Jan Fostier

i-ADHoRe 3.0—fast and sensitive detection of genomic homology in extremely large data sets

Comparative genomics is a powerful means to gain insight into the evolutionary processes that shape the genomes of related species. As the number of sequenced genomes increases, the development of software to perform accurate cross-species analyses becomes indispensable. However, many implementations that have the ability to compare multiple genomes exhibit unfavorable computational and memory requirements, limiting the number of genomes that can be analyzed in one run. Here, we present a software package to unveil genomic homology based on the identification of conservation of gene content and gene order (collinearity), i-ADHoRe 3.0, and its application to eukaryotic genomes. The use of efficient algorithms and support for parallel computing enable the analysis of large-scale data sets. Unlike other tools, i-ADHoRe can process the Ensembl data set, containing 49 species, in 1 h. Furthermore, the profile search is more sensitive to detect degenerate genomic homology than chaining pairwise collinearity information based on transitive homology. From ultra-conserved collinear regions between mammals and birds, by integrating coexpression information and protein–protein interactions, we identified more than 400 regions in the human genome showing significant functional coherence. The different algorithmical improvements ensure that i-ADHoRe 3.0 will remain a powerful tool to study genome evolution.

An Asynchronous Parallel MLFMA for Scattering at Multiple Dielectric Objects

In this paper, a new strategy for the parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed memory computers is presented. By using an asynchronous implementation of the parallel MLFMA, an efficient parallelization scheme is obtained when multiple dielectric objects are involved in the simulation. Furthermore, a better spreading of the communication through time is obtained, avoiding both communication in bursts and synchronization at each MLFMA level. This proves especially beneficial when slower interconnection networks are used.

O(1) Computation of Legendre polynomials and Gauss-Legendre nodes and weights for parallel computing

A self-contained set of algorithms is proposed for the fast evaluation of Legendre polynomials of arbitrary degree and argument

Halvade: scalable sequence analysis with MapReduce.

\in [-1,1]

An Open-Source Implementation for Full-Wave 2D Scattering by Million-Wavelength-Size Objects

. More specifically the time required to evaluate any Legendre polynomial, regardless of argument and degree, is bounded by a constant; i.e., the complexity is

A greedy, graph-based algorithm for the alignment of multiple homologous gene lists

\mathcal{O}(1)

Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations

. The proposed algorithm also immediately yields an

Full-Wave Simulations of Electromagnetic Scattering Problems With Billions of Unknowns

\mathcal{O}(1)

A GRID Computer Implementation of the Multilevel Fast Multipole Algorithm for Full-Wave Analysis of Optical Devices

algorithm for computing an arbitrary Gauss--Legendre quadrature node. Such a capability is crucial for efficiently performing certain parallel computations with high order Legendre polynomials, such as computing an integral in parallel by means of Gauss--Legendre quadrature and the parallel evaluation of Legendre series. In order to achieve the

Pathway relevance ranking for tumor samples through network-based data integration

\mathcal{O}(1)