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Unigenefinder

UnigeneFinder is an automated pipeline designed to simplify and improve gene prediction from de novo transcriptome assemblies, especially for non-model organisms that lack a reference genome. By integrating multiple clustering methods, UnigeneFinder significantly reduces the redundancy that often inflates the number of transcripts in raw assemblies. The pipeline outputs a set of primary transcripts, coding sequences, and protein sequences analogous to those typically available for high-quality reference genomes. This enables downstream analyses such as differential gene expression, ortholog identification, and evolutionary studies to be conducted with greater accuracy. It also calculates gene expression for putative unigenes as well as all transcript isoforms.

The pipeline is implemented in Python and is fully automated, making it accessible to a wide range of users. It is designed to run efficiently on both high-performance computing (HPC) systems and personal computers. For ease of use, UnigeneFinder is distributed with all necessary dependencies encapsulated in a Singularity container, ensuring smooth installation and execution on both Linux and Windows systems.

Code: Github Repository
From: The Rhee Lab

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Romero

Proteome-Wide Surface Analysis Code is from the upcoming preprint by Romero et al: Protein surface chemistry encodes an adaptive resistance to desiccation

Paulette Sofía Romero-Pérez, Haley M. Moran, Azeem Horani, Alexander Truong, Edgar Manriquez-Sandoval, John F. Ramirez, Alec Martinez, Edith Gollub, Kara Hunter, Jeffrey M. Lotthammer, Ryan J. Emenecker, Thomas C. Boothby, Alex S. Holehouse, Stephen D. Fried, Shahar Sukenik

Code: Github Repository
From: The Sukenik Lab, the Holehouse Lab, and the Boothby Lab