A Brief Review: The Z-curve Theory and its Application in Genome Analysis

Inference of gene regulatory networks based on directed graph convolutional networks

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Mitochondrial genome plasticity of mammalian species

There is an ongoing process in which mitochondrial sequences are being integrated into the nuclear genome. The importance of these sequences has already been revealed in cancer biology, forensic, phylogenetic studies and in the evolution of the eukaryotic genetic information. Human and numerous model organisms' genomes were described from those sequences point of view. Furthermore, recent studies were published on the patterns of these nuclear localised mitochondrial sequences in different taxa.However, the results of the previously released studies are difficult to compare due to the lack of standardised methods and/or using few numbers of genomes. Therefore, in this paper our primary goal is to establish a uniform mining pipeline to explore these nuclear localised mitochondrial sequences.Our results show that the frequency of several repetitive elements is higher in the flanking regions of these sequences than expected. A machine learning model reveals that the flanking regions' repetitive elements and different structural characteristics are highly influential during the integration process.In this paper, we introduce a general mining pipeline for all mammalian genomes. The workflow is publicly available and is believed to serve as a validated baseline for future research in this field. We confirm the widespread opinion, on - as to our current knowledge - the largest dataset, that structural circumstances and events corresponding to repetitive elements are highly significant. An accurate model has also been trained to predict these sequences and their corresponding flanking regions.

Unified mRNA Subcellular Localization Predictor based on machine learning techniques

BACKGROUND

The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.

METHODS

In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).

RESULTS

The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.

AVAILABILITY

We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis

Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precisely predict the human ORIs of various lengths by combining the Z-curve method with deep learning approach. Compared with existing methods, Ori-FinderH exhibits superior performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.9616 for K562 cell line in 10-fold cross-validation. In addition, we also established a cross-cell-line predictive model, which yielded a further improved AUC of 0.9706. The model was subsequently employed as a fitness function to support genetic algorithm for generating artificial ORIs. Sequence analysis through iORI-Euk revealed that a vast majority of the created sequences, specifically 98% or more, incorporate at least one ORI for three cell lines (Hela, MCF7 and K562). This innovative approach could provide more efficient, accurate and comprehensive information for experimental investigation, thereby further advancing the development of this field.

MSLP: mRNA subcellular localization predictor based on machine learning techniques

BACKGROUND

Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.

METHODS

In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.

RESULTS

Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method  in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.

AVAILABILITY

We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP .

Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework

Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene's transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning-based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework.

Multifarious aspects of the chaos game representation and its applications in biological sequence analysis

Chaos game representation (CGR) has been successfully applied in bioinformatics for over 30 years. Since then, many further extensions were announced. Numerical encoding of biological sequences is especially convenient in the visualisation process, free-alignment methods and input preparation for machine learning techniques. The development and applications of CGR have embraced mainly linear nucleotide sequences. However, there were also some attempts to create a representation of proteins. The latter need to be more sophisticated, as arbitrary coordinates for amino acids do not reflect their properties which is crucial during the encoding process. In this paper, the authors summarised various variations of CGRs and their limitations. We began by studying the PROSITE motifs and showed the immense number of amino acid properties employed by different proteins. To this aim, we harnessed the Principal Component Analysis (PCA) and studied the relation between explained variance and the number of features that describe them. It appeared that even after many reductions, about 50 features are non-redundant. This was the reason we introduced an embedding concept from natural language processing which enables adjusting features for a given list of sequences. We presented a simple neural network architecture with one hidden layer and one neuron within it and showed it provides satisfactory results in phylogenetic tree construction in ND5 and SPARC protein cases. To this aim, we transformed CGR representations for all considered sequences using Discrete Fourier Transform (DFT) and applied Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm. Moreover, we indicated some similarities between CGR and Recurrent Neural Networks (RNN). In the end, we attempted to include information about the RNA secondary structure and defined some measures to validate biological significance. We studied their properties and showed on ALMV-3 example its usefulness.

SkewDB, a comprehensive database of GC and 10 other skews for over 30,000 chromosomes and plasmids

GC skew denotes the relative excess of G nucleotides over C nucleotides on the leading versus the lagging replication strand of eubacteria. While the effect is small, typically around 2.5%, it is robust and pervasive. GC skew and the analogous TA skew are a localized deviation from Chargaff’s second parity rule, which states that G and C, and T and A occur with (mostly) equal frequency even within a strand. Different bacterial phyla show different kinds of skew, and differing relations between TA and GC skew. This article introduces an open access database (https://skewdb.org) of GC and 10 other skews for over 30,000 chromosomes and plasmids. Further details like codon bias, strand bias, strand lengths and taxonomic data are also included. The SkewDB can be used to generate or verify hypotheses. Since the origins of both the second parity rule and GC skew itself are not yet satisfactorily explained, such a database may enhance our understanding of prokaryotic DNA.

Measurement(s)	Imbalances in the use of DNA nucleotides
Technology Type(s)	Next Generation Sequencing
Factor Type(s)	Position within DNA sequence • Organism type
Sample Characteristic - Organism	bacterium • archaea
Sample Characteristic - Environment	Varying
Sample Characteristic - Location	World

OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae

DNA Replication plays the most crucial part in biological inheritance, ensuring an even flow of genetic information from parent to offspring. The beginning site of DNA Replication which is called the Origin of Replication (ORI), plays a significant role in understanding the molecular mechanisms and genomic analysis of DNA. Hence, it is paramount to accurately identify the origin of replication to gain a more accurate understanding of the biochemical and genomic properties of DNA. In this paper, We have proposed a new approach named OriC-ENS that uses sequence-based feature extraction techniques, K-mer, K-gapped Mono-Di, and Di Mono, and an ensemble classification technique that uses majority voting for the identification of Origin of Replication. We have used three SVM classifiers, one for the K-mer features and two more for K-Gapped Mono-Di and K-Gapped Di-mono features. Finally, we used majority voting to combine the prediction by each predictor. Experimental results on the S. Cerevisiae dataset have shown that our method achieves an accuracy of 91.62 % which outperforms other state-of-the-art methods by a significant margin. We have also tested our method using other evaluation metrics such as Matthews Correlation Coefficient (MCC), Area Under Curve(AUC), Sensitivity, and Specificity, where it has achieved a score of 0.83, 0.98, 0.90, and 0.92 respectively. We have further evaluated our model on an independent test set collected from OriDB, consisting of the sequences of Schizosaccharomyces pombe where we have seen that our model can predict the origin of replication efficiently and with great precision. We have made our python-based source code available at https://github.com/MehediAzim/OriC-ENS.

The robustness of the chaos game representation to mutations and its application in free-alignment methods

Numerical representation of biological sequences plays an important role in bioinformatics and has many practical applications. One of the most popular approaches is the chaos game representation. In this paper, the authors propose a novel look into chaos game construction - an analytical description of this procedure. This type enables to build more general number sequences using different weight functions. The authors suggest three conditions that these functions should hold. Additionally, they present some criteria to compare them and check whether they provide a unique representation. One of the most important advantages of our approach is the possibility to construct such a description that is less sensitive to mutations and as a result, give more reliable values for free-alignment phylogenetic trees constructions. Finally, the authors applied the DFT method using four types of functions and compared the obtained results using the BLAST tool.

Ori-Finder 3: a web server for genome-wide prediction of replication origins in Saccharomyces cerevisiae

DNA replication is a fundamental process in all organisms; this event initiates at sites termed origins of replication. The characteristics of eukaryotic replication origins are best understood in Saccharomyces cerevisiae. For this species, origin prediction algorithms or web servers have been developed based on the sequence features of autonomously replicating sequences (ARSs). However, their performances are far from satisfactory. By utilizing the Z-curve methodology, we present a novel pipeline, Ori-Finder 3, for the computational prediction of replication origins in S. cerevisiae at the genome-wide level based solely on DNA sequences. The ARS exhibiting both an AT-rich stretch and ARS consensus sequence element can be predicted at the single-nucleotide level. For the identified ARSs in the S. cerevisiae reference genome, 83 and 60% of the top 100 and top 300 predictions matched the known ARS records, respectively. Based on Ori-Finder 3, we subsequently built a database of the predicted ARSs identified in more than a hundred S. cerevisiae genomes. Consequently, we developed a user-friendly web server including the ARS prediction pipeline and the predicted ARSs database, which can be freely accessed at http://tubic.tju.edu.cn/Ori-Finder3.

The morbid cutaneous anatomy of the human genome revealed by a bioinformatic approach

Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.

[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature]

In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence dataset was transformed into three-dimensional coordinates, PWM of the nucleosome sequences was calculated and the similarity score was obtained. After integrating them, a nucleosome feature model based on the comprehensive DNA sequences was obtained and named CSeqFM. We calculated the Euclidean distance between nucleosome sequence candidates or linker sequences and CSeqFM model as the feature dataset, and put the feature datasets into the support vector machine (SVM) for training and testing by ten-fold cross-validation. The results showed that the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of identifying nucleosome positioning for S. cerevisiae were 97.1%, 96.9%, 94.2% and 0.89, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.980 1. Compared with another z-curve method, it was found that our method had better identifying effect and each evaluation performance showed better superiority. CSeqFM method was applied to identify nucleosome positioning for other three species, including C. elegans, H. sapiens and D. melanogaster. The results showed that AUCs of the three species were all higher than 0.90, and CSeqFM method also showed better stability and effectiveness compared with iNuc-STNC and iNuc-PseKNC methods, which is further demonstrated that CSeqFM method has strong reliability and good identification performance.

ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning

Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu's method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo's method 'iNuc-PseKNC', the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning.

Comprehensive Analysis of Replication Origins in Saccharomyces cerevisiae Genomes

DNA replication initiates from multiple replication origins (ORIs) in eukaryotes. Discovery and characterization of replication origins are essential for a better understanding of the molecular mechanism of DNA replication. In this study, the features of autonomously replicating sequences (ARSs) in Saccharomyces cerevisiae have been comprehensively analyzed as follows. Firstly, we carried out the analysis of the ARSs available in S. cerevisiae S288C. By evaluating the sequence similarity of experimentally established ARSs, we found that 94.32% of ARSs are unique across the whole genome of S. cerevisiae S288C and those with high sequence similarity are prone to locate in subtelomeres. Subsequently, we built a non-redundant dataset with a total of 520 ARSs, which are based on ARSs annotation of S. cerevisiae S288C from SGD and then supplemented with those from OriDB and DeOri databases. We conducted a large-scale comparison of ORIs among the diverse budding yeast strains from a population genomics perspective. We found that 82.7% of ARSs are not only conserved in genomic sequence but also relatively conserved in chromosomal position. The non-conserved ARSs tend to distribute in the subtelomeric regions. We also conducted a pan-genome analysis of ARSs among the S. cerevisiae strains, and a total of 183 core ARSs existing in all yeast strains were determined. We extracted the genes adjacent to replication origins among the 104 yeast strains to examine whether there are differences in their gene functions. The result showed that the genes involved in the initiation of DNA replication, such as orc3, mcm2, mcm4, mcm6, and cdc45, are conservatively located adjacent to the replication origins. Furthermore, we found the genes adjacent to conserved ARSs are significantly enriched in DNA binding, enzyme activity, transportation, and energy, whereas for the genes adjacent to non-conserved ARSs are significantly enriched in response to environmental stress, metabolites biosynthetic process and biosynthesis of antibiotics. In general, we characterized the replication origins from the genome-wide and population genomics perspectives, which would provide new insights into the replication mechanism of S. cerevisiae and facilitate the design of algorithms to identify genome-wide replication origins in yeast.

Recent development of Ori-Finder system and DoriC database for microbial replication origins

DNA replication begins at replication origins in all three domains of life. Identification and characterization of replication origins are important not only in providing insights into the structure and function of the replication origins but also in understanding the regulatory mechanisms of the initiation step in DNA replication. The Z-curve method has been used in the identification of replication origins in archaeal genomes successfully since 2002. Furthermore, the Web servers of Ori-Finder and Ori-Finder 2 have been developed to predict replication origins in both bacterial and archaeal genomes based on the Z-curve method, and the replication origins with manual curation have been collected into an online database, DoriC. Ori-Finder system and DoriC database are currently used in the research field of DNA replication origins in prokaryotes, including: (i) identification of oriC regions in bacterial and archaeal genomes; (ii) discovery and analysis of the conserved sequences within oriC regions; and (iii) strand-biased analysis of bacterial genomes.

Up to now, more and more predicted results by Ori-Finder system were supported by subsequent experiments, and Ori-Finder system has been used to identify the replication origins in > 100 newly sequenced prokaryotes in their genome reports. In addition, the data in DoriC database have been widely used in the large-scale analyses of replication origins and strand bias in prokaryotic genomes. Here, we review the development of Ori-Finder system and DoriC database as well as their applications. Some future directions and aspects for extending the application of Ori-Finder and DoriC are also presented.

Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses

Accurate detection of pathogenic viruses has become highly imperative. This is because viral diseases constitute a huge threat to human health and wellbeing on a global scale. However, both traditional and recent techniques for viral detection suffer from various setbacks. In codicil, some of the existing alignment-free methods are also limited with respect to viral detection accuracy. In this paper, we present the development of an alignment-free, digital signal processing based method for pathogenic viral detection named Z-Curve Genomic Cesptral Coefficients (ZCGCC). To evaluate the method, ZCGCC were computed from twenty six pathogenic viral strains extracted from the ViPR corpus. Naïve Bayesian classifier, which is a popular machine learning method was experimentally trained and validated using the extracted ZCGCC and other alignment-free methods in the literature. Comparative results show that the proposed ZCGCC gives good accuracy (93.0385%) and improved performance to existing alignment-free methods.

Quantitative analysis of correlation between AT and GC biases among bacterial genomes

Due to different replication mechanisms between the leading and lagging strands, nucleotide composition asymmetries widely exist in bacterial genomes. A general consideration reveals that the leading strand is enriched in Guanine (G) and Thymine (T), and the lagging strand shows richness in Adenine (A) and Cytosine (C). However, some bacteria like Bacillus subtilis have been discovered composing more A than T in the leading strand. To investigate the difference, we analyze the nucleotide asymmetry from the aspect of AT and GC bias correlations. In this study, we propose a windowless method, the Z-curve Correlation Coefficient (ZCC) index, based on the Z-curve method, and analyzed more than 2000 bacterial genomes. We find that the majority of bacteria reveal negative correlations between AT and GC biases, while most genomes in Firmicutes and Tenericutes have positive ZCC indexes. The presence of PolC, purine asymmetry and stronger genes preference in the leading strand are not confined to Firmicutes, but also likely to happen in other phyla dominated by positive ZCC indexes. This method also provides a new insight into other relevant features like aerobism, and can be applied to analyze the correlation between RY (Purine and Pyrimidine) and MK (Amino and Keto) bias and so on.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

Background

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.

Results

An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.

Conclusion

RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users.

A complete annotation of the chromosomes of the cellulase producer Trichoderma reesei provides insights in gene clusters, their expression and reveals genes required for fitness

BACKGROUND

Investigations on a few eukaryotic model organisms showed that many genes are non-randomly distributed on chromosomes. In addition, chromosome ends frequently possess genes that are important for the fitness of the organisms. Trichoderma reesei is an industrial producer of enzymes for food, feed and biorefinery production. Its seven chromosomes have recently been assembled, thus making an investigation of its chromosome architecture possible.

RESULTS

We manually annotated and mapped 9194 ORFs on their respective chromosomes and investigated the clustering of the major gene categories and of genes encoding carbohydrate-active enzymes (CAZymes), and the relationship between clustering and expression. Genes responsible for RNA processing and modification, amino acid metabolism, transcription, translation and ribosomal structure and biogenesis indeed showed loose clustering, but this had no impact on their expression. A third of the genes encoding CAZymes also occurred in loose clusters that also contained a high number of genes encoding small secreted cysteine-rich proteins. Five CAZyme clusters were located less than 50 kb apart from the chromosome ends. These genes exhibited the lowest basal (but not induced) expression level, which correlated with an enrichment of H3K9 methylation in the terminal 50 kb areas indicating gene silencing. No differences were found in the expression of CAZyme genes present in other parts of the chromosomes. The putative subtelomeric areas were also enriched in genes encoding secreted proteases, amino acid permeases, enzyme clusters for polyketide synthases (PKS)-non-ribosomal peptide synthase (NRPS) fusion proteins (PKS-NRPS) and proteins involved in iron scavenging. They were strongly upregulated during conidiation and interaction with other fungi.

CONCLUSIONS

Our findings suggest that gene clustering on the T. reesei chromosomes occurs but generally has no impact on their expression. CAZyme genes, located in subtelomers, however, exhibited a much lower basal expression level. The gene inventory of the subtelomers suggests a major role of competition for nitrogen and iron supported by antibiosis for the fitness of T. reesei. The availability of fully annotated chromosomes will facilitate the use of genetic crossings in identifying still unknown genes responsible for specific traits of T. reesei.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors

As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, which recruit the transcriptional machinery through binding to a variety of regulatory proteins (known as sigma factors). The identification of the promoter regions is very challenging because these regions do not adhere to specific sequence patterns or motifs and are difficult to determine experimentally. Machine learning represents a promising and cost-effective approach for computational identification of prokaryotic promoter regions. However, the quality of the predictors depends on several factors including: i) training data; ii) data representation; iii) classification algorithms; iv) evaluation procedures. In this work, we create several variants of E. coli promoter data sets and utilize them to experimentally examine the effect of these factors on the predictive performance of E. coli σ⁷⁰ promoter models. Our results suggest that under some combinations of the first three criteria, a prediction model might perform very well on cross-validation experiments while its performance on independent test data is drastically very poor. This emphasizes the importance of evaluating promoter region predictors using independent test data, which corrects for the over-optimistic performance that might be estimated using the cross-validation procedure. Our analysis of the tested models shows that good prediction models often perform well despite how the non-promoter data was obtained. On the other hand, poor prediction models seems to be more sensitive to the choice of non-promoter sequences. Interestingly, the best performing sequence-based classifiers outperform the best performing structure-based classifiers on both cross-validation and independent test performance evaluation experiments. Finally, we propose a meta-predictor method combining two top performing sequence-based and structure-based classifiers and compare its performance with some of the state-of-the-art E. coli σ⁷⁰ promoter prediction methods.