Lipid Metabolism and Comparative Genomics

Unilever asked the Study Group to focus on two problems. The first concerned dysregulated lipid metabolism which is a feature of many diseases including metabolic syndrome, obesity and coronary heart disease. The Study Group was asked to develop a model of the kinetics of lipoprotein metabolism between healthy and obese states incorporating the activities of key enzymes. The second concerned the use of comparative genomics in understanding and comparing metabolic networks in bacterium. Comparative genomics is a method to make inferences on the genome of a new organism using information of a previously charaterised organism. The first mathematical question is how one would quantify such a metabolic map in a statistical sense, in particular, where there are different levels of confidence for presense of different parts of the map. The next and most important question is how one can design a measurement strategy to maximise the confidence in the accuracy of the metabolic map

Comparative Analysis of Muscle Transcriptome between Pig Genotypes Identifies Genes and Regulatory Mechanisms Associated to Growth, Fatness and Metabolism.

Iberian ham production includes both purebred (IB) and Duroc-crossbred (IBxDU) Iberian pigs, which show important differences in meat quality and production traits, such as muscle growth and fatness. This experiment was conducted to investigate gene expression differences, transcriptional regulation and genetic polymorphisms that could be associated with the observed phenotypic differences between IB and IBxDU pigs. Nine IB and 10 IBxDU pigs were slaughtered at birth. Morphometric measures and blood samples were obtained and samples from Biceps femoris muscle were employed for compositional and transcriptome analysis by RNA-Seq technology. Phenotypic differences were evident at this early age, including greater body size and weight in IBxDU and greater Biceps femoris intramuscular fat and plasma cholesterol content in IB newborns. We detected 149 differentially expressed genes between IB and IBxDU neonates (p < 0.01 and Fold-Change > 1. 5). Several were related to adipose and muscle tissues development (DLK1, FGF21 or UBC). The functional interpretation of the transcriptomic differences revealed enrichment of functions and pathways related to lipid metabolism in IB and to cellular and muscle growth in IBxDU pigs. Protein catabolism, cholesterol biosynthesis and immune system were functions enriched in both genotypes. We identified transcription factors potentially affecting the observed gene expression differences. Some of them have known functions on adipogenesis (CEBPA, EGRs), lipid metabolism (PPARGC1B) and myogenesis (FOXOs, MEF2D, MYOD1), which suggest a key role in the meat quality differences existing between IB and IBxDU hams. We also identified several polymorphisms showing differential segregation between IB and IBxDU pigs. Among them, non-synonymous variants were detected in several transcription factors as PPARGC1B and TRIM63 genes, which could be associated to altered gene function. Taken together, these results provide information about candidate genes, metabolic pathways and genetic polymorphisms potentially involved in phenotypic differences between IB and IBxDU pigs associated to meat quality and production traits

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix