<p>Like you mentioned, having read pairs can help with alignment.
Your FASTQ for a single-end sequencing run will look something like this:</p>
<pre><code>@SEQ_ID_0001
AGCTAGCGCGGTTGGCTTAGCGACT
+
!''*((((***+))%%%++)(%%%%
@SEQ_ID_0002
AGGTGGTTGTAGGGAAAAAAGTCTC
+
!''*((((***+))%%%++)(%%%%
...
</code></pre>
<p>There is not necessarily a relationship between <code>@SEQ_ID_0001</code> and <code>@SEQ_ID_0002</code>.
They could come from neigbouring regions in the genome or they could come from different species if that's what your experiment involves.
So aligning <code>@SEQ_ID_0001</code> doesn't exactly give you information about where <code>SEQ_ID_0002</code> goes.</p>
<p><code>@SEQ_ID_0001</code> could equally map to multiple locations in the genome with the same probability.
How do we decide where it &quot;should&quot; go?
We can randomly distribute multi-mapped reads or do some other fancy stuff, but that usually relies on facts about the data as a whole, and not something specific to the original DNA molecule that sequencing read came from.</p>
<p>If we have paired-end sequencing, then like your diagram shows, you know that read 1 (<code>R1</code>) and read 2 (<code>R2</code>; these have the same <code>@SEQ_ID</code>) will relate to each other in some way.
<code>@SEQ_ID_0001 R1</code> may be multi-mapped, but if <code>@SEQ_ID_0001 R2</code> isn't, then we know that <code>R1</code> should probably be relatively close to <code>R2</code>.
Because of how you prepare your DNA samples before sequencing, you'll have some information about the insert length distribution.
You can use this information with sequence aligners to better identify where <code>R1</code> should be placed.</p>
<p>You can also do this if both <code>R1</code> and <code>R2</code> are multi-mapped; certain pairwise alignments will be more likely than others, even if the marginal alignment probabilities for each read are the same.</p>
<p>Finally, there are other unexpected benefits that you can get from paired-end sequencing.
All of the above applies to just the mapping step, but applications like structural variant detection and chromosome conformation capture really benefit from paired-end sequencing.</p>
<p>If you have single-end reads for structural variant detection, then you will only detect breakpoints if the read overlaps a breakpoint.
But if you have paired-end, you not only have twice the amount of DNA sequenced to look for breakpoints, you can also estimate if there is a breakpoint between the two reads (in the inner distance from your diagram) if the two reads align really far apart from each other, or on separate chromosomes.
The idea is similar for chromosome conformation capture and the ligation junction sites.</p>

CC BY-SA 4.0
2020-09-21T19:27:49.040Z
2020-09-21T19:27:49.040Z
James Hawley
<p>In paired-end sequencing, you have an expected fragment length. So if Read 1 maps to two different places, whereas Read 2 only maps to one place. Then you can determine which one is more likely for Read 1 to map to. It cannot be too far away from Read 2 given the expected fragment length.</p>
<p>It is not necessarily true that paired-end sequencing gets twice the amount of data. The total amount of data can be determined when running sequencing. If single-end has 20M reads, I can just stop sequencing at 20M as well for paired-end.</p>

CC BY-SA 4.0
2020-09-21T00:16:32.260Z
2020-09-21T00:16:32.260Z
Phoenix Mu
<p>One of the advantages of paired end sequencing over single end is that it doubles the amount of data. Another <strong>supposed</strong> advantage is that it leads to more accurate reads because if say Read 1 (see picture below) maps to two different regions of the genome, Read 2 can be used to help determine which one of the two regions makes more sense. However, I don't understand how this second advantage pertains only to paired end sequencing. With single-end sequencing, couldn't you just use the read that comes right after read 1 to essentially get a longer net read?
<a href="https://i.sstatic.net/xqJYt.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xqJYt.png" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2020-09-12T19:14:22.250Z
An explanation for the downvote would be appreciated
An Ignorant Wanderer
CC BY-SA 4.0
2020-09-12T20:12:15.777Z
You seem to think you can put the reads in order and they somehow join up, the reality is there is not order to the reads and it's only by assembling them like a jigsaw puzzle or aligning them to a reference genome that you can infer their location. I didn't downvote
Chris_Rands
CC BY-SA 4.0
2020-09-13T01:59:51.607Z
@Chris_Rands thank you for your answer. Hmm, could you elaborate more as to why there is no order? I mean, when we're obtaining the reads, aren't we starting with a primer at one end of the fragment and working our way to the other end to generate the multiple reads that make up that fragment?
An Ignorant Wanderer
CC BY-SA 4.0
2020-09-13T18:22:18.870Z
You have a picture of an Illumina read, which clearly does not work by 'working your way' from one end all the way through to the other.  Why technology are you talking about that works as you suggest, and why did you post a picture of an Illumina read when it's obviously not what you are talking about?  Why are you talking about multiple reads when you are asserting that there is an inherent ordering of single end reads?
swbarnes2
CC BY-SA 4.0
2020-09-15T07:48:03.623Z
There isn't any "order" that could be assumed from the FASTQ file.  That's why after you align reads, you often have to sort the generated SAM/BAM file.
Ray
CC BY-SA 4.0
2020-09-15T14:46:09.160Z
@Ray so if there is no order to the FASTQ file, then how is it possible to infer which reads are paired together (in the case of paired end sequencing)?
An Ignorant Wanderer
CC BY-SA 4.0
2020-09-16T06:35:05.137Z
Sorry for the confusion!  My "there is no order in the FASTQ file" comment refers to single-ended sequencing -- a single file.  For paired-end sequencing, the reads are usually in two separate files:  one for forward, the other for reverse.  Each read has a name, and the names are unique within a file.  Between files, they are matched up to form a pair by name.  So, yes, they are ordered in the sense that read called "ABC" is the 100th read in the forward file and it should be the 100th read in the reverse file.  A good program would also double-check the names match, too!
Ray
CC BY-SA 4.0
2020-09-16T06:37:04.057Z
But within either file, there is no meaning between the 99th read and the 100th read (i.e., why it is the 100th read).  Thus, returning to single-ended sequencing (i.e., one file), there is no order that can be assumed.  I think some programs accept paired-end sequencing as a single file so that reads are interleaved.  Since they alternate, you can create two files from that easily, if you wanted to.
Ray
CC BY-SA 4.0
2020-09-16T17:52:43.547Z
@Ray okay thanks this makes sense now
An Ignorant Wanderer
CC BY-SA 4.0
2020-09-17T04:40:36.983Z
@AnIgnorantWanderer You're welcome!  Just to emphasize what someone else hinted, this refers to Illumina reads and how it (BaseSpace or equivalent programs) changes the images to FASTQ files.  Another sequencing technology might choose another format that is still FASTQ, but *maybe* order of reads exists.  (I don't know of one off hand, but I won't rule that out that it exists now or in the future.)  Good luck!
Ray
<rna-seq>
Advantages of paired-end sequencing compared to single end
# Advantages of paired-end sequencing compared to single end

(Asked by: username_0 on 2020-09-12)

One of the advantages of paired end sequencing over single end is that it doubles the amount of data. Another **supposed** advantage is that it leads to more accurate reads because if say Read 1 (see picture below) maps to two different regions of the genome, Read 2 can be used to help determine which one of the two regions makes more sense. However, I don't understand how this second advantage pertains only to paired end sequencing. With single-end sequencing, couldn't you just use the read that comes right after read 1 to essentially get a longer net read?
[![enter image description here](https://i.sstatic.net/xqJYt.png)](https://i.sstatic.net/xqJYt.png)

**Tags:** rna-seq
> **Comments:**
> **username_0**: An explanation for the downvote would be appreciated  
> **username_1**: You seem to think you can put the reads in order and they somehow join up, the reality is there is not order to the reads and it's only by assembling them like a jigsaw puzzle or aligning them to a reference genome that you can infer their location. I didn't downvote  
> **username_0**: @Chris_Rands thank you for your answer. Hmm, could you elaborate more as to why there is no order? I mean, when we're obtaining the reads, aren't we starting with a primer at one end of the fragment and working our way to the other end to generate the multiple reads that make up that fragment?  
> **username_2**: You have a picture of an Illumina read, which clearly does not work by 'working your way' from one end all the way through to the other.  Why technology are you talking about that works as you suggest, and why did you post a picture of an Illumina read when it's obviously not what you are talking about?  Why are you talking about multiple reads when you are asserting that there is an inherent ordering of single end reads?  
> **username_3**: There isn't any "order" that could be assumed from the FASTQ file.  That's why after you align reads, you often have to sort the generated SAM/BAM file.  
> **username_0**: @Ray so if there is no order to the FASTQ file, then how is it possible to infer which reads are paired together (in the case of paired end sequencing)?  
> **username_3**: Sorry for the confusion!  My "there is no order in the FASTQ file" comment refers to single-ended sequencing -- a single file.  For paired-end sequencing, the reads are usually in two separate files:  one for forward, the other for reverse.  Each read has a name, and the names are unique within a file.  Between files, they are matched up to form a pair by name.  So, yes, they are ordered in the sense that read called "ABC" is the 100th read in the forward file and it should be the 100th read in the reverse file.  A good program would also double-check the names match, too!  
> **username_3**: But within either file, there is no meaning between the 99th read and the 100th read (i.e., why it is the 100th read).  Thus, returning to single-ended sequencing (i.e., one file), there is no order that can be assumed.  I think some programs accept paired-end sequencing as a single file so that reads are interleaved.  Since they alternate, you can create two files from that easily, if you wanted to.  
> **username_0**: @Ray okay thanks this makes sense now  
> **username_3**: @AnIgnorantWanderer You're welcome!  Just to emphasize what someone else hinted, this refers to Illumina reads and how it (BaseSpace or equivalent programs) changes the images to FASTQ files.  Another sequencing technology might choose another format that is still FASTQ, but *maybe* order of reads exists.  (I don't know of one off hand, but I won't rule that out that it exists now or in the future.)  Good luck!  

---

## 2 answers

### Answer by username_4 on 2020-09-21. Score: 2 | Accepted answer

Like you mentioned, having read pairs can help with alignment.
Your FASTQ for a single-end sequencing run will look something like this:
@SEQ_ID_0001
AGCTAGCGCGGTTGGCTTAGCGACT
+
!''*((((***+))%%%++)(%%%%
@SEQ_ID_0002
AGGTGGTTGTAGGGAAAAAAGTCTC
+
!''*((((***+))%%%++)(%%%%
...

There is not necessarily a relationship between @SEQ_ID_0001 and @SEQ_ID_0002.
They could come from neigbouring regions in the genome or they could come from different species if that's what your experiment involves.
So aligning @SEQ_ID_0001 doesn't exactly give you information about where SEQ_ID_0002 goes.
@SEQ_ID_0001 could equally map to multiple locations in the genome with the same probability.
How do we decide where it "should" go?
We can randomly distribute multi-mapped reads or do some other fancy stuff, but that usually relies on facts about the data as a whole, and not something specific to the original DNA molecule that sequencing read came from.
If we have paired-end sequencing, then like your diagram shows, you know that read 1 (R1) and read 2 (R2; these have the same @SEQ_ID) will relate to each other in some way.
@SEQ_ID_0001 R1 may be multi-mapped, but if @SEQ_ID_0001 R2 isn't, then we know that R1 should probably be relatively close to R2.
Because of how you prepare your DNA samples before sequencing, you'll have some information about the insert length distribution.
You can use this information with sequence aligners to better identify where R1 should be placed.
You can also do this if both R1 and R2 are multi-mapped; certain pairwise alignments will be more likely than others, even if the marginal alignment probabilities for each read are the same.
Finally, there are other unexpected benefits that you can get from paired-end sequencing.
All of the above applies to just the mapping step, but applications like structural variant detection and chromosome conformation capture really benefit from paired-end sequencing.
If you have single-end reads for structural variant detection, then you will only detect breakpoints if the read overlaps a breakpoint.
But if you have paired-end, you not only have twice the amount of DNA sequenced to look for breakpoints, you can also estimate if there is a breakpoint between the two reads (in the inner distance from your diagram) if the two reads align really far apart from each other, or on separate chromosomes.
The idea is similar for chromosome conformation capture and the ligation junction sites.


---

### Answer by username_5 on 2020-09-21. Score: 1

In paired-end sequencing, you have an expected fragment length. So if Read 1 maps to two different places, whereas Read 2 only maps to one place. Then you can determine which one is more likely for Read 1 to map to. It cannot be too far away from Read 2 given the expected fragment length.

It is not necessarily true that paired-end sequencing gets twice the amount of data. The total amount of data can be determined when running sequencing. If single-end has 20M reads, I can just stop sequencing at 20M as well for paired-end.


---

