Search

Your search keyword '"Furlanello C"' showing total 219 results
Start Over Author "Furlanello C"
219 results on '"Furlanello C"'

1. Supervised classification of combined copy number and gene expression data

Author

Riccadonna S., Jurman G., Merler S., Paoli S., Quattrone A., and Furlanello C.

Subjects
Biotechnology, TP248.13-248.65
Abstract

In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

Published
2007
Full Text
View/download PDF

2. In-field grape berries counting for yield estimation using dilated CNNs

Author

Coviello, L., Cristoforetti, M., Jurman, G., and Furlanello, C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning
Abstract

Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost, leveraging existing technologies and agronomists' skills. As a contribution along this direction we demonstrate a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting.

Published
2019

4. Towards a scientific blockchain framework for reproducible data analysis

Author

Furlanello, C., De Domenico, M., Jurman, G., and Bussola, N.

Subjects
Computer Science - Computers and Society, Computer Science - Cryptography and Security, Quantitative Biology - Quantitative Methods
Abstract

Publishing reproducible analyses is a long-standing and widespread challenge for the scientific community, funding bodies and publishers. Although a definitive solution is still elusive, the problem is recognized to affect all disciplines and lead to a critical system inefficiency. Here, we propose a blockchain-based approach to enhance scientific reproducibility, with a focus on life science studies and precision medicine. While the interest of encoding permanently into an immutable ledger all the study key information-including endpoints, data and metadata, protocols, analytical methods and all findings-has been already highlighted, here we apply the blockchain approach to solve the issue of rewarding time and expertise of scientists that commit to verify reproducibility. Our mechanism builds a trustless ecosystem of researchers, funding bodies and publishers cooperating to guarantee digital and permanent access to information and reproducible results. As a natural byproduct, a procedure to quantify scientists' and institutions' reputation for ranking purposes is obtained., Comment: 8 pages, 1 figure

Published
2017

5. Evaluating sources of variability in pathway profiling

Author

Barla, A., Riccadonna, S., Masecchia, S., Squillario, M., Filosi, M., Jurman, G., and Furlanello, C.

Subjects
Quantitative Biology - Molecular Networks
Abstract

A bioinformatics platform is introduced aimed at identifying models of disease-specific pathways, as well as a set of network measures that can quantify changes in terms of global structure or single link disruptions.The approach integrates a network comparison framework with machine learning molecular profiling. The platform includes different tools combined in one Open Source pipeline, supporting reproducibility of the analysis. We describe here the computational pipeline and explore the main sources of variability that can affect the results, namely the classifier, the feature ranking/selection algorithm, the enrichment procedure, the inference method and the networks comparison function. The proposed pipeline is tested on a microarray dataset of late stage Parkinsons' Disease patients together with healty controls. Choosing different machine learning approaches we get low pathway profiling overlapping in terms of common enriched elements. Nevertheless, they identify different but equally meaningful biological aspects of the same process, suggesting the integration of information across different methods as the best overall strategy. All the elements of the proposed pipeline are available as Open Source Software: availability details are provided in the main text.

Published
2012

6. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

Author

Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., Esposito A., Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., and Esposito A.

Abstract

Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Published
2022

10. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Author

Grapotte M., Saraswat M., Bessiere C., Menichelli C., Ramilowski J. A., Severin J., Hayashizaki Y., Itoh M., Tagami M., Murata M., Kojima-Ishiyama M., Noma S., Noguchi S., Kasukawa T., Hasegawa A., Suzuki H., Nishiyori-Sueki H., Frith M. C., Abugessaisa I., Aitken S., Aken B. L., Alam I., Alam T., Alasiri R., Alhendi A. M. N., Alinejad-Rokny H., Alvarez M. J., Andersson R., Arakawa T., Araki M., Arbel T., Archer J., Archibald A. L., Arner E., Arner P., Asai K., Ashoor H., Astrom G., Babina M., Baillie J. K., Bajic V. B., Bajpai A., Baker S., Baldarelli R. M., Balic A., Bansal M., Batagov A. O., Batzoglou S., Beckhouse A. G., Beltrami A. P., Beltrami C. A., Bertin N., Bhattacharya S., Bickel P. J., Blake J. A., Blanchette M., Bodega B., Bonetti A., Bono H., Bornholdt J., Bttcher M., Bougouffa S., Boyd M., Breda J., Brombacher F., Brown J. B., Bult C. J., Burroughs A. M., Burt D. W., Busch A., Caglio G., Califano A., Cameron C. J., Cannistraci C. V., Carbone A., Carlisle A. J., Carninci P., Carter K. W., Cesselli D., Chang J. -C., Chen J. C., Chen Y., Chierici M., Christodoulou J., Ciani Y., Clark E. L., Coskun M., Dalby M., Dalla E., Daub C. O., Davis C. A., de Hoon M. J. L., de Rie D., Denisenko E., Deplancke B., Detmar M., Deviatiiarov R., Di Bernardo D., Diehl A. D., Dieterich L. C., Dimont E., Djebali S., Dohi T., Dostie J., Drablos F., Edge A. S. B., Edinger M., Ehrlund A., Ekwall K., Elofsson A., Endoh M., Enomoto H., Enomoto S., Faghihi M., Fagiolini M., Farach-Carson M. C., Faulkner G. J., Favorov A., Fernandes A. M., Ferrai C., Forrest A. R. R., Forrester L. M., Forsberg M., Fort A., Francescatto M., Freeman T. C., Frith M., Fukuda S., Funayama M., Furlanello C., Furuno M., Furusawa C., Gao H., Gazova I., Gebhard C., Geier F., Geijtenbeek T. B. H., Ghosh S., Ghosheh Y., Gingeras T. R., Gojobori T., Goldberg T., Goldowitz D., Gough J., Greco D., Gruber A. J., Guhl S., Guigo R., Guler R., Gusev O., Gustincich S., Ha T. J., Haberle V., Hale P., Hallstrom B. M., Hamada M., Handoko L., Hara M., Harbers M., Harrow J., Harshbarger J., Hase T., Hashimoto K., Hatano T., Hattori N., Hayashi R., Herlyn M., Hettne K., Heutink P., Hide W., Hitchens K. J., Sui S. H., 't Hoen P. A. C., Hon C. C., Hori F., Horie M., Horimoto K., Horton P., Hou R., Huang E., Huang Y., Hugues R., Hume D., Ienasescu H., Iida K., Ikawa T., Ikemura T., Ikeo K., Inoue N., Ishizu Y., Ito Y., Ivshina A. V., Jankovic B. R., Jenjaroenpun P., Johnson R., Jorgensen M., Jorjani H., Joshi A., Jurman G., Kaczkowski B., Kai C., Kaida K., Kajiyama K., Kaliyaperumal R., Kaminuma E., Kanaya T., Kaneda H., Kapranov P., Kasianov A. S., Katayama T., Kato S., Kawaguchi S., Kawai J., Kawaji H., Kawamoto H., Kawamura Y. I., Kawasaki S., Kawashima T., Kempfle J. S., Kenna T. J., Kere J., Khachigian L., Kiryu H., Kishima M., Kitajima H., Kitamura T., Kitano H., Klaric E., Klepper K., Klinken S. P., Kloppmann E., Knox A. J., Kodama Y., Kogo Y., Kojima M., Kojima S., Komatsu N., Komiyama H., Kono T., Koseki H., Koyasu S., Kratz A., Kukalev A., Kulakovskiy I., Kundaje A., Kunikata H., Kuo R., Kuo T., Kuraku S., Kuznetsov V. A., Kwon T. J., Larouche M., Lassmann T., Law A., Le-Cao K. -A., Lecellier C. -H., Lee W., Lenhard B., Lennartsson A., Li K., Li R., Lilje B., Lipovich L., Lizio M., Lopez G., Magi S., Mak G. K., Makeev V., Manabe R., Mandai M., Mar J., Maruyama K., Maruyama T., Mason E., Mathelier A., Matsuda H., Medvedeva Y. A., Meehan T. F., Mejhert N., Meynert A., Mikami N., Minoda A., Miura H., Miyagi Y., Miyawaki A., Mizuno Y., Morikawa H., Morimoto M., Morioka M., Morishita S., Moro K., Motakis E., Motohashi H., Mukarram A. K., Mummery C. L., Mungall C. J., Murakawa Y., Muramatsu M., Nagasaka K., Nagase T., Nakachi Y., Nakahara F., Nakai K., Nakamura K., Nakamura Y., Nakazawa T., Nason G. P., Nepal C., Nguyen Q. H., Nielsen L. K., Nishida K., Nishiguchi K. M., Nishiyori H., Nitta K., Notredame C., Ogishima S., Ohkura N., Ohno H., Ohshima M., Ohtsu T., Okada Y., Okada-Hatakeyama M., Okazaki Y., Oksvold P., Orlando V., Ow G. S., Ozturk M., Pachkov M., Paparountas T., Parihar S. P., Park S. -J., Pascarella G., Passier R., Persson H., Philippens I. H., Piazza S., Plessy C., Pombo A., Ponten F., Poulain S., Poulsen T. M., Pradhan S., Prezioso C., Pridans C., Qin X. -Y., Quackenbush J., Rackham O., Ramilowski J., Ravasi T., Rehli M., Rennie S., Rito T., Rizzu P., Robert C., Roos M., Rost B., Roudnicky F., Roy R., Rye M. B., Sachenkova O., Saetrom P., Sai H., Saiki S., Saito M., Saito A., Sakaguchi S., Sakai M., Sakaue S., Sakaue-Sawano A., Sandelin A., Sano H., Sasamoto Y., Sato H., Saxena A., Saya H., Schafferhans A., Schmeier S., Schmidl C., Schmocker D., Schneider C., Schueler M., Schultes E. A., Schulze-Tanzil G., Semple C. A., Seno S., Seo W., Sese J., Sheng G., Shi J., Shimoni Y., Shin J. W., SimonSanchez J., Sivertsson A., Sjostedt E., Soderhall C., Laurent G. S., Stoiber M. H., Sugiyama D., Summers K. M., Suzuki A. M., Suzuki K., Suzuki M., Suzuki N., Suzuki T., Swanson D. J., Swoboda R. K., Taguchi A., Takahashi H., Takahashi M., Takamochi K., Takeda S., Takenaka Y., Tam K. T., Tanaka H., Tanaka R., Tanaka Y., Tang D., Taniuchi I., Tanzer A., Tarui H., Taylor M. S., Terada A., Terao Y., Testa A. C., Thomas M., Thongjuea S., Tomii K., Triglia E. T., Toyoda H., Tsang H. G., Tsujikawa M., Uhlen M., Valen E., van de Wetering M., van Nimwegen E., Velmeshev D., Verardo R., Vitezic M., Vitting-Seerup K., von Feilitzen K., Voolstra C. R., Vorontsov I. E., Wahlestedt C., Wasserman W. W., Watanabe K., Watanabe S., Wells C. A., Winteringham L. N., Wolvetang E., Yabukami H., Yagi K., Yamada T., Yamaguchi Y., Yamamoto M., Yamamoto Y., Yamanaka Y., Yano K., Yasuzawa K., Yatsuka Y., Yo M., Yokokura S., Yoneda M., Yoshida E., Yoshida Y., Yoshihara M., Young R., Young R. S., Yu N. Y., Yumoto N., Zabierowski S. E., Zhang P. G., Zucchelli S., Zwahlen M., Chatelain C., Brehelin L., Grapotte, M., Saraswat, M., Bessiere, C., Menichelli, C., Ramilowski, J. A., Severin, J., Hayashizaki, Y., Itoh, M., Tagami, M., Murata, M., Kojima-Ishiyama, M., Noma, S., Noguchi, S., Kasukawa, T., Hasegawa, A., Suzuki, H., Nishiyori-Sueki, H., Frith, M. C., Abugessaisa, I., Aitken, S., Aken, B. L., Alam, I., Alam, T., Alasiri, R., Alhendi, A. M. N., Alinejad-Rokny, H., Alvarez, M. J., Andersson, R., Arakawa, T., Araki, M., Arbel, T., Archer, J., Archibald, A. L., Arner, E., Arner, P., Asai, K., Ashoor, H., Astrom, G., Babina, M., Baillie, J. K., Bajic, V. B., Bajpai, A., Baker, S., Baldarelli, R. M., Balic, A., Bansal, M., Batagov, A. O., Batzoglou, S., Beckhouse, A. G., Beltrami, A. P., Beltrami, C. A., Bertin, N., Bhattacharya, S., Bickel, P. J., Blake, J. A., Blanchette, M., Bodega, B., Bonetti, A., Bono, H., Bornholdt, J., Bttcher, M., Bougouffa, S., Boyd, M., Breda, J., Brombacher, F., Brown, J. B., Bult, C. J., Burroughs, A. M., Burt, D. W., Busch, A., Caglio, G., Califano, A., Cameron, C. J., Cannistraci, C. V., Carbone, A., Carlisle, A. J., Carninci, P., Carter, K. W., Cesselli, D., Chang, J. -C., Chen, J. C., Chen, Y., Chierici, M., Christodoulou, J., Ciani, Y., Clark, E. L., Coskun, M., Dalby, M., Dalla, E., Daub, C. O., Davis, C. A., de Hoon, M. J. L., de Rie, D., Denisenko, E., Deplancke, B., Detmar, M., Deviatiiarov, R., Di Bernardo, D., Diehl, A. D., Dieterich, L. C., Dimont, E., Djebali, S., Dohi, T., Dostie, J., Drablos, F., Edge, A. S. B., Edinger, M., Ehrlund, A., Ekwall, K., Elofsson, A., Endoh, M., Enomoto, H., Enomoto, S., Faghihi, M., Fagiolini, M., Farach-Carson, M. C., Faulkner, G. J., Favorov, A., Fernandes, A. M., Ferrai, C., Forrest, A. R. R., Forrester, L. M., Forsberg, M., Fort, A., Francescatto, M., Freeman, T. C., Frith, M., Fukuda, S., Funayama, M., Furlanello, C., Furuno, M., Furusawa, C., Gao, H., Gazova, I., Gebhard, C., Geier, F., Geijtenbeek, T. B. H., Ghosh, S., Ghosheh, Y., Gingeras, T. R., Gojobori, T., Goldberg, T., Goldowitz, D., Gough, J., Greco, D., Gruber, A. J., Guhl, S., Guigo, R., Guler, R., Gusev, O., Gustincich, S., Ha, T. J., Haberle, V., Hale, P., Hallstrom, B. M., Hamada, M., Handoko, L., Hara, M., Harbers, M., Harrow, J., Harshbarger, J., Hase, T., Hashimoto, K., Hatano, T., Hattori, N., Hayashi, R., Herlyn, M., Hettne, K., Heutink, P., Hide, W., Hitchens, K. J., Sui, S. H., 't Hoen, P. A. C., Hon, C. C., Hori, F., Horie, M., Horimoto, K., Horton, P., Hou, R., Huang, E., Huang, Y., Hugues, R., Hume, D., Ienasescu, H., Iida, K., Ikawa, T., Ikemura, T., Ikeo, K., Inoue, N., Ishizu, Y., Ito, Y., Ivshina, A. V., Jankovic, B. R., Jenjaroenpun, P., Johnson, R., Jorgensen, M., Jorjani, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kajiyama, K., Kaliyaperumal, R., Kaminuma, E., Kanaya, T., Kaneda, H., Kapranov, P., Kasianov, A. S., Katayama, T., Kato, S., Kawaguchi, S., Kawai, J., Kawaji, H., Kawamoto, H., Kawamura, Y. I., Kawasaki, S., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L., Kiryu, H., Kishima, M., Kitajima, H., Kitamura, T., Kitano, H., Klaric, E., Klepper, K., Klinken, S. P., Kloppmann, E., Knox, A. J., Kodama, Y., Kogo, Y., Kojima, M., Kojima, S., Komatsu, N., Komiyama, H., Kono, T., Koseki, H., Koyasu, S., Kratz, A., Kukalev, A., Kulakovskiy, I., Kundaje, A., Kunikata, H., Kuo, R., Kuo, T., Kuraku, S., Kuznetsov, V. A., Kwon, T. J., Larouche, M., Lassmann, T., Law, A., Le-Cao, K. -A., Lecellier, C. -H., Lee, W., Lenhard, B., Lennartsson, A., Li, K., Li, R., Lilje, B., Lipovich, L., Lizio, M., Lopez, G., Magi, S., Mak, G. K., Makeev, V., Manabe, R., Mandai, M., Mar, J., Maruyama, K., Maruyama, T., Mason, E., Mathelier, A., Matsuda, H., Medvedeva, Y. A., Meehan, T. F., Mejhert, N., Meynert, A., Mikami, N., Minoda, A., Miura, H., Miyagi, Y., Miyawaki, A., Mizuno, Y., Morikawa, H., Morimoto, M., Morioka, M., Morishita, S., Moro, K., Motakis, E., Motohashi, H., Mukarram, A. K., Mummery, C. L., Mungall, C. J., Murakawa, Y., Muramatsu, M., Nagasaka, K., Nagase, T., Nakachi, Y., Nakahara, F., Nakai, K., Nakamura, K., Nakamura, Y., Nakazawa, T., Nason, G. P., Nepal, C., Nguyen, Q. H., Nielsen, L. K., Nishida, K., Nishiguchi, K. M., Nishiyori, H., Nitta, K., Notredame, C., Ogishima, S., Ohkura, N., Ohno, H., Ohshima, M., Ohtsu, T., Okada, Y., Okada-Hatakeyama, M., Okazaki, Y., Oksvold, P., Orlando, V., Ow, G. S., Ozturk, M., Pachkov, M., Paparountas, T., Parihar, S. P., Park, S. -J., Pascarella, G., Passier, R., Persson, H., Philippens, I. H., Piazza, S., Plessy, C., Pombo, A., Ponten, F., Poulain, S., Poulsen, T. M., Pradhan, S., Prezioso, C., Pridans, C., Qin, X. -Y., Quackenbush, J., Rackham, O., Ramilowski, J., Ravasi, T., Rehli, M., Rennie, S., Rito, T., Rizzu, P., Robert, C., Roos, M., Rost, B., Roudnicky, F., Roy, R., Rye, M. B., Sachenkova, O., Saetrom, P., Sai, H., Saiki, S., Saito, M., Saito, A., Sakaguchi, S., Sakai, M., Sakaue, S., Sakaue-Sawano, A., Sandelin, A., Sano, H., Sasamoto, Y., Sato, H., Saxena, A., Saya, H., Schafferhans, A., Schmeier, S., Schmidl, C., Schmocker, D., Schneider, C., Schueler, M., Schultes, E. A., Schulze-Tanzil, G., Semple, C. A., Seno, S., Seo, W., Sese, J., Sheng, G., Shi, J., Shimoni, Y., Shin, J. W., Simonsanchez, J., Sivertsson, A., Sjostedt, E., Soderhall, C., Laurent, G. S., Stoiber, M. H., Sugiyama, D., Summers, K. M., Suzuki, A. M., Suzuki, K., Suzuki, M., Suzuki, N., Suzuki, T., Swanson, D. J., Swoboda, R. K., Taguchi, A., Takahashi, H., Takahashi, M., Takamochi, K., Takeda, S., Takenaka, Y., Tam, K. T., Tanaka, H., Tanaka, R., Tanaka, Y., Tang, D., Taniuchi, I., Tanzer, A., Tarui, H., Taylor, M. S., Terada, A., Terao, Y., Testa, A. C., Thomas, M., Thongjuea, S., Tomii, K., Triglia, E. T., Toyoda, H., Tsang, H. G., Tsujikawa, M., Uhlen, M., Valen, E., van de Wetering, M., van Nimwegen, E., Velmeshev, D., Verardo, R., Vitezic, M., Vitting-Seerup, K., von Feilitzen, K., Voolstra, C. R., Vorontsov, I. E., Wahlestedt, C., Wasserman, W. W., Watanabe, K., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Yabukami, H., Yagi, K., Yamada, T., Yamaguchi, Y., Yamamoto, M., Yamamoto, Y., Yamanaka, Y., Yano, K., Yasuzawa, K., Yatsuka, Y., Yo, M., Yokokura, S., Yoneda, M., Yoshida, E., Yoshida, Y., Yoshihara, M., Young, R., Young, R. S., Yu, N. Y., Yumoto, N., Zabierowski, S. E., Zhang, P. G., Zucchelli, S., Zwahlen, M., Chatelain, C., Brehelin, L., Institute of Biotechnology, Biosciences, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), National Institute of Advanced Industrial Science and Technology (AIST), SANOFI Recherche, University of British Columbia (UBC), Experimental Immunology, Infectious diseases, AII - Infectious diseases, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)

Subjects
0301 basic medicine, General Physics and Astronomy, Genome, Mice, 0302 clinical medicine, Transcription (biology), Promoter Regions, Genetic, Transcription Initiation, Genetic, 0303 health sciences, Multidisciplinary, 1184 Genetics, developmental biology, physiology, High-Throughput Nucleotide Sequencing, Neurodegenerative Diseases, 222 Other engineering and technologies, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], humanities, Enhancer Elements, Genetic, Microsatellite Repeat, Transcription Initiation Site, Sequence motif, Transcription Initiation, Human, Enhancer Elements, Neural Networks, Science, 610 Medicine & health, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Promoter Regions, 03 medical and health sciences, Computer, Deep Learning, Tandem repeat, Genetic, Clinical Research, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Machine learning, Genetics, Animals, Humans, Polymorphism, Enhancer, Transcriptomics, Gene, A549 Cell, 030304 developmental biology, Polymorphism, Genetic, Neurodegenerative Disease, Base Sequence, Animal, Genome, Human, Human Genome, Computational Biology, Promoter, General Chemistry, 113 Computer and information sciences, Cap analysis gene expression, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Cardiovascular and Metabolic Diseases, A549 Cells, Minion, Generic health relevance, 3111 Biomedicine, Neural Networks, Computer, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery, FANTOM consortium, Microsatellite Repeats
Abstract

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., Nature Communications, 12 (1), ISSN:2041-1723

Published
2020

11. Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

Author

Gong, B, Li, D, Kusko, R, Novoradovskaya, N, Zhang, Y, Wang, S, Pabón-Peña, C, Zhang, Z, Lai, K, Cai, W, LoCoco, JS, Lader, E, Richmond, TA, Mittal, VK, Liu, LC, Johann, DJ, Willey, JC, Bushel, PR, Yu, Y, Xu, C, Chen, G, Burgess, D, Cawley, S, Giorda, K, Haseley, N, Qiu, F, Wilkins, K, Arib, H, Attwooll, C, Babson, K, Bao, L, Bao, W, Lucas, AB, Best, H, Bhandari, A, Bisgin, H, Blackburn, J, Blomquist, TM, Boardman, L, Burgher, B, Butler, DJ, Chang, CJ, Chaubey, A, Chen, T, Chierici, M, Chin, CR, Close, D, Conroy, J, Coleman, JC, Craig, DJ, Crawford, E, del Pozo, A, Deveson, IW, Duncan, D, Eterovic, AK, Fan, X, Foox, J, Furlanello, C, Ghosal, A, Glenn, S, Guan, M, Haag, C, Hang, X, Happe, S, Hennigan, B, Hipp, J, Hong, H, Horvath, K, Hu, J, Hung, LY, Jarosz, M, Kerkhof, J, Kipp, B, Kreil, DP, Łabaj, P, Lapunzina, P, Li, P, Li, QZ, Li, W, Li, Z, Liang, Y, Liu, S, Liu, Z, Ma, C, Marella, N, Martín-Arenas, R, Megherbi, DB, Meng, Q, Mieczkowski, PA, Morrison, T, Muzny, D, Ning, B, Parsons, BL, Paweletz, CP, Pirooznia, M, Qu, W, Raymond, A, Rindler, P, Ringler, R, Sadikovic, B, Gong, B, Li, D, Kusko, R, Novoradovskaya, N, Zhang, Y, Wang, S, Pabón-Peña, C, Zhang, Z, Lai, K, Cai, W, LoCoco, JS, Lader, E, Richmond, TA, Mittal, VK, Liu, LC, Johann, DJ, Willey, JC, Bushel, PR, Yu, Y, Xu, C, Chen, G, Burgess, D, Cawley, S, Giorda, K, Haseley, N, Qiu, F, Wilkins, K, Arib, H, Attwooll, C, Babson, K, Bao, L, Bao, W, Lucas, AB, Best, H, Bhandari, A, Bisgin, H, Blackburn, J, Blomquist, TM, Boardman, L, Burgher, B, Butler, DJ, Chang, CJ, Chaubey, A, Chen, T, Chierici, M, Chin, CR, Close, D, Conroy, J, Coleman, JC, Craig, DJ, Crawford, E, del Pozo, A, Deveson, IW, Duncan, D, Eterovic, AK, Fan, X, Foox, J, Furlanello, C, Ghosal, A, Glenn, S, Guan, M, Haag, C, Hang, X, Happe, S, Hennigan, B, Hipp, J, Hong, H, Horvath, K, Hu, J, Hung, LY, Jarosz, M, Kerkhof, J, Kipp, B, Kreil, DP, Łabaj, P, Lapunzina, P, Li, P, Li, QZ, Li, W, Li, Z, Liang, Y, Liu, S, Liu, Z, Ma, C, Marella, N, Martín-Arenas, R, Megherbi, DB, Meng, Q, Mieczkowski, PA, Morrison, T, Muzny, D, Ning, B, Parsons, BL, Paweletz, CP, Pirooznia, M, Qu, W, Raymond, A, Rindler, P, Ringler, R, and Sadikovic, B

Abstract

Background: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. Results: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5–20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. Conclusion: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Published
2021

12. Reporting guidelines for human microbiome research: the STORMS checklist

Author

Mirzayi, C, Renson, A, Furlanello, C, Sansone, SA, Zohra, F, Elsafoury, S, Geistlinger, L, Kasselman, LJ, Eckenrode, K, van de Wijgert, J, Loughman, Amy, Marques, FZ, MacIntyre, DA, Arumugam, M, Azhar, R, Beghini, F, Bergstrom, K, Bhatt, A, Bisanz, JE, Braun, J, Bravo, HC, Buck, GA, Bushman, F, Casero, D, Clarke, G, Collado, MC, Cotter, PD, Cryan, JF, Demmer, RT, Devkota, S, Elinav, E, Escobar, JS, Fettweis, J, Finn, RD, Fodor, AA, Forslund, S, Franke, A, Gilbert, J, Grice, E, Haibe-Kains, B, Handley, S, Herd, P, Holmes, S, Jacobs, JP, Karstens, L, Knight, R, Knights, D, Koren, O, Kwon, DS, Langille, M, Lindsay, B, McGovern, D, McHardy, AC, McWeeney, S, Mueller, NT, Nezi, L, Olm, M, Palm, N, Pasolli, E, Raes, J, Redinbo, MR, Rühlemann, M, Balfour Sartor, R, Schloss, PD, Schriml, L, Segal, E, Shardell, M, Sharpton, T, Smirnova, E, Sokol, H, Sonnenburg, JL, Srinivasan, S, Thingholm, LB, Turnbaugh, PJ, Upadhyay, V, Walls, RL, Wilmes, P, Yamada, T, Zeller, G, Zhang, M, Zhao, N, Zhao, L, Bao, W, Culhane, A, Devanarayan, V, Dopazo, J, Fan, X, Fischer, M, Jones, W, Kusko, R, Mason, CE, Mercer, TR, Scherer, A, Shi, L, Thakkar, S, Tong, W, Wolfinger, R, Hunter, C, Mirzayi, C, Renson, A, Furlanello, C, Sansone, SA, Zohra, F, Elsafoury, S, Geistlinger, L, Kasselman, LJ, Eckenrode, K, van de Wijgert, J, Loughman, Amy, Marques, FZ, MacIntyre, DA, Arumugam, M, Azhar, R, Beghini, F, Bergstrom, K, Bhatt, A, Bisanz, JE, Braun, J, Bravo, HC, Buck, GA, Bushman, F, Casero, D, Clarke, G, Collado, MC, Cotter, PD, Cryan, JF, Demmer, RT, Devkota, S, Elinav, E, Escobar, JS, Fettweis, J, Finn, RD, Fodor, AA, Forslund, S, Franke, A, Gilbert, J, Grice, E, Haibe-Kains, B, Handley, S, Herd, P, Holmes, S, Jacobs, JP, Karstens, L, Knight, R, Knights, D, Koren, O, Kwon, DS, Langille, M, Lindsay, B, McGovern, D, McHardy, AC, McWeeney, S, Mueller, NT, Nezi, L, Olm, M, Palm, N, Pasolli, E, Raes, J, Redinbo, MR, Rühlemann, M, Balfour Sartor, R, Schloss, PD, Schriml, L, Segal, E, Shardell, M, Sharpton, T, Smirnova, E, Sokol, H, Sonnenburg, JL, Srinivasan, S, Thingholm, LB, Turnbaugh, PJ, Upadhyay, V, Walls, RL, Wilmes, P, Yamada, T, Zeller, G, Zhang, M, Zhao, N, Zhao, L, Bao, W, Culhane, A, Devanarayan, V, Dopazo, J, Fan, X, Fischer, M, Jones, W, Kusko, R, Mason, CE, Mercer, TR, Scherer, A, Shi, L, Thakkar, S, Tong, W, Wolfinger, R, and Hunter, C

Published
2021

17. Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma

Author

Melaiu, O., Chierici, M., Lucarini, V., Jurman, G., Conti, L. A., De Vito, R., Boldrini, R., Cifaldi, L., Castellano, A., Furlanello, C., Barnaba, V., Locatelli, Franco, Fruci, D., Locatelli F. (ORCID:0000-0002-7976-3654), Melaiu, O., Chierici, M., Lucarini, V., Jurman, G., Conti, L. A., De Vito, R., Boldrini, R., Cifaldi, L., Castellano, A., Furlanello, C., Barnaba, V., Locatelli, Franco, Fruci, D., and Locatelli F. (ORCID:0000-0002-7976-3654)

Abstract

Tumor-infiltrating lymphocytes play an essential role in improving clinical outcome of neuroblastoma (NB) patients, but their relationship with other tumor-infiltrating immune cells in the T cell-inflamed tumors remains poorly investigated. Here we show that dendritic cells (DCs) and natural killer (NK) cells are positively correlated with T-cell infiltration in human NB, both at transcriptional and protein levels, and associate with a favorable prognosis. Multiplex imaging displays DC/NK/T cell conjugates in the tumor microenvironment of low-risk NB. Remarkably, this connection is further strengthened by the identification of gene signatures related to DCs and NK cells able to predict survival of NB patients and strongly correlate with the expression of PD-1 and PD-L1. In summary, our findings unveil a key prognostic role of DCs and NK cells and indicate their related gene signatures as promising tools for the identification of clinical biomarkers to better define risk stratification and survival of NB patients.

Published
2020

21. Deep Learning for Abnormal Movement Detection using Wearable Sensors: Case Studies on Stereotypical Motor Movements in Autism and Freezing of Gait in Parkinson's Disease

Author

Furlanello, C., Marchiori, E., Laarhoven, T. van, Mohammadian Rad, N., Furlanello, C., Marchiori, E., Laarhoven, T. van, and Mohammadian Rad, N.

Abstract

International Doctorate School in Information and Communication Technologies, 26 mei 2019, Promotores : Furlanello, C., Marchiori, E., Laarhoven, T. van, Item does not contain fulltext

Published
2019

26. Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders

Author

Mohammadian Rad, N., Mostafa Kia, S., Zarbo, C., Laarhoven, T. van, Jurman, G., Venuti, P., Marchiori, E., Furlanello, C., Mohammadian Rad, N., Mostafa Kia, S., Zarbo, C., Laarhoven, T. van, Jurman, G., Venuti, P., Marchiori, E., and Furlanello, C.

Abstract

Contains fulltext : 181993.pdf (preprint version ) (Closed access) Contains fulltext : 181993.pdf (Publisher’s version ) (Open Access)

Published
2018

27. Coupling early warning services, crowdsourcing, and modelling for improved decision support and wildfire emergency management

Author

Bielski, C., primary, O'Brien, V., additional, Whitmore, C., additional, Ylinen, K., additional, Juga, I., additional, Nurmi, P., additional, Kilpinen, J., additional, Porras, I., additional, Sole, J.M., additional, Gamez, P., additional, Navarro, M., additional, Alikadic, A., additional, Gobbi, A., additional, Furlanello, C., additional, Zeug, G., additional, Weirathe, M., additional, Martinez, J., additional, Yuste, R., additional, Castro, S., additional, Moreno, V., additional, Velin, T., additional, and Rossi, C., additional

Published
2017
Full Text
View/download PDF

28. PD-L1 is a therapeutic target of the bromodomain inhibitor JQ1 and, combined with HLA class I, a promising prognostic biomarker in neuroblastoma

Author

Melaiu, O, Mina, M, Chierici, M, Boldrini, R, Jurman, G, Romania, P, D'Alicandro, V, Benedetti, MC, Castellano, A, Liu, T, Furlanello, C, Locatelli, F, Fruci, D, Melaiu, O, Mina, M, Chierici, M, Boldrini, R, Jurman, G, Romania, P, D'Alicandro, V, Benedetti, MC, Castellano, A, Liu, T, Furlanello, C, Locatelli, F, and Fruci, D

Abstract

Purpose: This study sought to evaluate the expression of programmed cell death-ligand-1 (PD-L1) and HLA class I on neuroblastoma cells and programmed cell death-1 (PD-1) and lymphocyte activation gene 3 (LAG3) on tumor-infiltrating lymphocytes to better define patient risk stratification and understand whether this tumor may benefit from therapies targeting immune checkpoint molecules. Experimental Design: In situ IHC staining for PD-L1, HLA class I, PD-1, and LAG3 was assessed in 77 neuroblastoma specimens, previously characterized for tumor-infiltrating T-cell density and correlated with clinical outcome. Surface expression of PD-L1 was evaluated by flow cytometry and IHC in neuroblastoma cell lines and tumors genetically and/or pharmacologically inhibited for MYC and MYCN. A dataset of 477 human primary neuroblastomas from GEO and ArrayExpress databases was explored for PD-L1, MYC, and MYCN correlation. Results: Multivariate Cox regression analysis demonstrated that the combination of PD-L1 and HLA class I tumor cell density is a prognostic biomarker for predicting overall survival in neuroblastoma patients (P = 0.0448). MYC and MYCN control the expression of PD-L1 in neuroblastoma cells both in vitro and in vivo. Consistently, abundance of PD-L1 transcript correlates with MYC expression in primary neuroblastoma. Conclusions: The combination of PD-L1 and HLA class I represents a novel prognostic biomarker for neuroblastoma. Phar-macologic inhibition of MYCN and MYC may be exploited to target PD-L1 and restore an efficient antitumor immunity in high-risk neuroblastoma.

Published
2017

29. PD-L1 is a therapeutic target of the bromodomain inhibitor JQ1 and, combined with HLA class I, a promising prognostic biomarker in neuroblastoma

Author

Melaiu, O., Mina, M., Chierici, M., Boldrini, R., Jurman, G., Romania, P., D'Alicandro, V., Benedetti, M. C., Castellano, A., Liu, T., Furlanello, C., Locatelli, Franco, Fruci, D., Locatelli F. (ORCID:0000-0002-7976-3654), Melaiu, O., Mina, M., Chierici, M., Boldrini, R., Jurman, G., Romania, P., D'Alicandro, V., Benedetti, M. C., Castellano, A., Liu, T., Furlanello, C., Locatelli, Franco, Fruci, D., and Locatelli F. (ORCID:0000-0002-7976-3654)

Abstract

Purpose: This study sought to evaluate the expression of programmed cell death-ligand-1 (PD-L1) and HLA class I on neuroblastoma cells and programmed cell death-1 (PD-1) and lymphocyte activation gene 3 (LAG3) on tumor-infiltrating lymphocytes to better define patient risk stratification and understand whether this tumor may benefit from therapies targeting immune checkpoint molecules. Experimental Design: In situ IHC staining for PD-L1, HLA class I, PD-1, and LAG3 was assessed in 77 neuroblastoma specimens, previously characterized for tumor-infiltrating T-cell density and correlated with clinical outcome. Surface expression of PD-L1 was evaluated by flow cytometry and IHC in neuroblastoma cell lines and tumors genetically and/or pharmacologically inhibited for MYC and MYCN. A dataset of 477 human primary neuroblastomas from GEO and ArrayExpress databases was explored for PD-L1, MYC, and MYCN correlation. Results: Multivariate Cox regression analysis demonstrated that the combination of PD-L1 and HLA class I tumor cell density is a prognostic biomarker for predicting overall survival in neuroblastoma patients (P = 0.0448). MYC and MYCN control the expression of PD-L1 in neuroblastoma cells both in vitro and in vivo. Consistently, abundance of PD-L1 transcript correlates with MYC expression in primary neuroblastoma. Conclusions: The combination of PD-L1 and HLA class I represents a novel prognostic biomarker for neuroblastoma. Phar-macologic inhibition of MYCN and MYC may be exploited to target PD-L1 and restore an efficient antitumor immunity in high-risk neuroblastoma.

Published
2017

30. Gut Microbiota Dysbiosis as Risk and Premorbid Factors of IBD and IBS Along the Childhood-Adulthood Transition

Author

Putignani, L, Del Chierico, F, Vernocchi, P, Cicala, M, Cucchiara, S, Dallapiccola, B, Dysbiotrack Study Group Collaborators: Alisi, A, Altomare, A, Aloi, M, Belzer, C, Cavalieri, D, Carapito, C, Comera, C, De Angelis, P, Delaere, F, De Filippo, C, de Vos, W, Dorè, J, Donati, C, Furlanello, C, Gasbarrini, A, Gil, C, Guarino, M, Hugot, Jp, Juste, C, Knol, J, Koletzko, B, Monnet, V, Nobili, V, Oozeer, R, Scaldaferri, F, Stensvold, Cr, Stronati, L, Tack, J, Theodoridis, G, Torre, G, and Urbani, A.

Subjects
digestive system, Gut Microbiota, Dysbiosis, Inflammatory Bowel Disease, Irritable Bowel Syndrome, Childhood
Published
2016

31. The statistical geometry of transcriptome divergence in cell-type evolution and cancer

Author

Liang, C, Alam, I, Albanese, D, Altschuler, G, Andersson, R, Arakawa, T, Archer, J, Arner, E, Arner, P, Babina, M, Baillie, K, Bajic, V, Baker, S, Balic, A, Balwierz, P, Beckhouse, A, Bertin, N, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, M, Califano, A, Cannistraci, C, Carbajo, D, Carninci, P, Chen, Yang, Chierici, M, Ciani, Y, Clevers, H, Dalla, Emiliano, Daub, C, Davis, C, De Hoon, M, De Lima Morais, D, Dermar, M, Diehl, A, Dimont, E, Dohl, T, Drabros, F, Edge, A, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson, Mc, Faulkner, G, Favorov, A, Fisher, M, Forrest, A, Francescatto, M, Freeman, T, Frith, M, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowithz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, T, Haberle, V, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Hayashizaki, Y, Herlyn, M, Heutink, P, Hide, W, Hitchens, K, Ho Sui, S, Hofmann, O, Hoof, I, Hori, F, Hume, D, Huminiecki, L, Iida, K, Ikawa, T, Ishizu, Y, Itoh, M, Jankovic, B, Jia, H, Jorgensen, M, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, A, Kasukawa, T, Katayama, S, Kato Ishikawa, S, Kawaguchi, S, Kawai, J, Kawaji, H, Kawamoto, H, Kawamura, Y, Kawashima, T, Kempfle, J, Kenna, T, Kere, J, Khachigian, L, Kitamura, T, Klinken, P, Knox, A, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kulakovskiy, I, Kwon, At, Laros, J, Lassmann, T, Lenhard, B, Lennartsson, A, Li, K, Lilji, B, Lipovich, L, Lizio, M, Mackay Sim, A, Makeev, V, Manabe, R, Mar, J, Marchand, B, Mathelier, A, Medvedeva, Y, Meehan, Tf, Mejhert, N, Meynert, A, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, C, Mungall, Cj, Murata, M, Nagao Sato, S, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, Ninomiya Fukuda, N, Nishiyori Sueki, H, Noma, S, Nozaki, T, Ogishima, S, Ohkura, N, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, D, Pain, A, Passier, R, Persson, H, Piazza, Silvano, Plessy, C, Pradhan Bhatt, S, Prendergast, J, Rackham, O, Ramilowski, J, Rashid, M, Ravasi, T, Rehli, M, Rizzu, P, Roncador, M, Roy, S, Rye, M, Saijyo, E, Sajantila, A, Saka, A, Sakaguchi, S, Sakai, M, Sandelin, A, Sato, H, Satoh, H, Suzana, S, Alka, S, Schaefer, U, Schmeier, S, Schmidl, C, Schneider, C, Schultes, Ea, Schulze Tanzil, G, Schwegmann, A, Semple, C, Sengstag, T, Severin, J, Sheng, G, Shimoji, H, Shimoni, Y, Shin, J, Simon, C, Sugiyama, D, Sugiyama, T, Summers, K, Suzuki, H, Suzuki, M, Suzuki, N, Swoboda, R, Hoen P, T, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Taylor, M, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, Van De Wetering, M, Van Den Berg, L, Van Nimwegen, E, Verardo, R, Vijayan, D, Vitezic, M, Vorontzov, I, Wasserman, W, Watanabe, S, Wells, C, Winteringham, L, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, Shin'Ichirou, Young, R, Zabierowski, Se, Zhang, P, Zhao, X, Zucchelli, Silvia, Forrest, Ar, Wagner, Gp, Hubrecht Institute for Developmental Biology and Stem Cell Research, AII - Amsterdam institute for Infection and Immunity, Infectious diseases, and Experimental Immunology

Subjects
Cell type, General Physics and Astronomy, rna-seq data, phylogenetic networks, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, Divergence, Transcriptome, Models, Settore BIO/13 - Biologia Applicata, Neoplasms, Humans, Genetics, Models, Statistical, Multidisciplinary, Statistical model, General Chemistry, Statistical, Biological Evolution, Body plan, Tree structure, Evolutionary biology, Cancer cell
Abstract

In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination.

Published
2015

32. Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation

Author

Morikawa, H, Ohkura, N, Vandenbon, A, Itoh, M, Nagao Sato, S, Kawaji, H, Lassmann, T, Carninci, P, Hayashizaki, Y, Forrest, Ar, Standley, Dm, Date, H, Sakaguchi, S, FANTOM Consortium (Forrest AR, Rehli, M, Baillie, Jk, de Hoon MJ, Haberle, V, Kulakovskiy, Iv, Lizio, M, Andersson, R, Mungall, Cj, Meehan, Tf, Schmeier, S, Bertin, N, Jørgensen, M, Dimont, E, Arner, E, Schmidl, C, Schaefer, U, Medvedeva, Ya, Plessy, C, Vitezic, M, Severin, J, Semple, Ca, Ishizu, Y, Francescatto, M, Alam, I, Albanese, D, Altschuler, Gm, Archer, Ja, Arner, P, Babina, M, Baker, S, Balwierz, Pj, Beckhouse, Ag, Pradhan Bhatt, S, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, Am, Califano, A, Cannistraci, Cv, Carbajo, D, Chen, Y, Chierici, M, Ciani, Y, Clevers, Hc, Dalla, E, Davis, Ca, Deplancke, B, Detmar, M, Diehl, Ad, Dohi, T, Drabløs, F, Edge, As, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson MC, Faulkner, Gj, Favorov, Av, Fisher, Me, Frith, Mc, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowitz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, Tj, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Herlyn, M, Hitchens, Kj, Ho Sui SJ, Hofmann, Om, Hoof, I, Hori, F, Huminiecki, L, Iida, K, Ikawa, T, Jankovic, Br, Jia, H, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, As, Kasukawa, T, Katayama, S, Kato, S, Kawaguchi, S, Kawamoto, H, Kawamura, Yi, Kawashima, T, Kempfle, Js, Kenna, Tj, Kere, J, Khachigian, Lm, Kitamura, T, Klinken, Sp, Knox, Aj, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kwon, At, Laros, Jf, Lee, W, Lennartsson, A, Li, K, Lilje, B, Lipovich, L, Mackay Sim, A, Manabe, R, Mar, Jc, Marchand, B, Mathelier, A, Mejhert, N, Meynert, A, Mizuno, Y, Morais, Da, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, Cl, Murata, M, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, van Nimwegen, E, Ninomiya, N, Nishiyori, H, Noma, S, Nozaki, T, Ogishima, S, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, Da, Pain, A, Passier, R, Patrikakis, M, Persson, H, Piazza, S, Prendergast, Jg, Rackham, Oj, Ramilowski, Ja, Rashid, M, Ravasi, T, Rizzu, P, Roncador, M, Roy, S, Rye, Mb, Saijyo, E, Sajantila, A, Saka, A, Sakai, M, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schneider, C, Schultes, Ea, Schulze Tanzil GG, Schwegmann, A, Sengstag, T, Sheng, G, Shimoji, H, Shimoni, Y, Shin, Jw, Simon, C, Sugiyama, D, Sugiyama, T, Suzuki, M, Swoboda, Rk, 't Hoen PA, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, van de Wetering, M, van den Berg LM, Verardo, R, Vijayan, D, Vorontsov, Ie, Wasserman, Ww, Watanabe, S, Wells, Ca, Winteringham, Ln, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, S, Zabierowski, Se, Zhang, Pg, Zhao, X, Zucchelli, S, Summers, Km, Suzuki, H, Daub, Co, Kawai, J, Heutink, P, Hide, W, Freeman, Tc, Lenhard, B, Bajic, Vb, Taylor, Ms, Makeev, Vj, Sandelin, A, Hume, Da, Hayashizaki, Y., AII - Amsterdam institute for Infection and Immunity, Infectious diseases, Experimental Immunology, and Hubrecht Institute for Developmental Biology and Stem Cell Research

Subjects
Transcription, Genetic, Regulatory T cell, T-Lymphocytes, Down-Regulation, chemical and pharmacologic phenomena, Biology, Inbred C57BL, T-Lymphocytes, Regulatory, Epigenesis, Genetic, Mice, Genetic, Settore BIO/13 - Biologia Applicata, medicine, Transcriptional regulation, Animals, Epigenetics, Gene, Inbred BALB C, Genetics, Regulation of gene expression, Mice, Inbred BALB C, Multidisciplinary, Binding Sites, FOXP3, hemic and immune systems, Forkhead Transcription Factors, DNA Methylation, Biological Sciences, Regulatory, Cap analysis gene expression, Mice, Inbred C57BL, medicine.anatomical_structure, Gene Expression Regulation, DNA methylation, Transcription, Epigenesis
Abstract

Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression.

Published
2014

33. Diet and the gut microbiota: how the gut: brain axis impacts on autism

Author

Tuohy, K.M., Venuti, P., Cuva, S., Furlanello, C., Gasperotti, M., Mancini, A., Ceppa, F.A., Cavalieri, D., De Filippo, C., Vrhovsek, U., Mena, P., Del Rio, D., and Fava, F.

Subjects
Lipid metabolism, Settore AGR/15 - SCIENZE E TECNOLOGIE ALIMENTARI, Blood brain barrier, Autism, Brain, Dietary pattern, Gut microbiota successional development, Microbiome, Neurological disorders, Amino acid
Published
2014

34. Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study

Author

Diaz-Lacava, A. N., Walier, M., Holler, D., Steffens, M., Gieger, C., Furlanello, C., Lamina, C., Wichmann, H. E., Becker, T., Diaz-Lacava, A. N., Walier, M., Holler, D., Steffens, M., Gieger, C., Furlanello, C., Lamina, C., Wichmann, H. E., and Becker, T.

Abstract

Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs;n. = 728). Genetic heterogeneity was evaluated with observed heterozygosity (H-Omicron). Best-fitting spatial autoregressive models were identified, using socio-demographic variables as covariates. Spatial analysis included surface interpolation and geostatistics of observed and predicted patterns. Prediction accuracy was quantified. Spatial autocorrelation was detected for both socio-demographic and genetic variables. Augsburg City and eastern suburban areas showed higher H-Omicron values. The selected model gave best predictions in suburban areas. Fine-scale patterns of genetic heterogeneity were observed. In accordance to literature, more urbanized areas showed higher levels of admixture. This approach showed efficacy for detecting and analyzing subtle patterns of genetic heterogeneity within small areas. It is scalable in number of loci, even up to whole-genome analysis. It may be suggested that this approach may be applicable to investigate the underlying genetic history that is, at least partially, embedded in geographic data.

Published
2015

35. Tumor-infiltrating T lymphocytes improve clinical outcome of therapy-resistant neuroblastoma

Author

Mina, M., Boldrini, R., Citti, A., Romania, P., D'Alicandro, V., Ioris, M. D., Castellano, A., Furlanello, C., Locatelli, Franco, Fruci, D., Locatelli F. (ORCID:0000-0002-7976-3654), Mina, M., Boldrini, R., Citti, A., Romania, P., D'Alicandro, V., Ioris, M. D., Castellano, A., Furlanello, C., Locatelli, Franco, Fruci, D., and Locatelli F. (ORCID:0000-0002-7976-3654)

Abstract

Neuroblastoma grows within an intricate network of different cell types including epithelial, stromal and immune cells. The presence of tumor-infiltrating T cells is considered an important prognostic indicator in many cancers, but the role of these cells in neuroblastoma remains to be elucidated. Herein, we examined the relationship between the type, density and organization of infiltrating T cells and clinical outcome within a large collection of neuroblastoma samples by quantitative analysis of immunohistochemical staining. We found that infiltrating T cells have a prognostic value greater than, and independent of, the criteria currently used to stage neuroblastoma. A variable in situ structural organization and different concurrent infiltration of T-cell subsets were detected in tumors with various outcomes. Low-risk neuroblastomas were characterized by a higher number of proliferating T cells and a more structured T-cell organization, which was gradually lost in tumors with poor prognosis. We defined an immunoscore based on the presence of CD3+, CD4+ and CD8+ infiltrating T cells that associates with favorable clinical outcome in MYCN-amplified tumors, improving patient survival when combined with the v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) status. These findings support the hypothesis that infiltrating T cells influence the behavior of neuroblastoma and might be of clinical importance for the treatment of patients.

Published
2015

36. 3D solarweb : a solar cadaster in the Italian alpine landscape

Author

Nex, F.C., Remondino, F., Agugiaro, G., De Flippi, R., Poletti, M., Furlanello, C., Menegon, S., Dallago, G., Fontanari, S., Sunar, F., al., et, Faculty of Geo-Information Science and Earth Observation, Department of Earth Observation Science, and UT-I-ITC-ACQUAL

Subjects
lcsh:Applied optics. Photonics, Engineering, 020209 energy, Cadastre, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Context (language use), 02 engineering and technology, Urban area, 7. Clean energy, lcsh:Technology, ADLIB-ART-4788, 0202 electrical engineering, electronic engineering, information engineering, 021101 geological & geomatics engineering, Remote sensing, geography, geography.geographical_feature_category, business.industry, lcsh:T, Photovoltaic system, lcsh:TA1501-1820, computer.file_format, Pipeline (software), Photogrammetry, lcsh:TA1-2040, Radiance, Raster graphics, business, lcsh:Engineering (General). Civil engineering (General), computer
Abstract

The paper presents the research carried out in the on-going 3DSolarWeb project to test and implement a complete pipeline for the generation of a solar cadastre of building roofs located in alpine areas. The project aims at providing reliable results in a costeffective way, using (low resolution) available data and new aerial imagery acquisitions as input. The environmental context is digitally represented using already existing low resolution LiDAR data (1–2 m resolution), while the urban area is modelled using high resolution aerial images (10–20 cm GSD) and photogrammetric DSM. Reliable models and algorithms for the estimation of the incoming sun radiance are then adopted and a WebGIS is set up for the interactive calculation of the photovoltaic (PV) potential in a raster-based form. The paper summarizes the entire pipeline and the results (Figure 1) achieved on the test areas to show the potentialities of the method and the web-based service.

Published
2013

40. Envirochange: simulazione degli effetti fotosanitari del cambiamento climatico sulla vite in Trentino

Author

Eccel, E, Caffarra, A, Cordano, E, Rinaldi, Monica Fernanda, Rossi, Vittorio, De Filippi, R, Droghetti, S, Zarbo, C, Furlanello, C, Storari, M, Gessler, Cesare, Tomozeiu, R, and Pertot, Ilaria

Subjects
Powdery mildew, Aspergillus, cambiamento climatico, Climate, Ochratoxines, Aspergillo, Vite, Grapevine, Clima, Ocratossine, Erysiphe necator, Settore AGR/12 - PATOLOGIA VEGETALE, Oidio
Published
2012

43. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Z, Labaj, PP, Li, S, Thierry-Mieg, J, Thierry-Mieg, D, Shi, W, Wang, C, Schroth, GP, Setterquist, RA, Thompson, JF, Jones, WD, Xiao, W, Xu, W, Jensen, RV, Kelly, R, Xu, J, Conesa, A, Furlanello, C, Gao, H, Hong, H, Jafari, N, Letovsky, S, Liao, Y, Lu, F, Oakeley, EJ, Peng, Z, Praul, CA, Santoyo-Lopez, J, Scherer, A, Shi, T, Smyth, GK, Staedtler, F, Sykacek, P, Tan, X-X, Thompson, EA, Vandesompele, J, Wang, MD, Wang, J, Wolfinger, RD, Zavadil, J, Auerbach, SS, Bao, W, Binder, H, Blomquist, T, Brilliant, MH, Bushel, PR, Cain, W, Catalano, JG, Chang, C-W, Chen, T, Chen, G, Chen, R, Chierici, M, Chu, T-M, Clevert, D-A, Deng, Y, Derti, A, Devanarayan, V, Dong, Z, Dopazo, J, Du, T, Fang, H, Fang, Y, Fasold, M, Fernandez, A, Fischer, M, Furio-Tari, P, Fuscoe, JC, Caiment, F, Gaj, S, Gandara, J, Ge, W, Gondo, Y, Gong, B, Gong, M, Gong, Z, Green, B, Guo, C, Guo, L, Guo, L-W, Hadfield, J, Hellemans, J, Hochreiter, S, Jia, M, Jian, M, Johnson, CD, Kay, S, Kleinjans, J, Lababidi, S, Levy, S, Li, Q-Z, Li, L, Li, P, Li, Y, Li, H, Li, J, Lin, SM, Lopez, FJ, Lu, X, Luo, H, Ma, X, Meehan, J, Megherbi, DB, Mei, N, Mu, B, Ning, B, Pandey, A, Perez-Florido, J, Perkins, RG, Peters, R, Phan, JH, Pirooznia, M, Qian, F, Qing, T, Rainbow, L, Rocca-Serra, P, Sambourg, L, Sansone, S-A, Schwartz, S, Shah, R, Shen, J, Smith, TM, Stegle, O, Stralis-Pavese, N, Stupka, E, Suzuki, Y, Szkotnicki, LT, Tinning, M, Tu, B, van Deft, J, Vela-Boza, A, Venturini, E, Walker, SJ, Wan, L, Wang, W, Wieben, ED, Willey, JC, Wu, P-Y, Xuan, J, Yang, Y, Ye, Z, Yin, Y, Yu, Y, Yuan, Y-C, Zhang, J, Zhang, KK, Zhang, W, Zhang, Y, Zhao, C, Zheng, Y, Zhou, Y, Zumbo, P, Tong, W, Kreil, DP, Mason, CE, Shi, L, Su, Z, Labaj, PP, Li, S, Thierry-Mieg, J, Thierry-Mieg, D, Shi, W, Wang, C, Schroth, GP, Setterquist, RA, Thompson, JF, Jones, WD, Xiao, W, Xu, W, Jensen, RV, Kelly, R, Xu, J, Conesa, A, Furlanello, C, Gao, H, Hong, H, Jafari, N, Letovsky, S, Liao, Y, Lu, F, Oakeley, EJ, Peng, Z, Praul, CA, Santoyo-Lopez, J, Scherer, A, Shi, T, Smyth, GK, Staedtler, F, Sykacek, P, Tan, X-X, Thompson, EA, Vandesompele, J, Wang, MD, Wang, J, Wolfinger, RD, Zavadil, J, Auerbach, SS, Bao, W, Binder, H, Blomquist, T, Brilliant, MH, Bushel, PR, Cain, W, Catalano, JG, Chang, C-W, Chen, T, Chen, G, Chen, R, Chierici, M, Chu, T-M, Clevert, D-A, Deng, Y, Derti, A, Devanarayan, V, Dong, Z, Dopazo, J, Du, T, Fang, H, Fang, Y, Fasold, M, Fernandez, A, Fischer, M, Furio-Tari, P, Fuscoe, JC, Caiment, F, Gaj, S, Gandara, J, Ge, W, Gondo, Y, Gong, B, Gong, M, Gong, Z, Green, B, Guo, C, Guo, L, Guo, L-W, Hadfield, J, Hellemans, J, Hochreiter, S, Jia, M, Jian, M, Johnson, CD, Kay, S, Kleinjans, J, Lababidi, S, Levy, S, Li, Q-Z, Li, L, Li, P, Li, Y, Li, H, Li, J, Lin, SM, Lopez, FJ, Lu, X, Luo, H, Ma, X, Meehan, J, Megherbi, DB, Mei, N, Mu, B, Ning, B, Pandey, A, Perez-Florido, J, Perkins, RG, Peters, R, Phan, JH, Pirooznia, M, Qian, F, Qing, T, Rainbow, L, Rocca-Serra, P, Sambourg, L, Sansone, S-A, Schwartz, S, Shah, R, Shen, J, Smith, TM, Stegle, O, Stralis-Pavese, N, Stupka, E, Suzuki, Y, Szkotnicki, LT, Tinning, M, Tu, B, van Deft, J, Vela-Boza, A, Venturini, E, Walker, SJ, Wan, L, Wang, W, Wieben, ED, Willey, JC, Wu, P-Y, Xuan, J, Yang, Y, Ye, Z, Yin, Y, Yu, Y, Yuan, Y-C, Zhang, J, Zhang, KK, Zhang, W, Zhang, Y, Zhao, C, Zheng, Y, Zhou, Y, Zumbo, P, Tong, W, Kreil, DP, Mason, CE, and Shi, L

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Published
2014

44. Repeatability of published microarray gene expression analyses

Author

Ioannidis, J. P., Allison, D. B., Ball, C. A., Coulibaly, I., Cui, X., Culhane, A. C., Falchi, M., Furlanello, C., Game, L., Jurman, G., Mangion, J., Mehta, T., Nitzberg, M., Page, G. P., Petretto, E., and van Noort, V.

Subjects
Genome-Wide Association Study/standards, Oligonucleotide Array Sequence Analysis/*standards, Data Interpretation, Statistical, Databases, Genetic, Publications/standards, Animals, Humans, Reproducibility of Results, Gene Expression Profiling/*standards
Abstract

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered. Nat Genet

Published
2009

49. Scenari di diffusione e controllo di una pandemia influenzale in Italia

Author

Ciofi degli Atti, M, Rizzo, C, Bella, A, Massari, M, Iannelli, M, Lunelli, A, Pugliese, A, Ripoll, J, Manfredi, P, SCALIA TOMBA, G, Merler, S, Jurman, G, and Furlanello, C

Subjects
Prevention and control, Influenza pandemic, Mathematical modelling, Vaccination, Antiviral prophylaxis, Quarantine measures, Influenza pandemic, Mathematical modelling, Prevention and control, Vaccination, Antiviral prophylaxis, Quarantine measures, Settore MAT/06 - Probabilita' e Statistica Matematica
Published
2006

Catalog

Books, media, physical & digital resources
See catalog results