<p><a href="http://fenderglass.github.io/Ragout/" rel="nofollow noreferrer">Ragout</a> worked well for me. It is a reference-based scaffolder.</p>

<pre><code>ragout.py --outdir output/ input/ragout.rcp &gt; message.txt 2&gt; error.txt --threads 8 --overwrite
</code></pre>

<p>The ragout.rcp file contains a 'recipe' with the reference sequence and the contigs in FASTA format.:</p>

<pre><code>.references = ensembl
.target = assembly

ensembl.fasta = ref.fa
assembly.fasta = contigs.fa
</code></pre>

<p>In my case, it reduced the number of contigs 10-fold and increased the N50 10-fold. It added N's between the contigs.</p>

CC BY-SA 3.0
2017-11-27T11:55:38.940Z
Please consider expanding your answer. How/when does it work well ? How will it help the OP in their case?
llrs
CC BY-SA 3.0
2017-11-27T13:52:53.037Z
I have added my code and what Ragout did for me. What does 'OP' mean?
Biomagician
CC BY-SA 3.0
2017-11-27T15:42:30.400Z
Great [edit]! OP comes from Original Poster to refer the one that asked the question. I hope this will be useful.
llrs
CC BY-SA 3.0
2017-11-27T11:49:21.643Z
2017-11-27T15:43:51.997Z
2017-11-27T15:43:51.997Z
Biomagician
<p>In some cases you might consider reference-based scaffolding, as per <a href="https://bioinformatics.stackexchange.com/a/2944/196">charlesdarwin's answer</a>. I would argue that in most cases this will only provide limited information, as this will only really provide you with the order of the contigs <em>as it is in your reference</em>, as opposed to how it is in your sampled organism. In that sense it really only provides pseudo-scaffolding that makes your assembly look artificially good.</p>

<p>In all cases you will have to check wether your raw data supports your scaffolding solution. </p>

<p>Example: you will never be able to call structural variants across contig boundaries if you use reference based scaffolding. </p>

<p>I would suggest you try different k-mer sized for your assembly algorithm, and look into dedicated scaffolding tools. Without knowing your type of sequencing data it is hard to suggest anything specific.</p>

CC BY-SA 3.0
2017-11-30T08:01:37.027Z
2017-11-30T08:01:37.027Z
holmrenser
<p>I am using Ray to assemble a about 7.2 kb picornavirus genome. I am using k-mer of 55. Following is the exact code that I used.</p>

<pre><code>mpiexec -n 1 Ray -k 55 -s 2-CA-SVA-2_S2_L001_R1_001.fastq_q30_trim_LR_15.fasta  -o rayd55tr
</code></pre>

<p>This worked quite well as I got a <code>scaffolds.fasta</code> file with two scaffolds one with 5730 bp and another with 1548 bp. </p>

<p>Now, I am confused how I can join those scaffolds to get one linear genome. I joined them manually and blasted just to check and I got something like below :<a href="https://i.sstatic.net/SVhyo.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/SVhyo.jpg" alt="Blast of the manually joined contigs"></a>. </p>

<p>Is there any tool to join these two contigs together to have one complete linear genome? I have reference genome too if needed.</p>

CC BY-SA 3.0
2017-11-25T17:15:11.427Z
What did you blast against? Your image just shows a few identical sequences, but we can't interpret any blast results without knowing what was blasted against what. Also, if you have a reference genome, why didn't you use that to assemble the contigs?
terdon
CC BY-SA 3.0
2017-11-26T07:48:57.250Z
Are there any good tools available for reference based contigs joining ?
LoveVirology
CC BY-SA 3.0
2017-11-30T07:56:40.393Z
It is quite important to know what kind of sequencing data you have used for people to provide accurate tips.
holmrenser
<ngs>
Joining scaffolds to get full genome?
# Joining scaffolds to get full genome?

(Asked by: username_0 on 2017-11-24)

I am using Ray to assemble a about 7.2 kb picornavirus genome. I am using k-mer of 55. Following is the exact code that I used.
mpiexec -n 1 Ray -k 55 -s 2-CA-SVA-2_S2_L001_R1_001.fastq_q30_trim_LR_15.fasta  -o rayd55tr

This worked quite well as I got a scaffolds.fasta file with two scaffolds one with 5730 bp and another with 1548 bp. 
Now, I am confused how I can join those scaffolds to get one linear genome. I joined them manually and blasted just to check and I got something like below :. 
Is there any tool to join these two contigs together to have one complete linear genome? I have reference genome too if needed.

**Tags:** ngs
> **Comments:**
> **username_1**: What did you blast against? Your image just shows a few identical sequences, but we can't interpret any blast results without knowing what was blasted against what. Also, if you have a reference genome, why didn't you use that to assemble the contigs?  
> **username_0**: Are there any good tools available for reference based contigs joining ?  
> **username_2**: It is quite important to know what kind of sequencing data you have used for people to provide accurate tips.  

---

## 2 answers

### Answer by username_3 on 2017-11-27. Score: 2

Ragout worked well for me. It is a reference-based scaffolder.
ragout.py --outdir output/ input/ragout.rcp > message.txt 2> error.txt --threads 8 --overwrite

The ragout.rcp file contains a 'recipe' with the reference sequence and the contigs in FASTA format.:
.references = ensembl
.target = assembly

ensembl.fasta = ref.fa
assembly.fasta = contigs.fa

In my case, it reduced the number of contigs 10-fold and increased the N50 10-fold. It added N's between the contigs.

> **Comments:**
> **username_4**: Please consider expanding your answer. How/when does it work well ? How will it help the OP in their case?  
> **username_3**: I have added my code and what Ragout did for me. What does 'OP' mean?  
> **username_4**: Great [edit]! OP comes from Original Poster to refer the one that asked the question. I hope this will be useful.  

---

### Answer by username_2 on 2017-11-30. Score: 0

In some cases you might consider reference-based scaffolding, as per [charlesdarwin's answer](https://bioinformatics.stackexchange.com/a/2944/196). I would argue that in most cases this will only provide limited information, as this will only really provide you with the order of the contigs *as it is in your reference*, as opposed to how it is in your sampled organism. In that sense it really only provides pseudo-scaffolding that makes your assembly look artificially good.

In all cases you will have to check wether your raw data supports your scaffolding solution.

Example: you will never be able to call structural variants across contig boundaries if you use reference based scaffolding.

I would suggest you try different k-mer sized for your assembly algorithm, and look into dedicated scaffolding tools. Without knowing your type of sequencing data it is hard to suggest anything specific.


---

