02685nas a2200349 4500008004100000022001400041245011500055210006900170260001600239300001400255490000700269520162000276653001201896653002201908653001101930653001301941653001301954653001101967653002401978653002002002653003102022653001302053100003102066700001902097700002002116700002202136700001802158700001802176700002802194700002002222856009302242 2017 eng d a1367-481100aReference genome assessment from a population scale perspective: an accurate profile of variability and noise.0 aReference genome assessment from a population scale perspective  c2017 Nov 15 a3511-35170 v333 a

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.

10aAnimals10aGenetic Variation10aGenome10aGenomics10aGenotype10aHumans10aModels, Statistical10aQuality Control10aReproducibility of Results10aSoftware1 aCarbonell-Caballero, José1 aAmadoz, Alicia1 aAlonso, Roberto1 aHidalgo, Marta, R1 aCubuk, Cankut1 aConesa, David1 aLópez-Quílez, Antonio1 aDopazo, Joaquin uhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx48202616nas a2200325 4500008004100000022001400041245012100055210006900176260001600245300000900261490000700270520154400277653002101821653001901842653002901861653002001890653003401910653001301944653001101957653003201968100002402000700002002024700001902044700001902063700002402082700001902106700002002125700002002145856012502165 2012 eng d a1362-496200aDiscovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.0 aDiscovering the hidden subnetwork component in a ranked list of  c2012 Nov 01 ae1580 v403 a

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

10aBipolar Disorder10aFanconi Anemia10aGene Regulatory Networks10aGenes, Neoplasm10aGenome-Wide Association Study10aGenomics10aHumans10aProtein Interaction Mapping1 aGarcía-Alonso, Luz1 aAlonso, Roberto1 aVidal, Enrique1 aAmadoz, Alicia1 aDe Maria, Alejandro1 aMinguez, Pablo1 aMedina, Ignacio1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/discovering-hidden-sub-network-component-ranked-list-genes-or-proteins-derived-genomic