@article {388, title = {Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Nov 15}, pages = {3511-3517}, abstract = {

MOTIVATION: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.

RESULTS: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.

AVAILABILITY AND IMPLEMENTATION: This tool is freely available at http://gitlab.com/carbonell/ces.

CONTACT: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Animals, Genetic Variation, Genome, Genomics, Genotype, Humans, Models, Statistical, Quality Control, Reproducibility of Results, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx482}, url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482}, author = {Carbonell-Caballero, Jos{\'e} and Amadoz, Alicia and Alonso, Roberto and Hidalgo, Marta R and Cubuk, Cankut and Conesa, David and L{\'o}pez-Qu{\'\i}lez, Antonio and Dopazo, Joaquin} } @article {542, title = {Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.}, journal = {Pharmacogenomics J}, volume = {10}, year = {2010}, month = {2010 Aug}, pages = {310-23}, abstract = {

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.

}, keywords = {Algorithms, Databases, Genetic, Endpoint Determination, Gene Expression Profiling, Genomics, Humans, Neural Networks, Computer, Oligonucleotide Array Sequence Analysis, Phenotype, Predictive Value of Tests, Proteins, Quality Control}, issn = {1473-1150}, doi = {10.1038/tpj.2010.35}, author = {Shi, W and Bessarabova, M and Dosymbekov, D and Dezso, Z and Nikolskaya, T and Dudoladova, M and Serebryiskaya, T and Bugrim, A and Guryanov, A and Brennan, R J and Shah, R and Dopazo, J and Chen, M and Deng, Y and Shi, T and Jurman, G and Furlanello, C and Thomas, R S and Corton, J C and Tong, W and Shi, L and Nikolsky, Y} }