Dear Mr. Scott

As I found previously in this paper that attached to this comment. They did the analysis with Muscle alignment were inferred with MEGA7 . Also there is a video explains how to work with Muscle alignment (MOLE-BLAST) https://youtu.be/WG06vjrfVxc

I hope this information help you.

Please inform me if you find other way.

Best regards,

I agree with Mohammed Hasan Nabeel Almashat, the first step is to align your data.

*One "easy" quick option may be going for the "One click workflows" on https://ngphylogeny.fr/workflows/oneclick/

The long but robust path:

Considering the sequences you are using, you should analyze the alignments with jModelTest to determine the best fit model and/or with partitionFinder to see if the full sequence should be computed under the same substitution model.

Do you have any sequence to root your analysis (or does any of those sequence is a root independent of your samples)?

In case the answer is yes, I may suggest you to make an exploratory analysis via PHYML: http://www.atgc-montpellier.fr/phyml/ utilizing the presents of all the features. If you want something more used in papers, go for RAxML. The CIPRES web server has the newer version (http://www.phylo.org/)

In the case the answer is no, I suggest you the SPLITSTREE software. (http://www.splitstree.org/), it is very helpful to resolve phylogenetic relations between individuals inside the same group and it has various algorithms to construct different trees. Is super user-friendly, and due the size of your data, it will work very fast.

Align the desired sequences using ClustalW and Neighbor-joining phylogenetic trees of maximum-likelihood distances can be constructed using the TreeView1.6.6 program (http://taxonomy.zoology.gla.ac.uk/rod/rod.html).

The trees constructed should be analyzed with the protdist program using the default run options with hundred bootstrapping.

Alternatively, evolutionary history can be inferred using the

Maximum Parsimony (MP) method in Mega6 program. The MP tree can be obtained using the Subtree-Pruning-Regrafting (SPR) algorithm with search level 1

in which the initial trees can be obtained by the random addition of sequences (10 replicates).