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Frédérique Lisacek

Publications and source records attributed to Frédérique Lisacek.

10 recordsLinked to original sources
PubMed2006

Proteome informatics II: bioinformatics for comparative proteomics.

The present review attempts to cover the most recent initiatives directed towards representing, storing, displaying and processing protein-related data suited to undertake "comparative proteomics" studies. Data interpretation is brought into focus. Efforts invested into analysing and interpreting experimental data increasingly express the need for adding meaning. This trend is perceptible in work dedicated to determining ontologies, modelling interaction networks, etc. In parallel, technical advances in computer science are spurred by the development of the Web and the growing need to channel and understand massive volumes of data. Biology benefits from these advances as an application of choice for many generic solutions. Some examples of bioinformatics solutions are discussed and directions for on-going and future work conclude the review.

Algorithms↗
PubMed2006

Web-based MS/MS data analysis.

This tutorial focuses on three MS/MS data analysis programs currently available via a web interface: Mascot, Phenyx and X!Tandem. Although these programs process the same input and often produce comparable outputs, subtle differences remain. The use of parameters that are requested in the on-line forms and the subsequent interpretation of results are illustrated and explained via a single example.

Amino Acids↗
PubMed2005

Using argumentation to retrieve articles with similar citations: an inquiry into improving related articles search in the MEDLINE digital library.

The aim of this study is to investigate the relationships between citations and the scientific argumentation found abstracts. We design a related article search task and observe how the argumentation can affect the search results. We extracted citation lists from a set of 3200 full-text papers originating from a narrow domain. In parallel, we recovered the corresponding MEDLINE records for analysis of the argumentative moves. Our argumentative model is founded on four classes: PURPOSE, METHODS, RESULTS and CONCLUSION. A Bayesian classifier trained on explicitly structured MEDLINE abstracts generates these argumentative categories. The categories are used to generate four different argumentative indexes. A fifth index contains the complete abstract, together with the title and the list of Medical Subject Headings (MeSH) terms. To appraise the relationship of the moves to the citations, the citation lists were used as the criteria for determining relatedness of articles, establishing a benchmark; it means that two articles are considered as "related" if they share a significant set of co-citations. Our results show that the average precision of queries with the PURPOSE and CONCLUSION features is the highest, while the precision of the RESULTS and METHODS features was relatively low. A linear weighting combination of the moves is proposed, which significantly improves retrieval of related articles.

Abstracting and Indexing↗
PubMed2005

Extracting key sentences with latent argumentative structuring.

PROBLEM: Key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles. Abstracts are also used for this purpose. However with usually more than 300 words, abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into 4 argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION. METHODS: We rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification is feasible on MEDLINE abstracts and should help user navigation in such repositories.

Bayes Theorem↗
PubMed2004

Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins of Escherichia coli K-12.

A curated set of 81 proven and 44 predicted lipoproteins of Escherichia coli K-12 was defined with the combined use of a literature survey, a variety of predictive tools and human expertise. The well-documented Gram-negative proteome of E. coli K-12 was chosen to assess how the different approaches complement each other and to ensure a stable definition of a consistent set of lipoproteins. The results of detailed analysis of such proteins at the level of a single proteome are presented, corroborated and rationalized.

Amino Acid Motifs↗
PubMed2004

Using protein motif combinations to update KEGG pathway maps and orthologue tables.

We have studied the projection of protein family data onto single bacterial translated genome as a solution to visualise relationships between families restricted to bacterial sequences. Any member of any type of family as defined in the Pfam database (domains, signatures, etc.) is considered as a protein module. Our first goal is to discover rules correlating the occurrence of modules with biochemical properties. To achieve this goal we have developed a platform to quantify information found in protein databases and to support the analysis of the nature of modules, their position and corresponding frequencies of occurrence (in isolation or in combination) in association with pathway knowledge as found in KEGG. This paper focuses on two pathways: the two-component system and the aminophosphonate metabolism, that are partially but not completely documented. Proteins involved in those pathways were listed separately in each organism to analyse module composition and rules constraining pathway interactions were identified. It is shown how these results can be used to update KEGG pathways and orthologue tables.

Animals↗
PubMed2003

Investigating protein domain combinations in complete proteomes.

Protein-related information is more accumulated rather than reduced to a synthetic view. Itemising properties of protein sequences is informative, so is the list of ingredients to do some cooking, but without a recipe, that is, quantification and chronology, understanding is incomplete. If the goal of accumulating information is to discover or reveal the function and related biochemical mechanisms, information has to be weighed and ordered. As a guideline, the weight of a piece of information should reflect how often it consistently occurs in various contexts. We propose a common sense approach to quantify and put data and information into perspective. Complete bacterial proteomes are individually mapped with the Pfam-A database of domains and protein family signatures in an attempt to assess the modularity of proteins at the level of a single proteome and the implications of a modular description of proteins for a functional interpretation. Poorly annotated proteins in the most documented bacteria (E. coli and B. subtilis) were considered in an attempt to formulate hypothesis on the basis of domain/module content.

Bacillus subtilis↗
PubMed2002

Probabilistic alignment of motifs with sequences.

MOTIVATION: Motif detection is an important component of the classification and annotation of protein sequences. A method for aligning motifs with an amino acid sequence is introduced. The motifs can be described by the secondary (i.e. functional, biophysical, etc.) characteristics of a signal or pattern to be detected. The results produced are based on the statistical relevance of the alignment. The method was targeted to avoid the problems (i.e. over-fitting, biological interpretation and mathematical soundness) encountered in other methods currently available. RESULTS: The method was tested on lipoprotein signals in B. subtilis yielding stable results. The results of signal prediction were consistent with other methods where literature was available. AVAILABILITY: An implementation of the motif alignment, refining and bootstrapping is available for public use online at http://www.expasy.org/tools/patoseq/

Amino Acid Motifs↗
PubMedDate not supplied

Deciphering the human nucleolar proteome.

Nucleoli are plurifunctional nuclear domains involved in the regulation of several major cellular processes such as ribosome biogenesis, the biogenesis of non-ribosomal ribonucleoprotein complexes, cell cycle, and cellular aging. Until recently, the protein content of nucleoli was poorly described. Several proteomic analyses have been undertaken to discover the molecular bases of the biological roles fulfilled by nucleoli. These studies have led to the identification of more than 700 proteins. Extensive bibliographic and bioinformatic analyses allowed the classification of the identified proteins into functional groups and suggested potential functions of 150 human proteins previously uncharacterized. The combination of improvements in mass spectrometry technologies, the characterization of protein complexes, and data mining will assist in furthering our understanding of the role of nucleoli in different physiological and pathological cell states.

Cell Nucleolus↗