How Genes Interact to Produce Function

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (1 January 2021) | Viewed by 5655

Special Issue Editor


E-Mail Website1 Website2
Guest Editor
1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
2. Garvan-Weizmann Centre for Cellular Genomics, Darlinghurst, NSW, Australia
Interests: transcriptomics; co-expression; X-linked disorders; sex differences in disease; meta-analysis

Special Issue Information

Dear Colleagues,

Genes interact either through direct physical interactions, i.e., through their products, such as a protein complex, or indirectly as parts of cohesive networks or pathways. Ultimately, these interactions modulate a phenotype, a combination of all the epispastic, pleiotropic, and system level effects. Functional relationships among genes are studied at varying levels of resolution (molecular to organismal to population), in a variety of model organisms (yeast to mice to humans), and for a wide (and often ill-defined) set of functions.

As we still do not know the number of interactions of many genes, their products, and function(s), how context-dependent their functions are, and to what degree function and interactions are conserved, work to decipher these questions involves a broad set of fields and scientific approaches.

This Special Issue aims to highlight the variety of computational methods and tools that the functional genomics community uses to study gene interactions and predict function.  

Dr. Sara Ballouz
Guest Editor

Manuscript Submission Information

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Keywords

  • Functional genomics
  • Systems biology
  • Gene networks
  • Interactomes
  • Gene function prediction
  • Co-expression

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Published Papers (1 paper)

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Review

18 pages, 903 KiB  
Review
Automatic Gene Function Prediction in the 2020’s
by Stavros Makrodimitris, Roeland C. H. J. van Ham and Marcel J. T. Reinders
Genes 2020, 11(11), 1264; https://doi.org/10.3390/genes11111264 - 27 Oct 2020
Cited by 19 | Viewed by 5356
Abstract
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field [...] Read more.
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology. Full article
(This article belongs to the Special Issue How Genes Interact to Produce Function)
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