title: A novel essential domain perspective for exploring gene essentiality.
abstract: BACKGROUND
  Genes with indispensable functions are identified as essential; however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain.
  
  
  RESULTS
  To identify such essential domains, we have developed an Expectation-Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10-23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred from ribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains.
  
  
  BACKGROUND
  huilu.bioinfo@gmail.com or Long.Lu@cchmc.org
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, People's Republic of China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Lu
authors.affiliation: State Key Laboratory of Genetic Engineering Institute of Biostatistics, School of Life Science, Fudan University, Shanghai 200433, People's Republic of China.
authors.forename: Yulan
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
authors.forename: Jingyuan
authors.initials: J
authors.lastname: Deng
authors.affiliation: Institute for Systems Biology, Jianghan University, Wuhan, Hubei, People's Republic of China.
authors.forename: Hai
authors.initials: H
authors.lastname: Peng
authors.affiliation: Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, People's Republic of China, Department of Bioengineering (MC 063), University of Illinois at Chicago, Chicago, IL 60607-7052, USA and Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Lu
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA, Institute for Systems Biology, Jianghan University, Wuhan, Hubei, People's Republic of China.
authors.forename: Long Jason
authors.initials: LJ
authors.lastname: Lu
mesh_terms: D001419:Bacteria / Q000235:genetics; D003198:Computer Simulation; D005658:Fungi / Q000235:genetics; D005798:Genes, Bacterial / Q000235:genetics*; D020043:Genes, Essential / Q000235:genetics*; D005800:Genes, Fungal / Q000235:genetics*; D008962:Models, Theoretical; D017434:Protein Structure, Tertiary; D012335:RNA, Ribosomal / Q000235:genetics
chemical_list: D012335:RNA, Ribosomal