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457 results on '"Linial, M."'

1. SPECTRUM OF AFFECTED GENES IN UKRAINIAN PATIENTS WITH PRIMARY MYELOFIBROSIS

Author

Poluben L., Chen S., Yuan X., Shumeiko O., Voznesensky O., Adam M., Fraenkel E., Rasnic R., Linial M., Klymenko S., Balk S.P., and Fraenkel P.G.

Subjects
primary myelofibrosis, mutation, gene., Medicine, Biology (General), QH301-705.5
Abstract

In this study we aimed to identify the spectrum of affected genes in Ukrainian patients diagnosed with primary myelofibrosis (PMF). DNA samples were obtained from peripheral blood leukocytes of 30 Ukrainian PMF patients. Using Whole Exome Sequencing, we detected previously reported and unreported sequence variants considered as pathogenic or potentially pathogenic. Canonical mutations of usual MPN-driver genes were detected in 70% of PMF patients, including JAK2V617F in 43.3%, MPLW515 in 10% and CALRmutations (type 1-like and 2-like) in 16.7% of patients. Also, a non-canonical MPLP222S variant was detected in one patient negative for these mutations. However, in cell culture assay, MPLP222S expression in Ba/F3 cells did not demonstrate differences in phosphorylation of JAK/STAT signaling proteins in response to TPO stimulation compared to MPLWT expression. Overall, more pathogenic and potentially pathogenic sequence variants were found among PMF patients negative for canonical mutations in three driver genes (JAK2, MPL andCALR), compared to patients, positive for one of these mutations. The mean numbers of variants were 5 (range: 4 7) versus 3.3 (range: 1 9), respectively (p = 0.03). The most frequently affected among Ukrainian PMF patients were genes ASXL1, PEG3, EZH2, ATM, U2AF1, andCDH23in addition to JAK2, MPL andCALR. Pathogenic or potentially pathogenic sequence variants of ASXL1and EZH2genes were identified in 23.3% and 16.7% of cases, respectively. Genes JARID2, RBBP8, RTEL1, SUZ12, BRCA2, LAMB4, NF1, RBM12B, RBM43, and RBP3 were recurrently affected in PMF patients who were also positive for usual mutations in three driver genes. Among PMF patients negative for usual mutations in three driver genes recurrently affected genes were DNMT3Aand TET2in addition to mentioned earlier, and some genetic variants were identified in RTEL1, SUZ12, CBL, CUX1, FLT3LGand UMODL1genes in single cases. Also, we identified several genes affected in single cases (KIT, SF3B1, GNAS) in PMF patients negative for canonical mutations in three driver genes which may be potential MPN drivers.

Published
2018
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2. Particle Assembly and Genome Packaging

Author

Linial, M. L., Eastman, S. W., Compans, R. W., editor, Cooper, M. D., editor, Ito, Y., editor, Koprowski, H., editor, Melchers, F., editor, Oldstone, M. B. A., editor, Olsnes, S., editor, Potter, M., editor, Vogt, P. K., editor, Wagner, H., editor, and Rethwilm, Axel, editor

Published
2003
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3. Retroviral RNA Packaging: Sequence Requirements and Implications

Author

Linial, M. L., Miller, A. D., Compans, R. W., editor, Cooper, M., editor, Koprowski, H., editor, McConnell, I., editor, Melchers, F., editor, Nussenzweig, V., editor, Oldstone, M., editor, Olsnes, S., editor, Potter, M., editor, Saedler, H., editor, Vogt, P. K., editor, Wagner, H., editor, Wilson, I., editor, Swanstrom, Ronald, editor, and Vogt, Peter K., editor

Published
1990
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14. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Y, Oron, TR, Clark, WT, Bankapur, AR, D'Andrea, D, Lepore, R, Funk, CS, Kahanda, I, Verspoor, KM, Ben-Hur, A, Koo, DCE, Penfold-Brown, D, Shasha, D, Youngs, N, Bonneau, R, Lin, A, Sahraeian, SME, Martelli, PL, Profiti, G, Casadio, R, Cao, R, Zhong, Z, Cheng, J, Altenhoff, A, Skunca, N, Dessimoz, C, Dogan, T, Hakala, K, Kaewphan, S, Mehryary, F, Salakoski, T, Ginter, F, Fang, H, Smithers, B, Oates, M, Gough, J, Toronen, P, Koskinen, P, Holm, L, Chen, C-T, Hsu, W-L, Bryson, K, Cozzetto, D, Minneci, F, Jones, DT, Chapman, S, Dukka, BKC, Khan, IK, Kihara, D, Ofer, D, Rappoport, N, Stern, A, Cibrian-Uhalte, E, Denny, P, Foulger, RE, Hieta, R, Legge, D, Lovering, RC, Magrane, M, Melidoni, AN, Mutowo-Meullenet, P, Pichler, K, Shypitsyna, A, Li, B, Zakeri, P, ElShal, S, Tranchevent, L-C, Das, S, Dawson, NL, Lee, D, Lees, JG, Sillitoe, I, Bhat, P, Nepusz, T, Romero, AE, Sasidharan, R, Yang, H, Paccanaro, A, Gillis, J, Sedeno-Cortes, AE, Pavlidis, P, Feng, S, Cejuela, JM, Goldberg, T, Hamp, T, Richter, L, Salamov, A, Gabaldon, T, Marcet-Houben, M, Supek, F, Gong, Q, Ning, W, Zhou, Y, Tian, W, Falda, M, Fontana, P, Lavezzo, E, Toppo, S, Ferrari, C, Giollo, M, Piovesan, D, Tosatto, SCE, del Pozo, A, Fernandez, JM, Maietta, P, Valencia, A, Tress, ML, Benso, A, Di Carlo, S, Politano, G, Savino, A, Rehman, HU, Re, M, Mesiti, M, Valentini, G, Bargsten, JW, van Dijk, ADJ, Gemovic, B, Glisic, S, Perovic, V, Veljkovic, V, Veljkovic, N, Almeida-e-Silva, DC, Vencio, RZN, Sharan, M, Vogel, J, Kansakar, L, Zhang, S, Vucetic, S, Wang, Z, Sternberg, MJE, Wass, MN, Huntley, RP, Martin, MJ, O'Donovan, C, Robinson, PN, Moreau, Y, Tramontano, A, Babbitt, PC, Brenner, SE, Linial, M, Orengo, CA, Rost, B, Greene, CS, Mooney, SD, Friedberg, I, Radivojac, P, Jiang, Y, Oron, TR, Clark, WT, Bankapur, AR, D'Andrea, D, Lepore, R, Funk, CS, Kahanda, I, Verspoor, KM, Ben-Hur, A, Koo, DCE, Penfold-Brown, D, Shasha, D, Youngs, N, Bonneau, R, Lin, A, Sahraeian, SME, Martelli, PL, Profiti, G, Casadio, R, Cao, R, Zhong, Z, Cheng, J, Altenhoff, A, Skunca, N, Dessimoz, C, Dogan, T, Hakala, K, Kaewphan, S, Mehryary, F, Salakoski, T, Ginter, F, Fang, H, Smithers, B, Oates, M, Gough, J, Toronen, P, Koskinen, P, Holm, L, Chen, C-T, Hsu, W-L, Bryson, K, Cozzetto, D, Minneci, F, Jones, DT, Chapman, S, Dukka, BKC, Khan, IK, Kihara, D, Ofer, D, Rappoport, N, Stern, A, Cibrian-Uhalte, E, Denny, P, Foulger, RE, Hieta, R, Legge, D, Lovering, RC, Magrane, M, Melidoni, AN, Mutowo-Meullenet, P, Pichler, K, Shypitsyna, A, Li, B, Zakeri, P, ElShal, S, Tranchevent, L-C, Das, S, Dawson, NL, Lee, D, Lees, JG, Sillitoe, I, Bhat, P, Nepusz, T, Romero, AE, Sasidharan, R, Yang, H, Paccanaro, A, Gillis, J, Sedeno-Cortes, AE, Pavlidis, P, Feng, S, Cejuela, JM, Goldberg, T, Hamp, T, Richter, L, Salamov, A, Gabaldon, T, Marcet-Houben, M, Supek, F, Gong, Q, Ning, W, Zhou, Y, Tian, W, Falda, M, Fontana, P, Lavezzo, E, Toppo, S, Ferrari, C, Giollo, M, Piovesan, D, Tosatto, SCE, del Pozo, A, Fernandez, JM, Maietta, P, Valencia, A, Tress, ML, Benso, A, Di Carlo, S, Politano, G, Savino, A, Rehman, HU, Re, M, Mesiti, M, Valentini, G, Bargsten, JW, van Dijk, ADJ, Gemovic, B, Glisic, S, Perovic, V, Veljkovic, V, Veljkovic, N, Almeida-e-Silva, DC, Vencio, RZN, Sharan, M, Vogel, J, Kansakar, L, Zhang, S, Vucetic, S, Wang, Z, Sternberg, MJE, Wass, MN, Huntley, RP, Martin, MJ, O'Donovan, C, Robinson, PN, Moreau, Y, Tramontano, A, Babbitt, PC, Brenner, SE, Linial, M, Orengo, CA, Rost, B, Greene, CS, Mooney, SD, Friedberg, I, and Radivojac, P

Abstract

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Published
2016

15. E-BioFlow: Different perspectives on scientific workflows

Author

Wassink, I., Rauwerda, H., van der Vet, P.E., Breit, T., Nijholt, Antinus, Elloumi, M., Küng, J., Linial, M., Murphy, R.F., Schneider, K., Toma, C., and RNA Biology & Applied Bioinformatics (SILS, FNWI)

Subjects
METIS-251064, HMI-HF: Human Factors, Knowledge management, Computer science, Windows Workflow Foundation, business.industry, CR-H.3.5, DB-WFM: WORKFLOW MANAGEMENT, Workflow engine, XPDL, Workflow technology, Data flow diagram, CR-C.5, Workflow, EWI-13039, Object type, business, Software engineering, IR-64864, Workflow management system
Abstract

We introduce a new type of workflow design system called e-BioFlow and illustrate it by means of a simple sequence alignment workflow. E-BioFlow, intended to model advanced scientific workflows, enables the user to model a workflow from three different but strongly coupled perspectives: the control flow perspective, the data flow perspective, and the resource perspective. All three perspectives are of equal importance, but workflow designers from different domains prefer different perspectives as entry points for their design, and a single workflow designer may prefer different perspectives in different stages of workflow design. Each perspective provides its own type of information, visualisation and support for validation. Combining these three perspectives in a single application provides a new and flexible way of modelling workflows.

Published
2008

16. Finding frequent subgraphs in biological networks via maximal item sets

Author

Zantema, H., Wagemans, S., Bosnacki, D., Elloumi, M., Küng, J., Linial, M., Murphy, R., Schneider, K., Toma, C., Algorithms, Geometry and Applications, Computational Biology, and Formal System Analysis

Subjects
Computer science, Kegg database, Data mining, computer.software_genre, computer, Biological network, Running time
Abstract

We describe an improvement of an algorithm for detecting frequently occurring patterns and modules in biological networks. The improvement is based on the observation that the problem of finding frequent network parts can be reduced to the problem of finding maximal frequent item sets (MFI). The MFI problem is a classical problem in the data mining community and there exist numerous efficient tools for it, most of them publicly available. We apply MFI tools to find frequent subgraphs in metabolic pathways from the KEGG database. Our experimental results show that, compared to the existing specialized tools for frequent subgraphs detection, the MFI tools coupled with an adequate postprocessing are much more efficient with regard to the running time and the size of the frequent graphs.

Published
2008

17. BioSapiens: a European network for integrated genome annotation

Author

Thornton, J., Tramontano, A., Roma, U., Valencia, A., Brunak, R., Antonarakis, S., Bork, P., Casadio, R., Danchin, A., Durbin, R., Frishman, D., Guigo, R., Heijne, G., Helden, J., Lengauer, T., Linial, M., Mott, R., Orengo, C., Jones, D., Patthy, L., Rychlewski, L., Schachter, V., Schomburg, D., Ukkonen, E., Veuthey, A. L., Vingron, M., Vriend, G., Nyberg, K., BioSapiens Network, Excellence, Université libre de Bruxelles (ULB), and Spinelli, Lionel

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; no abstract

Published
2005

18. The automated function prediction SIG looks back at 2013 and prepares for 2014.

Author

Wass, Mark N., Mooney, S. D., Linial, M., Radivojac, P., Friedberg, I., Wass, Mark N., Mooney, S. D., Linial, M., Radivojac, P., and Friedberg, I.

Abstract

The mission of the Automated Function Prediction Special Interest Group (AFP-SIG) is to coalesce the community of computational biologists, experimental biologists and biocurators who are addressing the challenge of protein function prediction, thereby sharing ideas and creating collaborations. The AFP-SIG holds annual meetings alongside the Intelligent Systems for Molecular Biology, the leading conference of the International Society for Computational Biology. The AFP–SIG also runs the ongoing Critical Assessment of Functional Annotation (CAFA) challenge (Radivojac et al., 2013.

Published
2014

20. A large-scale evaluation of computational protein function prediction

Author

Radivojac, P, Clark, WT, Oron, TR, Schnoes, AM, Wittkop, T, Sokolov, A, Graim, K, Funk, C, Verspoor, K, Ben-Hur, A, Pandey, G, Yunes, JM, Talwalkar, AS, Repo, S, Souza, ML, Piovesan, D, Casadio, R, Wang, Z, Cheng, J, Fang, H, Goughl, J, Koskinen, P, Toronen, P, Nokso-Koivisto, J, Holm, L, Cozzetto, D, Buchan, DWA, Bryson, K, Jones, DT, Limaye, B, Inamdar, H, Datta, A, Manjari, SK, Joshi, R, Chitale, M, Kihara, D, Lisewski, AM, Erdin, S, Venner, E, Lichtarge, O, Rentzsch, R, Yang, H, Romero, AE, Bhat, P, Paccanaro, A, Hamp, T, Kassner, R, Seemayer, S, Vicedo, E, Schaefer, C, Achten, D, Auer, F, Boehm, A, Braun, T, Hecht, M, Heron, M, Hoenigschmid, P, Hopf, TA, Kaufmann, S, Kiening, M, Krompass, D, Landerer, C, Mahlich, Y, Roos, M, Bjorne, J, Salakoski, T, Wong, A, Shatkay, H, Gatzmann, F, Sommer, I, Wass, MN, Sternberg, MJE, Skunca, N, Supek, F, Bosnjak, M, Panov, P, Dzeroski, S, Smuc, T, Kourmpetis, YAI, van Dijk, ADJ, ter Braak, CJF, Zhou, Y, Gong, Q, Dong, X, Tian, W, Falda, M, Fontana, P, Lavezzo, E, Di Camillo, B, Toppo, S, Lan, L, Djuric, N, Guo, Y, Vucetic, S, Bairoch, A, Linial, M, Babbitt, PC, Brenner, SE, Orengo, C, Rost, B, Mooney, SD, Friedberg, I, Radivojac, P, Clark, WT, Oron, TR, Schnoes, AM, Wittkop, T, Sokolov, A, Graim, K, Funk, C, Verspoor, K, Ben-Hur, A, Pandey, G, Yunes, JM, Talwalkar, AS, Repo, S, Souza, ML, Piovesan, D, Casadio, R, Wang, Z, Cheng, J, Fang, H, Goughl, J, Koskinen, P, Toronen, P, Nokso-Koivisto, J, Holm, L, Cozzetto, D, Buchan, DWA, Bryson, K, Jones, DT, Limaye, B, Inamdar, H, Datta, A, Manjari, SK, Joshi, R, Chitale, M, Kihara, D, Lisewski, AM, Erdin, S, Venner, E, Lichtarge, O, Rentzsch, R, Yang, H, Romero, AE, Bhat, P, Paccanaro, A, Hamp, T, Kassner, R, Seemayer, S, Vicedo, E, Schaefer, C, Achten, D, Auer, F, Boehm, A, Braun, T, Hecht, M, Heron, M, Hoenigschmid, P, Hopf, TA, Kaufmann, S, Kiening, M, Krompass, D, Landerer, C, Mahlich, Y, Roos, M, Bjorne, J, Salakoski, T, Wong, A, Shatkay, H, Gatzmann, F, Sommer, I, Wass, MN, Sternberg, MJE, Skunca, N, Supek, F, Bosnjak, M, Panov, P, Dzeroski, S, Smuc, T, Kourmpetis, YAI, van Dijk, ADJ, ter Braak, CJF, Zhou, Y, Gong, Q, Dong, X, Tian, W, Falda, M, Fontana, P, Lavezzo, E, Di Camillo, B, Toppo, S, Lan, L, Djuric, N, Guo, Y, Vucetic, S, Bairoch, A, Linial, M, Babbitt, PC, Brenner, SE, Orengo, C, Rost, B, Mooney, SD, and Friedberg, I

Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

21. Paving the future: finding suitable ISMB venues

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Rost, B., Gaasterland, T., Lengauer, T., Linial, M., Markel, S., McKay, B. J. M., Schneider, Reinhard, Horton, P., Kelso, J., Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Rost, B., Gaasterland, T., Lengauer, T., Linial, M., Markel, S., McKay, B. J. M., Schneider, Reinhard, Horton, P., and Kelso, J.

Abstract

ISCB, the International Society for Computational Biology, organizes the largest event in the field of computational biology and bioinformatics, namely the annual ISMB, the international conference on Intelligent Systems for Molecular Biology. This year at ISMB 2012 in Long Beach, ISCB celebrated the 20th anniversary of its flagship meeting. ISCB is a young, lean and efficient society that aspires to make a significant impact with only limited resources. Many constraints make the choice of venues for ISMB a tough challenge. Here, we describe those challenges and invite the contribution of ideas for solutions.

Published
2012

22. Advancing cell biology through proteomics in space and time (PROSPECTS)

Author

Lamond, A.I., Uhlen, M., Horning, S., Makarov, A., Robinson, C.V., Serrano, L., Hartl, F.U., Baumeister, W., Werenskiold, A.K., Mann, M., Andersen, J.S., Vorm, O., Linial, M., Aebersold, R., Lamond, A.I., Uhlen, M., Horning, S., Makarov, A., Robinson, C.V., Serrano, L., Hartl, F.U., Baumeister, W., Werenskiold, A.K., Mann, M., Andersen, J.S., Vorm, O., Linial, M., and Aebersold, R.

Abstract

The term "proteomics" encompasses the large-scale detection and analysis of proteins and their post-translational modifications. Driven by major improvements in mass spectrometric instrumentation, methodology, and data analysis, the proteomics field has burgeoned in recent years. It now provides a range of sensitive and quantitative approaches for measuring protein structures and dynamics that promise to revolutionize our understanding of cell biology and molecular mechanisms in both human cells and model organisms. The Proteomics Specification in Time and Space (PROSPECTS) Network is a unique EU-funded project that brings together leading European research groups, spanning from instrumentation to biomedicine, in a collaborative five year initiative to develop new methods and applications for the functional analysis of cellular proteins. This special issue of Molecular and Cellular Proteomics presents 16 research papers reporting major recent progress by the PROSPECTS groups, including improvements to the resolution and sensitivity of the Orbitrap family of mass spectrometers, systematic detection of proteins using highly characterized antibody collections, and new methods for absolute as well as relative quantification of protein levels. Manuscripts in this issue exemplify approaches for performing quantitative measurements of cell proteomes and for studying their dynamic responses to perturbation, both during normal cellular responses and in disease mechanisms. Here we present a perspective on how the proteomics field is moving beyond simply identifying proteins with high sensitivity toward providing a powerful and versatile set of assay systems for characterizing proteome dynamics and thereby creating a new "third generation" proteomics strategy that offers an indispensible tool for cell biology and molecular medicine.

Published
2012

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