<p>Fungi, ain't a kingdom most of us will know much about. The only thing I can guess at is that in bacteria there has resurgence in methods for <em>de novo</em> assembly specifically aimed at reducing the total number of contigs in an assembly. The 'new wave' is summarised in this response here, <a href="https://bioinformatics.stackexchange.com/questions/20336/improving-prokaryotic-assembly-with-other-contig-scaffold-level-data">Improving prokaryotic assembly with other contig/scaffold-level data?</a>. This is potentially very exciting, except the OP reported they didn't work ;-(</p>
<p>A reasonable guess is your colleague has deployed these approaches to reduce the number of contigs. HOWEVER, what is appropriate for bacteria, may not be appropriate for fungi, alternatively it may be an improvement. Both bateria and fungi are small genomes, but beyond that there could notable differences.</p>
<p>If fungi are dominated by multigene families these might be being removed in the new wave <em>de novo</em> assemblies (they are designed for bacteria). The collapse of multigene families absolutely occurs in other eukaryotes with small genomes, but they have to be really big on multigene families (&gt;10% of genes in their genome). If that was true you would probably know about it.</p>
<p>Alternative, this could simply be poor gene identification in the first assembly resulting in an artificial jump in the number of genes.</p>
<p><strong>Solution?</strong> Either way you should be able to figure which genes are different between the assemblies and precisely what type of genes are differing, e.g. are these large multi-gene families. Personally, I would pipe the predicted genes from both assemblies through <code>eggNOG</code>. This might be useful, but not really what it's designed for, but it will return the KEGG nos.  More explicit methods based around comparing the RNAseq reads against the assembly via e.g. <code>bedtools</code> or <a href="https://wikis.utexas.edu/display/bioiteam/Gene+Counting" rel="nofollow noreferrer">these guys</a> here recommend <code> htseq</code>. This might be a better way to rephrase the question.</p>
<p>Thus I recommend:</p>
<ol>
<li>Finding out which <em>de novo</em> methods were used between first and second fungi genome assemblies. Do any of those methods match this post, <a href="https://bioinformatics.stackexchange.com/questions/20336/improving-prokaryotic-assembly-with-other-contig-scaffold-level-data">Improving prokaryotic assembly with other contig/scaffold-level data?</a></li>
<li>Consider a more explicit genomics study to examine RNAseq reads against the genome, or comparison between predicted no. genes between genomes.</li>
<li>Having said all that ... once you've done your DEG you  identify the KEGG no. and then simply compare the lists directly to find out which genes are different between the assemblies. This is an easy database operation to perform, e.g. via a <code>merge</code> function (separate question).</li>
</ol>
<p>I'd check point 1 and then go for point 3 - because it's something I easily know how to do. Thats just my opinion, a purist would likely explore this via point 2.</p>
<p><strong>Conclusion</strong> What I am certain about is that for a species where very little might be known it is better to perform an investigation rather than make an assumption that might prove later to be wrong.</p>

CC BY-SA 4.0
2023-03-23T13:48:51.743Z
2023-03-23T13:59:28.623Z
2023-03-23T13:59:28.623Z
M__
<p>I have a set of RNAseq data for a couple of relatively under-studied species.</p>
<p>They're fungi, of the order Hypocreales. The assemblies we're working with are de novo - reference seqs don't actually exist yet for most of our species. What the collaborator provided was updated genome assemblies in 60-90 fragments as opposed to the original 250-300, but obviously that's still not full resolution. As for the gene families, not a whole lot is known... in fact our species' genes are still not well described and we're mostly working off of predicted orthology.</p>
<p>During the analysis, a collaborator created new DNA scaffolds for these species which we'd now like to use.</p>
<p>However when I redid our RNAseq using the existing reads with the new scaffolds, one of our species had the number of differentially-expressed genes drop by anywhere from 20-50% per condition.</p>
<p>As the reads themselves haven't changed, we weren't expecting significant changes in differential gene expression, and indeed for our other species changes have been minimal.</p>
<p>Does anybody know what might cause this kind of change?</p>
<p>Thank you!</p>

CC BY-SA 4.0
2023-03-23T20:04:38.667Z
You mention RNAseq reads, but a DNA scaffold. How are you mapping the reads? Could you please show the command line that was used for read mapping, and give some more detail about how you calculated differential expression?
gringer
<rna-seq>
RNAseq: Why would using a more complete scaffold reduce DEGs?
# RNAseq: Why would using a more complete scaffold reduce DEGs?

(Asked by: username_0 on 2023-03-22)

I have a set of RNAseq data for a couple of relatively under-studied species.

They're fungi, of the order Hypocreales. The assemblies we're working with are de novo - reference seqs don't actually exist yet for most of our species. What the collaborator provided was updated genome assemblies in 60-90 fragments as opposed to the original 250-300, but obviously that's still not full resolution. As for the gene families, not a whole lot is known... in fact our species' genes are still not well described and we're mostly working off of predicted orthology.

During the analysis, a collaborator created new DNA scaffolds for these species which we'd now like to use.

However when I redid our RNAseq using the existing reads with the new scaffolds, one of our species had the number of differentially-expressed genes drop by anywhere from 20-50% per condition.

As the reads themselves haven't changed, we weren't expecting significant changes in differential gene expression, and indeed for our other species changes have been minimal.

Does anybody know what might cause this kind of change?

Thank you!

**Tags:** rna-seq
> **Comments:**
> **username_1**: You mention RNAseq reads, but a DNA scaffold. How are you mapping the reads? Could you please show the command line that was used for read mapping, and give some more detail about how you calculated differential expression?  

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## 1 answers

### Answer by username_2 on 2023-03-23. Score: 1

Fungi, ain't a kingdom most of us will know much about. The only thing I can guess at is that in bacteria there has resurgence in methods for *de novo* assembly specifically aimed at reducing the total number of contigs in an assembly. The 'new wave' is summarised in this response here, [Improving prokaryotic assembly with other contig/scaffold-level data?](https://bioinformatics.stackexchange.com/questions/20336/improving-prokaryotic-assembly-with-other-contig-scaffold-level-data). This is potentially very exciting, except the OP reported they didn't work ;-(

A reasonable guess is your colleague has deployed these approaches to reduce the number of contigs. HOWEVER, what is appropriate for bacteria, may not be appropriate for fungi, alternatively it may be an improvement. Both bateria and fungi are small genomes, but beyond that there could notable differences.

If fungi are dominated by multigene families these might be being removed in the new wave *de novo* assemblies (they are designed for bacteria). The collapse of multigene families absolutely occurs in other eukaryotes with small genomes, but they have to be really big on multigene families (>10% of genes in their genome). If that was true you would probably know about it.

Alternative, this could simply be poor gene identification in the first assembly resulting in an artificial jump in the number of genes.

**Solution?** Either way you should be able to figure which genes are different between the assemblies and precisely what type of genes are differing, e.g. are these large multi-gene families. Personally, I would pipe the predicted genes from both assemblies through `eggNOG`. This might be useful, but not really what it's designed for, but it will return the KEGG nos. More explicit methods based around comparing the RNAseq reads against the assembly via e.g. `bedtools` or [these guys](https://wikis.utexas.edu/display/bioiteam/Gene+Counting) here recommend  `htseq`. This might be a better way to rephrase the question.

Thus I recommend:

1. Finding out which *de novo* methods were used between first and second fungi genome assemblies. Do any of those methods match this post, [Improving prokaryotic assembly with other contig/scaffold-level data?](https://bioinformatics.stackexchange.com/questions/20336/improving-prokaryotic-assembly-with-other-contig-scaffold-level-data)
2. Consider a more explicit genomics study to examine RNAseq reads against the genome, or comparison between predicted no. genes between genomes.
3. Having said all that ... once you've done your DEG you identify the KEGG no. and then simply compare the lists directly to find out which genes are different between the assemblies. This is an easy database operation to perform, e.g. via a `merge` function (separate question).

I'd check point 1 and then go for point 3 - because it's something I easily know how to do. Thats just my opinion, a purist would likely explore this via point 2.

**Conclusion** What I am certain about is that for a species where very little might be known it is better to perform an investigation rather than make an assumption that might prove later to be wrong.


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