The thing with phylogenetics is that people tend to underestimate how complex the analytical machinary is . People want to charge in guns blazing , but there ' s a lot of careful thought that needs to go into an analysis . It is very easy to get some answer , but getting a good answer reliable and credible , not just what I want to see takes some finess . I highly recommend poking around the TreeThinkers website to see what they have written that pertains to your analyses . You should certainly read as much as you can of the manuals for the programs you want to use , and please don ' t hesitate to ask questions you ' re welcome to PM me . In general when you want to infer a phylogeny , you want to 0 Keep a log of what you ' re doing . Keep input files and output files . At the very least , your alignment , the input commands to your phylogeny inference program , and your tree files not just the picture of the tree should end up in your supplemental material or on . This is for reproducibility and anyone who wants to build on your work . It ' s not hard if you plan in advance . 1 Align all the sequences . Not all alignment programs were created equal , so some caution is important . MUSCLE and MAFFT are pretty consistently good across benchmarks , they ' re pretty safe choices . Look at the alignment . If it ' s a protein coding gene , did the alignment induce frameshifts that cause premature stop codons ? If it did , you can consider trying to remedy this by hand , but otherwise don ' t go around 2 Consider partitioning and substitution models . Sadly , CIPRES doesn ' t have which will do both at the same time . 2 A When you analyze data that are partitioned , you ' re going to infer a single topology so all partitions tell you about the relationships of the species , but you ' ll allow for differences in rates of evolution and how the evolutionary process works in the partitions . Partitioning is important when you have an analysis with multiple genes andor protein coding sequence for a generibosomal RNA . Between selection and wobble , different codon positions are known to often evolve at different rates andor under different substitution models . Stems and loops also tend to show this pattern , but instead of third codons being fast , it ' s stems . If your sequences are short , you may not be able to partition effectively , since you need a reasonable amount of data in any partition to fit a model of evolution . But if you have a few hundred sites of something that you think may evolve differently , say 900 sites of cellulase , leaving 300 per codon position , you should think about the partitioning scheme . You can try multiple schemes and choose the best objectively according to their likelihoods usually accounting for the number of parameters in each scheme with the AIC or BIC . 2 B Substitution models are a huge part of how we extract information from the alignment . Programs like jModelTest which is on CIPRES will help you choose which model is best for each of the partitions in your model . This is important because choosing a model that doesn ' t fit your data can lead your phylogenetic inference astray . If you use a ML method , you can ' t just throw the most complicated model at the problem . This is really just the standard biasvariance trade off in all statistical model selection . Bayesian methods seem to be better at dealing with an overly complicated model for the data , which is pretty convenient . 3 Infer the phylogeny . Be patient , this step can take time . Be very careful with what you ' re telling the program , make sure it ' s doing what you want . Many programs have defaults you may not expect or want . 4 Make sure your inference was stable . For Bayesian inference this means diagnosing the MCMC . For ML inference , it means conducting multiple search replicates to make sure you aren ' t stuck at a local optimum . 5 Characterize uncertainty . The issue is that we ' re rarely completely certain about the relationships in our tree , real data is messy . We need some way to ask how certain we are about the relationships we observed in our phylogeny . In ML analyses , this means performing nonparametric bootstraps . Bayesian analyses get this for free , with posterior probabilities for every split in the tree .