Fariza Tahi

A new challenge for compression algorithms: genetic sequences

Universal data compression algorithms fail to compress genetic sequences. It is due to the specificity of this particular kind of “text.” We analyze in some detail the properties of the sequences, which cause the failure of classical algorithms. We then present a lossless algorithm, biocompress-2, to compress the information contained in DNA and RNA sequences, based on the detection of regularities, such as the presence of palindromes. The algorithm combines substitutional and statistical methods, and to the best of our knowledge, leads to the highest compression of DNA. The results, although not satisfactory, give insight to the necessary correlation between compression and comprehension of genetic sequences.

Compression of DNA sequences

The authors propose a lossless algorithm based on regularities, such as the presence of palindromes, in the DNA. The results obtained, although not satisfactory, are far beyond classical algorithms.<<ETX>>

SAPHIR: a physiome core model of body fluid homeostasis and blood pressure regulation

We present the current state of the development of the SAPHIR project (a Systems Approach for PHysiological Integration of Renal, cardiac and respiratory function). The aim is to provide an open-source multi-resolution modelling environment that will permit, at a practical level, a plug-and-play construction of integrated systems models using lumped-parameter components at the organ/tissue level while also allowing focus on cellular- or molecular-level detailed sub-models embedded in the larger core model. Thus, an in silico exploration of gene-to-organ-to-organism scenarios will be possible, while keeping computation time manageable. As a first prototype implementation in this environment, we describe a core model of human physiology targeting the short- and long-term regulation of blood pressure, body fluids and homeostasis of the major solutes. In tandem with the development of the core models, the project involves database implementation and ontology development.

A fast ab-initio method for predicting miRNA precursors in genomes

miRNAs are small non coding RNA structures which play important roles in biological processes. Finding miRNA precursors in genomes is therefore an important task, where computational methods are required. The goal of these methods is to select potential pre-miRNAs which could be validated by experimental methods. With the new generation of sequencing techniques, it is important to have fast algorithms that are able to treat whole genomes in acceptable times. We developed an algorithm based on an original method where an approximation of miRNA hairpins are first searched, before reconstituting the pre-miRNA structure. The approximation step allows a substantial decrease in the number of possibilities and thus the time required for searching. Our method was tested on different genomic sequences, and was compared with CID-miRNA, miRPara and VMir. It gives in almost all cases better sensitivity and selectivity. It is faster than CID-miRNA, miRPara and VMir: it takes ∼30 s to process a 1 MB sequence, when VMir takes 30 min, miRPara takes 20 h and CID-miRNA takes 55 h. We present here a fast ab-initio algorithm for searching for pre-miRNA precursors in genomes, called miRNAFold. miRNAFold is available at http://EvryRNA.ibisc.univ-evry.fr/.

Identification of genes involved in ceramide-dependent neuronal apoptosis using cDNA arrays

BackgroundCeramide is important in many cell responses, such as proliferation, differentiation, growth arrest and apoptosis. Elevated ceramide levels have been shown to induce apoptosis in primary neuronal cultures and neuronally differentiated PC 12 cells.ResultsTo investigate gene expression during ceramide-dependent apoptosis, we carried out a global study of gene expression in neuronally differentiated PC 12 cells treated with C2-ceramide using an array of 9,120 cDNA clones. Although the criteria adopted for differential hybridization were stringent, modulation of expression of 239 genes was identified during the effector phase of C2-ceramide-induced cell death. We have made an attempt at classifying these genes on the basis of their putative functions, first with respect to known effects of ceramide or ceramide-mediated transduction systems, and then with respect to regulation of cell growth and apoptosis.ConclusionsOur cell-culture model has enabled us to establish a profile of gene expression during the effector phase of ceramide-mediated cell death. Of the 239 genes that met the criteria for differential hybridization, 10 correspond to genes previously involved in C2-ceramide or TNF-α signaling pathways and 20 in neuronal disorders, oncogenesis or more broadly in the regulation of proliferation. The remaining 209 genes, with or without known functions, constitute a pool of genes potentially implicated in the regulation of neuronal cell death.

Towards a piRNA prediction using multiple kernel fusion and support vector machine

Motivation: Piwi-interacting RNA (piRNA) is the most recently discovered and the least investigated class of Argonaute/Piwi protein-interacting small non-coding RNAs. The piRNAs are mostly known to be involved in protecting the genome from invasive transposable elements. But recent discoveries suggest their involvement in the pathophysiology of diseases, such as cancer. Their identification is therefore an important task, and computational methods are needed. However, the lack of conserved piRNA sequences and structural elements makes this identification challenging and difficult. Results: In the present study, we propose a new modular and extensible machine learning method based on multiple kernels and a support vector machine (SVM) classifier for piRNA identification. Very few piRNA features are known to date. The use of a multiple kernels approach allows editing, adding or removing piRNA features that can be heterogeneous in a modular manner according to their relevance in a given species. Our algorithm is based on a combination of the previously identified features [sequence features (k-mer motifs and a uridine at the first position) and piRNAs cluster feature] and a new telomere/centromere vicinity feature. These features are heterogeneous, and the kernels allow to unify their representation. The proposed algorithm, named piRPred, gives promising results on Drosophila and Human data and outscores previously published piRNA identification algorithms. Availability and implementation: piRPred is freely available to non-commercial users on our Web server EvryRNA http://EvryRNA.ibisc.univ-evry.fr Contact: tahi@ibisc.univ-evry.fr

Tfold: efficient in silico prediction of non-coding RNA secondary structures

Predicting RNA secondary structures is a very important task, and continues to be a challenging problem, even though several methods and algorithms are proposed in the literature. In this article, we propose an algorithm called Tfold, for predicting non-coding RNA secondary structures. Tfold takes as input a RNA sequence for which the secondary structure is searched and a set of aligned homologous sequences. It combines criteria of stability, conservation and covariation in order to search for stems and pseudoknots (whatever their type). Stems are searched recursively, from the most to the least stable. Tfold uses an algorithm called SSCA for selecting the most appropriate sequences from a large set of homologous sequences (taken from a database for example) to use for the prediction. Tfold can take into account one or several stems considered by the user as belonging to the secondary structure. Tfold can return several structures (if requested by the user) when ‘rival’ stems are found. Tfold has a complexity of O(n2), with n the sequence length. The developed software, which offers several different uses, is available on the web site: http://tfold.ibisc.univ-evry.fr/TFold.

ncRNAclassifier: a tool for detection and classification of transposable element sequences in RNA hairpins

BackgroundInverted repeat genes encode precursor RNAs characterized by hairpin structures. These RNA hairpins are then metabolized by biosynthetic pathways to produce functional small RNAs. In eukaryotic genomes, short non-autonomous transposable elements can have similar size and hairpin structures as non-coding precursor RNAs. This resemblance leads to problems annotating small RNAs.ResultsWe mapped all microRNA precursors from miRBASE to several genomes and studied the repetition and dispersion of the corresponding loci. We then searched for repetitive elements overlapping these loci. We developed an automatic method called ncRNAclassifier to classify pre-ncRNAs according to their relationship with transposable elements (TEs). We showed that there is a correlation between the number of scattered occurrences of ncRNA precursor candidates and the presence of TEs. We applied ncRNAclassifier on six chordate genomes and report our findings. Among the 1,426 human and 721 mouse pre-miRNAs of miRBase, we identified 235 and 68 mis-annotated pre-miRNAs respectively corresponding completely to TEs.ConclusionsWe provide a tool enabling the identification of repetitive elements in precursor ncRNA sequences. ncRNAclassifier is available athttp://EvryRNA.ibisc.univ-evry.fr.

miRBoost: boosting support vector machines for microRNA precursor classification

Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising. However these approaches require the use of training data, which is problematic due to an imbalance in the number of miRNAs (positive data) and non-miRNAs (negative data), which leads to a degradation of their performance. In order to address this issue, we present an ensemble method that uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. The algorithm, miRBoost, performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction. The software miRBoost is available on our web server http://EvryRNA.ibisc.univ-evry.fr.

Behaviour Preservation of a Biological Regulatory Network when Embedded into a Larger Network

The main contribution of this work is a mathematical theorem which establishes a necessary and sufficient condition to preserve the behaviour of a genetic regulatory network when it is embedded into a larger network. We adopt the modelling approach of Rene Thomas, which provides a discrete representation of biological regulatory networks. This framework is entirely formalized using labelled graphs with semantics defined in terms of state graphs with transitions. Our theorem offers the possibility to automatically verify whether a subnetwork has autonomous behaviour. It will allow biologists to better identify relevant sets of genes which should be studied together.