text: user15713: I'm a wet-lab biologist desperately trying to get to grips with some Bioinformatics.
  I have some shotgun Illumina paired-end data from a gut microbiome project I'm working on. I'm considering how to go about generating contigs from reads. I've been recommended to use SPAdes which I'm reading up on at the moment.
  Originally I was thinking of using FLASh however I was told it might not be suitable for metagenomic data.
  My question simply is, what makes certain programs suitable for merging paired-end reads from a genome but not a metagenome? I find myself a little spoilt for choice (eg. FLASh/SPAdes/BBmap... )  with bioinformatics tools I'm always concerned I'm picking the wrong one.
  Should I ideally be running my data through a couple of tools and comparing them?
  
  user2108: NB: "merging PE reads" (such as e.g. FLASH does) is not the same as "sequence assembly from PE reads" (what MetaSPAdes does). The latter generates contigs, the former generates (some) merged shotgun library inserts. I think you want sequence assembly, i.e. a tool like MetaSPAdes.
  Don't be tricked by the loose language of "merging" that is sometimes applied to the sequence assembly problem. It is much more complicated than that.
  If you want to learn more about sequence assembly, I suggest you read the sequence assembly wikipedia page.
  I would probably recommend MegaHit over metaSPAdes, because it requires much less memory / runtime, and lower frequency of errors.
  But generally speaking, both programs are building different flavors of de Bruijn Graphs from metagenomic reads to generate contigs/scaffolds. This is the accepted method by this point for Illumina metagenomic data, because it is the method that is capable of handling the amount of small pieces of data in a tractable fashion. Alternatives such as overlap-layout-consensus graphs or string graphs just don't work as well for the metagenomic problem.
  You are probably looking for methods that talk about complicated, computationally complex assembly algorithms and graphical methods for summarizing lots of short reads. You are not looking for simple read processing/alignment tools like BBMap/BBTools or FLASH. To reiterate, simple read merging tools will not give you assembled contigs.
  
original_text: 15713: <p>I'm a wet-lab biologist desperately trying to get to grips with some Bioinformatics.</p>
  <p>I have some shotgun Illumina paired-end data from a gut microbiome project I'm working on. I'm considering how to go about generating contigs from reads. I've been recommended to use SPAdes which I'm reading up on at the moment.</p>
  <p>Originally I was thinking of using FLASh however I was told it might not be suitable for metagenomic data.</p>
  <p><strong>My question simply is, what makes certain programs suitable for merging paired-end reads from a genome but not a metagenome?</strong> I find myself a little spoilt for choice (eg. FLASh/SPAdes/BBmap... )  with bioinformatics tools I'm always concerned I'm picking the wrong one.</p>
  <p>Should I ideally be running my data through a couple of tools and comparing them?</p>
   2108: <p>NB: &quot;merging PE reads&quot; (such as e.g. FLASH does) is not the same as &quot;sequence assembly from PE reads&quot; (what MetaSPAdes does). The latter generates contigs, the former generates (some) merged shotgun library inserts. I think you want sequence assembly, i.e. a tool like MetaSPAdes.</p>
  <p><strong>Don't be tricked by the loose language of &quot;merging&quot; that is sometimes applied to the sequence assembly problem. It is much more complicated than that.</strong></p>
  <p>If you want to learn more about sequence assembly, I suggest you read the <a href="https://en.wikipedia.org/wiki/Sequence_assembly" rel="nofollow noreferrer">sequence assembly</a> wikipedia page.</p>
  <p>I would probably recommend <a href="https://github.com/voutcn/megahit" rel="nofollow noreferrer">MegaHit</a> over metaSPAdes, because it requires much less memory / runtime, and lower frequency of errors.</p>
  <p>But generally speaking, both programs are building different flavors of <a href="https://www.cs.jhu.edu/%7Elangmea/resources/lecture_notes/assembly_dbg.pdf" rel="nofollow noreferrer">de Bruijn Graphs</a> from metagenomic reads to generate contigs/scaffolds. This is the accepted method by this point for Illumina metagenomic data, because it is the method that is capable of handling the amount of small pieces of data in a tractable fashion. Alternatives such as <a href="https://www.cs.jhu.edu/%7Elangmea/resources/lecture_notes/assembly_olc.pdf" rel="nofollow noreferrer">overlap-layout-consensus graphs</a> or <a href="https://bioinformaticshome.com/tools/wga/descriptions/SGA.html" rel="nofollow noreferrer">string graphs</a> just don't work as well for the metagenomic problem.</p>
  <p>You are probably looking for methods that talk about complicated, computationally complex assembly algorithms and graphical methods for summarizing lots of short reads. You are not looking for simple read processing/alignment tools like BBMap/BBTools or FLASH. <strong>To reiterate, simple read merging tools will not give you assembled contigs.</strong></p>
  