Bernard Moret

Biographie

Bernard M.E. Moret was born in Vevey, Switzerland, received baccalauréats in Latin-Greek and Latin-Mathematics, then did a Diploma in Electrical Engineering at EPFL. After working for 2 years for Omega and Swiss Timing on the development of real-time OS for sports applications, he left for the US. He received his PhD in Electrical Engineering from the U. of Tennessee in 1980 and joined the Department of Computer Science at the University of New Mexico (UNM) that fall. He served as Chairman of the department from 1991 till 1993 and eventually retired in summer 2006 to join the School of Computer and Communication Sciences at EPFL. (You can read about his work at UNM on his (archived) personal and laboratory web pages at UNM.) He was appointed group leader for phylogenetics at the Swiss Institute for Bioinformatics (SIB). From 2009 until his retirement, he was also in charge of the BS and MS programs in Computer Science and Associate Dean for Education. He founded the ACM Journal of Experimental Algorithmics (JEA) and served as its Editor-in-Chief for 7 years; he also helped found the IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), where he served as Associate Editor until 2008. He founded the annual Workshop on Algorithms in Bioinformatics (WABI) and chairs its steering committee, and he serves on the steering committee of the Workshop on Algorithm Engineering and Experiments (ALENEX). Until summer 2008, he chaired the Biodata Management and Analysis (BDMA) study section of the US National Institutes of Health (NIH); now he is a charter member of the NIH College of Reviewers. He led a team of over 50 biologists, computer scientists, and mathematicians in the CIPRES (Cyber Infrastructure for Phylogenetic Research) project, funded by the US National Science Foundation (NSF) for US$ 12 million over 5 years. He has published nearly 150 papers in computational biology, under funding from the US NSF, the Alfred P. Sloan foundation, the IBM Corporation, the US NIH, the Swiss NSF, and SystemsX.ch. He is a Fellow of the ISCB (International Society for Computational Biology). His Erdös number is 2 and (as of 2020) his h-index is 48.

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https://people.epfl.ch/bernard.moret?lang=fr

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NEMo: An Evolutionary Model With Modularity for PPI Networks

Qijia Jiang, Bernard Moret, Gabriela Clara Racz, Min Ye, Xiuwei Zhang

Modeling the evolution of biological networks is a major challenge. Biological networks are usually represented as graphs; evolutionary events not only include addition and removal of vertices and edges but also duplication of vertices and their associated edges. Since duplication is viewed as a primary driver of genomic evolution, recent work has focused on duplication-based models. Missing from these models is any embodiment of modularity, a widely accepted attribute of biological networks. Some models spontaneously generate modular structures, but none is known to maintain and evolve them. We describe network evolution with modularity (NEMo), a new model that embodies modularity. NEMo allows modules to appear and disappear and to fission and to merge, all driven by the underlying edge-level events using a duplication-based process. We also introduce measures to compare biological networks in terms of their modular structure; we present comparisons between NEMo and existing duplication-based models and run our measuring tools on both generated and published networks. Index indicates

IEEE Press2017

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Many important questions in molecular biology, evolution, and biomedicine can be addressed by comparative genomic approaches. One of the basic tasks when comparing genomes is the definition of measures of similarity (or dissimilarity) between two genomes, for example, to elucidate the phylogenetic relationships between species. The power of different genome comparison methods varies with the underlying formal model of a genome. The simplest models impose the strong restriction that each genome under study must contain the same genes, each in exactly one copy. More realistic models allow several copies of a gene in a genome. One speaks of gene families, and comparative genomic methods that allow this kind of input are called gene family-based. The most powerfulbut also most complexmodels avoid this preprocessing of the input data and instead integrate the family assignment within the comparative analysis. Such methods are called gene family-free. In this article, we study an intermediate approach between family-based and family-free genomic similarity measures. Introducing this simpler model, called gene connections, we focus on the combinatorial aspects of gene family-free genome comparison. While in most cases, the computational costs to the general family-free case are the same, we also find an instance where the gene connections model has lower complexity. Within the gene connections model, we define three variants of genomic similarity measures that have different expression powers. We give polynomial-time algorithms for two of them, while we show NP-hardness for the third, most powerful one. We also generalize the measures and algorithms to make them more robust against recent local disruptions in gene order. Our theoretical findings are supported by experimental results, proving the applicability and performance of our newly defined similarity measures.

Mary Ann Liebert2017

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On Computing Breakpoint Distances for Genomes with Duplicate Genes

Bernard Moret, Mingfu Shao

A fundamental problem in comparative genomics is to compute the distance between two genomes in terms of its higher level organization (given by genes or syntenic blocks). For two genomes without duplicate genes, we can easily define (and almost always efficiently compute) a variety of distance measures, but the problem is NP-hard under most models when genomes contain duplicate genes. To tackle duplicate genes, three formulations (exemplar, maximum matching, and any matching) have been proposed, all of which aim to build a matching between homologous genes so as to minimize some distance measure. Of the many distance measures, the breakpoint distance (the number of nonconserved adjacencies) was the first one to be studied and remains of significant interest because of its simplicity and model-free property. The three breakpoint distance problems corresponding to the three formulations have been widely studied. Although we provided last year a solution for the exemplar problem that runs very fast on full genomes, computing optimal solutions for the other two problems has remained challenging. In this article, we describe very fast, exact algorithms for these two problems. Our algorithms rely on a compact integer-linear program that we further simplify by developing an algorithm to remove variables, based on new results on the structure of adjacencies and matchings. Through extensive experiments using both simulations and biological data sets, we show that our algorithms run very fast (in seconds) on mammalian genomes and scale well beyond. We also apply these algorithms (as well as the classic orthology tool MSOAR) to create orthology assignment, then compare their quality in terms of both accuracy and coverage. We find that our algorithm for the any matching formulation significantly outperforms other methods in terms of accuracy while achieving nearly maximum coverage.

Mary Ann Liebert, Inc2017

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On Computing Breakpoint Distances for Genomes with Duplicate Genes

Bernard Moret, Mingfu Shao

Mary Ann Liebert, Inc2017