Completed on 25 May 2018 by Konrad Foerstner.
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The manuscript by Johnson et al. describe the re-analysis of the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP). The authors have generate a new computational pipeline for the de novo assembly (using Trinity de novo) of the RNA-Seq reads of several hundred transcriptomes as well as downstream a set of scripts to compare the outcome with the results of the original publication (which used Trans-ABySS for the assembly).
The current manuscript is a great example that shows the value of revisiting old data sets with new computational tools. The authors put strong focus on reproducibility of their analysis. The effort for this should not be underestimated and the work can serve as a blueprint for similar data re-analysis projects.
I see no major issue in this work but still would like to have a few smaller ones addressed:
* The manuscript is currently rather descriptive and has only a few explanations why there are certain differences in the presented assembly approaches. E.g. what are the reasons for the observation displayed in Figure 4 that there so many more unique k-mers in the DIB than in the NCGR set? Maybe not all results can be explained mechanistically but least at some potential reasons could be discussed.
* The authors write: "We used a different pipeline than the original one used to create the NCGR assemblies, in part because new software was available  and in part because of new trimming guidelines ". Is  really the correct reference here? If so this has to be further explained.
* I think figures 2, 3 and 5 are not red green blind safe.
* In the script collection uploaded to Zenodo I personally would have removed the "pycache" folder and the containing Python byte code files (*pyc). Or do they have any purpose / contain useful information?
* The supplementary notebooks could additionally be uploaded as ipyn files.
* The authors have a configuration file for user specif paths but this is not strictly used. In "dibMMETSPconfiguration.py" another "basedir" variable is set and in trimqc.py even the full path for Trimmomatic is set ("/mnt/home/ljcohen/bin/Trimmomatic-0.33/trimmomatic-0.33.jar"). This make the reuse of the framework harder.
* While I understand that it is sometime needed due to dependencies on old libraries I would like to discourage the use of Python 2.7 (aka "legacy Python") in currently research projects and would strongly recommend to use a current Python version (3 and higher) instead.