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1. StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem

Author

Alkan, C, Cameron, DL, Dong, R, Papenfuss, AT, Alkan, C, Cameron, DL, Dong, R, and Papenfuss, AT

Abstract

SUMMARY: StructuralVariantAnnotation is an R/Bioconductor package that provides a framework for decoupling downstream analysis of structural variant breakpoints from upstream variant calling methods. It standardizes the representational format from BEDPE, or any of the three different notations supported by VCF into a breakpoint GRanges data structure suitable for use by the wider Bioconductor ecosystem. It handles both transitive breakpoints and duplication/insertion notational differences of identical variants-both common scenarios when comparing short/long read-based call sets that confound downstream analysis. StructuralVariantAnnotation provides the caller-agnostic foundation needed for a R/Bioconductor ecosystem of structural variant annotation, classification and interpretation tools able to handle both simple and complex genomic rearrangements. AVAILABILITY AND IMPLEMENTATION: StructuralVariantAnnotation is implemented in R and available for download as the Bioconductor StructuralVariantAnnotation package. Details can be found at https://www.bioconductor.org/packages/release/bioc/html/StructuralVariantAnnotation.html. It has been released under a GPL license.

Published
2022

2. Sequence analysis expam-high-resolution analysis of metagenomes using distance trees

Author

Alkan, C, Solari, SM, Young, RB, Marcelino, VR, Forster, SC, Alkan, C, Solari, SM, Young, RB, Marcelino, VR, and Forster, SC

Abstract

SUMMARY: Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing. AVAILABILITY AND IMPLEMENTATION: expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Published
2022

4. Microencapsulation of thermochromic sytems containing fluoran dyes by poly (methyl methacrylate) wall for textile applications [Tekstil uygulamalari için fluoran boyalar I?çeren termokromik sistemlerin poli (Metil Metakrilat) duvar ile mikrokapsülasyonu]

Author

Özkayalar S., Aksoy S.A., Tözüm M.S., Alkan C., and Tokat Gaziosmanpaşa Üniversitesi

Subjects
Color change, Thermochromic microcapsule, Fluoran dye, Heat storage
Abstract

In this study, microencapsulation of thermochromic systems containing fluoran dye was aimed. For this purpose, thermochromic systems containing fluoran dye, Bisphenol A and 1-tetradecanol were microencapsulated with poly(methyl methacrylate) using emulsion polymerization method. Thermal properties and thermal stability of the microcapsules were investigated by differential scanning calorimetry (DSC) and thermal gravimetric (TG) analyzes. The thermo-regulation property of the microcapsules was tested by T-history analysis consisting of temperature sensor and data logger. The temperature-sensitive reversible discoloration properties of the microcapsules were determined by photographs taken at temperatures above and below the activation temperature. According to DSC results, microcapsules had latent heat storage in the range of 152.6-185.8 J/g. It was determined that the microcapsules, which were colored below the activation temperature, lost their color above the activation temperature and exhibited reversible color change property depending on change in temperature. From TG analysis results, it was determined that microcapsules had sufficient thermal resistance for textile applications. © Chamber of Textile Engineers.

Published
2020

6. Preparation of poly(methyl methacrylate-co-ethylene glycol dimethacrylate-co-glycidyl methacrylate) walled thermochromic microcapsules and their application to cotton fabrics

Author

Tözüm, Müyesser Selda, Alkan C., Alay Aksoy S., and Uşak Üniversitesi, Mühendislik Fakültesi, Tekstil Mühendisliği Bölümü

Subjects
emulsion polymerization, copolymers, dyes/pigments, textiles
Abstract

This study focused on fabrication of the thermochromic microcapsules and their application to the cotton fabric. In this study, thermochromic systems composed of crystal violet lactone, bisphenol A, and 1-tetradecanol were prepared and microencapsulated by emulsion polymerization method in poly(methyl methacrylate-co-ethylene glycol dimethacrylate-co-glycidyl methacrylate) wall. The microcapsules were analyzed by Fourier transform infrared spectroscopy, scanning electron microscope, transmission electron microscope, differential scanning calorimetry, and thermogravimetric analysis. Their thermoregulating property was tested by T-history test. The results revealed that microcapsules with smooth surfaces, core–shell structured, and spherical shape were successfully produced. The latent heat storage capacity of the microcapsules decreased from 202 J g?1 to 167 J g?1 when their shell/core ratio changed from 0.5/1 to 2/1. Microcapsules were adequately had sufficient thermal resistance to the temperatures they will encounter during their application to textile products and their usage. According to the UV–visible spectroscopy analysis and color measurements, the microcapsules exhibited reversible color change from blue to colorless and vice versa. Besides, the microcapsule impregnated fabric was able to absorb latent heat energy of 21.79 J g?1 at around 35 °C and had cooling effect. According to the colorimetric parameters, the fabric was at blue color at room temperature and became colorless when heated to the temperature above the melting point of thermochromic system. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 137, 48815. © 2019 Wiley Periodicals, Inc. 4602?D2?16 This study was funded by a Scientific Research Project of Suleyman Demirel University (Project no: 4602?D2?16).

Published
2019

8. Computational pan-genomics: Status, promises and challenges

Author

Marschall, T. (Tanja), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam M.), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn A.), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul J.P.), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca D.), Cijvat, R. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (E.), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar W.), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John C.), Neerincx, P.B.T. (Pieter B T), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (S.), Raphael, B.J. (Benjamin J.), Reinert, K. (Knut), Ridder, D. (Dick) de, de Ridder, J. (Jeroen), Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley D.), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), Schönhuth, A. (Alexander), Marschall, T. (Tanja), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam M.), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn A.), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul J.P.), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca D.), Cijvat, R. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (E.), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar W.), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John C.), Neerincx, P.B.T. (Pieter B T), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (S.), Raphael, B.J. (Benjamin J.), Reinert, K. (Knut), Ridder, D. (Dick) de, de Ridder, J. (Jeroen), Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley D.), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), and Schönhuth, A. (Alexander)

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations a

Published
2018
Full Text
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9. Computational pan-genomics: status, promises and challenges.

Author

Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., Schönhuth, A., Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., and Schönhuth, A.

Abstract

Contains fulltext : 190288.pdf (publisher's version ) (Open Access)

Published
2018

10. Computational pan-genomics: status, promises and challenges

Author

The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), Schönhuth, A. (Alexander), The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), and Schönhuth, A. (Alexander)

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Published
2018
Full Text
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11. Computational pan-genomics: status, promises and challenges

Author

Marschall, T, Marz, M, Abeel, T, Dijkstra, L, Dutilh, BE, Ghaffaari, A, Kersey, P, Kloosterman, WP, Makinen, V, Novak, AM, Paten, B, Porubsky, D, Rivals, E, Alkan, C, Baaijens, J A, de Bakker, PIW, Boeva, V, Bonnal, RJP, Chiaromonte, F, Chikhi, R, Ciccarelli, FD, Cijvat, R, Datema, E, Duijn, Cornelia, Eichler, EE, Ernst, C, Eskin, E, Garrison, E, El-Kebir, M, Klau, GW, Korbel, JO, Lameijer, EW, Langmead, B, Martin, M, Medvedev, P, Mu, JC, Neerincx, P, Ouwens, K, Peterlongo, P, Pisanti, N, Rahmann, S, Raphael, B, Reinert, K, Ridder, D, Ridder, J (Jannemarie), Schlesner, M, Schulz-Trieglaff, O, Sanders, AD, Sheikhizadeh, S, Shneider, C, Smit, S, Valenzuela, D, Wang, JY, Wessels, L, Zhang, Y, Guryev, V, Vandin, F, Ye, K, Schonhuth, A, Marschall, T, Marz, M, Abeel, T, Dijkstra, L, Dutilh, BE, Ghaffaari, A, Kersey, P, Kloosterman, WP, Makinen, V, Novak, AM, Paten, B, Porubsky, D, Rivals, E, Alkan, C, Baaijens, J A, de Bakker, PIW, Boeva, V, Bonnal, RJP, Chiaromonte, F, Chikhi, R, Ciccarelli, FD, Cijvat, R, Datema, E, Duijn, Cornelia, Eichler, EE, Ernst, C, Eskin, E, Garrison, E, El-Kebir, M, Klau, GW, Korbel, JO, Lameijer, EW, Langmead, B, Martin, M, Medvedev, P, Mu, JC, Neerincx, P, Ouwens, K, Peterlongo, P, Pisanti, N, Rahmann, S, Raphael, B, Reinert, K, Ridder, D, Ridder, J (Jannemarie), Schlesner, M, Schulz-Trieglaff, O, Sanders, AD, Sheikhizadeh, S, Shneider, C, Smit, S, Valenzuela, D, Wang, JY, Wessels, L, Zhang, Y, Guryev, V, Vandin, F, Ye, K, and Schonhuth, A

Published
2018

12. Divergent origins and concerted expansion of two segmental duplications on chromosome 16

Author

Eichler, E. E., Johnson, M. E., Alkan, C., Tuzun, E., Sahinalp, C., Misceo, D., Archidiacono, N., and Rocchi, M.

Subjects
Human chromosomes -- Research, Cladistic analysis -- Research, Genomes -- Research, Genetic research, Biological sciences
Abstract

A model of rapid expansion and evolutionary turnover among the genomes of man and the great apes is supported by the evolutionary history of the two chromosome duplications. The data suggest that the two copies of genomic sequence are brought together during the chimpanzee/human divergence and are subsequently duplicated as a larger cassette within the human lineage.

Published
2001

13. A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTD

Author

Renton, Ae, Majounie, E, Waite, A, Simón Sánchez, J, Rollinson, S, Gibbs, Jr, Schymick, Jc, Laaksovirta, H, van Swieten, Jc, Myllykangas, L, Kalimo, H, Paetau, A, Abramzon, Y, Remes, Am, Kaganovich, A, Scholz, Sw, Duckworth, J, Ding, J, Harmer, Dw, Hernandez, Dg, Johnson, Jo, Mok, K, Ryten, M, Trabzuni, D, Guerreiro, Rj, Orrell, Rw, Neal, J, Murray, A, Pearson, J, Jansen, Ie, Sondervan, D, Seelaar, H, Blake, D, Young, K, Halliwell, N, Callister, Jb, Toulson, G, Richardson, A, Gerhard, A, Snowden, J, Mann, D, Neary, D, Nalls, Ma, Peuralinna, T, Jansson, L, Isoviita, Vm, Kaivorinne, Al, Hölttä Vuori, M, Ikonen, E, Sulkava, R, Benatar, M, Wuu, J, Chiò, A, Restagno, G, Borghero, G, Sabatelli, M, Italsgen, Consortium, Heckerman, D, Rogaeva, E, Zinman, L, Rothstein, Jd, Sendtner, M, Drepper, C, Eichler, Ee, Alkan, C, Abdullaev, Z, Pack, Sd, Dutra, A, Pak, E, Hardy, J, Singleton, A, Williams, Nm, Heutink, P, Pickering Brown, S, Morris, Hr, Tienari, Pj, Traynor, Bj, Calvo, A, Cammarosano, S, Moglia, C, Canosa, A, Gallo, S, Brunetti, M, Ossola, I, Mora, G, Marinou, K, Papetti, L, Conte, A, Luigetti, M, La Bella, V, Spataro, R, Colletti, T, Battistini, S, Giannini, Fabio, Ricci, C, Caponnetto, C, Mancardi, G, Mandich, P, Salvi, F, Bartolomei, I, Mandrioli, J, Sola, P, Corbo, M, Lunetta, C, Penco, S, Monsurrò, Mr, Tedeschi, G, Conforti, Fl, Volanti, P, Floris, G, Cannas, A, Piras, V, Murru, Mr, Marrosu, Mg, Pugliatti, M, Ticca, A, Simone, I, Logroscino, G, Neuroscience Campus Amsterdam - Systems Biology of the Synapse, Neuroscience Campus Amsterdam - Neurodegeneration, Renton, Ae, Majounie, E, Waite, A, Simón Sánchez, J, Rollinson, S, Gibbs, Jr, Schymick, Jc, Laaksovirta, H, van Swieten, Jc, Myllykangas, L, Kalimo, H, Paetau, A, Abramzon, Y, Remes, Am, Kaganovich, A, Scholz, Sw, Duckworth, J, Ding, J, Harmer, Dw, Hernandez, Dg, Johnson, Jo, Mok, K, Ryten, M, Trabzuni, D, Guerreiro, Rj, Orrell, Rw, Neal, J, Murray, A, Pearson, J, Jansen, Ie, Sondervan, D, Seelaar, H, Blake, D, Young, K, Halliwell, N, Callister, Jb, Toulson, G, Richardson, A, Gerhard, A, Snowden, J, Mann, D, Neary, D, Nalls, Ma, Peuralinna, T, Jansson, L, Isoviita, Vm, Kaivorinne, Al, Hölttä Vuori, M, Ikonen, E, Sulkava, R, Benatar, M, Wuu, J, Chiò, A, Restagno, G, Borghero, G, Sabatelli, M, Italsgen, Consortium, Heckerman, D, Rogaeva, E, Zinman, L, Rothstein, Jd, Sendtner, M, Drepper, C, Eichler, Ee, Alkan, C, Abdullaev, Z, Pack, Sd, Dutra, A, Pak, E, Hardy, J, Singleton, A, Williams, Nm, Heutink, P, Pickering Brown, S, Morris, Hr, Tienari, Pj, COLLABORATORS: Calvo A, Traynor B. J., Cammarosano, S, Moglia, C, Canosa, A, Gallo, S, Brunetti, M, Ossola, I, Mora, G, Marinou, K, Papetti, L, Conte, A, Luigetti, M, La Bella, V, Spataro, R, Colletti, T, Battistini, S, Giannini, F, Ricci, C, Caponnetto, C, Mancardi, G, Mandich, P, Salvi, F, Bartolomei, I, Mandrioli, J, Sola, P, Corbo, M, Lunetta, C, Penco, S, Monsurro', Maria Rosaria, Tedeschi, Gioacchino, Conforti, Fl, Volanti, P, Floris, G, Cannas, A, Piras, V, Murru, Mr, Marrosu, Mg, Pugliatti, M, Ticca, A, Simone, I, Logroscino, G., Neurology, Human genetics, NCA - Systems Biology of the Synapse, and NCA - Neurodegeneration

Subjects
Male, Genotype, Neuroscience(all), Population, Biology, TARDBP, Chromosomes, 03 medical and health sciences, 0302 clinical medicine, Alleles, Amyotrophic Lateral Sclerosis, genetics, Chromosomes, Human, Pair 9, Female, Finland, Frontotemporal Dementia, genetics, Genetic Predisposition to Disease, Genotype, Haplotypes, Humans, Male, Microsatellite Repeats, Pedigree, Polymorphism, Single Nucleotide, SDG 3 - Good Health and Well-being, C9orf72, Humans, genetics, Genetic Predisposition to Disease, Polymorphism, education, Alleles, Finland, 030304 developmental biology, Genetics, 0303 health sciences, education.field_of_study, General Neuroscience, Haplotype, Amyotrophic Lateral Sclerosis, Charged multivesicular body protein 2B, DNA Repeat Expansion, 3. Good health, Pedigree, C9orf72 Protein, Haplotypes, Frontotemporal Dementia, Female, Trinucleotide repeat expansion, 030217 neurology & neurosurgery, Pair 9, Microsatellite Repeats
Abstract

The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases. We have previously shown that a founder haplotype, covering the MOBKL2b, IFNK, and C9ORF72 genes, is present in the majority of cases linked to this region. Here we show that there is a large hexanucleotide (GGGGCC) repeat expansion in the first intron of C9ORF72 on the affected haplotype. This repeat expansion segregates perfectly with disease in the Finnish population, underlying 46.0% of familial ALS and 21.1% of sporadic ALS in that population. Taken together with the D90A SOD1 mutation, 87% of familial ALS in Finland is now explained by a simple monogenic cause. The repeat expansion is also present in one-third of familial ALS cases of outbred European descent, making it the most common genetic cause of these fatal neurodegenerative diseases identified to date. © 2011 Elsevier Inc.

Published
2011

14. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

Author

Delaneau O., Marchini J., McVeanh G.A., Donnelly P., Lunter G., Marchini J.L., Myers, S., Gupta-Hinch, A., Iqbal, Z., Mathieson I., Rimmer, A., Xifara, D.K., Kerasidou, A., Churchhouse, C., Altshuler, D.M., Gabriel, S.B., Lander, E.S., Gupta, N., Daly, M.J., DePristo, M.A., Banks, E., Bhatia G., Carneiro, M.O., Del Angel G., Genovese G., Handsaker, R.E., Hartl, C., McCarroll, S.A., Nemesh J.C., Poplin, R.E., Schaffner, S.F., Shakir, K., Sabeti P.C., Grossman, S.R., Tabrizi, S., Tariyal, R., Li H., Reich, D., Durbin, R.M., Hurles, M.E., Balasubramaniam, S., Burton J., Danecek P., Keane, T.M., Kolb-Kokocinski, A., McCarthy, S., Stalker J., Quail, M., Ayub Q., Chen, Y., Coffey, A.J., Colonna V., Huang, N., Jostins L., Scally, A., Walter, K., Xue, Y., Zhang, Y., Blackburne, B., Lindsay, S.J., Ning, Z., Frankish, A., Harrow J., Chris, T.-S., Abecasis G.R., Kang H.M., Anderson P., Blackwell, T., Busonero F., Fuchsberger, C., Jun G., Maschio, A., Porcu, E., Sidore, C., Tan, A., Trost, M.K., Bentley, D.R., Grocock, R., Humphray, S., James, T., Kingsbury, Z., Bauer, M., Cheetham, R.K., Cox, T., Eberle, M., Murray L., Shaw, R., Chakravarti, A., Clark, A.G., Keinan, A., Rodriguez-Flores J.L., De LaVega F.M., Degenhardt J., Eichler, E.E., Flicek P., Clarke L., Leinonen, R., Smith, R.E., Zheng-Bradley X., Beal, K., Cunningham F., Herrero J., McLaren W.M., Ritchie G.R.S., Barker J., Kelman G., Kulesha, E., Radhakrishnan, R., Roa, A., Smirnov, D., Streeter I., Toneva I., Gibbs, R.A., Dinh H., Kovar, C., Lee, S., Lewis L., Muzny, D., Reid J., Wang, M., Yu F., Bainbridge, M., Challis, D., Evani, U.S., Lu J., Nagaswamy, U., Sabo, A., Wang, Y., Yu J., Fowler G., Hale W., Kalra, D., Green, E.D., Knoppers, B.M., Korbel J.O., Rausch, T., Sttz, A.M., Lee, C., Griffin L., Hsieh, C.-H., Mills, R.E., Von Grotthuss, M., Zhang, C., Shi X., Lehrach H., Sudbrak, R., Amstislavskiy V.S., Lienhard, M., Mertes F., Sultan, M., Timmermann, B., Yaspo, M.L., Herwig, S.R., Mardis, E.R., Wilson, R.K., Fulton L., Fulton, R., Weinstock G.M., Chinwalla, A., Ding L., Dooling, D., Koboldt, D.C., McLellan, M.D., Wallis J.W., Wendl, M.C., Zhang Q., Marth G.T., Garrison, E.P., Kural, D., Lee W.-P., Leong W.F., Ward, A.N., Wu J., Zhang, M., Nickerson, D.A., Alkan, C., Hormozdiari F., Ko, A., Sudmant P.H., Schmidt J.P., Davies, C.J., Gollub J., Webster, T., Wong, B., Zhan, Y., Sherry, S.T., Xiao, C., Church, D., Ananiev V., Belaia, Z., Beloslyudtsev, D., Bouk, N., Chen, C., Cohen, R., Cook, C., Garner J., Hefferon, T., Kimelman, M., Liu, C., Lopez J., Meric P., Ostapchuk, Y., Phan L., Ponomarov, S., Schneider V., Shekhtman, E., Sirotkin, K., Slotta, D., Zhang H., Wang J., Fang X., Guo X., Jian, M., Jiang H., Jin X., Li G., Li J., Li, Y., Liu X., Lu, Y., Ma X., Tai, S., Tang, M., Wang, B., Wang G., Wu H., Wu, R., Yin, Y., Zhang W., Zhao J., Zhao, M., Zheng X., Lachlan H., Fang L., Li Q., Li, Z., Lin H., Liu, B., Luo, R., Shao H., Xie, Y., Ye, C., Yu, C., Zheng H., Zhu H., Cai H., Cao H., Su, Y., Tian, Z., Yang H., Yang L., Zhu J., Cai, Z., Albrecht, M.W., Borodina, T.A., Auton, A., Yoon, S.C., Lihm J., Makarov V., Jin H., Kim W., Kim, K.C., Gottipati, S., Jones, D., Cooper, D.N., Ball, E.V., Stenson P.D., Barnes, B., Kahn, S., Ye, K., Batzer, M.A., Konkel, M.K., Walker J.A., MacArthur, D.G., Lek, M., Shriver, M.D., Bustamante, C.D., Gravel, S., Kenny, E.E., Kidd J.M., Lacroute P., Maples, B.K., Moreno-Estrada, A., Zakharia F., Henn, B., Sandoval, K., Byrnes J.K., Halperin, E., Baran, Y., Craig, D.W., Christoforides, A., Izatt, T., Kurdoglu, A.A., Sinari, S.A., Homer, N., Squire, K., Sebat J., Bafna V., Burchard, E.G., Hernandez, R.D., Gignoux, C.R., Haussler, D., Katzman, S.J., Kent W.J., Howie, B., Ruiz-Linares, A., Dermitzakis, E.T., Lappalainen, T., Devine, S.E., Maroo, A., Tallon L.J., Rosenfeld J.A., Michelson L.P., Angius, A., Cucca F., Sanna, S., Bigham, A., Jones, C., Reinier F., Lyons, R., Schlessinger, D., Awadalla P., Hodgkinson, A., Oleksyk, T.K., Martinez-Cruzado J.C., Fu, Y., Xiong, M., Jorde L., Witherspoon, D., Xing J., Browning, B.L., Hajirasouliha I., Chen, K., Albers, C.A., Gerstein, M.B., Abyzov, A., Chen J., Habegger L., Harmanci, A.O., Mu X.J., Sisu, C., Balasubramanian, S., Jin, M., Khurana, E., Clarke, D., Michaelson J.J., OSullivan, C., Barnes, K.C., Gharani, N., Toji L.H., Gerry, N., Kaye J.S., Kent, A., Mathias, R., Ossorio P.N., Parker, M., Rotimi, C.N., Royal, C.D., Tishkoff, S., Via, M., Bodmer W., Bedoya G., Yang G., You, C.J., Garcia-Montero, A., Orfao, A., Dutil J., Brooks L.D., Felsenfeld, A.L., McEwen J.E., Clemm, N.C., Guyer, M.S., Peterson J.L., Duncanson, A., Dunn, M., Peltonen L., and 1000 Genomes Project Consortium

Subjects
haplotype, genetic association, genotype, General Physics and Astronomy, Genome-wide association study, genetic analysis, gene sequence, Biology, gene frequency, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, polymorphism, Gene Frequency, single nucleotide polymorphism, Humans, chromosome, human, 1000 Genomes Project, indel mutation, genome, Alleles, Genetic association, Genetics, Whole genome sequencing, Multidisciplinary, accuracy, Genome, Human, Haplotype, allele, article, reference database, General Chemistry, Microarray Analysis, chromosome 20, Haplotypes, Human genome, microarray analysis, Imputation (genetics), Algorithms, SNP array, Genome-Wide Association Study
Abstract

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved.

Published
2014

15. Great ape genetic diversity and population history

Author

Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, Lorente-Galdos B, Veeramah KR, Woerner AE, O'Connor TD, Santpere G, Cagan A, Theunert C, Casals F, Laayouni H, Munch K, Hobolth A, Halager AE, Malig M, Hernandez-Rodriguez J, Hernando-Herraez I, Prxfcfer K, Pybus M, Johnstone L, Lachmann M, Alkan C, Twigg D, Petit N, Baker C, Hormozdiari F, Fernandez-Callejo M, Dabad M, Wilson ML, Stevison L, Camprubxed C, Carvalho T, Ruiz-Herrera A, Vives L, Mele M, Abello T, Kondova I, Bontrop RE, Pusey A, Lankester F, and K

Published
2013

17. An integrated map of genetic variation from 1,092 human genomes

Author

Altshuler, DM, Durbin, RM, Abecasis, GR, Bentley, DR, Chakravarti, A, Clark, AG, Donnelly, P, Eichler, EE, Flicek, P, Gabriel, SB, Gibbs, RA, Green, ED, Hurles, ME, Knoppers, BM, Korbel, JO, Lander, ES, Lee, C, Lehrach, H, Mardis, ER, Marth, GT, McVean, GA, Nickerson, DA, Schmidt, JP, Sherry, ST, Wang, J, Wilson, RK, Dinh, H, Kovar, C, Lee, S, Lewis, L, Muzny, D, Reid, J, Wang, M, Fang, X, Guo, X, Jian, M, Jiang, H, Jin, X, Li, G, Li, J, Li, Y, Li, Z, Liu, X, Lu, Y, Ma, X, Su, Z, Tai, S, Tang, M, Wang, B, Wang, G, Wu, H, Wu, R, Yin, Y, Zhang, W, Zhao, J, Zhao, M, Zheng, X, Zhou, Y, Gupta, N, Clarke, L, Leinonen, R, Smith, RE, Zheng-Bradley, X, Grocock, R, Humphray, S, James, T, Kingsbury, Z, Sudbrak, R, Albrecht, MW, Amstislavskiy, VS, Borodina, TA, Lienhard, M, Mertes, F, Sultan, M, Timmermann, B, Yaspo, M-L, Fulton, L, Fulton, R, Weinstock, GM, Balasubramaniam, S, Burton, J, Danecek, P, Keane, TM, Kolb-Kokocinski, A, McCarthy, S, Stalker, J, Quail, M, Davies, CJ, Gollub, J, Webster, T, Wong, B, Zhan, Y, Auton, A, Yu, F, Bainbridge, M, Challis, D, Evani, US, Lu, J, Nagaswamy, U, Sabo, A, Wang, Y, Yu, J, Coin, LJM, Fang, L, Li, Q, Lin, H, Liu, B, Luo, R, Qin, N, Shao, H, Xie, Y, Ye, C, Yu, C, Zhang, F, Zheng, H, Zhu, H, Garrison, EP, Kural, D, Lee, W-P, Leong, WF, Ward, AN, Wu, J, Zhang, M, Griffin, L, Hsieh, C-H, Mills, RE, Shi, X, Von Grotthuss, M, Zhang, C, Daly, MJ, DePristo, MA, Banks, E, Bhatia, G, Carneiro, MO, Del Angel, G, Genovese, G, Handsaker, RE, Hartl, C, McCarroll, SA, Nemesh, JC, Poplin, RE, Schaffner, SF, Shakir, K, Yoon, SC, Lihm, J, Makarov, V, Jin, H, Kim, W, Kim, KC, Rausch, T, Beal, K, Cunningham, F, Herrero, J, McLaren, WM, Ritchie, GRS, Gottipati, S, Keinan, A, Rodriguez-Flores, JL, Sabeti, PC, Grossman, SR, Tabrizi, S, Tariyal, R, Cooper, DN, Ball, EV, Stenson, PD, Barnes, B, Bauer, M, Cheetham, RK, Cox, T, Eberle, M, Kahn, S, Murray, L, Peden, J, Shaw, R, Ye, K, Batzer, MA, Konkel, MK, Walker, JA, MacArthur, DG, Lek, M, Herwig, R, Shriver, MD, Bustamante, CD, Byrnes, JK, De la Vega, FM, Gravel, S, Kenny, EE, Kidd, JM, Lacroute, P, Maples, BK, Moreno-Estrada, A, Zakharia, F, Halperin, E, Baran, Y, Craig, DW, Christoforides, A, Homer, N, Izatt, T, Kurdoglu, AA, Sinari, SA, Squire, K, Xiao, C, Sebat, J, Bafna, V, Burchard, EG, Hernandez, RD, Gignoux, CR, Haussler, D, Katzman, SJ, Kent, WJ, Howie, B, Ruiz-Linares, A, Dermitzakis, ET, Lappalainen, T, Devine, SE, Maroo, A, Tallon, LJ, Rosenfeld, JA, Michelson, LP, Kang, HM, Anderson, P, Angius, A, Bigham, A, Blackwell, T, Busonero, F, Cucca, F, Fuchsberger, C, Jones, C, Jun, G, Lyons, R, Maschio, A, Porcu, E, Reinier, F, Sanna, S, Schlessinger, D, Sidore, C, Tan, A, Trost, MK, Awadalla, P, Hodgkinson, A, Lunter, G, Marchini, JL, Myers, S, Churchhouse, C, Delaneau, O, Gupta-Hinch, A, Iqbal, Z, Mathieson, I, Rimmer, A, Xifara, DK, Oleksyk, TK, Fu, Y, Xiong, M, Jorde, L, Witherspoon, D, Xing, J, Browning, BL, Alkan, C, Hajirasouliha, I, Hormozdiari, F, Ko, A, Sudmant, PH, Chen, K, Chinwalla, A, Ding, L, Dooling, D, Koboldt, DC, McLellan, MD, Wallis, JW, Wendl, MC, Zhang, Q, Tyler-Smith, C, Albers, CA, Ayub, Q, Chen, Y, Coffey, AJ, Colonna, V, Huang, N, Jostins, L, Li, H, Scally, A, Walter, K, Xue, Y, Zhang, Y, Gerstein, MB, Abyzov, A, Balasubramanian, S, Chen, J, Clarke, D, Habegger, L, Harmanci, AO, Jin, M, Khurana, E, Mu, XJ, Sisu, C, Degenhardt, J, Stuetz, AM, Church, D, Michaelson, JJ, Ben, B, Lindsay, SJ, Ning, Z, Frankish, A, Harrow, J, Fowler, G, Hale, W, Kalra, D, Barker, J, Kelman, G, Kulesha, E, Radhakrishnan, R, Roa, A, Smirnov, D, Streeter, I, Toneva, I, Vaughan, B, Ananiev, V, Belaia, Z, Beloslyudtsev, D, Bouk, N, Chen, C, Cohen, R, Cook, C, Garner, J, Hefferon, T, Kimelman, M, Liu, C, Lopez, J, Meric, P, O'Sullivan, C, Ostapchuk, Y, Phan, L, Ponomarov, S, Schneider, V, Shekhtman, E, Sirotkin, K, Slotta, D, Zhang, H, Barnes, KC, Beiswanger, C, Cai, H, Cao, H, Gharani, N, Henn, B, Jones, D, Kaye, JS, Kent, A, Kerasidou, A, Mathias, R, Ossorio, PN, Parker, M, Reich, D, Rotimi, CN, Royal, CD, Sandoval, K, Su, Y, Tian, Z, Tishkoff, S, Toji, LH, Via, M, Yang, H, Yang, L, Zhu, J, Bodmer, W, Bedoya, G, Ming, CZ, Yang, G, You, CJ, Peltonen, L, Garcia-Montero, A, Orfao, A, Dutil, J, Martinez-Cruzado, JC, Brooks, LD, Felsenfeld, AL, McEwen, JE, Clemm, NC, Duncanson, A, Dunn, M, Guyer, MS, Peterson, JL, 1000 Genomes Project Consortium, Dermitzakis, Emmanouil, Universitat de Barcelona, Massachusetts Institute of Technology. Department of Biology, Altshuler, David, and Lander, Eric S.

Subjects
Natural selection, LOCI, Genome-wide association study, Evolutionary biology, Continental Population Groups/genetics, Human genetic variation, VARIANTS, Genoma humà, Binding Sites/genetics, 0302 clinical medicine, RARE, Sequence Deletion/genetics, WIDE ASSOCIATION, ddc:576.5, Copy-number variation, MUTATION, Exome sequencing, transcription factor, Conserved Sequence, Human evolution, Sequence Deletion, Genetics, RISK, 0303 health sciences, Multidisciplinary, Continental Population Groups, 1000 Genomes Project Consortium, Genetic analysis, Genomics, Polymorphism, Single Nucleotide/genetics, Research Highlight, 3. Good health, Algorithm, Multidisciplinary Sciences, Genetic Variation/genetics, Map, Science & Technology - Other Topics, Conserved Sequence/genetics, Integrated approach, General Science & Technology, Genetics, Medical, Haplotypes/genetics, Biology, Polymorphism, Single Nucleotide, Evolution, Molecular, 03 medical and health sciences, Genetic variation, Humans, Transcription Factors/metabolism, POPULATION-STRUCTURE, 1000 Genomes Project, Polymorphism, Nucleotide Motifs, Alleles, 030304 developmental biology, COPY NUMBER VARIATION, Science & Technology, Binding Sites, Human genome, Genome, Human, Racial Groups, Genetic Variation, Genetics, Population, Haplotypes, Genome, Human/genetics, untranslated RNA, 030217 neurology & neurosurgery, Transcription Factors, Genome-Wide Association Study
Abstract

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations., National Institutes of Health (U.S.) (Grant RC2HL102925), National Institutes of Health (U.S.) (Grant U54HG3067)

Published
2012

18. A draft sequence and preliminary analysis of the Neandertal genome

Author

Green, R.E., Krause, J., Briggs, A.W., Maricic, T., Stenzel, U., Kircher, M., Patterson, N., Li, H., Zhai, W., M. Hsi-Yang, Fritz, Hansen, N.F., Durand, E.Y., Malaspinas, A.-S., Jensen, J.D., Marques-Bonet, T., Alkan, C., Prüfer, K., Meyer, M., Burbano, H.A., Good, J.M., Schultz, R., Aximu-Petri, A., Butthof, A., Höber, B., Höffner, B., Siegemund, M., Weihmann, A., Nusbaum, C., Lander, E.S., Russ, C., Novod, N., Affourtit, J., Egholm, M., Verna, C., Rudan, P., Brajković, D., Kućan, Z., Gušić, I., Doronichev, V.B., Golovanova, L.V., Lalueza-Fox, C., Rasilla M. De, La, Fortea, J., Rosas, A., Schmitz, R., Johnson, P., Eichler, E.E., Falush, D., Birney, E., Mullikin, J.C., Slatkin, M., Nielsen, R., Kelso, J., Lachmann, M., Reich, D., Pääbo, S., Laboratoire de mécanique des sols, structures et matériaux (MSSMat), CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Origine, structure et évolution de la biodiversité (OSEB), and Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SHS.ANTHRO-BIO]Humanities and Social Sciences/Biological anthropology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2010

21. Virus discovery in sandflies, from field studies to phylogenetic tree building: classic methods versus novel methods, what do we need to address public health impact?

Author

Bichaud, L, primary, Alkan, C, additional, Piorkowski, G, additional, Zhioua, E, additional, Bitam, I, additional, Izri, A, additional, Moin Vaziri, V, additional, Alten, B, additional, Ozbel, Y, additional, Kasap, OE, additional, De Lamballerie, X, additional, and Charrel, RN, additional

Published
2014
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23. The electronic transport properties [Co-2(L)L '(dioxane)(2)(NO3)(2)](NO3)(2) of the complex

Author

Yakuphanoglu, F, Aydogdu, Y, Gomleksiz, M, Sekerci, M, Agan, S, Alkan, C, and Kırıkkale Üniversitesi

Subjects
electrical conductivity, semiconductor, complex
Abstract

WOS: 000182126000015 The temperature dependence of the electrical conductivity, microstructure and crystal structure properties of the [Co,(L)L'(dioxane)(2)(NO3)(2)](NO3)(2) complex was investigated. The X-ray diffraction showed that the [Co,(L)L'(dioxane)(2) (NO3)(2)](NO3)(2) has a triclinic structure. Temperature dependence of the conductivity curve of the complex exhibited two conduction regions, and while increase of conductivity with temperature in the first region is of hopping conduction which takes place in localized states, the conduction in the second region is of Arrhenius-type. These conduction mechanisms are related to intermolecular and intramolecular conduction progresses. The electrical conductivity of the [Co-2(L)L'(dioxane)(2)(NO3)(2)](NO3)(2) complex was 2.57 x 10(-7) (S/cm) at room temperature with activation energy of 0.46 eV which showed semiconductivity behavior. (C) 2002 Published by Elsevier Science B.V.

Published
2003

37. Pacific symposium on biocomputing 2011

Author

Bebek, G., Chance, M., Koyuturk, M., Price, N. D., La Vega, F. M., Bustamante, C. D., Leal, S. M., James Foster, Moore, J., Bernauer, J., Flores, S., Huang, X., Shin, S., Zhou, R., Alkan, C., Capriotti, E., Hormozdiari, F., Eskin, E., Kann, M. G., Alterovitz, G., Cavalcanti, S., Wang, M., and Ramoni, M. F.

39. Synthesis, characterization and antibacterial activity of imidazole derivatives of 1,10-phenanthroline and their Cu(II), Co(II) and Ni(II) complexes

Author

Gomleksiz, M., Alkan, C., Belgin Erdem, and Kırşehir Ahi Evran Üniversitesi, Fen-Edebiyat Fakültesi, Kimya Bölümü

Subjects
antibacterial activity, 1-10-Phenanthroline, Complex, Antibacterial activity, 1,10-Phenanthroline, imidazole, complex, antibacterial activity, 1,10-Phenanthroline, Imidazole, complex, imidazole
Abstract

WOS: 000318758500013 Six new CuL1 (L-1 = 4-bromo-2-(1H-imidazo[4,5-f][1,10]phenanthroline-2-yl)phenol), CoL1, NiL1, CuL2 (L-2 = 2-(1H-imidazo[4,5-f] [1,10]phenanthroline-2-yl)-5-methoxyphenol), CoL2 and NiL2 complexes were synthesized. L-1 and L-2 ligands were prepared by the condensation of 1,10-phenanthroline-5,6-dione with 5-bromosalicylaldehyde and 2-hydroxy-4-methoxybenzaldehyde, respectively. The structures of the compounds were determined by elemental analyses, IR, UV-visible, H-1-NMR, TGA, magnetic susceptibilities and molar conductance measurements. It is observed that the synthesized complexes have tetragonal and distorted square pyramidal geometrical structures. Antibacterial activity of the ligands and their metal complexes were tested against selected bacteria by disc diffusion method. Firat University Research FoundationFirat University We are grateful to Firat University Research Foundation for the support of this research.

43. The electronic transport properties of the [Co2(L)L′(dioxane)2(NO3)2](NO3)2 complex

Author

Yakuphanoglu, F., Aydogdu, Y., Gömleksiz, M., Sekerci, M., Agan, S., and Alkan, C.

Subjects
*ELECTRIC conductivity, *COMPLEX compounds
Abstract

The temperature dependence of the electrical conductivity, microstructure and crystal structure properties of the [Co2(L)L′(dioxane)2(NO3)2](NO3)2 complex was investigated. The X-ray diffraction showed that the [Co2(L)L′(dioxane)2(NO3)2](NO3)2 has a triclinic structure. Temperature dependence of the conductivity curve of the complex exhibited two conduction regions, and while increase of conductivity with temperature in the first region is of hopping conduction which takes place in localized states, the conduction in the second region is of Arrhenius-type. These conduction mechanisms are related to intermolecular and intramolecular conduction progresses. The electrical conductivity of the [Co2(L)L′(dioxane)2(NO3)2](NO3)2 complex was 2.57×10−7 (S/cm) at room temperature with activation energy of 0.46 eV which showed semiconductivity behavior. [Copyright &y& Elsevier]

Published
2003
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44. Developing a poly(ethylene glycol)/cellulose phase change reactive composite for cooling application

Author

Özgül Gök, Yeliz Konuklu, Cemil Alkan, Gök, Ö., Hakkari University, Faculty of Engineering, Department of Chemical Engineering, Hakkari, Turkey -- Alkan, C., Tokat Gaziosmanpaşa University, Faculty of Arts and Sciences, Department of Chemistry, Tokat, Turkey -- Konuklu, Y., Niğde Ömer Halisdemir University, Faculty of Arts and Sciences, Department of Chemistry, Niğde, Turkey, Niğde Ömer Halisdemir University, Nanotechnology Application and Research Center, Niğde, Turkey, Hakkari Üniversitesi, Mühendislik Fakültesi, Kimya Mühendisliği Bölümü, 0-Belirlenecek, and 0-Belirlenecek

Subjects
Latent heat, Materials science, Composite number, Composite, 02 engineering and technology, 010402 general chemistry, 01 natural sciences, Thermal energy storage, chemistry.chemical_compound, Differential scanning calorimetry, Naturel Sciences, Thermal stability, Cellulose, Thermal analysis, Polyurethane, Renewable Energy, Sustainability and the Environment, Poly(ethylene glycol), 021001 nanoscience & nanotechnology, Phase-change material, 0104 chemical sciences, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials, chemistry, Chemical engineering, sense organs, 0210 nano-technology, Ethylene glycol, Phase change material
Abstract

In this study, it was carried out the development of a poly(ethylene glycol)/cellulose phase change reactive composite as latent heat storage material for cooling application and determination of its thermal stability. Poly(ethylene glycol) (PEG) was grafted onto a cellulose backbone as solid–solid phase change material. The change in the surface morphology of the PEG1000 was studied by using a polarised optical microscopy (POM) instrument. Thermal analysis and thermal stability upon usage of the phase change composite material were performed by using a differential scanning calorimeter (DSC) instrument. The chemical analysis of the PCM composite was carried out by using a fourier transform infrared spectroscopy (FTIR) instrument. Phase change composite melted and crystallized 10 times to determine the thermal stability. The PEG1000 grafted cellulose phase change composite before 10 times thermal cycling absorbed 78.6 J/g of heat at 7.7 °C and released 74.4 J/g at - 5.4 °C whereas the PEG1000 grafted cellulose phase change composite after 10 times thermal cycling absorbed 92.7 J/g of heat at 8.3 °C and released 83.2 J/g at 1 °C during melting and freezing respectively. It was observed that Cp of the PEG1000 grafted cellulose sample was decreased compared to Cp of the pristine PEG1000. There was a considerable difference between crystal structures of pristine PEG1000 and PEG1000 grafted cellulose phase change composite. The disappearance of diisocyanate peak of TDI observed at 2228.34 cm -1 in PEG1000 grafted cellulose sample before and after 10 times thermal cycling was accepted as the evidence of polyurethane formation. As a result, PEG1000 grafted cellulose phase change composite was found as a potential thermal energy storage material for cooling application in relevant systems. © 2018, This work was supported by Research Fund of the Hakkari University . Project Number: FM2017BAP2 . The experimental work of this study was carried out in Polymer Research Laboratory at Chemistry Department of Tokat Gaziosmanpaşa University. The authors would like to thank Dr. Derya KAHRAMAN DÖĞÜŞCÜ for her expert assistance in experimental studies.

Published
2019

45. Genetic history of an archaic hominin group from Denisova Cave in Siberia

Author

Heng Li, Michael P. Richards, Adrian W. Briggs, Richard E. Green, Michael V. Shunkov, Bence Viola, Eric Durand, Can Alkan, Mark Stoneking, A.P. Derevianko, Svante Pääbo, Jeffrey M. Good, Udo Stenzel, Montgomery Slatkin, Jean-Jacques Hublin, Tomislav Maricic, Janet Kelso, Tomas Marques-Bonet, Johannes Krause, Nick Patterson, Sahra Talamo, Swapan Mallick, Martin Kircher, Philip L. F. Johnson, Qiaomei Fu, Evan E. Eichler, Matthias Meyer, David Reich, Reich D., Green R.E., Kircher M., Krause J., Patterson N., Durand E.Y., Viola B., Briggs A.W., Stenzel U., Johnson P.L.F., Maricic T., Good J.M., Marques-Bonet T., Alkan C., Fu Q., Mallick S., Li H., Meyer M., Eichler E.E., Stoneking M., Richards M., Talamo S., Shunkov M.V., Derevianko A.P., Hublin J.-J., Kelso J., Slatkin M., and Paabo S.

Subjects
Gene Flow, Asia, Neanderthal, Homínids, Molecular Sequence Data, education, Population, Zoology, Archaic humans, Neanderthal genome project, DNA, Mitochondrial, Article, Finger Phalanges, biology.animal, ADN mitocondrial -- Genètica, Animals, Humans, Denisovan, Phylogeny, education.field_of_study, Genome, Multidisciplinary, biology, Animal, Fossils, Recent African origin of modern humans, Finger Phalange, Fossil, Hominidae, biology.organism_classification, Europe, Siberia, Homo sapiens, Anatomically modern human, Melanesia, Tooth, Human
Abstract

8 páginas, 4 figuras, 1 tabla.-- Artículo Open Access.-- et al., Using DNA extracted from a finger bone found in Denisova Cave in southern Siberia, we have sequenced the genome of an archaic hominin to about 1.9-fold coverage. This individual is from a group that shares a common origin with Neanderthals. This population was not involved in the putative gene flow from Neanderthals into Eurasians; however, the data suggest that it contributed 4–6% of its genetic material to the genomes of present-day Melanesians. We designate this hominin population ‘Denisovans’ and suggest that it may have been widespread in Asia during the Late Pleistocene epoch. A tooth found in Denisova Cave carries a mitochondrial genome highly similar to that of the finger bone. This tooth shares no derived morphological features with Neanderthals or modern humans, further indicating that Denisovans have an evolutionary history distinct from Neanderthals and modern humans., The Presidential Innovation Fund of the Max Planck Society and the Krekeler Foundation provided financial support. M.S. was supported by a US National Institutes of Health grant (R01-GM40282). The National Science Foundation provided an International Postdoctoral Fellowship (OISE-0754461) to J.M.G., a Fellowship in Biological Informatics to P.L.F.J. and a HOMINID grant (1032255) to D.R.

Published
2010

46. New Insights into Centromere Organization and Evolution from the White-Cheeked Gibbon and Marmoset

Author

G. Della Valle, Claudia Rita Catacchio, Can Alkan, Francesca Antonacci, Maria Francesca Cardone, Mario Ventura, Maika Malig, Mariano Rocchi, Giuliana Giannuzzi, Angelo Cellamare, Evan E. Eichler, Cellamare A, Catacchio CR, Alkan C, Giannuzzi G, Antonacci F, Cardone MF, Della Valle G, Malig M, Rocchi M, Eichler EE, and Ventura M.

Subjects
Primates, Centromere, Sequence assembly, ALPHA-SATELLITE DNA, Genome, GIBBON AND MARMOSET GENOME, Cell Line, 03 medical and health sciences, chemistry.chemical_compound, Phylogenetics, Hylobates, biology.animal, Genetics, Animals, Humans, Molecular Biology, CENTROMERE EVOLUTION, Ecology, Evolution, Behavior and Systematics, Research Articles, CENTROMERIC SEQUENCES, 030304 developmental biology, New World monkey, 0303 health sciences, biology, 030302 biochemistry & molecular biology, Marmoset, Callithrix, biology.organism_classification, Biological Evolution, body regions, chemistry, DNA
Abstract

The evolutionary history of alpha-satellite DNA, the major component of primate centromeres, is hardly defined because of the difficulty in its sequence assembly and its rapid evolution when compared with most genomic sequences. By using several approaches, we have cloned, sequenced, and characterized alpha-satellite sequences from two species representing critical nodes in the primate phylogeny: the white-cheeked gibbon, a lesser ape, and marmoset, a New World monkey. Sequence analyses demonstrate that white-cheeked gibbon and marmoset alpha-satellite sequences are formed by units of approximately 171 and approximately 342 bp, respectively, and they both lack the high-order structure found in humans and great apes. Fluorescent in situ hybridization characterization shows a broad dispersal of alpha-satellite in the white-cheeked gibbon genome including centromeric, telomeric, and chromosomal interstitial localizations. On the other hand, centromeres in marmoset appear organized in highly divergent dimers roughly of 342 bp that show a similarity between monomers much lower than previously reported dimers, thus representing an ancient dimeric structure. All these data shed light on the evolution of the centromeric sequences in Primates. Our results suggest radical differences in the structure, organization, and evolution of alpha-satellite DNA among different primate species, supporting the notion that 1) all the centromeric sequence in Primates evolved by genomic amplification, unequal crossover, and sequence homogenization using a 171 bp monomer as the basic seeding unit and 2) centromeric function is linked to relatively short repeated elements, more than higher-order structure. Moreover, our data indicate that complex higher-order repeat structures are a peculiarity of the hominid lineage, showing the more complex organization in humans.

Published
2009

47. Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.

Author

Huang J, Wu Y, Wang F, Fang Y, Nan Y, Alkan C, Abraham D, Liao C, Xu L, Gao Z, Wu W, Zhu L, Chen Z, Lally P, Bangerter N, Setsompop K, Guo Y, Rueckert D, Wang G, and Yang G

Abstract

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

Published
2024
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48. AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes.

Author

Kim JS, Firtina C, Cavlak MB, Cali DS, Hajinazar N, Alser M, Alkan C, and Mutlu O

Abstract

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants.

Published
2024
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49. AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI.

Author

Alkan C, Mardani M, Liao C, Li Z, Vasanawala SS, and Pauly JM

Abstract

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Published
2024
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50. Distinct Pathological Changes in Preweaning Mice Infected with Live-Attenuated Rift Valley Fever Virus Strains.

Author

Alkan C, Jurado-Cobena E, and Ikegami T

Subjects
Animals, Mice, Virulence, Female, Rift Valley fever virus pathogenicity, Rift Valley fever virus immunology, Rift Valley fever virus genetics, Rift Valley Fever virology, Rift Valley Fever pathology, Rift Valley Fever prevention & control, Rift Valley Fever immunology, Vaccines, Attenuated immunology, Vaccines, Attenuated administration & dosage, Viral Vaccines immunology, Viral Vaccines administration & dosage, Mice, Inbred C57BL, Disease Models, Animal
Abstract

Rift Valley fever (RVF) is a mosquito-borne zoonotic viral disease endemic to Africa and the Middle East. Live-attenuated RVF vaccines have been studied for both veterinary and human use due to their strong immunogenicity and cost-effective manufacturing. The live-attenuated MP-12 vaccine has been conditionally approved for veterinary use in the U.S.A., and next-generation live-attenuated RVF vaccine candidates are being actively researched. Assessing the virulence phenotype of vaccine seeds or lots is crucial for managing vaccine safety. Previously, preweaning 19-day-old outbred CD1 mice have been used to evaluate the MP-12 strain. This study aimed to characterize the relative virulence of three live-attenuated RVF vaccine strains in 19-day-old inbred C57BL/6 mice: the recombinant MP-12 (rMP-12), the RVax-1, and the ∆NSs-∆NSm-rZH501 strains. Although this mouse model did not show dose-dependent pathogenesis, mice that succumbed to the infection exhibited distinct brain pathology. Mice infected with ∆NSs-∆NSm-rZH501 showed an infiltration of inflammatory cells associated with infected neurons, and focal lesions formed around virus-infected cells. In contrast, mice infected with rMP-12 or RVax-1 showed a minimal association of inflammatory cells in the brain, yet the virus spread diffusely. The preweaning model is likely useful for evaluating host responses to attenuated RVFV strains, although further refinement may be necessary to quantitate the virulence among different RVFV strains or vaccine lots.

Published
2024
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