GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

Transcriptomic and Bioinformatic Analyses Identifying a Central Mif-Cop9-Nf-kB Signaling Network in Innate Immunity Response of Ciona robusta.

The Ascidian C. robusta is a powerful model for studying innate immunity. LPS induction activates inflammatory-like reactions in the pharynx and the expression of several innate immune genes in granulocyte hemocytes such as cytokines, for instance, macrophage migration inhibitory factors (CrMifs). This leads to intracellular signaling involving the Nf-kB signaling cascade that triggers downstream pro-inflammatory gene expression. In mammals, the COP9 (Constitutive photomorphogenesis 9) signalosome (CSN) complex also results in the activation of the NF-kB pathway. It is a highly conserved complex in vertebrates, mainly engaged in proteasome degradation which is essential for maintaining processes such as cell cycle, DNA repair, and differentiation. In the present study, we used bioinformatics and in-silico analyses combined with an in-vivo LPS exposure strategy, next-generation sequencing (NGS), and qRT-PCR to elucidate molecules and the temporal dynamics of Mif cytokines, Csn signaling components, and the Nf-κB signaling pathway in C. robusta. A qRT-PCR analysis of immune genes selected from transcriptome data revealed a biphasic activation of the inflammatory response. A phylogenetic and STRING analysis indicated an evolutionarily conserved functional link between the Mif-Csn-Nf-kB axis in ascidian C. robusta during LPS-mediated inflammation response, finely regulated by non-coding molecules such as microRNAs (miRNAs).

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA-mRNA interaction networks. siRNAs, in fact, are small RNA molecules that are able to bind to target genes and silence them. These events make siRNAs key molecules as RNA interference agents in many biological interaction networks related to severe diseases such as cancer. In particular, our GNN approach allows for the prediction of the siRNA efficacy, which measures the siRNA's ability to bind and silence a gene target. Tested on benchmark datasets, our proposed method overcomes other machine learning algorithms, including the state-of-the-art predictor based on the convolutional neural network, reaching a Pearson correlation coefficient of approximately 73.6%. Finally, we proposed a case study where the efficacy of a set of siRNAs is predicted for a gene of interest. To the best of our knowledge, GNNs were used for the first time in this scenario.

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs.

In consideration of the increasing prevalence of COVID-19 cases in several countries and the resulting demand for unbiased sequencing approaches, we performed a direct RNA sequencing (direct RNA seq.) experiment using critical oropharyngeal swab samples collected from Italian patients infected with SARS-CoV-2 from the Palermo region in Sicily. Here, we identified the sequences SARS-CoV-2 directly in RNA extracted from critical samples using the Oxford Nanopore MinION technology without prior cDNA retrotranscription. Using an appropriate bioinformatics pipeline, we could identify mutations in the nucleocapsid (N) gene, which have been reported previously in studies conducted in other countries. In conclusion, to the best of our knowledge, the technique used in this study has not been used for SARS-CoV-2 detection previously owing to the difficulties in the extraction of RNA of sufficient quantity and quality from routine oropharyngeal swabs. Despite these limitations, this approach provides the advantages of true native RNA sequencing and does not include amplification steps that could introduce systematic errors. This study can provide novel information relevant to the current strategies adopted in SARS-CoV-2 next-generation sequencing.

Impact of the flame retardant 2,2'4,4'-tetrabromodiphenyl ether (PBDE-47) in THP-1 macrophage-like cell function via small extracellular vesicles.

2,2'4,4'-tetrabromodiphenyl ether (PBDE-47) is one of the most widespread environmental brominated flame-retardant congeners which has also been detected in animal and human tissues. Several studies have reported the effects of PBDEs on different health issues, including neurobehavioral and developmental disorders, reproductive health, and alterations of thyroid function. Much less is known about its immunotoxicity. The aim of our study was to investigate the effects that treatment of THP-1 macrophage-like cells with PBDE-47 could have on the content of small extracellular vesicles' (sEVs) microRNA (miRNA) cargo and their downstream effects on bystander macrophages. To achieve this, we purified sEVs from PBDE-47 treated M(LPS) THP-1 macrophage-like cells (sEVsPBDE+LPS) by means of ultra-centrifugation and characterized their miRNA cargo by microarray analysis detecting the modulation of 18 miRNAs. Furthermore, resting THP-1 derived M(0) macrophage-like cells were cultured with sEVsPBDE+LPS, showing that the treatment reshaped the miRNA profiles of 12 intracellular miRNAs. This dataset was studied in silico, identifying the biological pathways affected by these target genes. This analysis identified 12 pathways all involved in the maturation and polarization of macrophages. Therefore, to evaluate whether sEVsPBDE+LPS can have some immunomodulatory activity, naïve M(0) THP-1 macrophage-like cells cultured with purified sEVsPBDE+LPS were studied for IL-6, TNF-α and TGF-ß mRNAs expression and immune stained with the HLA-DR, CD80, CCR7, CD38 and CD209 antigens and analyzed by flow cytometry. This analysis showed that the PBDE-47 treatment does not induce the expression of specific M1 and M2 cytokine markers of differentiation and may have impaired the ability to make immunological synapses and present antigens, down-regulating the expression of HLA-DR and CD209 antigens. Overall, our study supports the model that perturbation of miRNA cargo by PBDE-47 treatment contributes to the rewiring of cellular regulatory pathways capable of inducing perturbation of differentiation markers on naïve resting M(0) THP-1 macrophage-like cells.

Transcriptomic Analyses Reveal 2 and 4 Family Members of Cytochromes P450 (CYP) Involved in LPS Inflammatory Response in Pharynx of Ciona robusta.

Cytochromes P450 (CYP) are enzymes responsible for the biotransformation of most endogenous and exogenous agents. The expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, and regulation by cytokines and hormones. In recent years, Ciona robusta, one of the closest living relatives of vertebrates, has become a model in various fields of biology, in particular for studying inflammatory response. Using an in vivo LPS exposure strategy, next-generation sequencing (NGS) and qRT-PCR combined with bioinformatics and in silico analyses, compared whole pharynx transcripts from naïve and LPS-exposed C. robusta, and we provide the first view of cytochrome genes expression and miRNA regulation in the inflammatory response induced by LPS in a hematopoietic organ. In C. robusta, cytochromes belonging to 2B,2C, 2J, 2U, 4B and 4F subfamilies were deregulated and miRNA network interactions suggest that different conserved and species-specific miRNAs are involved in post-transcriptional regulation of cytochrome genes and that there could be an interplay between specific miRNAs regulating both inflammation and cytochrome molecules in the inflammatory response in C. robusta.

T Cells Expressing Receptor Recombination/Revision Machinery Are Detected in the Tumor Microenvironment and Expanded in Genomically Over-unstable Models.

Tumors undergo dynamic immunoediting as part of a process that balances immunologic sensing of emerging neoantigens and evasion from immune responses. Tumor-infiltrating lymphocytes (TIL) comprise heterogeneous subsets of peripheral T cells characterized by diverse functional differentiation states and dependence on T-cell receptor (TCR) specificity gained through recombination events during their development. We hypothesized that within the tumor microenvironment (TME), an antigenic milieu and immunologic interface, tumor-infiltrating peripheral T cells could reexpress key elements of the TCR recombination machinery, namely, Rag1 and Rag2 recombinases and Tdt polymerase, as a potential mechanism involved in the revision of TCR specificity. Using two syngeneic invasive breast cancer transplantable models, 4T1 and TS/A, we observed that Rag1, Rag2, and Dntt in situ mRNA expression characterized rare tumor-infiltrating T cells. In situ expression of the transcripts was increased in coisogenic Mlh1-deficient tumors, characterized by genomic overinstability, and was also modulated by PD-1 immune-checkpoint blockade. Through immunolocalization and mRNA hybridization analyses, we detected the presence of rare TDT+RAG1/2+ cells populating primary tumors and draining lymph nodes in human invasive breast cancer. Analysis of harmonized single-cell RNA-sequencing data sets of human cancers identified a very small fraction of tumor-associated T cells, characterized by the expression of recombination/revision machinery transcripts, which on pseudotemporal ordering corresponded to differentiated effector T cells. We offer thought-provoking evidence of a TIL microniche marked by rare transcripts involved in TCR shaping.

2,2'4,4'-Tetrabromodiphenyl Ether (PBDE-47) Modulates the Intracellular miRNA Profile, sEV Biogenesis and Their miRNA Cargo Exacerbating the LPS-Induced Pro-Inflammatory Response in THP-1 Macrophages.

The 2,2'4,4'-tetrabromodiphenyl ether (PBDE-47) is one of the most prominent PBDE congeners detected in the environment and in animal and human tissues. Animal model experiments suggested the occurrence of PBDE-induced immunotoxicity leading to different outcomes and recently we demonstrated that this substance can impair macrophage and basophil activities. In this manuscript, we decided to further examine the effects induced by PBDE-47 treatment on innate immune response by looking at the intracellular expression profile of miRNAs as well as the biogenesis, cargo content and activity of human M(LPS) macrophage cell-derived small extracellular vesicles (sEVs). Microarray and in silico analysis demonstrated that PBDE-47 can induce some epigenetic effects in M(LPS) THP-1 cells modulating the expression of a set of intracellular miRNAs involved in biological pathways regulating the expression of estrogen-mediated signaling and immune responses with particular reference to M1/M2 differentiation. In addition to the cell-intrinsic modulation of intracellular miRNAs, we demonstrated that PBDE-47 could also interfere with the biogenesis of sEVs increasing their number and selecting a de novo population of sEVs. Moreover, PBDE-47 induced the overload of specific immune related miRNAs in PBDE-47 derived sEVs. Finally, culture experiments with naïve M(LPS) macrophages demonstrated that purified PBDE-47 derived sEVs can modulate macrophage immune response exacerbating the LPS-induced pro-inflammatory response inducing the overexpression of the IL-6 and the MMP9 genes. Data from this study demonstrated that PBDE-47 can perturb the innate immune response at different levels modulating the intracellular expression of miRNAs but also interfering with the biogenesis, cargo content and functional activity of M(LPS) macrophage cell-derived sEVs.

ceRNA Network Regulation of TGF-ß, WNT, FOXO, Hedgehog Pathways in the Pharynx of Ciona robusta.

The transforming growth factor-ß (TGF-ß) family of cytokines performs a multifunctional signaling, which is integrated and coordinated in a signaling network that involves other pathways, such as Wintless, Forkhead box-O (FOXO) and Hedgehog and regulates pivotal functions related to cell fate in all tissues. In the hematopoietic system, TGF-ß signaling controls a wide spectrum of biological processes, from immune system homeostasis to the quiescence and self-renewal of hematopoietic stem cells (HSCs). Recently an important role in post-transcription regulation has been attributed to two type of ncRNAs: microRNAs and pseudogenes. Ciona robusta, due to its philogenetic position close to vertebrates, is an excellent model to investigate mechanisms of post-transcriptional regulation evolutionarily highly conserved in immune homeostasis. The combined use of NGS and bioinformatic analyses suggests that in the pharynx, the hematopoietic organ of Ciona robusta, the Tgf-ß, Wnt, Hedgehog and FoxO pathways are involved in tissue homeostasis, as they are in human. Furthermore, ceRNA network interactions and 3'UTR elements analyses of Tgf-ß, Wnt, Hedgehog and FoxO pathways genes suggest that different miRNAs conserved (cin-let-7d, cin-mir-92c, cin-mir-153), species-specific (cin-mir-4187, cin-mir-4011a, cin-mir-4056, cin-mir-4150, cin-mir-4189, cin-mir-4053, cin-mir-4016, cin-mir-4075), pseudogenes (ENSCING00000011392, ENSCING00000018651, ENSCING00000007698) and mRNA 3'UTR elements are involved in post-transcriptional regulation in an integrated way in C. robusta.

BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics.

The 16th Annual Meeting of the Bioinformatics Italian Society was held in Palermo, Italy, on June 26-28, 2019. More than 80 scientific contributions were presented, including 4 keynote lectures, 31 oral communications and 49 posters. Also, three workshops were organised before and during the meeting. Full papers from some of the works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions.

BACKGROUND: Non-coding RNAs include different classes of molecules with regulatory functions. The most studied are microRNAs (miRNAs) that act directly inhibiting mRNA expression or protein translation through the interaction with a miRNAs-response element. Other RNA molecules participate in the complex network of gene regulation. They behave as competitive endogenous RNA (ceRNA), acting as natural miRNA sponges to inhibit miRNA functions and modulate the expression of RNA messenger (mRNA). It became evident that understanding the ceRNA-miRNA-mRNA crosstalk would increase the functional information across the transcriptome, contributing to identify new potential biomarkers for translational medicine. RESULTS: We present miRTissue ce, an improvement of our original miRTissue web service. By introducing a novel computational pipeline, miRTissue ce provides an easy way to search for ceRNA interactions in several cancer tissue types. Moreover it extends the functionalities of previous miRTissue release about miRNA-target interaction in order to provide a complete insight about miRNA mediated regulation processes. miRTissue ce is freely available at http://tblab.pa.icar.cnr.it/mirtissue.html . CONCLUSIONS: The study of ceRNA networks and its dynamics in cancer tissue could be applied in many fields of translational biology, as the investigation of new cancer biomarker, both diagnostic and prognostic, and also in the investigation of new therapeutic strategies of intervention. In this scenario, miRTissue ce can offer a powerful instrument for the analysis and characterization of ceRNA-ceRNA interactions in different tissue types, representing a fundamental step in order to understand more complex regulation mechanisms.

MiRNA therapeutics based on logic circuits of biological pathways.

BACKGROUND: In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. RESULTS: In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. CONCLUSIONS: The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes.

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

miRTissue: a web application for the analysis of miRNA-target interactions in human tissues.

BACKGROUND: microRNAs act as regulators of gene expression interacting with their gene targets. Current bioinformatics services, such as databases of validated miRNA-target interactions and prediction tools, usually provide interactions without any information about what tissue that interaction is more likely to appear nor information about the type of interactions, causing mRNA degradation or translation inhibition respectively. RESULTS: In this work, we introduce miRTissue, a web application that combines validated miRNA-target interactions with statistical correlation among expression profiles of miRNAs, genes and proteins in 15 different human tissues. Validated interactions are taken from the miRTarBase database, while expression profiles are downloaded from The Cancer Genome Atlas repository. As a result, the service provides a tissue-specific characterisation of each couple of miRNA and gene together with its statistical significance (p-value). The inclusion of protein data also allows providing the type of interaction. Moreover, miRTissue offers several views for analysing interactions, focusing for example on the comparison between different cancer types or different tissue conditions. All the results are freely downloadable in the most common formats. CONCLUSIONS: miRTissue fills a gap concerning current bioinformatics services related to miRNA-target interactions because it provides a tissue-specific context to each validated interaction and the type of interaction itself. miRTissue is easily browsable allowing the user to select miRNAs, genes, cancer types and tissue conditions. The results can be sorted according to p-values to immediately identify those interactions that are more likely to occur in a given tissue. miRTissue is available at http://tblab.pa.icar.cnr.it/mirtissue.html.

BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources.

BACKGROUND: Several online databases provide a large amount of biomedical data of different biological entities. These resources are typically stored in systems implementing their own data model, user interface and query language. On the other hand, in many bioinformatics scenarios there is often the need to use more than one resource. The availability of a single bioinformatics platform that integrates many biological resources and services is, for those reasons a fundamental issue. DESCRIPTION: Here, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available sources. BioGraph is built upon our previously developed graph database called BioGraphDB, that integrates and stores heterogeneous biological resources and make them available by means of a common structure and a unique query language. BioGraph implements state-of-the-art technologies and provides pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results. CONCLUSION: We present a case study about functional analysis of microRNA in breast cancer in order to demonstrate the functionalities of the system. BioGraph is freely available at http://biograph.pa.icar.cnr.it . Source files are available on GitHub at https://github.com/IcarPA-TBlab/BioGraph.

Deep learning models for bacteria taxonomic classification of metagenomic data.

BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. RESULTS: To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. CONCLUSIONS: In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

nRC: non-coding RNA Classifier based on structural features.

MOTIVATION: Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. RESULTS: We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. CONCLUSION: The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

MiRNATIP: a SOM-based miRNA-target interactions predictor.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability. RESULTS: We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 %, respectively for Homo sapiens and for C. elegans. All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ . CONCLUSIONS: Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

OBJECTIVES: In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. METHODS: In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". RESULTS: The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. CONCLUSIONS: Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments.

Probabilistic topic modeling for the analysis and classification of genomic sequences.

BACKGROUND: Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. METHODS: The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. RESULTS AND CONCLUSIONS: We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased.