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Jason Macrander
PeerJ Author & Reviewer
250 Points

Contributions by role

Author 135
Preprint Author 70
Reviewer 45

Contributions by subject area

Bioinformatics
Data Mining and Machine Learning
Evolutionary Studies
Marine Biology
Molecular Biology
Zoology
Toxicology
Ecology

Jason C Macrander

PeerJ Author & Reviewer

Summary

I am an evolutionary biologist using comparative ‘omics approaches in sea anemones and other venomous animals to address questions concerning venom evolution, protein function, and the physiology of symbiosis. In my research, I use sea anemones as a model to understand how evolutionary and ecological factors influence biological interactions and molecular diversity from a phylogenetic perspective. I am currently a Postdoctoral Fellow in the laboratory of Dr. Adam Reitzel at the University of North Carolina, Charlotte.

Bioinformatics Evolutionary Studies Genetics Marine Biology Toxicology Zoology

Past or current institution affiliations

Florida Southern College
Ohio State University
University of North Carolina at Charlotte

Work details

Assistant Professor of Marine Biology

Florida Southern College
August 2018
Biology

Postdoctoral Fellow

University of North Carolina at Charlotte
August 2016 - July 2018
Department of Biology
Reitzel Lab

Identities

@Jason_Macrander

Websites

  • Google Scholar
  • FSC Faculty Profile

PeerJ Contributions

  • Articles 1
  • Preprints 2
  • Feedback 1
  • Questions 1
July 31, 2018
Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data
Jason Macrander, Jyothirmayi Panda, Daniel Janies, Marymegan Daly, Adam M. Reitzel
https://doi.org/10.7717/peerj.5361 PubMed 30083468
July 19, 2018 - Version: 1
Linear Mitochondrial genome in Anthozoa (Cnidaria): A case study in Ceriantharia
Sergio N Stampar, Michael B Broe, Jason Macrander, Adam M Reitzel, Marymegan Daly
https://doi.org/10.7287/peerj.preprints.27042v1
March 19, 2018 - Version: 1
Venomix: A simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data
Jason Macrander, Jyothirmayi Panda, Daniel Janies, Marymegan Daly, Adam M Reitzel
https://doi.org/10.7287/peerj.preprints.26733v1

Provided feedback on

08 Feb 2019

TOXIFY: a deep learning approach to classify animal venom proteins

Hello, I am very much looking forward to giving this a try, but I noticed that you referenced Venomix as a time-consuming pipeline. We designed it specifically not to be one. Fr...

1 Question

0
Not a question just a note.
about Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions