Article

# Discriminate the Falsely Predicted Protein-Coding Genes in Aeropyrum Pernix K1 Genome Based on Graphical Representation

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## Abstract

The problem that how many protein-coding genes exist in Aeropyrum pernix K1 genome has confused many scientists since 1999. In this paper, we attempt to re-identify the protein-coding genes in this genome by proposing a modified method based on I-TN curve. Consequently, all of the 727 experimentally validated protein-coding genes and 726 of the corresponding negative samples are correctly predicted respectively, then an accuracy of 99.93% of self-test is obtained. In the Jackknife test, two positive samples and two negative samples are falsely predicted, respectively, and then the accuracy of cross-validation is 99.72%. In the testing set, all of the 132 putative genes are correctly predicted as protein-coding and 14 out of the 841 hypothetical genes are predicted as non-coding, the number of protein-coding genes is reduced to 1686 instead of 1700. Further analysis shows the performance of the reannotating algorithm is comparable to other prevalent programs, and the present method is much simple and efficient. We implement the reannotating algorithm trained by Aeropyrum pernix K1 to Chlorobium tepidum TLS genome, and 217 hypothetical genes are predicted as non-coding. Sufficient sequences analysis indicates most of them are random sequences that are falsely predicted as protein-coding genes. In addition, we also perform some significative analysis aiming to the influence of artificial parameters on the graphical representation approaches, which may provide helpful information for related researches.

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... One of the typical re-annotation cases of archaea was associated with Aeropyrum pernix K1, in which protein-coding genes were over-annotated up to 60% by the original sequencing institute.4–6 It is lucky that this major error has been corrected by using proteome approaches and bioinformatics methods.7–10 Amsacta moorei entomopoxvirus may have the most over-annotated protein-coding genes among sequenced viruses.11 ...
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We evaluate a number of computer programs designed to predict the structure of protein coding genes in genomic DNA sequences. Computational gene identification is set to play an increasingly important role in the development of the genome projects, as emphasis turns from mapping to large-scale sequencing. The evaluation presented here serves both to assess the current status of the problem and to identify the most promising approaches to ensure further progress. The programs analyzed were uniformly tested on a large set of vertebrate sequences with simple gene structure, and several measures of predictive accuracy were computed at the nucleotide, exon, and protein product levels. The results indicated that the predictive accuracy of the programs analyzed was lower than originally found. The accuracy was even lower when considering only those sequences that had recently been entered and that did not show any similarity to previously entered sequences. This indicates that the programs are overly dependent on the particularities of the examples they learn from. For most of the programs, accuracy in this test set ranged from 0.60 to 0.70 as measured by the Correlation Coefficient (where 1.0 corresponds to a perfect prediction and 0.0 is the value expected for a random prediction), and the average percentage of exons exactly identified was less than 50%. Only those programs including protein sequence database searches showed substantially greater accuracy. The accuracy of the programs was severely affected by relatively high rates of sequence errors. Since the set on which the programs were tested included only relatively short sequences with simple gene structure, the accuracy of the programs is likely to be even lower when used for large uncharacterized genomic sequences with complex structure. While in such cases, programs currently available may still be of great use in pinpointing the regions likely to contain exons, they are far from being powerful enough to elucidate its genomic structure completely.
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The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark.hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.
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The relationship between the synonymous codon usage and protein secondary structural elements (alpha helices and beta sheets) were reinvestigated by taking structural information of proteins from Protein Data Bank (PDB) and their corresponding mRNA sequences from GenBank for four different organisms E. coli, B. subtilis, S. cerevisiae, and Homo sapiens. It was observed that synonymous codon families have non-random codon usage, but there does not exist any species invariant universal correlation between the synonymous codon usage and protein secondary structural elements. The secondary structural units of proteins can be distinguished from the occurrences of bases at the second codon position.
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The Z curve is a three-dimensional space curve constituting the unique representation of a given DNA sequence in the sense that each can be uniquely reconstructed from the other. Based on the Z curve, a new protein coding gene-finding algorithm specific for the yeast genome at better than 95% accuracy has been proposed. Six cross-validation tests were performed to confirm the above accuracy. Using the new algorithm, the number of protein coding genes in the yeast genome is re-estimated. The estimate is based on the assumption that the unknown genes have similar statistical properties to the known genes. It is found that the number of protein coding genes in the 16 yeast chromosomes is </=5645, significantly smaller than the 5800-6000 which is widely accepted, and much larger than the 4800 estimated by another group recently. The mitochondrial genes were not included into the above estimate. A codingness index called the YZ score (YZ OE [0,1]) is proposed to recognize protein coding genes in the yeast genome. Among the ORFs annotated in the MIPS (Munich Information Centre for Protein Sequences) database, those recognized as non-coding by the present algorithm are listed in this paper in detail. The criterion for a coding or non-coding ORF is simply decided by YZ > 0.5 or YZ < 0.5, respectively. The YZ scores for all the ORFs annotated in the MIPS database have been calculated and are available on request by sending e-mail to the corresponding author.
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The published sequence of the Vibrio cholerae genome indicates that, in addition to the genes that encode proteins of known and unknown function, there are 1577 ORFs identified as conserved hypothetical or hypothetical gene candidates. Because the annotation is not 100% accurate, it is not known which of the 1577 ORFs are true protein-coding genes. In this paper, an algorithm based on the Z curve method, with sensitivity, specificity and accuracy greater than 98%, is used to solve this problem. Twenty-fold cross-validation tests show that the accuracy of the algorithm is 98.8%. A detailed discussion of the mechanism of the algorithm is also presented. It was found that 172 of the 1577 ORFs are unlikely to be protein-coding genes. The number of protein-coding genes in the V. cholerae genome was re-estimated and found to be approximately 3716. This result should be of use in microarray analysis of gene expression in the genome, because the cost of preparing chips may be somewhat decreased. A computer program was written to calculate a coding score called VCZ for gene identification in the genome. Coding/noncoding is simply determined by VCZ > 0/VCZ < 0. The program is freely available on request for academic use.
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The 2694 ORFs originally annotated as potential genes in the genome of Aeropyrum pernix can be categorized into three clusters (A, B, C), according to their nucleotide composition at three codon positions. Coding potential was found to be responsible for the phenomenon of three clusters in a 9-dimensional space derived from the nucleotide composition of ORFs: ORFs assigned to cluster A are coding ones, while those assigned to clusters B and C are non-coding ORFs. A "codingness" index called the AZ score is defined based on a clustering method used to recognize protein-coding genes in the A. pernix genome. The criterion for a coding or non-coding ORF is based on the AZ score. ORFs with AZ > 0 or AZ < 0 are coding or non-coding, respectively. Consequently, 620 out of 632 ORFs with putative functions based on the original annotation are contained in cluster A, which have positive AZ scores. In addition, all 29 ORFs encoding putative or conserved proteins newly added in RefSeq annotation also have positive AZ scores. Accordingly, the number of re-recognized protein-coding genes in the A. pernix genome is 1610, which is significantly less than 2694 in the original annotation and also much less than 1841 in the RefSeq annotation curated by NCBI staff. Annotation information of re-recognized genes and their AZ scores are available at: http://tubic.tju.edu.cn/Aper/.
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Using the Z curve method, the protein-coding genes in AmEPV genome are re-predicted. On the basis of the parameters trained on the experimentally validated genes, all of the 30 experimentally validated genes and 67 putative genes are predicted correctly as coding genes. The sensitivities of the present method for self-test and cross-validation are all 100% based on these test sets. Thirty-eight annotated conserved and hypothetical genes are predicted as non-coding ORFs. The number of re-predicted protein-coding genes in AmEPV is 256. It is significantly less than the number 294 reported in the original annotation. After extending the present method trained in AeEPV genome to the other entomopoxvirus genome, it is found that 116 of the 123 known and putative genes are predicted correctly as coding. Six of the seven falsely missed genes are less than 300bp. The present method could be extended to other poxvirus genomes with or without adaptation of training sets.
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Over-annotation of hypothetical ORFs is a common phenomenon in bacterial genomes, which necessitates confirming the coding reliability of hypothetical ORFs and then predicting their functions. The important plant pathogen Erwinia carotovora subsp. atroseptica SCRI1043 (Eca1043) is a typical case because more than a quarter of its annotated ORFs are hypothetical. Our analysis focuses on annotation of Eca1043 hypothetical ORFs, and comprises two efforts: (a) based on the Z-curve method, 49 originally annotated hypothetical ORFs are recognized as noncoding, this is further supported by principal components analysis and other evidence; and (b) using sequence-alignment tools and some functional resources, more than a half of the hypothetical genes were assigned functions. The potential functions of 427 hypothetical genes are summarized according to the cluster of orthologous groups functional category. Moreover, 114 and 86 hypothetical genes are recognized as putative 'membrane proteins' and 'exported proteins', respectively. Reannotation of Eca1043 hypothetical ORFs will benefit research into the lifestyle, metabolism and pathogenicity of the important plant pathogen. Also, our study proffers a model for the reannotation of hypothetical ORFs in microbial genomes.