Gene Regulatory Networks from Multifactorial Perturbations Using Graphical Lasso: Application to the DREAM4 Challenge

A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network

Demography and mating system shape the genome-wide impact of purifying selection in Arabis alpina

YesPlant mating systems have profound effects on levels and structuring of genetic variation and can affect the impact of natural selection. Although theory predicts that intermediate outcrossing rates may allow plants to prevent accumulation of deleterious alleles, few studies have empirically tested this prediction using genomic data. Here, we study the effect of mating system on purifying selection by conducting population-genomic analyses on whole-genome resequencing data from 38 European individuals of the arctic-alpine crucifer Arabis alpina. We find that outcrossing and mixed-mating populations maintain genetic diversity at similar levels, whereas highly self-fertilizing Scandinavian A. alpina show a strong reduction in genetic diversity, most likely as a result of a postglacial colonization bottleneck. We further find evidence for accumulation of genetic load in highly self-fertilizing populations, whereas the genome-wide impact of purifying selection does not differ greatly between mixed-mating and outcrossing populations. Our results demonstrate that intermediate levels of outcrossing may allow efficient selection against harmful alleles, whereas demographic effects can be important for relaxed purifying selection in highly selfing populations. Thus, mating system and demography shape the impact of purifying selection on genomic variation in A. alpina. These results are important for an improved understanding of the evolutionary consequences of mating system variation and the maintenance of mixed-mating strategies.This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1707492115/-/DCSupplemental

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature

Finished Genome of the Fungal Wheat Pathogen Mycosphaerella graminicola Reveals Dispensome Structure, Chromosome Plasticity, and Stealth Pathogenesis.

The plant-pathogenic fungus Mycosphaerella graminicola (asexual stage: Septoria tritici) causes septoria tritici blotch, a disease that greatly reduces the yield and quality of wheat. This disease is economically important in most wheat-growing areas worldwide and threatens global food production. Control of the disease has been hampered by a limited understanding of the genetic and biochemical bases of pathogenicity, including mechanisms of infection and of resistance in the host. Unlike most other plant pathogens, M. graminicola has a long latent period during which it evades host defenses. Although this type of stealth pathogenicity occurs commonly in Mycosphaerella and other Dothideomycetes, the largest class of plant-pathogenic fungi, its genetic basis is not known. To address this problem, the genome of M. graminicolawas sequenced completely. The finished genome contains 21 chromosomes, eight of which could be lost with no visible effect on the fungus and thus are dispensable. This eight-chromosome dispensome is dynamic in field and progeny isolates, is different from the core genome in gene and repeat content, and appears to have originated by ancient horizontal transfer from an unknown donor. Synteny plots of the M. graminicola chromosomes versus those of the only other sequenced Dothideomycete, Stagonospora nodorum, revealed conservation of gene content but not order or orientation, suggesting a high rate of intra-chromosomal rearrangement in one or both species. This observed “mesosynteny” is very different from synteny seen between other organisms. A surprising feature of the M. graminicolagenome compared to other sequenced plant pathogens was that it contained very few genes for enzymes that break down plant cell walls, which was more similar to endophytes than to pathogens. The stealth pathogenesis of M. graminicola probably involves degradation of proteins rather than carbohydrates to evade host defenses during the biotrophic stage of infection and may have evolved from endophytic ancestors

Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering

Let be a given nn square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters. In additive fuzzy clustering, the nK fuzzy memberships matrix is found by least-squares approximation of the off-diagonal elements of by inner products of rows of . By contrast, kernelized fuzzy c-means is not least-squares and requires an additional fuzziness parameter. The aim is to popularize additive fuzzy clustering by interpreting it as a latent class model, whereby the elements of are modeled as the probability that two individuals share the same class on the basis of the assignment probability matrix . Two new algorithms are provided, a brute force genetic algorithm (differential evolution) and an iterative row-wise quadratic programming algorithm of which the latter is the more effective. Simulations showed that (1) the method usually has a unique solution, except in special cases, (2) both algorithms reached this solution from random restarts and (3) the number of clusters can be well estimated by AIC. Additive fuzzy clustering is computationally efficient and combines attractive features of both the vector model and the cluster model.

Genome-Wide Computational Function Prediction of Arabidopsis Proteins by Integration of Multiple Data Sources1[C][W][OA]

Although Arabidopsis (Arabidopsis thaliana) is the best studied plant species, the biological role of one-third of its proteins is still unknown. We developed a probabilistic protein function prediction method that integrates information from sequences, protein-protein interactions, and gene expression. The method was applied to proteins from Arabidopsis. Evaluation of prediction performance showed that our method has improved performance compared with single source-based prediction approaches and two existing integration approaches. An innovative feature of our method is that it enables transfer of functional information between proteins that are not directly associated with each other. We provide novel function predictions for 5,807 proteins. Recent experimental studies confirmed several of the predictions. We highlight these in detail for proteins predicted to be involved in flowering and floral organ development

MitoRS, a method for high throughput, sensitive, and accurate detection of mitochondrial DNA heteroplasmy

Abstract Background Mitochondrial dysfunction is linked to numerous pathological states, in particular related to metabolism, brain health and ageing. Nuclear encoded gene polymorphisms implicated in mitochondrial functions can be analyzed in the context of classical genome wide association studies. By contrast, mitochondrial DNA (mtDNA) variants are more challenging to identify and analyze for several reasons. First, contrary to the diploid nuclear genome, each cell carries several hundred copies of the circular mitochondrial genome. Mutations can therefore be present in only a subset of the mtDNA molecules, resulting in a heterogeneous pool of mtDNA, a situation referred to as heteroplasmy. Consequently, detection and quantification of variants requires extremely accurate tools, especially when this proportion is small. Additionally, the mitochondrial genome has pseudogenized into numerous copies within the nuclear genome over the course of evolution. These nuclear pseudogenes, named NUMTs, must be distinguished from genuine mtDNA sequences and excluded from the analysis. Results Here we describe a novel method, named MitoRS, in which the entire mitochondrial genome is amplified in a single reaction using rolling circle amplification. This approach is easier to setup and of higher throughput when compared to classical PCR amplification. Sequencing libraries are generated at high throughput exploiting a tagmentation-based method. Fine-tuned parameters are finally applied in the analysis to allow detection of variants even of low frequency heteroplasmy. The method was thoroughly benchmarked in a set of experiments designed to demonstrate its robustness, accuracy and sensitivity. The MitoRS method requires 5 ng total DNA as starting material. More than 96 samples can be processed in less than a day of laboratory work and sequenced in a single lane of an Illumina HiSeq flow cell. The lower limit for accurate quantification of single nucleotide variants has been measured at 1% frequency. Conclusions The MitoRS method enables the robust, accurate, and sensitive analysis of a large number of samples. Because it is cost effective and simple to setup, we anticipate this method will promote the analysis of mtDNA variants in large cohorts, and may help assessing the impact of mtDNA heteroplasmy on metabolic health, brain function, cancer progression, or ageing