z
2017-08-10T18:12:40.087Z
finswimmer
<p>The earliest mention of the 30x paradigm I could find is in the original Illumina whole-genome sequencing paper: <a href="https://www.nature.com/nature/journal/v456/n7218/full/nature07517.html" rel="noreferrer">Bentley, 2008</a>. Specifically, in Figure 5, they show that most SNPs have been found, and that there are few uncovered/uncalled bases by the time you reach 30x: <a href="https://i.sstatic.net/42eHQ.png" rel="noreferrer"><img src="https://i.sstatic.net/42eHQ.png" alt="30xSequencingDepth"></a></p>

<p>These days, 30x is still a common standard, but large-scale germline sequencing projects are often pushing down closer to 25x and finding it adequate. Every group doing this seriously has done power calculations based on specifics of their machines and prep (things like error rates and read lengths matter!).</p>

<p>Cancer genomics is going in the other direction. When you have to contend with purity, ploidy, and subclonal populations, much more coverage than 30x is needed. Our group showed in <a href="http://www.cell.com/cell-systems/abstract/S2405-4712(15)00113-1" rel="noreferrer">this 2015 paper</a> that even 300x whole-genome coverage of a tumor was likely missing real rare variants in a tumor. </p>

<p>On the whole, the sequence coverage you need really depends on what questions you're asking, and I'd recommend that anyone designing a sequencing experiment consult with both a sequencing expert and a statistician beforehand (and it's even better if those are the same person!)</p>

CC BY-SA 3.0
2017-08-04T17:50:16.997Z
This is as far as I could trace back, but would love to know if anyone can find an earlier mention of 30x as a standard depth!
chrisamiller
CC BY-SA 3.0
2017-08-04T18:03:58.743Z
That is likely the earliest.  Previous technologies could not reach that depth at a reasonable cost.
Bioathlete
CC BY-SA 3.0
2017-08-04T15:46:51.593Z
2017-08-04T15:46:51.593Z
chrisamiller
<p>A bit of a historical question on a number, 30 times coverage, that's become so familiar in the field: why do we sequence the human genome at 30x coverage?</p>

<p>My question has two parts:</p>

<ul>
<li>Who came up with the 30x value and why?</li>
<li>Does the value need to be updated to reflect today's state-of-the-art?</li>
</ul>

<p>In summary, if the 30x value is a number that was based on the old Solexa GAIIx 2x35bp reads and error rates, and the current standard Illumina sequencing is 2x150bp, does the 30x value need updating?</p>

<p><a href="https://i.sstatic.net/GGkq0.png" rel="noreferrer"><img src="https://i.sstatic.net/GGkq0.png" alt="enter image description here"></a></p>

<genome><ngs><phylogenetics>
Why sequence the human genome at 30x coverage?
# Why sequence the human genome at 30x coverage?

(Asked by: username_0 on 2017-08-04)

A bit of a historical question on a number, 30 times coverage, that's become so familiar in the field: why do we sequence the human genome at 30x coverage?

My question has two parts:

* Who came up with the 30x value and why?
* Does the value need to be updated to reflect today's state-of-the-art?

In summary, if the 30x value is a number that was based on the old Solexa GAIIx 2x35bp reads and error rates, and the current standard Illumina sequencing is 2x150bp, does the 30x value need updating?

[![enter image description here](https://i.sstatic.net/GGkq0.png)](https://i.sstatic.net/GGkq0.png)

**Tags:** genome, ngs, phylogenetics

---

## 4 answers

### Answer by username_7 on 2017-08-04. Score: 30 | Accepted answer

The earliest mention of the 30x paradigm I could find is in the original Illumina whole-genome sequencing paper: [Bentley, 2008](https://www.nature.com/nature/journal/v456/n7218/full/nature07517.html). Specifically, in Figure 5, they show that most SNPs have been found, and that there are few uncovered/uncalled bases by the time you reach 30x: [![30xSequencingDepth](https://i.sstatic.net/42eHQ.png)](https://i.sstatic.net/42eHQ.png)

These days, 30x is still a common standard, but large-scale germline sequencing projects are often pushing down closer to 25x and finding it adequate. Every group doing this seriously has done power calculations based on specifics of their machines and prep (things like error rates and read lengths matter!).

Cancer genomics is going in the other direction. When you have to contend with purity, ploidy, and subclonal populations, much more coverage than 30x is needed. Our group showed in [this 2015 paper](http://www.cell.com/cell-systems/abstract/S2405-4712(15)00113-1) that even 300x whole-genome coverage of a tumor was likely missing real rare variants in a tumor.

On the whole, the sequence coverage you need really depends on what questions you're asking, and I'd recommend that anyone designing a sequencing experiment consult with both a sequencing expert and a statistician beforehand (and it's even better if those are the same person!)

> **Comments:**
> **username_7**: This is as far as I could trace back, but would love to know if anyone can find an earlier mention of 30x as a standard depth!  
> **username_8**: That is likely the earliest.  Previous technologies could not reach that depth at a reasonable cost.  

---

### Answer by username_1 on 2017-08-06. Score: 7

Solexa Inc. sequenced NA12878 chrX to ~30x in early 2007, which later became part of [Bentley (2008)](https://www.ncbi.nlm.nih.gov/pubmed/18987734). This, I believe, was the first time that 30x showed up. I don't recall they had a particular reason for that. Figure 5 in the published paper was just aftermath. It does not really explain why not 25x or 35x, given that the curves between 25x and 35x in that figure are about linear.

In the abstract of [Ajay et al (2011)](https://www.ncbi.nlm.nih.gov/pubmed/21771779), the authors argued "the current recommendation of ~30x coverage is not adequate". Nonetheless, the discussion section seems to suggest 50–60x would be necessary with GAIIx, but 35x was adequate with HiSeq2000 plus better recent chemistry. Overall, this paper provides a more thorough analysis. The data quality at that time is also closer to data we produce today.

The required coverage is largely determined by two factors: read placement bias (e.g. GC bias) and base/mapping error rate. While GC bias has been reduced with the PCR-free protocol, base error rate has been going the downward since HiSeq2500. I guess 30x coverage would be necessary if you want to achieve the sensitivity with the older 30x data. Illumina, as a sequencing service provider, and our sequencing facility still insist on the 30x threshold.

> **Comments:**
> **username_2**: Interestingly enough we also found in Sudbery et al, 2009 (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-10-r112), that the number of continuous regions without a break in coverage was more or less linear in the number of sequenced reads between 25x and 60x when sequencing the mouse genome.  

---

### Answer by username_3 on 2017-08-04. Score: 6

30 times coverage is not unique to this problem, but number **30** has its empirical role in statistics:

> In statistical analysis, the rule of three states that if a certain event did not occur in a sample with n subjects, the interval from 0 to 3/n is a 95% confidence interval for the rate of occurrences in the population. **When n is greater than 30, this is a good approximation to results from more sensitive tests.**

source: [Wikipedia: Rule of three (statistics)](https://en.wikipedia.org/wiki/Rule_of_three_(statistics))

Similarly, you can search for related questions like this one:

* [What is the rationale behind the magic number 30 in statistics?](https://www.researchgate.net/post/What_is_the_rationale_behind_the_magic_number_30_in_statistics)

In line with this, I have seen data processing in other disciplines which required **n ≥ 30** for sufficient reliability of results.

> **Comments:**
> **username_3**: In this type of community, I would expect some opponenture under the answer but I am getting only downvotes so far (curent score: +6/-3).  

---

### Answer by username_4 on 2017-08-10. Score: 0

The point I always miss in the discussion about coverage is, that no one tells how it was calculated. Were duplicates removed? How do you count overlapping paired-end reads? As 2 or 1? Just to point out two things that have influence on the coverage.

> **Comments:**
> **username_5**: Anyone who counts overlapping paired-end reads as 2 is doing it wrong. There's never an excuse for that.  
> **username_6**: @DevonRyan why? Overlapping parts of pair-end reads are two independent technical replicates of the same region. What is the difference to overlap of two reads that are not paired, should they also be counted as one? Of course if you merge overlapping reads into longer sequences before an assembly, then you got to count it once, but otherwise I do not see a reason why they should be counted as one...  
> **username_5**: @KamilSJaron They represent one sequenced fragment, counting them otherwise is lying.  
> **username_6**: @DevonRyan Same fragment, but different sequencing and it is sequencing coverage, not a fragment coverage. I do not get your point.  
> **username_5**: @KamilSJaron You're being overly literal. The point of the metric is to assess how much data you have per position. Overlapping PE reads don't represent different data points, that's why they're treated as a single unit in variant and peak calling.  
> **username_4**: In my opinion the coverage should represent the number of different molecules. So counting overlapping paired reads and removing duplicates is mandatory. Otherwise the read counts is just a more or less useless number.  
> **username_7**: @KamilSJaron, if you want to utilize that information, there are algorithms that collapse the overlapping segments of such reads in an intelligent way (e.g. for each overlapping base, use the one with the highest quality and discard the other). Devon is right that they absolutely should not be treated as independent events for variant discovery, depth calc, etc.  

---

