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Showing 13 to 24 of 52 entries
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Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments.

Nature biotechnology

Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C.
PMID: 23873083
Nat Biotechnol. 2013 Aug;31(8):748-52. doi: 10.1038/nbt.2642. Epub 2013 Jul 21.

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of...

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Wills QF, Livak KJ, Tipping AJ, et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013;31(8):748-52doi: 10.1038/nbt.2642.
Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. (2013). Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nature biotechnology, 31(8), 748-52. https://doi.org/10.1038/nbt.2642
Wills, Quin F, et al. "Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments." Nature biotechnology vol. 31,8 (2013): 748-52. doi: https://doi.org/10.1038/nbt.2642
Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013 Aug;31(8):748-52. doi: 10.1038/nbt.2642. Epub 2013 Jul 21. PMID: 23873083.
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A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations.

PloS one

Venet D, Detours V, Bersini H.
PMID: 23251415
PLoS One. 2012;7(12):e51013. doi: 10.1371/journal.pone.0051013. Epub 2012 Dec 12.

BACKGROUND: The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures...

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Venet D, Detours V, Bersini H. A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. PLoS One. 2012;7(12):e51013doi: 10.1371/journal.pone.0051013.
Venet, D., Detours, V., & Bersini, H. (2012). A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. PloS one, 7(12), e51013. https://doi.org/10.1371/journal.pone.0051013
Venet, David, et al. "A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations." PloS one vol. 7,12 (2012): e51013. doi: https://doi.org/10.1371/journal.pone.0051013
Venet D, Detours V, Bersini H. A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. PLoS One. 2012;7(12):e51013. doi: 10.1371/journal.pone.0051013. Epub 2012 Dec 12. PMID: 23251415; PMCID: PMC3520972.
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Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.

BMC bioinformatics

van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA.
PMID: 22559006
BMC Bioinformatics. 2012 May 04;13:80. doi: 10.1186/1471-2105-13-80.

BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require...

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van Wieringen WN, Unger K, Leday GG, et al. Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses. BMC Bioinformatics. 2012;13:80doi: 10.1186/1471-2105-13-80.
van Wieringen, W. N., Unger, K., Leday, G. G., Krijgsman, O., de Menezes, R. X., Ylstra, B., & van de Wiel, M. A. (2012). Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses. BMC bioinformatics, 1380. https://doi.org/10.1186/1471-2105-13-80
van Wieringen, Wessel N, et al. "Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses." BMC bioinformatics vol. 13 (2012): 80. doi: https://doi.org/10.1186/1471-2105-13-80
van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA. Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses. BMC Bioinformatics. 2012 May 04;13:80. doi: 10.1186/1471-2105-13-80. PMID: 22559006; PMCID: PMC3475006.
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An electronic analog of synthetic genetic networks.

PloS one

Hellen EH, Volkov E, Kurths J, Dana SK.
PMID: 21829723
PLoS One. 2011;6(8):e23286. doi: 10.1371/journal.pone.0023286. Epub 2011 Aug 04.

An electronic analog of a synthetic genetic network known as the repressilator is proposed. The repressilator is a synthetic biological clock consisting of a cyclic inhibitory network of three negative regulatory genes which produces oscillations in the expressed protein...

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Hellen EH, Volkov E, Kurths J, et al. An electronic analog of synthetic genetic networks. PLoS One. 2011;6(8):e23286doi: 10.1371/journal.pone.0023286.
Hellen, E. H., Volkov, E., Kurths, J., & Dana, S. K. (2011). An electronic analog of synthetic genetic networks. PloS one, 6(8), e23286. https://doi.org/10.1371/journal.pone.0023286
Hellen, Edward H, et al. "An electronic analog of synthetic genetic networks." PloS one vol. 6,8 (2011): e23286. doi: https://doi.org/10.1371/journal.pone.0023286
Hellen EH, Volkov E, Kurths J, Dana SK. An electronic analog of synthetic genetic networks. PLoS One. 2011;6(8):e23286. doi: 10.1371/journal.pone.0023286. Epub 2011 Aug 04. PMID: 21829723; PMCID: PMC3150412.
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debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues.

Bioinformatics (Oxford, England)

Chen L, Wu CT, Wang N, Herrington DM, Clarke R, Wang Y.
PMID: 32219387
Bioinformatics. 2020 Jun 01;36(12):3927-3929. doi: 10.1093/bioinformatics/btaa205.

SUMMARY: We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically...

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Chen L, Wu CT, Wang N, et al. debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues. Bioinformatics. 2020;36(12):3927-3929doi: 10.1093/bioinformatics/btaa205.
Chen, L., Wu, C. T., Wang, N., Herrington, D. M., Clarke, R., & Wang, Y. (2020). debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues. Bioinformatics (Oxford, England), 36(12), 3927-3929. https://doi.org/10.1093/bioinformatics/btaa205
Chen, Lulu, et al. "debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues." Bioinformatics (Oxford, England) vol. 36,12 (2020): 3927-3929. doi: https://doi.org/10.1093/bioinformatics/btaa205
Chen L, Wu CT, Wang N, Herrington DM, Clarke R, Wang Y. debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues. Bioinformatics. 2020 Jun 01;36(12):3927-3929. doi: 10.1093/bioinformatics/btaa205. PMID: 32219387; PMCID: PMC7320609.
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Disordered and dead, but in good company: How a catalytically inactive UTX retains its function.

Molecular cell

Cantu Oliveros H, Sabari BR.
PMID: 34798044
Mol Cell. 2021 Nov 18;81(22):4577-4578. doi: 10.1016/j.molcel.2021.10.021.

Shi et al. (2021) demonstrate that tumor-suppressive and developmental functions of UTX require an intrinsically disordered region (IDR) capable of condensate formation. These results provide further evidence for the functional role of IDR-mediated spatial organization in regulating gene expression...

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Cantu Oliveros H, Sabari BR. Disordered and dead, but in good company: How a catalytically inactive UTX retains its function. Mol Cell. 2021;81(22):4577-4578doi: 10.1016/j.molcel.2021.10.021.
Cantu Oliveros, H., & Sabari, B. R. (2021). Disordered and dead, but in good company: How a catalytically inactive UTX retains its function. Molecular cell, 81(22), 4577-4578. https://doi.org/10.1016/j.molcel.2021.10.021
Cantu Oliveros, Heankel, and Sabari, Benjamin R. "Disordered and dead, but in good company: How a catalytically inactive UTX retains its function." Molecular cell vol. 81,22 (2021): 4577-4578. doi: https://doi.org/10.1016/j.molcel.2021.10.021
Cantu Oliveros H, Sabari BR. Disordered and dead, but in good company: How a catalytically inactive UTX retains its function. Mol Cell. 2021 Nov 18;81(22):4577-4578. doi: 10.1016/j.molcel.2021.10.021. PMID: 34798044.
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[Role of microRNAs in erectile dysfunction: An update].

Zhonghua nan ke xue = National journal of andrology

Yao LY, Cong R, Meng XH, Song NH.
PMID: 33382227
Zhonghua Nan Ke Xue. 2020 Nov;26(10):934-937.

MicroRNAs (miRNAs) are short non-coding RNAs consisting of approximately 19－23 nucleotides and involved in many pathological and physiological processes by regulating post-transcriptional gene expressions. ED is one of the common male sexual dysfunctions seriously affecting the patient's quality of...

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Yao LY, Cong R, Meng XH, et al. [Role of microRNAs in erectile dysfunction: An update]. Zhonghua Nan Ke Xue. 2020;26(10):934-937
Yao, L. Y., Cong, R., Meng, X. H., & Song, N. H. (2020). [Role of microRNAs in erectile dysfunction: An update]. Zhonghua nan ke xue = National journal of andrology, 26(10), 934-937.
Yao, Liang-Yu, et al. "[Role of microRNAs in erectile dysfunction: An update]." Zhonghua nan ke xue = National journal of andrology vol. 26,10 (2020): 934-937.
Yao LY, Cong R, Meng XH, Song NH. [Role of microRNAs in erectile dysfunction: An update]. Zhonghua Nan Ke Xue. 2020 Nov;26(10):934-937. Chinese. PMID: 33382227.
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Genetic and gene expression signatures in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)

Patsopoulos NA, De Jager PL.
PMID: 31965883
Mult Scler. 2020 Apr;26(5):576-581. doi: 10.1177/1352458519898332. Epub 2020 Jan 22.

Multiple sclerosis (MS) exhibits a well-documented increased incidence in individuals with respective family history, that is, is a heritable disease. In the last decade, genome-wide association studies have enabled the agnostic interrogation of the whole genome at a large...

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Patsopoulos NA, De Jager PL. Genetic and gene expression signatures in multiple sclerosis. Mult Scler. 2020;26(5):576-581doi: 10.1177/1352458519898332.
Patsopoulos, N. A., & De Jager, P. L. (2020). Genetic and gene expression signatures in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England), 26(5), 576-581. https://doi.org/10.1177/1352458519898332
Patsopoulos, Nikolaos A, and De Jager, Philip L. "Genetic and gene expression signatures in multiple sclerosis." Multiple sclerosis (Houndmills, Basingstoke, England) vol. 26,5 (2020): 576-581. doi: https://doi.org/10.1177/1352458519898332
Patsopoulos NA, De Jager PL. Genetic and gene expression signatures in multiple sclerosis. Mult Scler. 2020 Apr;26(5):576-581. doi: 10.1177/1352458519898332. Epub 2020 Jan 22. PMID: 31965883.
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Maximizing the reusability of gene expression data by predicting missing metadata.

PLoS computational biology

Lung PY, Zhong D, Pang X, Li Y, Zhang J.
PMID: 33156882
PLoS Comput Biol. 2020 Nov 06;16(11):e1007450. doi: 10.1371/journal.pcbi.1007450. eCollection 2020 Nov.

Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of...

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Lung PY, Zhong D, Pang X, et al. Maximizing the reusability of gene expression data by predicting missing metadata. PLoS Comput Biol. 2020;16(11):e1007450doi: 10.1371/journal.pcbi.1007450.
Lung, P. Y., Zhong, D., Pang, X., Li, Y., & Zhang, J. (2020). Maximizing the reusability of gene expression data by predicting missing metadata. PLoS computational biology, 16(11), e1007450. https://doi.org/10.1371/journal.pcbi.1007450
Lung, Pei-Yau, et al. "Maximizing the reusability of gene expression data by predicting missing metadata." PLoS computational biology vol. 16,11 (2020): e1007450. doi: https://doi.org/10.1371/journal.pcbi.1007450
Lung PY, Zhong D, Pang X, Li Y, Zhang J. Maximizing the reusability of gene expression data by predicting missing metadata. PLoS Comput Biol. 2020 Nov 06;16(11):e1007450. doi: 10.1371/journal.pcbi.1007450. eCollection 2020 Nov. PMID: 33156882; PMCID: PMC7673503.
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Graph transformation for enzymatic mechanisms.

Bioinformatics (Oxford, England)

Andersen JL, Fagerberg R, Flamm C, Fontana W, Kolčák J, Laurent CVFP, Merkle D, Nøjgaard N.
PMID: 34252947
Bioinformatics. 2021 Jul 12;37:i392-i400. doi: 10.1093/bioinformatics/btab296.

MOTIVATION: The design of enzymes is as challenging as it is consequential for making chemical synthesis in medical and industrial applications more efficient, cost-effective and environmentally friendly. While several aspects of this complex problem are computationally assisted, the drafting...

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Andersen JL, Fagerberg R, Flamm C, et al. Graph transformation for enzymatic mechanisms. Bioinformatics. 2021;37:i392-i400doi: 10.1093/bioinformatics/btab296.
Andersen, J. L., Fagerberg, R., Flamm, C., Fontana, W., Kolčák, J., Laurent, C. V. F. P., Merkle, D., & Nøjgaard, N. (2021). Graph transformation for enzymatic mechanisms. Bioinformatics (Oxford, England), 37i392-i400. https://doi.org/10.1093/bioinformatics/btab296
Andersen, Jakob L, et al. "Graph transformation for enzymatic mechanisms." Bioinformatics (Oxford, England) vol. 37 (2021): i392-i400. doi: https://doi.org/10.1093/bioinformatics/btab296
Andersen JL, Fagerberg R, Flamm C, Fontana W, Kolčák J, Laurent CVFP, Merkle D, Nøjgaard N. Graph transformation for enzymatic mechanisms. Bioinformatics. 2021 Jul 12;37:i392-i400. doi: 10.1093/bioinformatics/btab296. PMID: 34252947; PMCID: PMC8686676.
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Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.

Bioinformatics (Oxford, England)

Seifert S, Gundlach S, Junge O, Szymczak S.
PMID: 32399562
Bioinformatics. 2020 Aug 01;36(15):4301-4308. doi: 10.1093/bioinformatics/btaa483.

MOTIVATION: High-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional...

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Seifert S, Gundlach S, Junge O, et al. Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study. Bioinformatics. 2020;36(15):4301-4308doi: 10.1093/bioinformatics/btaa483.
Seifert, S., Gundlach, S., Junge, O., & Szymczak, S. (2020). Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study. Bioinformatics (Oxford, England), 36(15), 4301-4308. https://doi.org/10.1093/bioinformatics/btaa483
Seifert, Stephan, et al. "Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study." Bioinformatics (Oxford, England) vol. 36,15 (2020): 4301-4308. doi: https://doi.org/10.1093/bioinformatics/btaa483
Seifert S, Gundlach S, Junge O, Szymczak S. Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study. Bioinformatics. 2020 Aug 01;36(15):4301-4308. doi: 10.1093/bioinformatics/btaa483. PMID: 32399562; PMCID: PMC7520048.
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TableButler - a Windows based tool for processing large data tables generated with high-throughput methods.

BMC bioinformatics

Schwager C, Wirkner U, Abdollahi A, Huber PE.
PMID: 19640300
BMC Bioinformatics. 2009 Jul 29;10:235. doi: 10.1186/1471-2105-10-235.

BACKGROUND: High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these...

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Schwager C, Wirkner U, Abdollahi A, et al. TableButler - a Windows based tool for processing large data tables generated with high-throughput methods. BMC Bioinformatics. 2009;10:235doi: 10.1186/1471-2105-10-235.
Schwager, C., Wirkner, U., Abdollahi, A., & Huber, P. E. (2009). TableButler - a Windows based tool for processing large data tables generated with high-throughput methods. BMC bioinformatics, 10235. https://doi.org/10.1186/1471-2105-10-235
Schwager, Christian, et al. "TableButler - a Windows based tool for processing large data tables generated with high-throughput methods." BMC bioinformatics vol. 10 (2009): 235. doi: https://doi.org/10.1186/1471-2105-10-235
Schwager C, Wirkner U, Abdollahi A, Huber PE. TableButler - a Windows based tool for processing large data tables generated with high-throughput methods. BMC Bioinformatics. 2009 Jul 29;10:235. doi: 10.1186/1471-2105-10-235. PMID: 19640300; PMCID: PMC2731053.
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Showing 13 to 24 of 52 entries