Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants.Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as ＂hypothetical＂ were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58.

Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach

Accurate identification of protein-coding regions （exons） in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identifica- tion methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

Background:Esophageal squamous cell carcinoma(ESCC)is the predominant subtype of esophageal carcinoma in China.This study was to develop a staging model to predict outcomes of patients with ESCC.Methods:Using Cox regression analysis,principal component analysis(PCA),partitioning clustering,Kaplan-Meier analysis,receiver operating characteristic(ROC)curve analysis,and classification and regression tree(CART)analysis,we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset.Results:Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes(PCGs)and 635 long non-coding RNAs(lncRNAs)were associated with the survival of patients with ESCC.PCA categorized these PCGs and lncRNAs into three principal components(PCs),which were used to cluster the patients into three groups.ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging(area under ROC curve[AUC]:0.69 vs.0.65,P<0.05).Accord-ingly,we constructed a molecular disaggregated model comprising one lncRNA and two PCGs,which we desig-nated as the LSB staging model using CART analysis in the GSE63624 dataset.This LSB staging model classified the GSE63622 dataset of patients into three different groups,and its effectiveness was validated by analysis of another cohort of 105 patients.Conclusions:The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

Hide and Seek: Protein-coding Sequences Inside ‘‘Non-coding” RNAs

Calcium homeostasis is crucial for muscle contractilityMuscle cells are critically dependent on calcium homeostasis. Without having the right amount of calcium ions just on the spot and coordinated in between muscle cells, no contraction can take place. Therefore, calcium homeostasis is one of the critical regulatory mechanisms in all muscle cells, including skeletal muscle and heart [1,2]. Ca2＋ adenosine triphosphatase the relaxation of muscle cells Sarco-endoplasmic reticulum （SERCA） is responsible for by pumping Ca2＋ into the sarcoplasmic reticulum （SR） .

A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions

Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of bio- logical signal processing. A modified filter method based on a statistically optimal null filter （SONF） theory is proposed for recognizing protein-coding regions. The square deviation gain （SDG） between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.

Transcriptome analysis in the beet webworm, Spoladea recurvalis （Lepidoptera： Crambidae）

The beet webworm, Spoladea recurvalis Fabricius, is a destructive pest on vegetable crops in tropics and subtropics; its main host plant is amaranth. It has become imperative to develop non-chemical methods to control S. recurvalis on amaranth. However, the lack of molecular information about this species has hindered the development of novel pest management strategies. In this study, high-throughput RNA sequencing covering de novo sequence assemblies, functional annotation of transcripts, gene function classification and enrichment was performed on S. recurvalis. Illumina sequencing generated a total of 120 435 transcript contigs ranging from 201 to 22 729 bases with a mean length of 688 bases. The assembled transcripts were subjected to Basic Local Alignment Search Tool- X （BLASTX） to obtain the annotations against non-redundant, Swiss-Prot, Clusters of Orthologous Groups （COG） and Kyoto Encyclopedia of Genes and Genomes （KEGG） protein databases. A subset of 58 225 transcript sequences returned hits from known proteins in the National Center for Biotechnology Information database, and the majority of the transcript sequences had the highest number of hits for Danausplexippus （50.43%）. A total of 1217 Gene Ontology-level 3 annotations were assigned to 51 805 transcripts, while 39 650 transcripts were predicted as functional protein-coding genes in the COG database and 20 037 transcripts were enriched to KEGG pathways. We identified 40 putative genes related to pheromone production and reception in S. recurvalis, with the expression of one gene between 0.29 and 1141.79 fragments per kilo base per million （FPKM） reads. The transcriptome sequence of S. recurvalis is a first step toward offering a comprehensive genomic resource which would enable better understanding of molecular mechanisms to enable development of effective pest management practices for this species.