BioGPS has become the valuable resource that it is because of the contributions from our wonderful user community. Thank you for contributing plugins, suggestions, and ideas–all of which have improved BioGPS for everyone. In order to celebrate the contributions of BioGPS users to the scientific research community, this series will feature publications and articles generated by BioGPS users. We sincerely hope you will join us in celebrating the fascinating work that YOU do.

BioGPS users study so many different subjects, it’s impossible not to be intrigued by their work. This week, we will feature an article from a evolutionary functional genomics team that studies how genes and genomes evolve through time and how these changes an help us better understand the function and interaction between genes during normal development/adult life and disease states: Correcting for Differential Transcript Coverage Reveals a Strong Relationship between Alternative Splicing and Organism Complexity by Lu Chen, Stephen J. Bush, Jaime M. Tovar-Corona, Atahualpa Castillo-Morales, and Araxi O. Urrutia. (DOI 10.1093/molbev/msu083)

Dr. Araxi O. Urrutia kindly answered our inquiries for this series.

  1. Who is the team behind the work that was published in Correcting for Differential Transcript Coverage Reveals a Strong Relationship between Alternative Splicing and Organism Complexity?
    This study was conducted by PhD students of my research team in the functional and evolutionary genomics lab at the University of Bath in the UK. All authors worked extremely hard on this project Three of the four students involved have now finished their studies and are now working as postdocs at Cambridge University, The Roslin Institute and the University of Bath.

  3. Please provide a brief summary of the findings reported in your article, Correcting for Differential Transcript Coverage Reveals a Strong Relationship between Alternative Splicing and Organism Complexity.

    In this manuscript we focus on the role of alternative splicing which has long been a top candidate for explaining variation in complexity among species. Previous attempts to establish the association of splicing and complexity were, in the authors’ own words, inconclusive due to the lack of comparable alternative splicing estimates. Using comparable indexes, obtained by analyzing over 39 million ESTs sequences available for 47 eukaryotic species through a random transcript method, we find that alternative splicing has steadily increased over the last 1400 million years of evolution in the lineage leading to humans is strongly associated with organism complexity (assayed as the number of cell types per species).

    Furthermore, when compared to other genomic functional parameters, alternative splicing is the dominant predictor, fully explaining the variance in complexity explained by gene number, protein length, proteome disorder and protein interactivity all previously linked to organism complexity. Importantly, we also show that the association between alternative splicing and organism complexity is not explained as a result of increased genetic drift associated with the reduction in effective population size in more complex species.

    Taken together our results support a strong association between alternative splicing and eukaryotic cell type diversification explaining other parameters’ link to complexity previously linked to complexity transforming our understanding into the importance of transcript diversification through alternative splicing in determining a genome’s functional information capacity.

    To the best of our knowledge, our study is the first to examine the role of alternative splicing as a predictor of organism complexity using a comparable index of alternative splicing. It also offers the first direct comparison of various functional genomic parameters previously linked with complexity. Our results firmly establish alternative splicing as a major parameter explaining variation in organism complexity and raise the possibility of a causative role of transcript diversification in organism complexity.


  5. How did the team learn about BioGPS?
    I read the paper presenting the data when it was first published and have used it in several1 additional2 publications34. It is a great resource for gene expression data.

  7. How did your team utilize BioGPS in this research?
    In this particular project we used BioGPS to estimate levels of gene expression in the mouse genome in order to correct our estimates of alternative splicing levels in mouse genes by levels of expression.

  9. What are some future directions for the team behind this research?
    We are planning to continue this research by testing the functionality of the expansion of alternative splicing levels in metazoan genomes.

  11. Please list any twitter accounts to follow in order to receive updates on this exciting research:
    We don’t have twitter at the moment… but we’ll send you accounts when we have them.

Thanks again to Dr. Araxi O. Urrutia for taking the time to answer our questions. Click here to read their fascinating article. Not only did these awesome researchers publish this article with Open Access so you can read it for free, but their other articles citing BioGPS are FREE as well!

Used BioGPS and cited it in your publication? Let us know! We would love to feature YOUR work, no matter how long ago it was published. BioGPS Featured Article Series only started recently, but we know your contributions to science is ongoing.

2016.06.16 – The links in this post were updated to lead to the original articles.

More awesome open access papers from the Urrutia lab

  1. Urrutia AO, Hurst LD. The signature of selection mediated by expression on human genes. Genome Res. 2003 Oct;13(10):2260-4. Epub 2003 Sep 15. []
  2. Zhang Y, Castillo-Morales A, Jiang M, Zhu Y, Hu L, Urrutia AO, Kong X, Hurst LD. Genes that escape X-inactivation in humans have high intraspecific variability in expression, are associated with mental impairment but are not slow evolving. Mol Biol Evol. 2013 Dec;30(12):2588-601. doi: 10.1093/molbev/mst148. Epub 2013 Sep 10 []
  3. Urrutia AO, Ocaña LB, Hurst LD. >Do Alu repeats drive the evolution of the primate transcriptome? Genome Biol. 2008;9(2):R25. doi: 10.1186/gb-2008-9-2-r25. Epub 2008 Feb 1. []
  4. Parmley JL, Urrutia AO, Potrzebowski L, Kaessmann H, Hurst LD. Splicing and the evolution of proteins in mammals. PLoS Biol. 2007 Feb;5(2):e14. []