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25 results on '"Emde, Anne-Katrin"'

1. Mid-pass whole-genome sequencing in a Malagasy cohort uncovers body composition associations

Author

Hamid, Iman, Raveloson, Séverine Nantenaina Stéphie, Spiral, Germain Jules, Ravelonjanahary, Soanorolalao, Raharivololona, Brigitte Marie, Randria, José Mahenina, Zafimaro, Mosa, Randriambola, Tsiorimanitra Aimée, Andriantsoa, Rota Mamimbahiny, Andriamahefa, Tojo Julio, Rafidison, Bodonomena Fitahiana Laza, Mughal, Mehreen, Emde, Anne-Katrin, Hendershott, Melissa, LeBaron von Baeyer, Sarah, Wasik, Kaja A., Ranaivoarisoa, Jean Freddy, Yerges-Armstrong, Laura, Castel, Stephane E., and Rakotoarivony, Rindra

Published
2024
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2. The gout epidemic in French Polynesia: a modelling study of data from the Ma’i u’u epidemiological survey

Author

Pascart, Tristan, Wasik, Kaja A, Preda, Cristian, Chune, Valérie, Torterat, Jérémie, Prud’homme, Nicolas, Nassih, Maryline, Martin, Agathe, Le Masson, Julien, Rodière, Vahinetua, Frogier, Sylvain, Canova, Georges, Pescheux, Jean-Paul, Shan Sei Fan, Charles, Jauffret, Charlotte, Claeys, Patrick, von Baeyer, Sarah LeBaron, Castel, Stephane E, Emde, Anne-Katrin, Yerges-Armstrong, Laura, Fox, Keolu, Leask, Megan, Vitagliano, Jean-Jacques, Graf, Sahara, Norberciak, Laurène, Raynal, Jacques, Dalbeth, Nicola, Merriman, Tony, Bardin, Thomas, and Oehler, Erwan

Published
2024
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3. Somatic whole genome dynamics of precancer in Barrett’s esophagus reveals features associated with disease progression

Author

Paulson, Thomas G., Galipeau, Patricia C., Oman, Kenji M., Sanchez, Carissa A., Kuhner, Mary K., Smith, Lucian P., Hadi, Kevin, Shah, Minita, Arora, Kanika, Shelton, Jennifer, Johnson, Molly, Corvelo, Andre, Maley, Carlo C., Yao, Xiaotong, Sanghvi, Rashesh, Venturini, Elisa, Emde, Anne-Katrin, Hubert, Benjamin, Imielinski, Marcin, Robine, Nicolas, Reid, Brian J., and Li, Xiaohong

Published
2022
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4. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Taliun, Daniel, Harris, Daniel N., Kessler, Michael D., Carlson, Jedidiah, Szpiech, Zachary A., Torres, Raul, Taliun, Sarah A. Gagliano, Corvelo, André, Gogarten, Stephanie M., Kang, Hyun Min, Pitsillides, Achilleas N., LeFaive, Jonathon, Lee, Seung-been, Tian, Xiaowen, Browning, Brian L., Das, Sayantan, Emde, Anne-Katrin, Clarke, Wayne E., Loesch, Douglas P., Shetty, Amol C., Blackwell, Thomas W., Smith, Albert V., Wong, Quenna, Liu, Xiaoming, Conomos, Matthew P., Bobo, Dean M., Aguet, François, Albert, Christine, Alonso, Alvaro, Ardlie, Kristin G., Arking, Dan E., Aslibekyan, Stella, Auer, Paul L., Barnard, John, Barr, R. Graham, Barwick, Lucas, Becker, Lewis C., Beer, Rebecca L., Benjamin, Emelia J., Bielak, Lawrence F., Blangero, John, Boehnke, Michael, Bowden, Donald W., Brody, Jennifer A., Burchard, Esteban G., Cade, Brian E., Casella, James F., Chalazan, Brandon, Chasman, Daniel I., Chen, Yii-Der Ida, Cho, Michael H., Choi, Seung Hoan, Chung, Mina K., Clish, Clary B., Correa, Adolfo, Curran, Joanne E., Custer, Brian, Darbar, Dawood, Daya, Michelle, de Andrade, Mariza, DeMeo, Dawn L., Dutcher, Susan K., Ellinor, Patrick T., Emery, Leslie S., Eng, Celeste, Fatkin, Diane, Fingerlin, Tasha, Forer, Lukas, Fornage, Myriam, Franceschini, Nora, Fuchsberger, Christian, Fullerton, Stephanie M., Germer, Soren, Gladwin, Mark T., Gottlieb, Daniel J., Guo, Xiuqing, Hall, Michael E., He, Jiang, Heard-Costa, Nancy L., Heckbert, Susan R., Irvin, Marguerite R., Johnsen, Jill M., Johnson, Andrew D., Kaplan, Robert, Kardia, Sharon L. R., Kelly, Tanika, Kelly, Shannon, Kenny, Eimear E., Kiel, Douglas P., Klemmer, Robert, Konkle, Barbara A., Kooperberg, Charles, Köttgen, Anna, Lange, Leslie A., Lasky-Su, Jessica, Levy, Daniel, Lin, Xihong, Lin, Keng-Han, Liu, Chunyu, Loos, Ruth J. F., Garman, Lori, Gerszten, Robert, Lubitz, Steven A., Lunetta, Kathryn L., Mak, Angel C. Y., Manichaikul, Ani, Manning, Alisa K., Mathias, Rasika A., McManus, David D., McGarvey, Stephen T., Meigs, James B., Meyers, Deborah A., Mikulla, Julie L., Minear, Mollie A., Mitchell, Braxton D., Mohanty, Sanghamitra, Montasser, May E., Montgomery, Courtney, Morrison, Alanna C., Murabito, Joanne M., Natale, Andrea, Natarajan, Pradeep, Nelson, Sarah C., North, Kari E., O’Connell, Jeffrey R., Palmer, Nicholette D., Pankratz, Nathan, Peloso, Gina M., Peyser, Patricia A., Pleiness, Jacob, Post, Wendy S., Psaty, Bruce M., Rao, D. C., Redline, Susan, Reiner, Alexander P., Roden, Dan, Rotter, Jerome I., Ruczinski, Ingo, Sarnowski, Chloé, Schoenherr, Sebastian, Schwartz, David A., Seo, Jeong-Sun, Seshadri, Sudha, Sheehan, Vivien A., Sheu, Wayne H., Shoemaker, M. Benjamin, Smith, Nicholas L., Smith, Jennifer A., Sotoodehnia, Nona, Stilp, Adrienne M., Tang, Weihong, Taylor, Kent D., Telen, Marilyn, Thornton, Timothy A., Tracy, Russell P., Van Den Berg, David J., Vasan, Ramachandran S., Viaud-Martinez, Karine A., Vrieze, Scott, Weeks, Daniel E., Weir, Bruce S., Weiss, Scott T., Weng, Lu-Chen, Willer, Cristen J., Zhang, Yingze, Zhao, Xutong, Arnett, Donna K., Ashley-Koch, Allison E., Barnes, Kathleen C., Boerwinkle, Eric, Gabriel, Stacey, Gibbs, Richard, Rice, Kenneth M., Rich, Stephen S., Silverman, Edwin K., Qasba, Pankaj, Gan, Weiniu, Papanicolaou, George J., Nickerson, Deborah A., Browning, Sharon R., Zody, Michael C., Zöllner, Sebastian, Wilson, James G., Cupples, L. Adrienne, Laurie, Cathy C., Jaquish, Cashell E., Hernandez, Ryan D., O’Connor, Timothy D., and Abecasis, Gonçalo R.

Published
2021
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7. Sequencing and curation strategies for identifying candidate glioblastoma treatments

Author

Frank, Mayu O., Koyama, Takahiko, Rhrissorrakrai, Kahn, Robine, Nicolas, Utro, Filippo, Emde, Anne-Katrin, Chen, Bo-Juen, Arora, Kanika, Shah, Minita, Geiger, Heather, Felice, Vanessa, Dikoglu, Esra, Rahman, Sadia, Fang, Alice, Vacic, Vladimir, Bergmann, Ewa A., Vogel, Julia L. Moore, Reeves, Catherine, Khaira, Depinder, Calabro, Anthony, Kim, Duyang, Lamendola-Essel, Michelle F., Esteves, Cecilia, Agius, Phaedra, Stolte, Christian, Boockvar, John, Demopoulos, Alexis, Placantonakis, Dimitris G., Golfinos, John G., Brennan, Cameron, Bruce, Jeffrey, Lassman, Andrew B., Canoll, Peter, Grommes, Christian, Daras, Mariza, Diamond, Eli, Omuro, Antonio, Pentsova, Elena, Orange, Dana E., Harvey, Stephen J., Posner, Jerome B., Michelini, Vanessa V., Jobanputra, Vaidehi, Zody, Michael C., Kelly, John, Parida, Laxmi, Wrzeszczynski, Kazimierz O., Royyuru, Ajay K., and Darnell, Robert B.

Published
2019
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8. PGBD5 promotes site-specific oncogenic mutations in human tumors

Author

Henssen, Anton G, Koche, Richard, Zhuang, Jiali, Jiang, Eileen, Reed, Casie, Eisenberg, Amy, Still, Eric, MacArthur, Ian C, Rodríguez-Fos, Elias, Gonzalez, Santiago, Puiggròs, Montserrat, Blackford, Andrew N, Mason, Christopher E, de Stanchina, Elisa, Gönen, Mithat, Emde, Anne-Katrin, Shah, Minita, Arora, Kanika, Reeves, Catherine, Socci, Nicholas D, Perlman, Elizabeth, Antonescu, Cristina R, Roberts, Charles W M, Steen, Hanno, Mullen, Elizabeth, Jackson, Stephen P, Torrents, David, Weng, Zhiping, Armstrong, Scott A, and Kentsis, Alex

Published
2017
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11. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response

Author

Beltran, Himisha, Eng, Kenneth, Mosquera, Juan Miguel, Sigaras, Alexandros, Romanel, Alessandro, Rennert, Hanna, Kossai, Myriam, Pauli, Chantal, Faltas, Bishoy, Fontugne, Jacqueline, Park, Kyung, Banfelder, Jason, Prandi, Davide, Madhukar, Neel, Zhang, Tuo, Padilla, Jessica, Greco, Noah, McNary, Terra J., Herrscher, Erick, Wilkes, David, MacDonald, Theresa Y., Xue, Hui, Vacic, Vladimir, Emde, Anne-Katrin, Oschwald, Dayna, Tan, Adrian Y., Chen, Zhengming, Collins, Colin, Gleave, Martin E., Wang, Yuzhuo, Chakravarty, Dimple, Schiffman, Marc, Kim, Robert, Campagne, Fabien, Robinson, Brian D., Nanus, David M., Tagawa, Scott T., Xiang, Jenny Z., Smogorzewska, Agata, Demichelis, Francesca, Rickman, David S., Sboner, Andrea, Elemento, Olivier, and Rubin, Mark A.

Published
2015
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13. Robust consensus computation

Author

Emde Anne-Katrin, Rausch Tobias, and Reinert Knut

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Published
2008
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18. Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations.

Author

Emde, Anne-Katrin, Phipps-Green, Amanda, Cadzow, Murray, Gallagher, C. Scott, Major, Tanya J., Merriman, Marilyn E., Topless, Ruth K., Takei, Riku, Dalbeth, Nicola, Murphy, Rinki, Stamp, Lisa K., de Zoysa, Janak, Wilcox, Philip L., Fox, Keolu, Wasik, Kaja A., Merriman, Tony R., and Castel, Stephane E.

Subjects
*WHOLE genome sequencing, *GENETIC variation, *HUMAN genetic variation, *HEALTH equity, *GENETICISTS
Abstract

Background: Historically, geneticists have relied on genotyping arrays and imputation to study human genetic variation. However, an underrepresentation of diverse populations has resulted in arrays that poorly capture global genetic variation, and a lack of reference panels. This has contributed to deepening global health disparities. Whole genome sequencing (WGS) better captures genetic variation but remains prohibitively expensive. Thus, we explored WGS at "mid-pass" 1-7x coverage. Results: Here, we developed and benchmarked methods for mid-pass sequencing. When applied to a population without an existing genomic reference panel, 4x mid-pass performed consistently well across ethnicities, with highc recall (98%) and precision (97.5%). Conclusion: Compared to array data imputed into 1000 Genomes, mid-pass performed better across all metrics and identified novel population-specific variants with potential disease relevance. We hope our work will reduce financial barriers for geneticists from underrepresented populations to characterize their genomes prior to biomedical genetic applications. [ABSTRACT FROM AUTHOR]

Published
2022
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20. A novel and well-defined benchmarking method for second generation read mapping

Author

Weese David, Emde Anne-Katrin, Holtgrewe Manuel, and Reinert Knut

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Second generation sequencing technologies yield DNA sequence data at ultra high-throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. The assessment of the quality of read mapping results is not straightforward and has not been formalized so far. Hence, it has not been easy to compare different read mapping approaches in a unified way and to determine which program is the best for what task. Results We present a new benchmark method, called Rabema (Read Alignment BEnchMArk), for read mappers. It consists of a strict definition of the read mapping problem and of tools to evaluate the result of arbitrary read mappers supporting the SAM output format. Conclusions We show the usefulness of the benchmark program by performing a comparison of popular read mappers. The tools supporting the benchmark are licensed under the GPL and available from http://www.seqan.de/projects/rabema.html.

Published
2011
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21. Analytical model of peptide mass cluster centres with applications

Author

Lehrach Hans, Emde Anne-Katrin, Farrow Malcolm, Wolski Witold E, Lalowski Maciej, and Reinert Knut

Subjects
Cytology, QH573-671
Abstract

Abstract Background The elemental composition of peptides results in formation of distinct, equidistantly spaced clusters across the mass range. The property of peptide mass clustering is used to calibrate peptide mass lists, to identify and remove non-peptide peaks and for data reduction. Results We developed an analytical model of the peptide mass cluster centres. Inputs to the model included, the amino acid frequencies in the sequence database, the average length of the proteins in the database, the cleavage specificity of the proteolytic enzyme used and the cleavage probability. We examined the accuracy of our model by comparing it with the model based on an in silico sequence database digest. To identify the crucial parameters we analysed how the cluster centre location depends on the inputs. The distance to the nearest cluster was used to calibrate mass spectrometric peptide peak-lists and to identify non-peptide peaks. Conclusion The model introduced here enables us to predict the location of the peptide mass cluster centres. It explains how the location of the cluster centres depends on the input parameters. Fast and efficient calibration and filtering of non-peptide peaks is achieved by a distance measure suggested by Wool and Smilansky.

Published
2006
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22. Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions.

Author

Brannon, A. Rose, Vakiani, Efsevia, Sylvester, Brooke E., Scott, Sasinya N., McDermott, Gregory, Shah, Ronak H., Kania, Krishan, Viale, Agnes, Oschwald, Dayna M., Vacic, Vladimir, Emde, Anne-Katrin, Cercek, Andrea, Yaeger, Rona, Kemeny, Nancy E., Saltz, Leonard B., Jinru Shia, D'Angelica, Michael I., Weiser, Martin R., Solit, David B., and Berger, Michael F.

Published
2014
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23. Disease variants in genomes of 44 centenarians.

Author

Freudenberg‐Hua, Yun, Freudenberg, Jan, Vacic, Vladimir, Abhyankar, Avinash, Emde, Anne‐Katrin, Ben‐Avraham, Danny, Barzilai, Nir, Oschwald, Dayna, Christen, Erika, Koppel, Jeremy, Greenwald, Blaine, Darnell, Robert B., Germer, Soren, Atzmon, Gil, and Davies, Peter

Subjects
GENOMES, HAPLOIDY, CENTENARIANS, GENETIC disorders, MEDICAL genetics
Abstract

To identify previously reported disease mutations that are compatible with extraordinary longevity, we screened the coding regions of the genomes of 44 Ashkenazi Jewish centenarians. Individual genome sequences were generated with 30× coverage on the Illumina HiSeq 2000 and single-nucleotide variants were called with the genome analysis toolkit ( GATK). We identified 130 coding variants that were annotated as 'pathogenic' or 'likely pathogenic' based on the ClinVar database and that are infrequent in the general population. These variants were previously reported to cause a wide range of degenerative, neoplastic, and cardiac diseases with autosomal dominant, autosomal recessive, and X-linked inheritance. Several of these variants are located in genes that harbor actionable incidental findings, according to the recommendations of the American College of Medical Genetics. In addition, we found risk variants for late-onset neurodegenerative diseases, such as the APOE ε4 allele that was even present in a homozygous state in one centenarian who did not develop Alzheimer's disease. Our data demonstrate that the incidental finding of certain reported disease variants in an individual genome may not preclude an extraordinarily long life. When the observed variants are encountered in the context of clinical sequencing, it is thus important to exercise caution in justifying clinical decisions. [ABSTRACT FROM AUTHOR]

Published
2014
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24. A novel and well-defined benchmarking method for second generation read mapping.

Author

Holtgrewe, Manuel, Emde, Anne-Katrin, Weese, David, and Reinert, Knut

Subjects
*NUCLEOTIDE sequence, *GENOMES, *DNA, *MATHEMATICAL mappings, *GENETICS
Abstract

Background: Second generation sequencing technologies yield DNA sequence data at ultra high-throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. The assessment of the quality of read mapping results is not straightforward and has not been formalized so far. Hence, it has not been easy to compare different read mapping approaches in a unified way and to determine which program is the best for what task. Results: We present a new benchmark method, called Rabema (Read Alignment BEnchMArk), for read mappers. It consists of a strict definition of the read mapping problem and of tools to evaluate the result of arbitrary read mappers supporting the SAM output format. Conclusions: We show the usefulness of the benchmark program by performing a comparison of popular read mappers. The tools supporting the benchmark are licensed under the GPL and available from http://www.seqan. de/projects/rabema.html. [ABSTRACT FROM AUTHOR]

Published
2011
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25. Robust consensus computation.

Author

Rausch, Tobias, Emde, Anne-Katrin, and Reinert, Knut

Subjects
*GENOMICS, *ALGORITHMS, *GRAPH theory, *NUCLEOTIDES, *HUMAN genetic variation
Abstract

Introduction High-throughput sequencing technologies with short read data pose a new challenge to the current three-phase assembly methodology: Overlap-Phase, Layout-Phase, and Consensus-Phase. We describe a new consensus method that is robust in the face of high coverage, shorter reads, and genomic variation. Methods Given an initial layout of the reads, we generate a consensus sequence and a multi-read alignment with the following protocol: (1) Computation of all necessary (with respect to the layout) pairwise overlap alignments. (2) Extraction of all gapless alignment segments and generation of a segment-based weighted overlap graph (see Fig. 1). Conflicts between segment matches are resolved using a novel multiple segment match refinement algorithm. (3) An adjustment of the edge-weights using a variant of the triplet extension pioneered in the T-Coffee package. By means of the triplet extension we increase the weights of clique-edges within the overlap graph and thus, these edges are more likely to be chosen in the subsequent progressive alignment stage. (4) A progressive graphbased alignment of the reads using the heaviest common subsequence algorithm and a guide tree computed from the pairwise alignment scores. Note that our algorithm does not align single nucleotides but the segments identified in the overlap alignment phase. This ensures that columns with genetic variation (e.g., SNPs) are preserved. (5) Output of the multi-read alignment, the gapped consensus and all positioned reads with their respective deltas. The output can be visualized in Hawkeye (see Fig. 2). Results We used a read simulator and real data from the NCBI trace archive to evaluate our consensus tool. The main parameters of the read simulator are the source sequence length, the average read length, the number of reads and the error rate per base call. In addition, multiple haplotypes can be simulated. Two further parameters, namely the number of SNPs and the number of indels, specify the genetic variation randomly introduced into these haplo-types. We performed two experiments: (1) Given a source sequence length of 10000, we simulated reads under different settings. The read length varied from 35 to 200, the coverage from 20x to 50x and the error rate per base call from 2% to 4%. In all cases, the computed gap-free consensus matched the simulated source sequence in each position with coverage > 2. (2) Given two haplotypes each of length 10000 with 100 SNPs and 5 Indels, we simulated reads of length 200, coverage 20 and 4% error rate. We then manually inspected the multi-read alignment with Hawkeye to evaluate the consensus in case of genetic variation (see Fig. 2). Conclusion The results on simulated data are encouraging and preliminary results on real data show that our consensus quality is comparable to other tools. It remains to be shown that our program outperforms other tools in diffficult settings, namely high coverage and short, error-prone read data. The consensus tool is part of the SeqAn library http:// www.seqan.de and the read simulator is available on request: rausch@inf.fu-berlin.de. [ABSTRACT FROM AUTHOR]

Published
2008
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