Identification of microRNA-Like RNAs in the Filamentous Fungus Trichoderma reesei by Solexa Sequencing

microRNAs (miRNAs) are non-coding small RNAs (sRNAs) capable of negatively regulating gene expression. Recently, microRNA-like small RNAs (milRNAs) were discovered in several filamentous fungi but not yet in Trichoderma reesei, an industrial filamentous fungus that can secrete abundant hydrolases. To explore the presence of milRNA in T. reesei and evaluate their expression under induction of cellulose, two T. reesei sRNA libraries of cellulose induction (IN) and non-induction (CON) were generated and sequenced using Solexa sequencing technology. A total of 726 and 631 sRNAs were obtained from the IN and CON samples, respectively. Global expression analysis showed an extensively differential expression of sRNAs in T. reesei under the two conditions. Thirteen predicted milRNAs were identified in T. reesei based on the short hairpin structure analysis. The milRNA profiles obtained in deep sequencing were further validated by RT-qPCR assay. Computational analysis predicted a number of potential targets relating to many processes including regulation of enzyme expression. The presence and differential expression of T. reesei milRNAs imply that milRNA might play a role in T. reesei growth and cellulase induction. This work lays foundation for further functional study of fungal milRNAs and their industrial application.     

Phylogenetic analysis of protein sequences based on a novel k-mer natural vector method

Based on the k-mer model for protein sequence, a novel k-mer natural vector method is proposed to characterize the features of k-mers in a protein sequence, in which the numbers and distributions of k-mers are considered. It is proved that the relationship between a protein sequence and its k-mer natural vector is one-to-one. Phylogenetic analysis of protein sequences therefore can be easily performed without requiring evolutionary models or human intervention. In addition, there exists no a criterion to choose a suitable k, and k has a great influence on obtaining results as well as computational complexity. In this paper, a compound k-mer natural vector is utilized to quantify each protein sequence. The results gotten from phylogenetic analysis on three protein datasets demonstrate that our new method can precisely describe the evolutionary relationships of proteins, and greatly heighten the computing efficiency.     

Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.     

FoQDE2-dependent milRNA promotes Fusarium oxysporum f. sp. cubense virulence by silencing a glycosyl hydrolase coding gene expression

MicroRNAs (miRNAs) are small non-coding RNAs that regulate protein-coding gene expression primarily found in plants and animals. Fungi produce microRNA-like RNAs (milRNAs) that are structurally similar to miRNAs and functionally important in various biological processes. The fungus Fusarium oxysporum f. sp. cubense (Foc) is the causal agent of Banana Fusarium vascular wilt that threatens global banana production. It remains uncharacterized about the biosynthesis and functions of milRNAs in Foc. In this study, we investigated the biological function of milRNAs contributing to Foc pathogenesis. Within 24 hours post infecting the host, the Argonaute coding gene FoQDE2, and two Dicer coding genes FoDCL1 and FoDCL2, all of which are involved in milRNA biosynthesis, were significantly induced. FoQDE2 deletion mutant exhibited decreased virulence, suggesting the involvement of milRNA biosynthesis in the Foc pathogenesis. By small RNA sequencing, we identified 364 small RNA-producing loci in the Foc genome, 25 of which were significantly down-regulated in the FoQDE2 deletion mutant, from which milR-87 was verified as a FoQDE2-depedent milRNA based on qRT-PCR and Northern blot analysis. Compared to the wild-type, the deletion mutant of milR-87 was significantly reduced in virulence, while overexpression of milR-87 enhanced disease severity, confirming that milR-87 is crucial for Foc virulence in the infection process. We furthermore identified FOIG_15013 (a glycosyl hydrolase-coding gene) as the direct target of milR-87 based on the expression of FOIG_15013-GFP fusion protein. The FOIG_15013 deletion mutant displayed similar phenotypes as the overexpression of milR-87, with a dramatic increase in the growth, conidiation and virulence. Transient expression of FOIG_15013 in Nicotiana benthamiana leaves activates the host defense responses. Collectively, this study documents the involvement of milRNAs in the manifestation of the devastating fungal disease in banana, and demonstrates the importance of milRNAs in the pathogenesis and other biological processes. Further analyses of the biosynthesis and expression regulation of fungal milRNAs may offer a novel strategy to combat devastating fungal diseases.     

Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions

Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.     

Mining statistically-solid <Emphasis Type="Italic">k</Emphasis>-mers for accurate NGS error correction

Background

NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f₀ to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.

Results

We propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f₀. To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer’s frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets.

Conclusion

The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.

     

K-mer natural vector and its application to the phylogenetic analysis of genetic sequences

Based on the well-known k-mer model, we propose a k-mer natural vector model for representing a genetic sequence based on the numbers and distributions of k-mers in the sequence. We show that there exists a one-to-one correspondence between a genetic sequence and its associated k-mer natural vector. The k-mer natural vector method can be easily and quickly used to perform phylogenetic analysis of genetic sequences without requiring evolutionary models or human intervention. Whole or partial genomes can be handled more effective with our proposed method. It is applied to the phylogenetic analysis of genetic sequences, and the obtaining results fully demonstrate that the k-mer natural vector method is a very powerful tool for analysing and annotating genetic sequences and determining evolutionary relationships both in terms of accuracy and efficiency.     

Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction

Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate prediction of plant lncRNA–protein interaction is imperative for subsequent functional studies. We present an integrative model, namely DRPLPI. Its uniqueness is that it predicts by multi-feature fusion. Structural and four groups of sequence features are used, including tri-nucleotide composition, gapped k-mer, recursive complement and binary profile. We design a multi-head self-attention long short-term memory encoder-decoder network to extract generative high-level features. To obtain robust results, DRPLPI combines categorical boosting and extra trees into a single meta-learner. Experiments on Zea mays and Arabidopsis thaliana obtained 0.9820 and 0.9652 area under precision/recall curve (AUPRC) respectively. The proposed method shows significant enhancement in the prediction performance compared with existing state-of-the-art methods.     

Mining statistically-solid k-mers for accurate NGS error correction

Background

NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f₀ to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.

Results

We propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f₀. To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer’s frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets.

Conclusion

The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.

     

CRAC: an integrated approach to the analysis of RNA-seq reads

A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.     

Optimal choice of k-mer in composition vector method for genome sequence comparison

Several proteins and genes are members of families that share a public evolutionary. In order to outline the evolutionary relationships and to recognize conserved patterns, sequence comparison becomes an emerging process. The current work investigates critically the k-mer role in composition vector method for comparing genome sequences. Generally, composition vector methods using k-mer are applied under choice of different value of k to compare genome sequences. For some values of k, results are satisfactory, but for other values of k, results are unsatisfactory. Standard composition vector method is carried out in the proposed work using 3-mer string length. In addition, special type of information based similarity index is used as a distance measure. It establishes that use of 3-mer and information based similarity index provide satisfactory results especially for comparison of whole genome sequences in all cases. These selections provide a sort of unified approach towards comparison of genome sequences.     

Cleavage of SNAP25 and Its Shorter Versions by the Protease Domain of Serotype A Botulinum Neurotoxin

Various substrates, catalysts, and assay methods are currently used to screen inhibitors for their effect on the proteolytic activity of botulinum neurotoxin. As a result, significant variation exists in the reported results. Recently, we found that one source of variation was the use of various catalysts, and have therefore evaluated its three forms. In this paper, we characterize three substrates under near uniform reaction conditions using the most active catalytic form of the toxin. Bovine serum albumin at varying optimum concentrations stimulated enzymatic activity with all three substrates. Sodium chloride had a stimulating effect on the full length synaptosomal-associated protein of 25 kDa (SNAP25) and its 66-mer substrates but had an inhibitory effect on the 17-mer substrate. We found that under optimum conditions, full length SNAP25 was a better substrate than its shorter 66-mer or 17-mer forms both in terms of k_cat, K_m, and catalytic efficiency k_cat/K_m. Assay times greater than 15 min introduced large variations and significantly reduced the catalytic efficiency. In addition to characterizing the three substrates, our results identify potential sources of variations in previous published results, and underscore the importance of using well-defined reaction components and assay conditions.