Selected article for: "ab initio gene prediction and gene prediction"
Author: Wang, Shiliang; Sundaram, Jaideep P; Spiro, David
Title: VIGOR, an annotation program for small viral genomes
  • Document date: 2010_9_7
    • ID: 0lbxvudt_33
    Snippet: The same set of influenza genomic sequences was also run using GeneMarkS and ZCURVE_V, two ab initio approach gene prediction tools for viral genomes ( Table 1 ). The specificity and sensitivity for GeneMarkS was 40.63% and 35.22% respectively; while the specificity and sensitivity for ZCURVE_V was 81.74% and 72.27%. Similar numbers of GenBank annotated genes were missed by both GeneMarkS (770 genes) and ZCUR-VE_V (841 genes). Manual inspection .....
    Document: The same set of influenza genomic sequences was also run using GeneMarkS and ZCURVE_V, two ab initio approach gene prediction tools for viral genomes ( Table 1 ). The specificity and sensitivity for GeneMarkS was 40.63% and 35.22% respectively; while the specificity and sensitivity for ZCURVE_V was 81.74% and 72.27%. Similar numbers of GenBank annotated genes were missed by both GeneMarkS (770 genes) and ZCUR-VE_V (841 genes). Manual inspection showed that the majority of the overlooked genes were PB1-F2, NS2 and M2 genes. Several studies have shown that embedded genes and splicing often pose problems for viral gene prediction algorithms [2, 14] . For example, ZCURVE_V could not identify the Tat gene correctly and missed the Rev completely when it was used to predict genes for the HIV-I virus [14] . Additionally, almost half of the GeneMarkS predictions for influenza genomes picked start codons upstream of the correct start codons.

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