Alexei Nordell-Markovits

Université de Sherbrooke, Шербрук, Quebec, Canada

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Publications (4)19.77 Total impact

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    ABSTRACT: The analysis of genomic data such as ChIP-Seq usually involves representing the signal intensity level over genes or other genetic features. This is often illustrated as a curve (representing the aggregate profile of a group of genes) or as a heatmap (representing individual genes). However, no specific resource dedicated to easily generating such profiles is currently available. We therefore built the versatile aggregate profiler (VAP), designed to be used by experimental and computational biologists to generate profiles of genomic datasets over groups of regions of interest, using either an absolute or a relative method. Graphical representation of the results is automatically generated, and subgrouping can be performed easily, based on the orientation of the flanking annotations. The outputs include statistical measures to facilitate comparisons between groups or datasets. We show that, through its intuitive design and flexibility, VAP can help avoid misinterpretations of genomics data. VAP is highly efficient and designed to run on laptop computers by using a memory footprint control, but can also be easily compiled and run on servers. VAP is accessible at http://lab-jacques.recherche.usherbrooke.ca/vap/.
    Full-text · Article · Apr 2014 · Nucleic Acids Research
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    ABSTRACT: Chromatin represents a repressive barrier to the process of ligand-dependent transcriptional activity of nuclear receptors. Here, we show that H3K27 methylation imposes ligand-dependent regulation of the oestrogen receptor α (ERα)-dependent apoptotic response via Bcl-2 in breast cancer cells. The activation of BCL2 transcription is dependent on the simultaneous inactivation of the H3K27 methyltransferase, EZH2, and the demethylation of H3K27 at a poised enhancer by the ERα-dependent recruitment of JMJD3 in hormone-dependent breast cancer cells. We also provide evidence that this pathway is modified in cells resistant to anti-oestrogen (AE), which constitutively express BCL2. We show that the lack of H3K27 methylation at BCL2 regulatory elements due to the inactivation of EZH2 by the HER2 pathway leads to this constitutive activation of BCL2 in these AE-resistant cells. Our results describe a mechanism in which the epigenetic state of chromatin affects ligand dependency during ERα-regulated gene expression.
    Preview · Article · Aug 2011 · The EMBO Journal
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    ABSTRACT: The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Significant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches.
    Full-text · Article · Feb 2009 · Canadian Journal of Electrical and Computer Engineering
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    Abdellali Kelil · Alexei Nordell-Markovits · Shengrui Wang
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    ABSTRACT: The classification of categorical sequences is a fundamental process in many application fields. A key issue is to extract and make use of significant features hidden behind the chronological and structural dependencies found in these sequences. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but sequences often have similar structural features in non-chronological order. In addition, these algorithms have serious difficulties to outperform domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. CLASS captures the significant patterns and reduces the influence of those representing merely noise. Moreover, CLASS employs a classifier called SNN for significant-nearest-neighbours, inspired from the K-nearest-neighbours with a dynamic estimation of K. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches.
    Full-text · Article · Jan 2009