Current Bioinformatics - Volume 14, Issue 7, 2019

Background: The advent of the Single-Molecule Real-time (SMRT) Isoform Sequencing (Iso-Seq) has paved the way to obtain longer full-length transcripts. This method was found to be much superior in identifying full-length splice variants and other post-transcriptional events as compared to the Next Generation Sequencing (NGS)-based short read sequencing (RNA-Seq). Several different bioinformatics tools to analyze the Iso-Seq data have been developed and some of them are still being refined to address different aspects of transcriptome complexity. However, a comprehensive summary of the available tools and their utility is still lacking. Objective: Here, we summarized the existing Iso-Seq analysis tools and presented an integrated bioinformatics pipeline for Iso-Seq analysis, which overcomes the limitations of NGS and generates long contiguous Full-Length Non-Chimeric (FLNC) reads for the analysis of posttranscriptional events. Results: In this review, we summarized recent applications of Iso-Seq in plants, which include improved genome annotations, identification of novel genes and lncRNAs, identification of fulllength splice isoforms, detection of novel Alternative Splicing (AS) and Alternative Polyadenylation (APA) events. In addition, we also discussed the bioinformatics pipeline for comprehensive Iso-Seq data analysis, including how to reduce the error rate in the reads and how to identify and quantify post-transcriptional events. Furthermore, the visualization approach of Iso-Seq was discussed as well. Finally, we discussed methods to combine Iso-Seq data with RNA-Seq for transcriptome quantification. Conclusion: Overall, this review demonstrates that the Iso-Seq is pivotal for analyzing transcriptome complexity and this new method offers unprecedented opportunities to comprehensively understand transcripts diversity.

Background: Blattella germanica is a widespread urban invader insect that can spread numerous types of human pathogens, including bacteria, fungi, and protozoa. Despite the medical significance of B. germanica, the genetic diversity of this species has not been investigated across its wide geographical distribution in China. Objective: In this study, the genetic variation of B. germanica was evaluated in central China. Methods: Fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and the 16S rRNA gene were amplified in 36 B. germanica isolates from 7 regions. The sequence data for COI and 16S rRNA genes were analyzed using bioinformatics methods. Results: In total, 13 haplotypes were found among the concatenated sequences. Each sampled population, and the total population, had high haplotype diversity (Hd) that was accompanied by low nucleotide diversity (Pi). Molecular genetic variation analysis indicated that 84.33% of the genetic variation derived from intra-region sequences. Phylogenetic analysis indicated that the B. germanica isolates from central China should be classified as a single population. Demographic analysis rejected the hypothesis of sudden population expansion of the B. germanica population. Conclusion: The 36 isolates of B. germanica sampled in this study had high genetic variation and belonged to the same species. They should be classified as a single population. The mismatch distribution analysis and BSP analysis did not support a demographic population expansion of the B. germanica population, which provided useful knowledge for monitoring changes in parasite populations for future control strategies.

Background: Studies of Positively Selected Genes (PSGs) in microorganisms and mammals have provided insights into the dynamics of genome evolution and the genetic basis of differences between species by using whole genome-wide scans. Systematic investigations and comparisons of PSGs in plants, however, are still limited. Objective: A systematic comparison of PSGs between the genomes of two cotton species, Gossypium arboreum (G. arboreum) and G. raimondii, will give the key answer for revealing molecular evolutionary differences in plants. Methods: Genome sequences of G. arboreum and G. raimondii were compared, including Whole Genome Duplication (WGD) events and genomic features such as gene number, gene length, codon bias index, evolutionary rate, number of expressed genes, and retention of duplicated copies. Results: Unlike the PSGs in G. raimondii, G. arboreum comprised more PSGs, smaller gene size and fewer expressed gene. In addition, the PSGs evolved at a higher rate of synonymous substitutions, but were subjected to lower selection pressure. The PSGs in G. arboreum were also retained with a lower number of duplicate gene copies than G. raimondii after a single WGD event involving Gossypium. Conclusion: These data indicate that PSGs in G. arboreum and G. raimondii differ not only in Ka/Ks, but also in their evolutionary, structural, and expression properties, indicating that divergence of G. arboreum and G. raimondii was associated with differences in PSGs in terms of evolutionary rates, gene length, expression patterns, and WGD retention in Gossypium.

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

Background: Plants respond to various stresses at the same time. Recent studies show that interactions of various phytohormones can play important roles in response to stresses. Objective: Although many studies have been done about the effects of the individual hormones, little information exists about the crosstalk among the hormone signalling pathways in plants. Methods: In this work, the weighted gene co-expression network analysis method was used to define modules containing genes with highly correlated expression patterns in response to abscisic acid, jasmonic acid, and salicylic acid in Arabidopsis. Results: Results indicate that plant hormones cause major changes the expression profile and control diverse cell functions, including response to environmental stresses and external factors, cell cycle, and antioxidant activity. In addition, AtbHLH15 and HY5 transcription factors can participate in phytochrome pathways in response to the phytohormones. It is probable that some Type III WRKY transcription factors control the response to bacterium separately from the other stresses. The E2Fa/DPa transcription factor also regulates the cell cycle. Conclusion: In general, many processes and pathways in plants may be regulated using a combination of abscisic acid, jasmonic acid, and salicylic acid.

Background: As one of the most important reversible protein post-translation modification types, ubiquitination plays a significant role in the regulation of many biological processes, such as cell division, signal transduction, apoptosis and immune response. Protein ubiquitination usually occurs when ubiquitin molecule is attached to a lysine on a target protein, which is also known as “lysine ubiquitination”. Objective: In order to investigate the molecular mechanisms of ubiquitination-related biological processes, the crucial first step is the identification of ubiquitination sites. However, conventional experimental methods in detecting ubiquitination sites are often time-consuming and a large number of ubiquitination sites remain unidentified. In this study, a ubiquitination site prediction method for Arabidopsis thaliana was developed using a Support Vector Machine (SVM). Methods: We collected 3009 experimentally validated ubiquitination sites on 1607 proteins in A. thaliana to construct the training set. Three feature encoding schemes were used to characterize the sequence patterns around ubiquitination sites, including AAC, Binary and CKSAAP. The maximum Relevance and Minimum Redundancy (mRMR) feature selection method was employed to reduce the dimensionality of input features. Five-fold cross-validation and independent tests were used to evaluate the performance of the established models. Results: As a result, the combination of AAC and CKSAAP encoding schemes yielded the best performance with the accuracy and AUC of 81.35% and 0.868 in the independent test. We also generated an online predictor termed as AraUbiSite, which is freely accessible at: http://systbio.cau.edu.cn/araubisite. Conclusion: We developed a well-performed prediction tool for large-scale ubiquitination site identification in A. thaliana. It is hoped that the current work will speed up the process of identification of ubiquitination sites in A. thaliana and help to further elucidate the molecular mechanisms of ubiquitination in plants.

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

Background: Rice straw as a plant photosynthesis product, is a valuable renewable resource and it contains protein, fat, cellulose, hemicellulose, lignin and ash. It has received wide attention for biogas can solve both the energy and environment problems. Objective: To improve the degradation rate of rice straw in aerobic and anaerobic bi-phase fermentation process. Methods: Different aerobic methods were adopted to improve the degradation rate of aerobic acid producing cellulose. Results: The results showed that in different ways of gas supply test experiments the total enzyme activity of aeration mode was higher than that of the stirring air supply mode, which indicated that the aeration mode was more favorable to the growth of mixed strains of Trichoderma and Aspergillus. The gas production of TS was 438.69 mL•g-1, which was higher than both the stirring group and control group. Conclusion: The degradation utilization rate of rice straw solid organic matter can be significantly improved using method of aeration mode, and the conversion of straw biomass into biogas was promoted.

Background: Phospholipase D (PLD)is closely related to browning and senescence of postharvest longan fruit. Objective: This study investigated the effects of 2-butanol (a PLD inhibitor) on the expression and regulation of PLD during storage of longan fruit at a low temperature. Methods: Senescence-related quality indices showed that the 2-butanol-treated fruit presented lower pericarp browning index, pulp breakdown index and total soluble solid value than the untreated fruit. Results: The fruit treated by 60 μL/L 2-butanol exhibited the strongest inhibition on senescence, which significantly delayed changes in weight, titratable acidity content, total soluble solid content and ascorbic acid content. This treatment maintained a high level of total phenolic content and caused significant inhibition on pericarp browning and pulp breakdown. Through ELISA method, 60 μL/L 2-butanol treatment also reduced PLD activity. Real-time RT-PCR (RT-qPCR) results showed that PLD mRNA expression level was inhibited by 60 μL/L 2-butanol within 15 days. Western-blotting results further confirmed the differential expression of PLD during storage, and a relatively higher expression for PLD protein was found in control compared to the 2-butanoltreated fruit during 15-d storage. Conclusion: These results provided a scientific basis and reference to further investigating postharvest longan quality maintenance by regulating the PLD gene expression.

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Objective: Biomedical data can be de-identified via micro-aggregation achieving k - anonymity privacy. However, the existing micro-aggregation algorithms result in low similarity within the equivalence classes, and thus, produce low-utility anonymous data when dealing with a sparse biomedical dataset. To balance data utility and anonymity, we develop a novel microaggregation framework. Methods: Combining a density-based clustering method and classical micro-aggregation algorithm, we propose a density-based second division micro-aggregation framework called DBTP . The framework allows the anonymous sets to achieve the optimal k- partition with an increased homogeneity of the tuples in the equivalence class. Based on the proposed framework, we propose a k − anonymity algorithm DBTP − MDAV and an l − diversity algorithm DBTP − l − MDAV to respond to different attacks. Conclusions: Experiments on real-life biomedical datasets confirm that the anonymous algorithms under the framework developed in this paper are superior to the existing algorithms for achieving high utility.

Background: At present, due to the limitation of hardware, software and network transmission performance, the medical diagnosis of medical CT image equipment is easy to be carried out based on the wrong image. In addition, due to the complex structure of human organs and unpredictable lesion location, it is difficult to judge the reliability of medical CT images, spatial localization of the lesion, two-dimensional slice images and shape based on stereotypes. Therefore, how to improve the efficiency of medical CT terminal and the image quality has become the key technology to improve the satisfaction of medical diagnosis and treatment. Objective: To improve the work efficiency of medical CT terminal and medical image transmission quality, with the medical CT terminal state and service quality. Methods: Firstly, from the view of throughput, packet loss rate, delay and so on, a QoS aware model for medical CT image transmission has been established. Then, with throughput, packet length, path loss, service area size, access point location, and the number of medical CT terminals, the performance change regulation of the medical CT image transmission is completed and the optimal quality of service guarantee parameters sequence is obtained. Next, the medical CT image big data autonomous collision control scheme is proposed. Results: The experimental and mathematical results verify the real-time performance, reliability, effectiveness and feasibility of the proposed medical CT image transmission anti-collision mechanism. Conclusion: The proposed scheme can satisfy the high-quality high demand for data transmission at the same time, according to a variety of user experience demand and real-time adjustment of medical CT terminal working state, which provides effective data quality assurance and optimization of the network source distribution, and also enhances the quality of medical image data transmission service.