<p>Welcome! I have some quick basic answers for the questions you're asking so hopefully this helps:</p>
<ol>
<li>Sequences for the same species represent variation/diversity/evolutionary changes. Think of humans; how do different people's DNA sequences represent the sequence of a human? This is especially important for phylogenetic studies.</li>
<li>For the most part, all sequences are best suited for alignment. The main thing to think about is the amount of data you have (if it's a lot of GB the alignment will take a lot of computing power), the type of sequnece data you have (simple rule of thumb is to not mix short read with long read sequences), and the tool you're using for alignment.</li>
<li>Always a good idea to think about orientation but most tools will account for orientation when aligning sequences.</li>
<li>This depends on what you're looking for but more often than not using the whole genomes is best as it lets you look at the whole picture instead of just a small portion but that's definitely a lot of computing power. If you're just looking at the mitochondria just focus on that region</li>
</ol>
<p>Hope this helps!</p>

CC BY-SA 4.0
2023-10-11T14:47:05.880Z
2023-10-11T14:47:05.880Z
rimo
<p>I am using this package <a href="https://pypi.org/project/nsdpy/" rel="nofollow noreferrer">nsdpy</a> to download genome sequences from <strong>NCBI nucleotide database</strong>.<br />
Specifically I am interested in the <strong>whole mitochondrial genome</strong> of different species, here I will use a subset of species just as an example.</p>
<p>My Query is:<br />
<code>&quot;{mitochondrion[Title]&quot; -i -t -L subset_species_list.txt </code><br />
Where :</p>
<ul>
<li><code>-i</code> Adds the taxonomic information to the information lines of the sequences written in the output files (not relevant for this example)</li>
<li><code>-t</code> option produces a tsv file with the following fields: Taxon name, SequenceID, TaxID, Taxonomic lineage, Sequence length, Sequence.</li>
<li><code>subset_species_list.txt</code> is a list of species, below you can see its content</li>
</ul>
<pre><code>Xenopus laevis
Gasterosteus aculeatus
Chironomus riparius
Rasbora heteromorpha
Planktolyngbya limnetica
</code></pre>
<p>The output file is quite long and includes the actual sequences. Here I will post the same file where I dropped the column for the sequence and the lineage to keep it compact</p>
<p><a href="https://i.sstatic.net/4QcyKl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4QcyKl.png" alt="table" /></a></p>
<p>My aim is to obtain phylogenetic distances between species.</p>
<p>My questions are:</p>
<ol>
<li>What do the sequences for the same species represent?</li>
<li>Which sequences are best suited for an alignment and how do I know that?</li>
<li>Should I also look into the orientation of the sequences or are they all on the forward strand?</li>
<li>Is it better if I filter for Coding sequences (CDS) instead of whole genomes</li>
</ol>
<p>Also if you noticed any rookie mistake in my process feel free to point that out. It is the first time for me working with DNA sequences and I really want to learn to correct way to do that.</p>

CC BY-SA 4.0
2023-10-11T13:32:23.017Z
Welcome to the site. This question is too broad could you limit the number of questions please?
M__
<phylogenetics><sequence-analysis>
What does the different sequences represent?
# What does the different sequences represent?

(Asked by: username_0 on 2023-10-11)

I am using this package nsdpy to download genome sequences from NCBI nucleotide database.
Specifically I am interested in the whole mitochondrial genome of different species, here I will use a subset of species just as an example.
My Query is:
"{mitochondrion[Title]" -i -t -L subset_species_list.txt 
Where :

-i Adds the taxonomic information to the information lines of the sequences written in the output files (not relevant for this example)
-t option produces a tsv file with the following fields: Taxon name, SequenceID, TaxID, Taxonomic lineage, Sequence length, Sequence.
subset_species_list.txt is a list of species, below you can see its content

Xenopus laevis
Gasterosteus aculeatus
Chironomus riparius
Rasbora heteromorpha
Planktolyngbya limnetica

The output file is quite long and includes the actual sequences. Here I will post the same file where I dropped the column for the sequence and the lineage to keep it compact

My aim is to obtain phylogenetic distances between species.
My questions are:

What do the sequences for the same species represent?
Which sequences are best suited for an alignment and how do I know that?
Should I also look into the orientation of the sequences or are they all on the forward strand?
Is it better if I filter for Coding sequences (CDS) instead of whole genomes

Also if you noticed any rookie mistake in my process feel free to point that out. It is the first time for me working with DNA sequences and I really want to learn to correct way to do that.

**Tags:** phylogenetics, sequence-analysis
> **Comments:**
> **username_1**: Welcome to the site. This question is too broad could you limit the number of questions please?  

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## 1 answers

### Answer by username_2 on 2023-10-11. Score: 1 | Accepted answer

Welcome! I have some quick basic answers for the questions you're asking so hopefully this helps:

1. Sequences for the same species represent variation/diversity/evolutionary changes. Think of humans; how do different people's DNA sequences represent the sequence of a human? This is especially important for phylogenetic studies.
2. For the most part, all sequences are best suited for alignment. The main thing to think about is the amount of data you have (if it's a lot of GB the alignment will take a lot of computing power), the type of sequnece data you have (simple rule of thumb is to not mix short read with long read sequences), and the tool you're using for alignment.
3. Always a good idea to think about orientation but most tools will account for orientation when aligning sequences.
4. This depends on what you're looking for but more often than not using the whole genomes is best as it lets you look at the whole picture instead of just a small portion but that's definitely a lot of computing power. If you're just looking at the mitochondria just focus on that region

Hope this helps!


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