title: Analysis of nucleosome positioning landscapes enables gene discovery in the human malaria parasite Plasmodium falciparum.
abstract: BACKGROUND
  Plasmodium falciparum, the deadliest malaria-causing parasite, has an extremely AT-rich (80.7 %) genome. Because of high AT-content, sequence-based annotation of genes and functional elements remains challenging. In order to better understand the regulatory network controlling gene expression in the parasite, a more complete genome annotation as well as analysis tools adapted for AT-rich genomes are needed. Recent studies on genome-wide nucleosome positioning in eukaryotes have shown that nucleosome landscapes exhibit regular characteristic patterns at the 5'- and 3'-end of protein and non-protein coding genes. In addition, nucleosome depleted regions can be found near transcription start sites. These unique nucleosome landscape patterns may be exploited for the identification of novel genes. In this paper, we propose a computational approach to discover novel putative genes based exclusively on nucleosome positioning data in the AT-rich genome of P. falciparum.
  
  
  RESULTS
  Using binary classifiers trained on nucleosome landscapes at the gene boundaries from two independent nucleosome positioning data sets, we were able to detect a total of 231 regions containing putative genes in the genome of Plasmodium falciparum, of which 67 highly confident genes were found in both data sets. Eighty-eight of these 231 newly predicted genes exhibited transcription signal in RNA-Seq data, indicative of active transcription. In addition, 20 out of 21 selected gene candidates were further validated by RT-PCR, and 28 out of the 231 genes showed significant matches using BLASTN against an expressed sequence tag (EST) database. Furthermore, 108 (47%) out of the 231 putative novel genes overlapped with previously identified but unannotated long non-coding RNAs. Collectively, these results provide experimental validation for 163 predicted genes (70.6%). Finally, 73 out of 231 genes were found to be potentially translated based on their signal in polysome-associated RNA-Seq representing transcripts that are actively being translated.
  
  
  CONCLUSIONS
  Our results clearly indicate that nucleosome positioning data contains sufficient information for novel gene discovery. As distinct nucleosome landscapes around genes are found in many other eukaryotic organisms, this methodology could be used to characterize the transcriptome of any organism, especially when coupled with other DNA-based gene finding and experimental methods (e.g., RNA-Seq).
authors.affiliation: Department of Cell Biology and Neuroscience, Institute for Integrative Genome Biology, Center for Disease Vector Research, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. xlu006@ucr.edu.
authors.forename: Xueqing Maggie
authors.initials: XM
authors.lastname: Lu
authors.affiliation: Department of Cell Biology and Neuroscience, Institute for Integrative Genome Biology, Center for Disease Vector Research, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. evelien.bunnik@ucr.edu.
authors.forename: Evelien M
authors.initials: EM
authors.lastname: Bunnik
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. neetipok@buffalo.edu.
authors.forename: Neeti
authors.initials: N
authors.lastname: Pokhriyal
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. sara.nasseri@email.ucr.edu.
authors.forename: Sara
authors.initials: S
authors.lastname: Nasseri
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. stelo@cs.ucr.edu.
authors.forename: Stefano
authors.initials: S
authors.lastname: Lonardi
authors.affiliation: Department of Cell Biology and Neuroscience, Institute for Integrative Genome Biology, Center for Disease Vector Research, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA. karine.leroch@ucr.edu.
authors.forename: Karine G
authors.initials: KG
authors.lastname: Le Roch
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D017125:Genes, Protozoan; D056726:Genetic Association Studies; D018503:Genome, Protozoan; D023281:Genomics / Q000379:methods; D006801:Humans; D016778:Malaria, Falciparum / Q000469:parasitology; D058977:Molecular Sequence Annotation; D009707:Nucleosomes / Q000378:metabolism*; D010963:Plasmodium falciparum / Q000235:genetics* / Q000378:metabolism*; D015203:Reproducibility of Results; D014158:Transcription, Genetic; D064878:Web Browser
chemical_list: D009707:Nucleosomes