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An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS.

Methods in molecular biology (Clifton, N.J.)

Silva GGZ, Lopes FAC, Edwards RA.
PMID: 28451970
Methods Mol Biol. 2017;1611:35-44. doi: 10.1007/978-1-4939-7015-5_4.

One of the main goals in metagenomics is to identify the functional profile of a microbial community from unannotated shotgun sequencing reads. Functional annotation is important in biological research because it enables researchers to identify the abundance of functional...

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Silva GGZ, Lopes FAC, Edwards RA. An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods Mol Biol. 2017;1611:35-44doi: 10.1007/978-1-4939-7015-5_4.
Silva, G. G. Z., Lopes, F. A. C., & Edwards, R. A. (2017). An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods in molecular biology (Clifton, N.J.), 161135-44. https://doi.org/10.1007/978-1-4939-7015-5_4
Silva, Genivaldo Gueiros Z, et al. "An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS." Methods in molecular biology (Clifton, N.J.) vol. 1611 (2017): 35-44. doi: https://doi.org/10.1007/978-1-4939-7015-5_4
Silva GGZ, Lopes FAC, Edwards RA. An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods Mol Biol. 2017;1611:35-44. doi: 10.1007/978-1-4939-7015-5_4. PMID: 28451970.
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Enzyme Annotation and Metabolic Reconstruction Using KEGG.

Methods in molecular biology (Clifton, N.J.)

Kanehisa M.
PMID: 28451977
Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11.

KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY...

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Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145doi: 10.1007/978-1-4939-7015-5_11.
Kanehisa, M. (2017). Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods in molecular biology (Clifton, N.J.), 1611135-145. https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa, Minoru. "Enzyme Annotation and Metabolic Reconstruction Using KEGG." Methods in molecular biology (Clifton, N.J.) vol. 1611 (2017): 135-145. doi: https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11. PMID: 28451977.
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MiRTargetLink--miRNAs, Genes and Interaction Networks.

International journal of molecular sciences

Hamberg M, Backes C, Fehlmann T, Hart M, Meder B, Meese E, Keller A.
PMID: 27089332
Int J Mol Sci. 2016 Apr 14;17(4):564. doi: 10.3390/ijms17040564.

Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool...

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Hamberg M, Backes C, Fehlmann T, et al. MiRTargetLink--miRNAs, Genes and Interaction Networks. Int J Mol Sci. 2016;17(4):564doi: 10.3390/ijms17040564.
Hamberg, M., Backes, C., Fehlmann, T., Hart, M., Meder, B., Meese, E., & Keller, A. (2016). MiRTargetLink--miRNAs, Genes and Interaction Networks. International journal of molecular sciences, 17(4), 564. https://doi.org/10.3390/ijms17040564
Hamberg, Maarten, et al. "MiRTargetLink--miRNAs, Genes and Interaction Networks." International journal of molecular sciences vol. 17,4 (2016): 564. doi: https://doi.org/10.3390/ijms17040564
Hamberg M, Backes C, Fehlmann T, Hart M, Meder B, Meese E, Keller A. MiRTargetLink--miRNAs, Genes and Interaction Networks. Int J Mol Sci. 2016 Apr 14;17(4):564. doi: 10.3390/ijms17040564. PMID: 27089332; PMCID: PMC4849020.
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23andMe wades further into drug discovery.

Nature biotechnology

Petrone J.
PMID: 29020001
Nat Biotechnol. 2017 Oct 11;35(10):897. doi: 10.1038/nbt1017-897.

No abstract available.

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Petrone J. 23andMe wades further into drug discovery. Nat Biotechnol. 2017;35(10):897doi: 10.1038/nbt1017-897.
Petrone, J. (2017). 23andMe wades further into drug discovery. Nature biotechnology, 35(10), 897. https://doi.org/10.1038/nbt1017-897
Petrone, Justin. "23andMe wades further into drug discovery." Nature biotechnology vol. 35,10 (2017): 897. doi: https://doi.org/10.1038/nbt1017-897
Petrone J. 23andMe wades further into drug discovery. Nat Biotechnol. 2017 Oct 11;35(10):897. doi: 10.1038/nbt1017-897. PMID: 29020001.
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Access Denied: The Controversy of Commercial Genetic Databases.

The Canadian journal of cardiology

Genest J.
PMID: 27161824
Can J Cardiol. 2016 Nov;32(11):1295-1296. doi: 10.1016/j.cjca.2016.03.018. Epub 2016 Apr 06.

No abstract available.

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Genest J. Access Denied: The Controversy of Commercial Genetic Databases. Can J Cardiol. 2016;32(11):1295-1296doi: 10.1016/j.cjca.2016.03.018.
Genest, J. (2016). Access Denied: The Controversy of Commercial Genetic Databases. The Canadian journal of cardiology, 32(11), 1295-1296. https://doi.org/10.1016/j.cjca.2016.03.018
Genest, Jacques. "Access Denied: The Controversy of Commercial Genetic Databases." The Canadian journal of cardiology vol. 32,11 (2016): 1295-1296. doi: https://doi.org/10.1016/j.cjca.2016.03.018
Genest J. Access Denied: The Controversy of Commercial Genetic Databases. Can J Cardiol. 2016 Nov;32(11):1295-1296. doi: 10.1016/j.cjca.2016.03.018. Epub 2016 Apr 06. PMID: 27161824.
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A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip.

IEEE journal of biomedical and health informatics

Valavanis I, Sifakis EG, Georgiadis P, Kyrtopoulos S, Chatziioannou AA.
PMID: 24808224
IEEE J Biomed Health Inform. 2014 May;18(3):817-23. doi: 10.1109/JBHI.2014.2298351.

High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic, and analytical pipelines for an efficient systems level analysis and interpretation. In this study, we utilize the Illumina's Infinium Human Methylation...

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Valavanis I, Sifakis EG, Georgiadis P, et al. A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE J Biomed Health Inform. 2014;18(3):817-23doi: 10.1109/JBHI.2014.2298351.
Valavanis, I., Sifakis, E. G., Georgiadis, P., Kyrtopoulos, S., & Chatziioannou, A. A. (2014). A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE journal of biomedical and health informatics, 18(3), 817-23. https://doi.org/10.1109/JBHI.2014.2298351
Valavanis, Ioannis, et al. "A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip." IEEE journal of biomedical and health informatics vol. 18,3 (2014): 817-23. doi: https://doi.org/10.1109/JBHI.2014.2298351
Valavanis I, Sifakis EG, Georgiadis P, Kyrtopoulos S, Chatziioannou AA. A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE J Biomed Health Inform. 2014 May;18(3):817-23. doi: 10.1109/JBHI.2014.2298351. PMID: 24808224.
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High-throughput translational medicine: challenges and solutions.

Advances in experimental medicine and biology

Sulakhe D, Balasubramanian S, Xie B, Berrocal E, Feng B, Taylor A, Chitturi B, Dave U, Agam G, Xu J, Börnigen D, Dubchak I, Gilliam TC, Maltsev N.
PMID: 24292961
Adv Exp Med Biol. 2014;799:39-67. doi: 10.1007/978-1-4614-8778-4_3.

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and...

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Sulakhe D, Balasubramanian S, Xie B, et al. High-throughput translational medicine: challenges and solutions. Adv Exp Med Biol. 2014;799:39-67doi: 10.1007/978-1-4614-8778-4_3.
Sulakhe, D., Balasubramanian, S., Xie, B., Berrocal, E., Feng, B., Taylor, A., Chitturi, B., Dave, U., Agam, G., Xu, J., Börnigen, D., Dubchak, I., Gilliam, T. C., & Maltsev, N. (2014). High-throughput translational medicine: challenges and solutions. Advances in experimental medicine and biology, 79939-67. https://doi.org/10.1007/978-1-4614-8778-4_3
Sulakhe, Dinanath, et al. "High-throughput translational medicine: challenges and solutions." Advances in experimental medicine and biology vol. 799 (2014): 39-67. doi: https://doi.org/10.1007/978-1-4614-8778-4_3
Sulakhe D, Balasubramanian S, Xie B, Berrocal E, Feng B, Taylor A, Chitturi B, Dave U, Agam G, Xu J, Börnigen D, Dubchak I, Gilliam TC, Maltsev N. High-throughput translational medicine: challenges and solutions. Adv Exp Med Biol. 2014;799:39-67. doi: 10.1007/978-1-4614-8778-4_3. PMID: 24292961.
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Identification of sample annotation errors in gene expression datasets.

Archives of toxicology

Lohr M, Hellwig B, Edlund K, Mattsson JS, Botling J, Schmidt M, Hengstler JG, Micke P, Rahnenführer J.
PMID: 26608184
Arch Toxicol. 2015 Dec;89(12):2265-72. doi: 10.1007/s00204-015-1632-4. Epub 2015 Nov 25.

The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and...

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Lohr M, Hellwig B, Edlund K, et al. Identification of sample annotation errors in gene expression datasets. Arch Toxicol. 2015;89(12):2265-72doi: 10.1007/s00204-015-1632-4.
Lohr, M., Hellwig, B., Edlund, K., Mattsson, J. S., Botling, J., Schmidt, M., Hengstler, J. G., Micke, P., & Rahnenführer, J. (2015). Identification of sample annotation errors in gene expression datasets. Archives of toxicology, 89(12), 2265-72. https://doi.org/10.1007/s00204-015-1632-4
Lohr, Miriam, et al. "Identification of sample annotation errors in gene expression datasets." Archives of toxicology vol. 89,12 (2015): 2265-72. doi: https://doi.org/10.1007/s00204-015-1632-4
Lohr M, Hellwig B, Edlund K, Mattsson JS, Botling J, Schmidt M, Hengstler JG, Micke P, Rahnenführer J. Identification of sample annotation errors in gene expression datasets. Arch Toxicol. 2015 Dec;89(12):2265-72. doi: 10.1007/s00204-015-1632-4. Epub 2015 Nov 25. PMID: 26608184; PMCID: PMC4673097.
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Bioinformatics on the cloud computing platform Azure.

PloS one

Shanahan HP, Owen AM, Harrison AP.
PMID: 25050811
PLoS One. 2014 Jul 22;9(7):e102642. doi: 10.1371/journal.pone.0102642. eCollection 2014.

We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse...

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Shanahan HP, Owen AM, Harrison AP. Bioinformatics on the cloud computing platform Azure. PLoS One. 2014;9(7):e102642doi: 10.1371/journal.pone.0102642.
Shanahan, H. P., Owen, A. M., & Harrison, A. P. (2014). Bioinformatics on the cloud computing platform Azure. PloS one, 9(7), e102642. https://doi.org/10.1371/journal.pone.0102642
Shanahan, Hugh P, et al. "Bioinformatics on the cloud computing platform Azure." PloS one vol. 9,7 (2014): e102642. doi: https://doi.org/10.1371/journal.pone.0102642
Shanahan HP, Owen AM, Harrison AP. Bioinformatics on the cloud computing platform Azure. PLoS One. 2014 Jul 22;9(7):e102642. doi: 10.1371/journal.pone.0102642. eCollection 2014. PMID: 25050811; PMCID: PMC4106841.
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DWFS: a wrapper feature selection tool based on a parallel genetic algorithm.

PloS one

Soufan O, Kleftogiannis D, Kalnis P, Bajic VB.
PMID: 25719748
PLoS One. 2015 Feb 26;10(2):e0117988. doi: 10.1371/journal.pone.0117988. eCollection 2015.

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification...

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Soufan O, Kleftogiannis D, Kalnis P, et al. DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS One. 2015;10(2):e0117988doi: 10.1371/journal.pone.0117988.
Soufan, O., Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015). DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PloS one, 10(2), e0117988. https://doi.org/10.1371/journal.pone.0117988
Soufan, Othman, et al. "DWFS: a wrapper feature selection tool based on a parallel genetic algorithm." PloS one vol. 10,2 (2015): e0117988. doi: https://doi.org/10.1371/journal.pone.0117988
Soufan O, Kleftogiannis D, Kalnis P, Bajic VB. DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS One. 2015 Feb 26;10(2):e0117988. doi: 10.1371/journal.pone.0117988. eCollection 2015. PMID: 25719748; PMCID: PMC4342225.
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Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae.

Plant & cell physiology

Yamamoto N, Kudo T, Fujiwara S, Takatsuka Y, Hirokawa Y, Tsuzuki M, Takano T, Kobayashi M, Suda K, Asamizu E, Yokoyama K, Shibata D, Tabata S, Yano K.
PMID: 26746174
Plant Cell Physiol. 2016 Jan;57(1):e6. doi: 10.1093/pcp/pcv195. Epub 2016 Jan 07.

Pleurochrysis is a coccolithophorid genus, which belongs to the Coccolithales in the Haptophyta. The genus has been used extensively for biological research, together with Emiliania in the Isochrysidales, to understand distinctive features between the two coccolithophorid-including orders. However, molecular...

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Yamamoto N, Kudo T, Fujiwara S, et al. Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant Cell Physiol. 2016;57(1):e6doi: 10.1093/pcp/pcv195.
Yamamoto, N., Kudo, T., Fujiwara, S., Takatsuka, Y., Hirokawa, Y., Tsuzuki, M., Takano, T., Kobayashi, M., Suda, K., Asamizu, E., Yokoyama, K., Shibata, D., Tabata, S., & Yano, K. (2016). Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant & cell physiology, 57(1), e6. https://doi.org/10.1093/pcp/pcv195
Yamamoto, Naoki, et al. "Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae." Plant & cell physiology vol. 57,1 (2016): e6. doi: https://doi.org/10.1093/pcp/pcv195
Yamamoto N, Kudo T, Fujiwara S, Takatsuka Y, Hirokawa Y, Tsuzuki M, Takano T, Kobayashi M, Suda K, Asamizu E, Yokoyama K, Shibata D, Tabata S, Yano K. Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae. Plant Cell Physiol. 2016 Jan;57(1):e6. doi: 10.1093/pcp/pcv195. Epub 2016 Jan 07. PMID: 26746174; PMCID: PMC4722176.
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Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PloS one

Davis AP, Wiegers TC, King BL, Wiegers J, Grondin CJ, Sciaky D, Johnson RJ, Mattingly CJ.
PMID: 27171405
PLoS One. 2016 May 12;11(5):e0155530. doi: 10.1371/journal.pone.0155530. eCollection 2016.

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/)...

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Davis AP, Wiegers TC, King BL, et al. Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One. 2016;11(5):e0155530doi: 10.1371/journal.pone.0155530.
Davis, A. P., Wiegers, T. C., King, B. L., Wiegers, J., Grondin, C. J., Sciaky, D., Johnson, R. J., & Mattingly, C. J. (2016). Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PloS one, 11(5), e0155530. https://doi.org/10.1371/journal.pone.0155530
Davis, Allan Peter, et al. "Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database." PloS one vol. 11,5 (2016): e0155530. doi: https://doi.org/10.1371/journal.pone.0155530
Davis AP, Wiegers TC, King BL, Wiegers J, Grondin CJ, Sciaky D, Johnson RJ, Mattingly CJ. Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One. 2016 May 12;11(5):e0155530. doi: 10.1371/journal.pone.0155530. eCollection 2016. PMID: 27171405; PMCID: PMC4865041.
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Showing 1 to 12 of 455 entries