Pelagic species identification by using a PNN neural network and echo-sounder data

For several years, a group of CNR researchers conducted acoustic surveys in the Sicily Channel to estimate the biomass of small pelagic species, their geographical distribution and their variations over time. The instrument used to carry out these surveys is the scientific echo-sounder, set for different frequencies. The processing of the back scattered signals in the volume of water under investigation determines the abundance of the species. These data are then correlated with the biological data of experimental catches, to attribute the composition of the various fish schools investigated. Of course, the recognition of the fish schools helps to produce very good results, that is very clo…

An Intelligent System for Building Bioinformatics Workflows

In this paper a new intelligent system designed to support the researcher in the development of a workflow for bio informatics experiments is presented. The proposed system is capable to suggest one or more strategies in order to resolve the selected problem and to support the user in the assembly of a workflow for complex experiments, using a a Knowledge base, representing the expertise about the application domain, and a Rule-Based system for decision-making activity. Moreover, the system can represent this workflow at different abstraction layers, freeing the user from implementation details and assisting him in the correct configuration of the algorithms. A sample workflow for protein c…

A New Model of Fuzzy System for Mobile Robots in Unknown Environment

Identification of Key miRNAs in Regulation of PPI Networks

In this paper, we explore the interaction between miRNA and deregulated proteins in some pathologies. Assuming that miRNA can influence mRNA and consequently the proteins regulation, we explore this connection by using an interaction matrix derived from miRNA-target data and PPI network interactions. From this interaction matrix and the set of deregulated proteins, we search for the miRNA subset that influences the deregulated proteins with a minimum impact on the not deregulated ones. This regulation problem can be formulated as a complex optimization problem. In this paper, we have tried to solve it by using the Genetic Algorithm Heuristic. As the main result, we have found a set of miRNA…

Use of Soft Topographic Maps for Clustering Bacteria on the Basis of their 16S rRNA Gene Sequence

A Decision Support System for Reverse Engineering Gene Regulatory Networks

In this paper we present a knowledge-based system that aims at helping scientists in the reverse engineering process of gene regulatory networks. The main motivation of the proposed approach is to support scientists in the choice of the wide variety of algorithms and methods currently applied in the literature to infer Gene Regulatory Networks starting from gene expression measured using microarray technology. The Decision Support System (DSS) architecture is based on an ontology to model the knowledge base, a logical reasoner that builds the workflow of tasks to be done starting from the user’s request and a set of rules, and, finally, an agenda that runs the algorithms and software schedu…

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

AbstractThe 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.

Knowledge organization for modelling workflows in Taverna environment

Today Workflow Management Systems (WFMS), like Taverna and Kepler, have a very important place in the everyday work of the scientist. These tools support the access to computational resources and act as interface for building complex data processing chains. The next step is to support decisions of the researcher on autonomously developing workflow parts guided by requirements of the scientist while she/he is working on the high-level goal of the experiment. To this aim, it is necessary an ontology to store the knowledge related to the experiments and tools used, and to make this knowledge available not only to the scientist, but also to a suitable artificial intelligent system. In this pape…

A Proposed Knowledge Based Approach for Solving Proteomics Issues

In this paper we present a novel knowledge-based approach that aims at helping scientists to face and resolve a large number of proteomics problem. The system architecture is based on an ontology to model the knowledge base, a reasoner that starting from the user's request and a set of rules builds the workflow of tasks to be done, and an executor that runs the algorithms and software scheduled by the reasoner. The system can interact with the user showing him intermediate results and several options in order to refine the workflow and supporting him to choose among different forks. Thanks to the presence of the knowledge base and the modularity provided by the ontology, the system can be e…

Direct RNA nanopore sequencing of SARS-CoV-2 extracted from critical material from swabs

ABSTRACTBackgroundIn 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 experiment using critical oropharyngeal swab samples collected from Italian patients infected with SARS-CoV-2 from the Palermo region in Sicily.MethodsHere, we identified the sequences SARS-CoV-2 directly in RNA extracted from critical samples using the Oxford Nanopore MinION technology without prior cDNA retro-transcription.ResultsUsing an appropriate bioinformatics pipeline, we could identify mutations in the nucleocapisid (N) gene, which have been reported previously in studies conducted in…

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

AbstractTumors 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 poten…

A Deep Learning Model for Epigenomic Studies

Epigenetics is the study of heritable changes in gene expression that does not involve changes to the underlying DNA sequence, i.e. a change in phenotype not involved by a change in genotype. At least three main factor seems responsible for epigenetic change including DNA methylation, histone modification and non-coding RNA, each one sharing having the same property to affect the dynamic of the chromatin structure by acting on Nucleosomes posi- tion. A nucleosome is a DNA-histone complex, where around 150 base pairs of double-stranded DNA is wrapped. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells, to form the Chromatin. Nucleosome positioning plays an imp…

A knowledge-based decision support system in bioinformatics: An application to protein complex extraction

Abstract Background We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems. Results We briefly present the KDSS' architecture and basic concepts used in the design of the knowl…

Normalised compression distance and evolutionary distance of genomic sequences: comparison of clustering results

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a time consuming procedure and the obtained dissimilarity results is not a metric. Recently, the normalised compression distance was introduced as a method to calculate the distance between two generic digital objects and it seems a suitable way to compare genomic strings. In this paper, the clustering and the non-linear mapping obtained using the evolutionary distance and the compression distance are compared, in order to understand if the two distances sets are similar.

An expert system hybrid architecture to support experiment management

Specific expert systems are used for supporting, speeding-up and adding precision to in silico experimentation in many domains. In particular, many experimentalists exhibit a growing interest in workflow management systems for making a pipeline of experiments. Unfortunately, these type of systems does not integrate a systematic approach or a support component for the workflow composition/reuse. For this reason, in this paper we propose a knowledge-based hybrid architecture for designing expert systems that are able to support experiment management. This architecture defines a reference cognitive space and a proper ontology that describe the state of a problem by means of three different per…

An ontological-based knowledge organization for bioinformatics workflow management system

Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are E…

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …

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

The transforming growth factor-β (TGF-β) 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-β 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 verte…

"A KNOWLEDGE-BASED EXPERT SYSTEM IN BIOINFORMATICS: AN APPLICATION TO REVERSE ENGINEERING GENE REGULATORY NETWORK"

The huge amount of biological data has spread the development of plenty of bionformatics tools, databases and web services. In order to face a computational biology problem, there not exist only a way, but different methodologies and strategies, with their own pros and cons, can be applied. In this PhD thesis I present a knowledge-based expert system that aims at helping a bionformatics researcher in the choice of the proper strategy and heuristic in order to resolve a bioinformatics issue. The Knowledge Base of the system is structured by means of an ontology and codes the expertise about the application domain. KB is organized into decision-making modules that introduce a set of metareaso…

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…

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.…

A Knowledge Based Decision Support System for Bioinformatics and System Biology

In this paper, we present a new Decision Support System for Bioinformatics and System Biology issues. Our system is based on a Knowledge base, representing the expertise about the application domain, and a Reasoner. The Reasoner, consulting the Knowledge base and according to the user’s request, is able to suggest one or more strategies in order to resolve the selected problem. Moreover, the system can build, at different abstraction layers, a workflow for the current problem on the basis of the user’s choices, freeing the user from implementation details and assisting him in the correct configuration of the algorithms. Two possible application scenarios will be introduced: the analysis of …

Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences

Several recent works have shown that K-mer sequence representation of a DNA sequence can be used for classification or identification of nucleosome positioning related sequences. This representation can be computationally expensive when k grows, making the complexity in spaces of exponential dimension. This issue effects significantly the classification task computed by a general machine learning algorithm used for the purpose of sequence classification. In this paper, we investigate the advantage offered by the so-called Variable Ranking Feature Selection method to select the most informative k − mers associated to a set of DNA sequences, for the final purpose of nucleosome/linker classifi…

Topographic Map of Gammaproteobacteria using 16S rRNA gene sequences

Soft Topographic Map for Clustering and Classification of Bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA…

Comparison of genomic sequences clustering using Normalized Compression Distance and Evolutionary Distance

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a long procedure and the obtained dissimilarity results is not a metric. Recently the normalized compression distance was introduced as a method to calculate the distance between two generic digital objects, and it seems a suitable way to compare genomic strings. In this paper the clustering and the mapping, obtained using a SOM, with the traditional evolutionary distance and the compression distance are compared in order to understand if the two distances sets are similar. The first results indicate that the two distances catch differen…

Topographic maps for clustering and fast identification of bacteria using 16s housekeeping gene

In microbial identification the standard method to attribute a specific name to a bacterial isolate relays on the comparison of morphologic and phenotypic characters to those described for type or typical strains. In the last years a new standard for identifying bacteria using genotypic information began to be developed. In this new approach phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so called "housekeeping genes". The most commonly used gene for taxonomic purposes for bacteria is the 16S rRNA. The goal of this chapter is to show that genotypic features can be used to build a topographic map for clustering of a lar…

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 countri…

Additional file 1 of Deep learning models for bacteria taxonomic classification of metagenomic data

Preliminary classification results. Preliminary classification results obtained training a model with a kind of input data, e.g. SG, and testing it with the other type of input data, e.g. AMP. (XLSX 9.52 kb)