epitopepredict: a tool for integrated MHC binding prediction
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Abstract
A key step in the cellular adaptive immune response is the presentation of antigens to T cells. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics, which allows in silico screening of peptides before experiments are performed. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open-source license from https://github.com/dmnfarrell/epitopepredict
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Now published in Gigabyte doi: 10.46471/gigabyte.13
This work has been peer reviewed in GigaByte, which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
**Reviewer 1. Yoonjoo Choi ** *Is the language of sufficient quality? Yes
There are some minor typos. Perhaps this would not be a matter in other systems or viewer - all "fi" do not appear on my computer (Mac OS Preview), e.g. "affinity" -> "a inity", "artificial" -> "arti cial".
*Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?
Yes. The purpose of this software is clearly stated and it will be very useful for researchers in relevant research fields.
Yes. The author recommended running this package on Linux machines, though it is written in …
Now published in Gigabyte doi: 10.46471/gigabyte.13
This work has been peer reviewed in GigaByte, which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
**Reviewer 1. Yoonjoo Choi ** *Is the language of sufficient quality? Yes
There are some minor typos. Perhaps this would not be a matter in other systems or viewer - all "fi" do not appear on my computer (Mac OS Preview), e.g. "affinity" -> "a inity", "artificial" -> "arti cial".
*Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?
Yes. The purpose of this software is clearly stated and it will be very useful for researchers in relevant research fields.
Yes. The author recommended running this package on Linux machines, though it is written in Python. It would be great for a non-linux user to run TEPITOPE and BasicMHC1 (for a quick epitope screen). I pip-installed it on both Ubuntu and Mac OS (just to see whether I can run TEPITOPE and BasicMHC1). The installation on Ubuntu was very easy and running fine. The Mac OS installation failed, but perhaps not the trouble of epitopepredict (brew installed Python 3.9.0).
*Have any claims of performance been sufficiently tested and compared to other commonly-used packages?
Yes. (Definitely not mandatory at all but) It would be great this package also provides a wrapper for the IEDB tools.
Recommendation: Minor Revisions.
**Reviewer 2. Jayaraman Valadi. ** *Is the language of sufficient quality?
Yes. There are lot of spelling mistakes. Must be corrected before acceptance.
*Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?
Yes. This is clearly explained In the manuscript
*Is the source code available, and has an appropriate Open Source Initiative license (https://opensource.org/licenses) been assigned to the code?
Yes. The source code is available on Github and it works as expected.
*Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?
No. The software depends on a number of external soft wares. Installation of the same need to be explained clearly in the manuscript.
*Is the documentation provided clear and user friendly?
Yes. Overall the documentation is good. Doc-Strings need minor improvements to make it more comprehensive.
*Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level?
Yes. This is well explained in manuscript.
*Have any claims of performance been sufficiently tested and compared to other commonly-used packages?
Yes. Adding a note on comparing the performance of different methods would be useful.
Additional Comments: The software developed is a python wrapper for a number of epitope prediction methods which are available. Unified architecture allows users to have easy access to all methods and compare the results of each method. Some of these methods/models have to be manually installed before the user can access it through the python wrapper. A new model trained by the authors has also been added additionally. users can utilize this prediction model without having to install any additional dependencies. Salient Features The software also supports visual comparison of predictions Users can select a target protein for epitope scanning users can prediction putative mhc1 and mhc2 epitopes using various predictive models using the python wrapper. Selection of best predictions possible Visual comparison of predictions from different predictive models possible.
Highlights the positions of putative epitopes on the target protein sequence
Overall the manuscript and software are quite comprehensive and can be accepted after minor revisions.
Recommendation: Minor Revisions
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