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117 results on '"Filosi, Michele"'

1. Nuclear and mitochondrial genetic variants associated with mitochondrial DNA copy number

Author

Koller, Adriana, Filosi, Michele, Weissensteiner, Hansi, Fazzini, Federica, Gorski, Mathias, Pattaro, Cristian, Schönherr, Sebastian, Forer, Lukas, Herold, Janina M., Stark, Klaus J., Döttelmayer, Patricia, Hicks, Andrew A., Pramstaller, Peter P., Würzner, Reinhard, Eckardt, Kai-Uwe, Heid, Iris M., Fuchsberger, Christian, Lamina, Claudia, and Kronenberg, Florian

Published
2024
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4. The person-to-person transmission landscape of the gut and oral microbiomes

Author

Valles-Colomer, Mireia, Blanco-Míguez, Aitor, Manghi, Paolo, Asnicar, Francesco, Dubois, Leonard, Golzato, Davide, Armanini, Federica, Cumbo, Fabio, Huang, Kun D., Manara, Serena, Masetti, Giulia, Pinto, Federica, Piperni, Elisa, Punčochář, Michal, Ricci, Liviana, Zolfo, Moreno, Farrant, Olivia, Goncalves, Adriana, Selma-Royo, Marta, Binetti, Ana G., Becerra, Jimmy E., Han, Bei, Lusingu, John, Amuasi, John, Amoroso, Loredana, Visconti, Alessia, Steves, Claire M., Falchi, Mario, Filosi, Michele, Tett, Adrian, Last, Anna, Xu, Qian, Qin, Nan, Qin, Huanlong, May, Jürgen, Eibach, Daniel, Corrias, Maria Valeria, Ponzoni, Mirco, Pasolli, Edoardo, Spector, Tim D., Domenici, Enrico, Collado, Maria Carmen, and Segata, Nicola

Published
2023
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5. Transcriptome analysis of the zebrafish atoh7−/− Mutant, lakritz, highlights Atoh7‐dependent genetic networks with potential implications for human eye diseases

Author

Covello, Giuseppina, Rossello, Fernando J, Filosi, Michele, Gajardo, Felipe, Duchemin, Anne‐Laure, Tremonti, Beatrice F, Eichenlaub, Michael, Polo, Jose M, Powell, David, Ngai, John, Allende, Miguel L, Domenici, Enrico, Ramialison, Mirana, and Poggi, Lucia

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Neurosciences, Eye Disease and Disorders of Vision, Aetiology, 2.1 Biological and endogenous factors, Eye, Ath5, human retina, inherited eye diseases, retinal ganglion cells, transcriptome analysis, Biological sciences
Abstract

Expression of the bHLH transcription protein Atoh7 is a crucial factor conferring competence to retinal progenitor cells for the development of retinal ganglion cells. Several studies have emerged establishing ATOH7 as a retinal disease gene. Remarkably, such studies uncovered ATOH7 variants associated with global eye defects including optic nerve hypoplasia, microphthalmia, retinal vascular disorders, and glaucoma. The complex genetic networks and cellular decisions arising downstream of atoh7 expression, and how their dysregulation cause development of such disease traits remains unknown. To begin to understand such Atoh7-dependent events in vivo, we performed transcriptome analysis of wild-type and atoh7 mutant (lakritz) zebrafish embryos at the onset of retinal ganglion cell differentiation. We investigated in silico interplays of atoh7 and other disease-related genes and pathways. By network reconstruction analysis of differentially expressed genes, we identified gene clusters enriched in retinal development, cell cycle, chromatin remodeling, stress response, and Wnt pathways. By weighted gene coexpression network, we identified coexpression modules affected by the mutation and enriched in retina development genes tightly connected to atoh7. We established the groundwork whereby Atoh7-linked cellular and molecular processes can be investigated in the dynamic multi-tissue environment of the developing normal and diseased vertebrate eye.

Published
2020

7. Differential network analysis and graph classification: a glocal approach

Author

Jurman, Giuseppe, Filosi, Michele, Riccadonna, Samantha, Visintainer, Roberto, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods
Abstract

Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification tasks. The HIM distance is defined as the one-parameter family of product metrics linearly combining the normalised Hamming distance H and the normalised Ipsen-Mikhailov spectral distance IM. The combination of the two components within a single metric allows overcoming their drawbacks and obtaining a measure that is simultaneously global and local. Furthermore, plugging the HIM kernel into a Support Vector Machine gives us a classification algorithm based on the HIM distance. First, we outline the theory underlying the metric construction. We introduce two diverse applications of the HIM distance and the HIM kernel to biological datasets. This versatility supports the adoption of the HIM family as a general tool for information extraction, quantifying difference among diverse in- stances of a complex system. An Open Source implementation of the HIM metrics is provided by the R package nettols and in its web interface ReNette., Comment: Submitted for BMTL 2015 Proceedings

Published
2016

10. DTW-MIC Coexpression Networks from Time-Course Data

Author

Riccadonna, Samantha, Jurman, Giuseppe, Visintainer, Roberto, Filosi, Michele, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying horizontal displacements (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on both synthetic and transcriptomic datasets.

Published
2012

11. Stability Indicators in Network Reconstruction

Author

Jurman, Giuseppe, Filosi, Michele, Visintainer, Roberto, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance when the gold standard is unknown is a difficult task. Here we propose to use a few reconstruction stability tools as a quantitative solution to this problem. We introduce four indicators to quantitatively assess the stability of a reconstructed network in terms of variability with respect to data subsampling. In particular, we give a measure of the mutual distances among the set of networks generated by a collection of data subsets (and from the network generated on the whole dataset) and we rank nodes and edges according to their decreasing variability within the same set of networks. As a key ingredient, we employ a global/local network distance combined with a bootstrap procedure. We demonstrate the use of the indicators in a controlled situation on a toy dataset, and we show their application on a miRNA microarray dataset with paired tumoral and non-tumoral tissues extracted from a cohort of 241 hepatocellular carcinoma patients.

Published
2012

12. Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

Author

Albanese, Davide, Filosi, Michele, Visintainer, Roberto, Riccadonna, Samantha, Jurman, Giuseppe, and Furlanello, Cesare

Subjects
Statistics - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties, and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large (n=1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Availability and Implementation: Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX)., Comment: Bioinformatics 2012, in press

Published
2012
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13. The HIM glocal metric and kernel for network comparison and classification

Author

Jurman, Giuseppe, Visintainer, Roberto, Filosi, Michele, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Mathematics - Combinatorics, Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of complex systems. Both tasks have been recently tackled via quite different strategies, even tailored ad-hoc for the investigated problem. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices. The new measure combines the local Hamming distance and the global spectral Ipsen-Mikhailov distance so to overcome the drawbacks affecting the two components separately. Building then the HIM kernel function derived from the HIM distance it is possible to move from network comparison to network classification via the Support Vector Machine (SVM) algorithm. Applications of HIM distance and HIM kernel in computational biology and social networks science demonstrate the effectiveness of the proposed functions as a general purpose solution., Comment: Frontiers of Network Analysis: Methods, Models, and Applications - NIPS 2013 Workshop

Published
2012

14. A machine learning pipeline for discriminant pathways identification

Author

Barla, Annalisa, Jurman, Giuseppe, Visintainer, Roberto, Squillario, Margherita, Filosi, Michele, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

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.

Published
2011

15. The interplay between oxidative stress and inflammation supports autistic-related behaviors in mice

Author

Pangrazzi, Luca, primary, Cerilli, Enrica, additional, Balasco, Luigi, additional, Tobia, Caterina, additional, Dall'O', Ginevra Matilde, additional, Chelini, Gabriele, additional, Perini, Samuel, additional, Filosi, Michele, additional, Ravizza, Teresa, additional, Vezzani, Annamaria, additional, Provenzano, Giovanni, additional, Pastore, Anna, additional, Weinberger, Birgit, additional, Domenici, Enrico, additional, and Bozzi, Yuri, additional

Published
2023
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18. Stability in GRN Inference

Author

Jurman, Giuseppe, primary, Filosi, Michele, additional, Visintainer, Roberto, additional, Riccadonna, Samantha, additional, and Furlanello, Cesare, additional

Published
2018
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19. The person-to-person transmission landscape of the gut and oral microbiomes

Author

European Commission, National Cancer Institute (US), European Research Council, Simons Foundation, EMBO, 0000-0002-1988-6054, 0000-0001-7386-5572, 0000-0003-0846-6529, 0000-0003-3732-1468, 0000-0001-6371-528X, 0000-0002-4539-4811, 0000-0001-9450-9235, 0000-0001-6661-4046, 0000-0001-5212-1101, 0000-0002-8640-2662, 0000-0003-4144-2019, 0000-0002-5646-1004, 0000-0002-3872-347X, 0000-0002-7623-217X, 0000-0002-9128-9414, 0000-0001-8438-2322, 0000-0001-7831-8420, 0000-0002-7316-0772, 0000-0002-9795-0365, 0000-0001-7436-6919, 0000-0002-1583-5794, Valles-Colomer, Mireia, Blanco-Míguez, Aitor, Manghi, Paolo, Asnicar, Francesco, Dubois, Leonard, Golzato, Davide, Armanini, Federica, Cumbo, Fabio, Huang, Kun D., Manara, Serena, Masetti, Giulia, Pinto, Federica, Piperni, Elisa, Punčochář, Michal, Ricci, Liviana, Zolfo, Moreno, Farrant, Olivia, Goncalves, Adriana, Selma-Royo, Marta, Binetti, Ana G., Becerra, Jimmy E., Han, Bei, Lusingu, John, Amuasi, John, Amoroso, Loredana, Visconti, Alessia, Steves, Claire M., Falchi, Mario, Filosi, Michele, Tett, Adrian, Last, Anna, Xu, Qian, Qin, Nan, Qin, Huanlong, May, Jürgen, Eibach, Daniel, Corrias, Maria Valeria, Ponzoni, Mirco, Pasolli, Edoardo, Spector, Tim D., Domenici, Enrico, Collado, María Carmen, Segata, Nicola, European Commission, National Cancer Institute (US), European Research Council, Simons Foundation, EMBO, 0000-0002-1988-6054, 0000-0001-7386-5572, 0000-0003-0846-6529, 0000-0003-3732-1468, 0000-0001-6371-528X, 0000-0002-4539-4811, 0000-0001-9450-9235, 0000-0001-6661-4046, 0000-0001-5212-1101, 0000-0002-8640-2662, 0000-0003-4144-2019, 0000-0002-5646-1004, 0000-0002-3872-347X, 0000-0002-7623-217X, 0000-0002-9128-9414, 0000-0001-8438-2322, 0000-0001-7831-8420, 0000-0002-7316-0772, 0000-0002-9795-0365, 0000-0001-7436-6919, 0000-0002-1583-5794, Valles-Colomer, Mireia, Blanco-Míguez, Aitor, Manghi, Paolo, Asnicar, Francesco, Dubois, Leonard, Golzato, Davide, Armanini, Federica, Cumbo, Fabio, Huang, Kun D., Manara, Serena, Masetti, Giulia, Pinto, Federica, Piperni, Elisa, Punčochář, Michal, Ricci, Liviana, Zolfo, Moreno, Farrant, Olivia, Goncalves, Adriana, Selma-Royo, Marta, Binetti, Ana G., Becerra, Jimmy E., Han, Bei, Lusingu, John, Amuasi, John, Amoroso, Loredana, Visconti, Alessia, Steves, Claire M., Falchi, Mario, Filosi, Michele, Tett, Adrian, Last, Anna, Xu, Qian, Qin, Nan, Qin, Huanlong, May, Jürgen, Eibach, Daniel, Corrias, Maria Valeria, Ponzoni, Mirco, Pasolli, Edoardo, Spector, Tim D., Domenici, Enrico, Collado, María Carmen, and Segata, Nicola

Abstract

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

Published
2023

22. Increased Levels of the Parkinson’s Disease-Associated Gene ITPKB Correlate with Higher Expression Levels of α-Synuclein, Independent of Mutation Status

Author

Di Leva, Francesca, primary, Filosi, Michele, additional, Oyston, Lisa, additional, Silvestri, Erica, additional, Picard, Anne, additional, Lavdas, Alexandros A., additional, Lobbestael, Evy, additional, Baekelandt, Veerle, additional, Neely, G. Gregory, additional, Pramstaller, Peter P., additional, Hicks, Andrew A., additional, and Corti, Corrado, additional

Published
2023
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25. Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

Author

Enrico Domenici, Samuel Perini, and Filosi Michele

Abstract

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues.

Published
2022

26. A Machine Learning Pipeline for Discriminant Pathways Identification

Author

Barla, Annalisa, Jurman, Giuseppe, Visintainer, Roberto, Squillario, Margherita, Filosi, Michele, Riccadonna, Samantha, Furlanello, Cesare, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Biganzoli, Elia, editor, Vellido, Alfredo, editor, Ambrogi, Federico, editor, and Tagliaferri, Roberto, editor

Published
2012
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27. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Author

Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, Mangravite, Lara M., Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, and Mangravite, Lara M.

Abstract

© 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

Published
2022

29. Natural SINEUP RNAs in Autism Spectrum Disorders: RAB11B-AS1 Dysregulation in a Neuronal CHD8 Suppression Model Leads to RAB11B Protein Increase

Author

Zarantonello, Giulia, primary, Arnoldi, Michele, additional, Filosi, Michele, additional, Tebaldi, Toma, additional, Spirito, Giovanni, additional, Barbieri, Anna, additional, Gustincich, Stefano, additional, Sanges, Remo, additional, Domenici, Enrico, additional, Di Leva, Francesca, additional, and Biagioli, Marta, additional

Published
2021
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32. Transcriptome analysis of the zebrafishatoh7−/−Mutant,lakritz, highlights Atoh7‐dependent genetic networks with potential implications for human eye diseases

Author

Covello, Giuseppina, primary, Rossello, Fernando J., additional, Filosi, Michele, additional, Gajardo, Felipe, additional, Duchemin, Anne‐Laure, additional, Tremonti, Beatrice F., additional, Eichenlaub, Michael, additional, Polo, Jose M., additional, Powell, David, additional, Ngai, John, additional, Allende, Miguel L., additional, Domenici, Enrico, additional, Ramialison, Mirana, additional, and Poggi, Lucia, additional

Published
2020
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34. Gene Net Weaver Dataset

Author

Furlanello, Cesare, Filosi, Michele, Visintainer, Roberto, Jurman, Giuseppe, and Riccadonna, Samantha

Subjects
bepress|Life Sciences|Biology
Abstract

The zip archive collects the data used in the "GeneNetWeaver Yeast & E. coli data" Section of the manuscript "DTW-MIC Coexpression Networks from Time-Course Data".The zip archive include a text file (GeneNetWeaver_settings.txt) with the Gene Net Weaver parameters used to generate both the networks and the time-course data, and three folders, one for each of the dataset Ecoli20, Ecoli50, Yeast20.Each folder include a text file (gene_names.txt) with the gene names, a tab-separated text file with the true networks as edgelist (true_network.tsv) and a tab-separated text file with the 10 instances of the time-course perturbation data (perturbation_time_series.tsv). The Yeast20 folder includes and additional tab-separated text file with the 4 instances of the time-course knock-out data (dualknockout_time_series.tsv).

Published
2016

40. A network medicine approach on microarray and Next generation Sequencing data

Author

Filosi, Michele, Furlanello, Cesare, and Jurman, Giuseppe

Subjects
BIO/18 GENETICA, FIS/07 FISICA APPLICATA (A BENI CULTURALI, AMBIENTALI, BIOLOGIA E MEDICINA), ING-INF/06 BIOINGEGNERIA ELETTRONICA E INFORMATICA, BIO/11 BIOLOGIA MOLECOLARE
Abstract

The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu.

Published
2014

44. A network medicine approach on microarray and Next generation Sequencing data

Author

Furlanello, Cesare, Jurman, Giuseppe, Filosi, Michele, Furlanello, Cesare, Jurman, Giuseppe, and Filosi, Michele

Abstract

The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu.

Published
2014

46. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Author

Wang, Charles, primary, Gong, Binsheng, additional, Bushel, Pierre R, additional, Thierry-Mieg, Jean, additional, Thierry-Mieg, Danielle, additional, Xu, Joshua, additional, Fang, Hong, additional, Hong, Huixiao, additional, Shen, Jie, additional, Su, Zhenqiang, additional, Meehan, Joe, additional, Li, Xiaojin, additional, Yang, Lu, additional, Li, Haiqing, additional, Łabaj, Paweł P, additional, Kreil, David P, additional, Megherbi, Dalila, additional, Gaj, Stan, additional, Caiment, Florian, additional, van Delft, Joost, additional, Kleinjans, Jos, additional, Scherer, Andreas, additional, Devanarayan, Viswanath, additional, Wang, Jian, additional, Yang, Yong, additional, Qian, Hui-Rong, additional, Lancashire, Lee J, additional, Bessarabova, Marina, additional, Nikolsky, Yuri, additional, Furlanello, Cesare, additional, Chierici, Marco, additional, Albanese, Davide, additional, Jurman, Giuseppe, additional, Riccadonna, Samantha, additional, Filosi, Michele, additional, Visintainer, Roberto, additional, Zhang, Ke K, additional, Li, Jianying, additional, Hsieh, Jui-Hua, additional, Svoboda, Daniel L, additional, Fuscoe, James C, additional, Deng, Youping, additional, Shi, Leming, additional, Paules, Richard S, additional, Auerbach, Scott S, additional, and Tong, Weida, additional

Published
2014
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50. Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation.

Author

FAY, DAMIEN, MOORE, ANDREW W., BROWN, KEN, FILOSI, MICHELE, and JURMAN, GIUSEPPE

Subjects
BAYESIAN analysis, GRAPHIC methods, PARAMETER estimation
Abstract

In this paper, we investigate Approximate Bayes Computation as a technique for estimating the parameters of graph generators relative to an observed graph. Specifically, we investigate six spectral graph metrics with a view to evaluating their suitability as summary statistics. The overall findings are that Approximate Bayesian Computation can result in reasonable estimates of the parameter posteriors, if the rank of the metrics is sufficiently high. For some graph metrics, biases can exist in the estimated parameters though these appear, empirically, to be small. We demonstrate that combining metrics to form a new summary statistic provides more robust estimates. Given these results, the authors then create two, somewhat arbitrary, graph generators and show how the parameters for these may be estimated with ease. In addition, we show how to apply model selection to determine which generator best explains the observed graph. [ABSTRACT FROM AUTHOR]

Published
2015
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