1. Preparing mitogenome data

Introduction

In this subsection, we will collect and consolidate our mitogenome data from multiple sources. Note that if you’re using the example data, you do not need to run any commands here apart from whatever is needed to copy over the data to your working directory

Data and software

The input data for this tutorial is one or more genbank-format files containing mitogenome sequences from which you want to build a phylogeny. See details below for how you might acquire these.

The files references_CCCPmitogenomes.gb, references_GBcoleoptera.gb and references_GBarthropods.gb within the sectionD archive can be used as example data.

Novel reference mitogenomes

We have generated novel mitogenome assemblies through sequencing of a set of reference specimens collected as part of same project as our example metabarcoding dataset. These mitogenomes were produced using the mitochondrial metagenomics (MMG) methodology [1]

These mitogenomes are stored in a genbank-format file called references_CCCPmitogenomes.gb file (see the Data and software box above). You might want to have a look at this file to see the format if you’re not familiar with it, although if it’s completely new keep reading to find out more.

Mitogenomes from GenBank

We want to supplement these mitogenomes with some good data from GenBank in order to generate a more comprehensive tree. We’ve already done this, but we’ll discuss our thought process.

While we will obviously want to acquire some Coleoptera data, we should also include representatives from other taxa. Most importantly, we want to be able to ensure that any OTUs that are not from Coleoptera will not be assigned to Coleoptera simply because there is nowhere else in the tree for them to go. While we focused on Coleoptera, it’s perfectly feasible that DNA from other taxa were present (see the OTU identification section for more information). Given that CO1 is a fast-evolving gene, and we are just using a short, highly variable region, we should not expect that deep-level (i.e. order-level) relationships will be properly resolved on a tree based solely on this data. In other words, we shouldn’t necessarily assume that the beetles and non-beetles will form separate monophyletic clades on a tree if only the metabarcode region is used to separate them. So, we will also use data for a set of other Arthropod orders.

If you’ve not previously acquired data from GenBank, or aren’t familiar with the GenBank file format, see this refresher on how to download data from GenBank. You’ll see that if we search for all Coleoptera or all Arthropods on GenBank, that we’ll return a dataset of many thousands of mitogenomes. Not only will building trees with this many sequences be very time-consuming, but it’ll make our trees very complicated to view and understand.

We have already searched GenBank and downloaded data for the Coleoptera and selected other Arthropods (Blattodea, Hemiptera, Lepidoptera, Diptera, Araneae, and Collembola), then filtered these down to three sequences per order for the other Arthropods and a subset of sequences for the Coleoptera, to give a broad overview without having too much data. We have also checked and verified the annotations are accurate.

Next steps

Now we should have three genbank-format files. The next step is to 2. Extract protein coding genes.

[1]	Alex Crampton-Platt, Douglas W Yu, Xin Zhou, Alfried P Vogler, Mitochondrial metagenomics: letting the genes out of the bottle, GigaScience, Volume 5, Issue 1, December 2016, s13742–016–0120–y, https://doi.org/10.1186/s13742-016-0120-y