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Dyliss tools for key actors identification

Our tools are based on formal systems. They aim at guiding the user to progressively reduce the space of models (gene or protein families, set of main actors involved in a system response, dynamical models) which are compatible with both knowledge and experimental observations.

Most of our tools are available both as stand-alone software and through portals such as Mobyle or Galaxy interfaces.

Among others, a goal of the team is to facilitate interplays between tools for biological data analysis and integration. 

Sequence analysis: product identification for complex functions

  • Protomata learner: Complex pattern discovery based on grammatical inference. Inference of precise protein signatures.
  • Logol: Complex pattern matching. Pattern syntax uses a String Variable Grammar-like formalism (ambiguities, insertions/ deletions, gaps, repeats and palindromes).

Integrative Biology: (constraint-based) toolbox for network filtering

  • MeTools (webserver, galaxy instance). Several tools to reconciliate and merge smbl files for metabolic metabolic and uniform their annotations with respect to the metacyc database.
  • Meneco (webserver, python package, galaxy instance). Gap-filling for a functional reconstruction of metabolic network. [input: genome & metabolic profiles]
  • Shogen (webserver, python package, galaxy instance). Finding shortest genome segments that regulate metabolic pathways. [input: genome & metabolic network].
  • Lombarde (webserver). Enhancing key causalities within a regulatory pathways. [input: genome, modules & several gene-expression datasets].
  • BioQuali cytoscape plugin for sign consistency detection and qualitative prediction. [input: regulatory network & one gene-expression dataset].
  • ingranalyze (webserver, python package, galaxy instance). Network repair. Qualitative prediction under inconsistencies. [input: regulatory network & one gene-expression dataset].
  • keyregulatorfinder Scoring regulators of a complete transcriptomic expression profile. [input: knowledge-based regulation and metabolic database & one transcriptomic array].

Dynamics: invariant-based prediction

  • Caspo (webserver/learning, webserver/control, python package, galaxy instance) Reasoning on the response of logical models (signaling networks) from prior knowledge and phospho-proteomics data. Study of the complete family of admissible models.
  • Cadbiom: state-chart like graphical language to investigating discrete synchronization events in biological networks.
  • POGG: Score the relevance of regulatory pathways w.r.t a higher-scale time-series quantitative phenotype (Markov chain model)  (collaboration with LINA-Nantes).
  • NutritionAnalyzer: Analysis of yield variability in a metabolic network w.r.t. input-output datasets.

Integration of our tools in larger software environments


Most of our softwares (see below) were designed as "bricks" that can combined through workflow application such as Mobyle. It worths considering them into larger dedicated environnements to benefit from the expertise of other research groups.