Answers.Body: <p>I asked that on Reddit. I didn't get an answer to all my questions but I got an answer for how to assemble the overlapping contigs.</p>
  <p>If I use Flye I can assemble these and get larger contigs, better NG50, and higher genome fraction.</p>
  <p><a href="https://i.sstatic.net/UaqXM.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UaqXM.png" alt="enter image description here" /></a>
  <strong>s-aligner + Flye assembly of SRR8357422</strong> Genome fraction:95,95% - Largest Alignment: 134.103 - NGA50: 134.103 - LGA50: 1</p>
  <p>Given that the data in the first assembly apart from being more useful itself in my opinion, is objectively more suitable to generate a better result in all senses with a simple additional step that takes a few seconds, my only possible conclusion is that it is overall a better result and therefore this sort of result will often be preferable.</p>
  <p>Of course, there are other aspects to consider, like error rate, that are not considered here as it wasn't part of the question (mainly because I think Quast is not adequate for calculating it). But BTW, mismatches per 100kbp are also lower.</p>
  <p>[EDIT] I want to add that this is not granted that in all cases Flye will generate a clearly better assembly.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-22T22:58:11.073Z
Answers.LastActivityDate: 2020-12-23T18:53:12.557Z
Answers.LastEditDate: 2020-12-23T18:53:12.557Z
Answers.UserDisplayName: juanjo75es
Body: <p>I have developed a software for de novo genome assembly. Its performance varies gradually according to how much data you employ. At initial stages it often produces contigs that look like that when aligned to a reference genome:</p>
  <p><a href="https://i.sstatic.net/4iNvj.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4iNvj.png" alt="s-aligner assembly of SRR8357422" /></a>
  <strong>Initial-stage s-aligner assembly of SRR8357422</strong>
  Genome fraction:98,8% -
  Largest Alignment: 61.544 -
  NGA50: 59.409 -
  LGA50: 2</p>
  <p>If I add more data, the contigs end up being larger, but that obviously takes more time. My question is, how does this result compare to, for example, one like that?</p>
  <p><a href="https://i.sstatic.net/RRAfB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/RRAfB.png" alt="SPAdes assembly of SRR8357422" /></a>
  <strong>SPAdes assembly of SRR8357422</strong>
  Genome fraction:93,46% -
  Largest Alignment: 128.391 -
  NGA50: 128.391 -
  LGA50: 1</p>
  <p>Note how you can construct a contig covering almost the full reference genome assembling green contigs in the first case. Therefore, a blast search of genes will likely let you find more genes given that it covers more genome fraction.</p>
  <p>Apart from determining what option is more convenient, I would also like to know how much more convenient is to have both results. Or in which cases it's more convenient.</p>
  <p>Last (but not least!), is there a way to generate automatically larger contigs from these overlapping contigs in assembly 1? I have tried using miniasm with no success. Even minimap2 seems not to find overlaps well enough. I am developing my own algorithm also for that but would like to know if there is another way.</p>
  <p>[EDITED on 17/12 to add another example with real data for both assemblies]</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-16T23:01:01.550Z
Comments.Text: Hi, could you please clarify your question a bit more; it's unclear to me. Asking "which one is a more convenient assembly?" suggests to me that there should be at least two comparisons, but you've only provided a figure for one. There is also additional information needed about the assembly and reference genome (i.e. are you doing kmer-based assembly; is the genome highly-repetitive; does it have Ns in the sequence).
Comments.UserDisplayName: gringer
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-16T23:20:39.837Z
Comments.Text: Hi @gringer, it's not a question for a single case. It's something that happens in different assemblies in different circumstances. It's a generic question. The example in the image is real and corresponds to a run in PRJEB32127 but I have not assembled it with another software yet. I mean, in case I obtained an assemblie with a single contig but lower coverage (for example a contig 100k long) , which one of the two assemblies would be more useful.
Comments.UserDisplayName: juanjo75es
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-16T23:24:54.513Z
Comments.Text: Maybe it's more clear with a more extreme case. Suppose it's a bacterial assembly (~2Mbp) and SPAdes get a contig with 3Mbp bit just covering 90% of the reference (the rest is chimeric) while the otrher assembler gets lots of contigs around 200k bp long but covering 99%. I'd like to know how important is to get larger contigs or if coverage and percentage of genes covered is more important.
Comments.UserDisplayName: juanjo75es
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-16T23:25:29.617Z
Comments.Text: I will lsee if I find a real case with real data...
Comments.UserDisplayName: juanjo75es
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-16T23:43:10.450Z
Comments.Text: I guess it's "genome fraction" where I said "coverage"...
Comments.UserDisplayName: juanjo75es
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-17T04:35:14.503Z
Comments.Text: If it's a general question about assembly algorithms, it won't really fit well into the StackExchange Question/Answer format. Bioinformatics Stack Exchange works best for single, specific issues. I recommend posting your question on the [bioinformatics subreddit](https://reddit.com/r/bioinformatics/submit).
Comments.UserDisplayName: gringer
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-17T13:15:31.427Z
Comments.Text: Yes, makes sense. I will post it there. I will see if I find an example clear enough too and edit the answer or write a new one.
Comments.UserDisplayName: juanjo75es
Tags: <assembly><reference-genome><genome-sequencing><scaffold>
Title: Which one is a more convenient assembly?
ThreadText: # Which one is a more convenient assembly?
  
  (Asked by: username_0 on 2020-12-16)
  
  I have developed a software for de novo genome assembly. Its performance varies gradually according to how much data you employ. At initial stages it often produces contigs that look like that when aligned to a reference genome:
  
  [![s-aligner assembly of SRR8357422](https://i.sstatic.net/4iNvj.png)](https://i.sstatic.net/4iNvj.png)
  **Initial-stage s-aligner assembly of SRR8357422**
  Genome fraction:98,8% -
  Largest Alignment: 61.544 -
  NGA50: 59.409 -
  LGA50: 2
  
  If I add more data, the contigs end up being larger, but that obviously takes more time. My question is, how does this result compare to, for example, one like that?
  
  [![SPAdes assembly of SRR8357422](https://i.sstatic.net/RRAfB.png)](https://i.sstatic.net/RRAfB.png)
  **SPAdes assembly of SRR8357422**
  Genome fraction:93,46% -
  Largest Alignment: 128.391 -
  NGA50: 128.391 -
  LGA50: 1
  
  Note how you can construct a contig covering almost the full reference genome assembling green contigs in the first case. Therefore, a blast search of genes will likely let you find more genes given that it covers more genome fraction.
  
  Apart from determining what option is more convenient, I would also like to know how much more convenient is to have both results. Or in which cases it's more convenient.
  
  Last (but not least!), is there a way to generate automatically larger contigs from these overlapping contigs in assembly 1? I have tried using miniasm with no success. Even minimap2 seems not to find overlaps well enough. I am developing my own algorithm also for that but would like to know if there is another way.
  
  [EDITED on 17/12 to add another example with real data for both assemblies]
  
  **Tags:** assembly, reference-genome, genome-sequencing, scaffold
  
  > **Comments:**
  > **username_1**: Hi, could you please clarify your question a bit more; it's unclear to me. Asking "which one is a more convenient assembly?" suggests to me that there should be at least two comparisons, but you've only provided a figure for one. There is also additional information needed about the assembly and reference genome (i.e. are you doing kmer-based assembly; is the genome highly-repetitive; does it have Ns in the sequence).  
  > **username_0**: Hi @username_1, it's not a question for a single case. It's something that happens in different assemblies in different circumstances. It's a generic question. The example in the image is real and corresponds to a run in PRJEB32127 but I have not assembled it with another software yet. I mean, in case I obtained an assemblie with a single contig but lower coverage (for example a contig 100k long) , which one of the two assemblies would be more useful.  
  > **username_0**: Maybe it's more clear with a more extreme case. Suppose it's a bacterial assembly (~2Mbp) and SPAdes get a contig with 3Mbp bit just covering 90% of the reference (the rest is chimeric) while the otrher assembler gets lots of contigs around 200k bp long but covering 99%. I'd like to know how important is to get larger contigs or if coverage and percentage of genes covered is more important.  
  > **username_0**: I will lsee if I find a real case with real data...  
  > **username_0**: I guess it's "genome fraction" where I said "coverage"...  
  > **username_1**: If it's a general question about assembly algorithms, it won't really fit well into the StackExchange Question/Answer format. Bioinformatics Stack Exchange works best for single, specific issues. I recommend posting your question on the [bioinformatics subreddit](https://reddit.com/r/bioinformatics/submit).  
  > **username_0**: Yes, makes sense. I will post it there. I will see if I find an example clear enough too and edit the answer or write a new one.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2020-12-22. Score: 0 | Accepted answer
  
  I asked that on Reddit. I didn't get an answer to all my questions but I got an answer for how to assemble the overlapping contigs.
  
  If I use Flye I can assemble these and get larger contigs, better NG50, and higher genome fraction.
  
  [![enter image description here](https://i.sstatic.net/UaqXM.png)](https://i.sstatic.net/UaqXM.png)
  **s-aligner + Flye assembly of SRR8357422** Genome fraction:95,95% - Largest Alignment: 134.103 - NGA50: 134.103 - LGA50: 1
  
  Given that the data in the first assembly apart from being more useful itself in my opinion, is objectively more suitable to generate a better result in all senses with a simple additional step that takes a few seconds, my only possible conclusion is that it is overall a better result and therefore this sort of result will often be preferable.
  
  Of course, there are other aspects to consider, like error rate, that are not considered here as it wasn't part of the question (mainly because I think Quast is not adequate for calculating it). But BTW, mismatches per 100kbp are also lower.
  
  [EDIT] I want to add that this is not granted that in all cases Flye will generate a clearly better assembly.
  
  
  ---
  
  