Computational Systems Biology Lab

by Aditi Jain

“A classic challenge in biology is to study the function of proteins. Of various functions, essential functions are very interesting, as they map to important indispensable genes in an organism. Experimentally identifying these genes is rather expensive and challenging. Computational predictions can help point in the right direction, to prioritise experiments. To date, experimental data are available for <100 organisms! On the other hand, sequencing data are available for 1000s of organisms, as also interactome (networks of interactions) data. In the paper, we posit that network information is crucial to predict essentiality and achieve excellent performance in prediction, by simply using network data,” explains Dr. Raman.

Read the whole blog at
https://rbcdsai.iitm.ac.in/blogs/predicting-genes-through-network-approach/

Access the NetGenes database at https://rbc-dsai-iitm.github.io/NetGenes/

Original Paper: 

• V. Senthamizhan, B. Ravindran, and K. Raman, “NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks,” Frontiers in Genetics, vol. 12, p. 1666, 2021.
  [bibtex]

  @article{Senthamizhan2021NetGenes,
    title = {{{NetGenes}}: {{A Database}} of {{Essential Genes Predicted Using Features From Interaction Networks}}},
    shorttitle = {{{NetGenes}}},
    author = {Senthamizhan, Vimaladhasan and Ravindran, Balaraman and Raman, Karthik},
    year = {2021},
    journal = {Frontiers in Genetics},
    volume = {12},
    pages = {1666},
    issn = {1664-8021},
    doi = {10.3389/fgene.2021.722198},
    pmid = {34630517},
    abstract = {Essential gene prediction models built so far are heavily reliant on sequence-based features, and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we apply our approach for the prediction of essential genes to organisms from the STRING database and host the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2,700+ bacteria predicted using features derived from STRING protein\textendash protein functional association networks. Housing a total of over 2.1 million genes, NetGenes offers various features like essentiality scores, annotations, and feature vectors for each gene. NetGenes database is available from https://rbc-dsai-iitm.github.io/NetGenes/.},
  }