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[Bioinformatics and Next -generation Sequencing].

Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti

Krejčí A, Müller P, Vojtěšek B.
PMID: 26374164
Klin Onkol. 2015;28:2S91-6. doi: 10.14735/amko20152s91.

Next-generation sequencing technologies are currently well-established in the research field and progressively find their way towards clinical applications. Sequencers produce vast amounts of data and therefore bioinformatics methods are needed for processing. Without computational methods, sequencing would not be...

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Krejčí A, Müller P, Vojtěšek B. Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klin Onkol. 2015;28:2S91-6doi: 10.14735/amko20152s91.
Krejčí, A., Müller, P., & Vojtěšek, B. (2015). Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti, 282S91-6. https://doi.org/10.14735/amko20152s91
Krejčí, A, et al. "Bioinformatika a sekvenování nové generace." [Bioinformatics and Next -generation Sequencing]. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti vol. 28 (2015): 2S91-6. doi: https://doi.org/10.14735/amko20152s91
Krejčí A, Müller P, Vojtěšek B. Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klin Onkol. 2015;28:2S91-6. doi: 10.14735/amko20152s91. Czech. PMID: 26374164.
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A heuristic approach to determine an appropriate number of topics in topic modeling.

BMC bioinformatics

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W.
PMID: 26424364
BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics...

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Zhao W, Chen JJ, Perkins R, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8doi: 10.1186/1471-2105-16-S13-S8.
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC bioinformatics, 16S8. https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao, Weizhong, et al. "A heuristic approach to determine an appropriate number of topics in topic modeling." BMC bioinformatics vol. 16 (2015): S8. doi: https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25. PMID: 26424364; PMCID: PMC4597325.
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Next generation sequencing: an application in forensic sciences?.

Annals of human biology

Alvarez-Cubero MJ, Saiz M, Martínez-García B, Sayalero SM, Entrala C, Lorente JA, Martinez-Gonzalez LJ.
PMID: 28948844
Ann Hum Biol. 2017 Nov;44(7):581-592. doi: 10.1080/03014460.2017.1375155. Epub 2017 Sep 26.

CONTEXT: Over the last few decades, advances in sequencing have improved greatly. One of the most important achievements of Next Generation Sequencing (NGS) is to produce millions of sequence reads in a short period of time, and to produce...

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Alvarez-Cubero MJ, Saiz M, Martínez-García B, et al. Next generation sequencing: an application in forensic sciences?. Ann Hum Biol. 2017;44(7):581-592doi: 10.1080/03014460.2017.1375155.
Alvarez-Cubero, M. J., Saiz, M., Martínez-García, B., Sayalero, S. M., Entrala, C., Lorente, J. A., & Martinez-Gonzalez, L. J. (2017). Next generation sequencing: an application in forensic sciences?. Annals of human biology, 44(7), 581-592. https://doi.org/10.1080/03014460.2017.1375155
Alvarez-Cubero, Maria Jesus, et al. "Next generation sequencing: an application in forensic sciences?." Annals of human biology vol. 44,7 (2017): 581-592. doi: https://doi.org/10.1080/03014460.2017.1375155
Alvarez-Cubero MJ, Saiz M, Martínez-García B, Sayalero SM, Entrala C, Lorente JA, Martinez-Gonzalez LJ. Next generation sequencing: an application in forensic sciences?. Ann Hum Biol. 2017 Nov;44(7):581-592. doi: 10.1080/03014460.2017.1375155. Epub 2017 Sep 26. PMID: 28948844.
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Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles.

Scientific reports

Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, Sangiao-Alvarellos S, Monserrat L, Arntz OJ, van de Loo FJ, Mateos J, Arufe MC.
PMID: 28262816
Sci Rep. 2017 Mar 06;7:43923. doi: 10.1038/srep43923.

Stem cells possess significant age-dependent differences in their immune-response profile. These differences were analysed by Next-Generation Sequencing of six age groups from bone marrow mesenchymal stem cells. A total of 9,628 genes presenting differential expression between age groups were...

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Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, et al. Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Sci Rep. 2017;7:43923doi: 10.1038/srep43923.
Fafián-Labora, J., Lesende-Rodriguez, I., Fernández-Pernas, P., Sangiao-Alvarellos, S., Monserrat, L., Arntz, O. J., van de Loo, F. J., Mateos, J., & Arufe, M. C. (2017). Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Scientific reports, 743923. https://doi.org/10.1038/srep43923
Fafián-Labora, J, et al. "Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles." Scientific reports vol. 7 (2017): 43923. doi: https://doi.org/10.1038/srep43923
Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, Sangiao-Alvarellos S, Monserrat L, Arntz OJ, van de Loo FJ, Mateos J, Arufe MC. Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Sci Rep. 2017 Mar 06;7:43923. doi: 10.1038/srep43923. PMID: 28262816; PMCID: PMC5338265.
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Preparing the next generation of genomicists: a laboratory-style course in medical genomics.

BMC medical genomics

Linderman MD, Bashir A, Diaz GA, Kasarskis A, Sanderson SC, Zinberg RE, Mahajan M, Shah H, Suckiel S, Zweig M, Schadt EE.
PMID: 26264128
BMC Med Genomics. 2015 Aug 12;8:47. doi: 10.1186/s12920-015-0124-y.

The growing gap between the demand for genome sequencing and the supply of trained genomics professionals is creating an acute need to develop more effective genomics education. In response we developed "Practical Analysis of Your Personal Genome", a novel...

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Linderman MD, Bashir A, Diaz GA, et al. Preparing the next generation of genomicists: a laboratory-style course in medical genomics. BMC Med Genomics. 2015;8:47doi: 10.1186/s12920-015-0124-y.
Linderman, M. D., Bashir, A., Diaz, G. A., Kasarskis, A., Sanderson, S. C., Zinberg, R. E., Mahajan, M., Shah, H., Suckiel, S., Zweig, M., & Schadt, E. E. (2015). Preparing the next generation of genomicists: a laboratory-style course in medical genomics. BMC medical genomics, 847. https://doi.org/10.1186/s12920-015-0124-y
Linderman, Michael D, et al. "Preparing the next generation of genomicists: a laboratory-style course in medical genomics." BMC medical genomics vol. 8 (2015): 47. doi: https://doi.org/10.1186/s12920-015-0124-y
Linderman MD, Bashir A, Diaz GA, Kasarskis A, Sanderson SC, Zinberg RE, Mahajan M, Shah H, Suckiel S, Zweig M, Schadt EE. Preparing the next generation of genomicists: a laboratory-style course in medical genomics. BMC Med Genomics. 2015 Aug 12;8:47. doi: 10.1186/s12920-015-0124-y. PMID: 26264128; PMCID: PMC4534145.
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AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing.

Bioinformatics (Oxford, England)

Shin S, Lee H, Son H, Paik S, Kim S.
PMID: 29126106
Bioinformatics. 2018 Apr 01;34(7):1232-1234. doi: 10.1093/bioinformatics/btx719.

SUMMARY: Ion Torrent sequencing is one of the most frequently used platforms in healthcare research and industry. Despite many advantages, platform-specific artifacts complicate efficient separation of true variants from errors, especially in variants with lower allele frequencies (

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Shin S, Lee H, Son H, et al. AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing. Bioinformatics. 2018;34(7):1232-1234doi: 10.1093/bioinformatics/btx719.
Shin, S., Lee, H., Son, H., Paik, S., & Kim, S. (2018). AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing. Bioinformatics (Oxford, England), 34(7), 1232-1234. https://doi.org/10.1093/bioinformatics/btx719
Shin, Sunguk, et al. "AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing." Bioinformatics (Oxford, England) vol. 34,7 (2018): 1232-1234. doi: https://doi.org/10.1093/bioinformatics/btx719
Shin S, Lee H, Son H, Paik S, Kim S. AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing. Bioinformatics. 2018 Apr 01;34(7):1232-1234. doi: 10.1093/bioinformatics/btx719. PMID: 29126106.
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Editorial: Advanced Big-Data Analysis Methods and Tools for High-Throughput Omics Technologies.

Combinatorial chemistry & high throughput screening

Huang T.
PMID: 28629312
Comb Chem High Throughput Screen. 2017;20(2):94-95. doi: 10.2174/138620732002170601151524.

No abstract available.

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Huang T. Editorial: Advanced Big-Data Analysis Methods and Tools for High-Throughput Omics Technologies. Comb Chem High Throughput Screen. 2017;20(2):94-95doi: 10.2174/138620732002170601151524.
Huang, T. (2017). Editorial: Advanced Big-Data Analysis Methods and Tools for High-Throughput Omics Technologies. Combinatorial chemistry & high throughput screening, 20(2), 94-95. https://doi.org/10.2174/138620732002170601151524
Huang, Tao. "Editorial: Advanced Big-Data Analysis Methods and Tools for High-Throughput Omics Technologies." Combinatorial chemistry & high throughput screening vol. 20,2 (2017): 94-95. doi: https://doi.org/10.2174/138620732002170601151524
Huang T. Editorial: Advanced Big-Data Analysis Methods and Tools for High-Throughput Omics Technologies. Comb Chem High Throughput Screen. 2017;20(2):94-95. doi: 10.2174/138620732002170601151524. PMID: 28629312.
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VCF-Explorer: filtering and analysing whole genome VCF files.

Bioinformatics (Oxford, England)

Akgün M, Demirci H.
PMID: 29036499
Bioinformatics. 2017 Nov 01;33(21):3468-3470. doi: 10.1093/bioinformatics/btx422.

SUMMARY: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most...

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Akgün M, Demirci H. VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics. 2017;33(21):3468-3470doi: 10.1093/bioinformatics/btx422.
Akgün, M., & Demirci, H. (2017). VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics (Oxford, England), 33(21), 3468-3470. https://doi.org/10.1093/bioinformatics/btx422
Akgün, Mete, and Demirci, Hüseyin. "VCF-Explorer: filtering and analysing whole genome VCF files." Bioinformatics (Oxford, England) vol. 33,21 (2017): 3468-3470. doi: https://doi.org/10.1093/bioinformatics/btx422
Akgün M, Demirci H. VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics. 2017 Nov 01;33(21):3468-3470. doi: 10.1093/bioinformatics/btx422. PMID: 29036499.
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Production and analytic bioinformatics for next-generation DNA sequencing.

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

Allcock RJ.
PMID: 24870128
Methods Mol Biol. 2014;1168:17-29. doi: 10.1007/978-1-4939-0847-9_2.

The bioinformatics requirements within the clinical environment are very specific, and analytic techniques need to be fit for purpose, robust, and predictable. At the same time, the bewildering amount of information produced during these analyses needs to be carefully...

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Allcock RJ. Production and analytic bioinformatics for next-generation DNA sequencing. Methods Mol Biol. 2014;1168:17-29doi: 10.1007/978-1-4939-0847-9_2.
Allcock, R. J. (2014). Production and analytic bioinformatics for next-generation DNA sequencing. Methods in molecular biology (Clifton, N.J.), 116817-29. https://doi.org/10.1007/978-1-4939-0847-9_2
Allcock, Richard James Nigel. "Production and analytic bioinformatics for next-generation DNA sequencing." Methods in molecular biology (Clifton, N.J.) vol. 1168 (2014): 17-29. doi: https://doi.org/10.1007/978-1-4939-0847-9_2
Allcock RJ. Production and analytic bioinformatics for next-generation DNA sequencing. Methods Mol Biol. 2014;1168:17-29. doi: 10.1007/978-1-4939-0847-9_2. PMID: 24870128.
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Scalable and cost-effective NGS genotyping in the cloud.

BMC medical genomics

Souilmi Y, Lancaster AK, Jung JY, Rizzo E, Hawkins JB, Powles R, Amzazi S, Ghazal H, Tonellato PJ, Wall DP.
PMID: 26470712
BMC Med Genomics. 2015 Oct 15;8:64. doi: 10.1186/s12920-015-0134-9.

BACKGROUND: While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust...

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Souilmi Y, Lancaster AK, Jung JY, et al. Scalable and cost-effective NGS genotyping in the cloud. BMC Med Genomics. 2015;8:64doi: 10.1186/s12920-015-0134-9.
Souilmi, Y., Lancaster, A. K., Jung, J. Y., Rizzo, E., Hawkins, J. B., Powles, R., Amzazi, S., Ghazal, H., Tonellato, P. J., & Wall, D. P. (2015). Scalable and cost-effective NGS genotyping in the cloud. BMC medical genomics, 864. https://doi.org/10.1186/s12920-015-0134-9
Souilmi, Yassine, et al. "Scalable and cost-effective NGS genotyping in the cloud." BMC medical genomics vol. 8 (2015): 64. doi: https://doi.org/10.1186/s12920-015-0134-9
Souilmi Y, Lancaster AK, Jung JY, Rizzo E, Hawkins JB, Powles R, Amzazi S, Ghazal H, Tonellato PJ, Wall DP. Scalable and cost-effective NGS genotyping in the cloud. BMC Med Genomics. 2015 Oct 15;8:64. doi: 10.1186/s12920-015-0134-9. PMID: 26470712; PMCID: PMC4608296.
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NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.

BMC genomics

Kim NK, Jayatillake RV, Spouge JL.
PMID: 23706083
BMC Genomics. 2013 May 25;14:349. doi: 10.1186/1471-2164-14-349.

BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.RESULTS: We propose a rigorous...

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Kim NK, Jayatillake RV, Spouge JL. NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC Genomics. 2013;14:349doi: 10.1186/1471-2164-14-349.
Kim, N. K., Jayatillake, R. V., & Spouge, J. L. (2013). NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC genomics, 14349. https://doi.org/10.1186/1471-2164-14-349
Kim, Nak-Kyeong, et al. "NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data." BMC genomics vol. 14 (2013): 349. doi: https://doi.org/10.1186/1471-2164-14-349
Kim NK, Jayatillake RV, Spouge JL. NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC Genomics. 2013 May 25;14:349. doi: 10.1186/1471-2164-14-349. PMID: 23706083; PMCID: PMC3672025.
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Next-Generation Proficiency Testing.

The Journal of molecular diagnostics : JMD

Segal JP.
PMID: 27236102
J Mol Diagn. 2016 Jul;18(4):469-70. doi: 10.1016/j.jmoldx.2016.05.001. Epub 2016 May 25.

This commentary highlights the article by Davies et al that demonstrates the feasibility of applying proficiency testing directly to the postsequencing analysis of next-generation sequencing data.

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Segal JP. Next-Generation Proficiency Testing. J Mol Diagn. 2016;18(4):469-70doi: 10.1016/j.jmoldx.2016.05.001.
Segal, J. P. (2016). Next-Generation Proficiency Testing. The Journal of molecular diagnostics : JMD, 18(4), 469-70. https://doi.org/10.1016/j.jmoldx.2016.05.001
Segal, Jeremy P. "Next-Generation Proficiency Testing." The Journal of molecular diagnostics : JMD vol. 18,4 (2016): 469-70. doi: https://doi.org/10.1016/j.jmoldx.2016.05.001
Segal JP. Next-Generation Proficiency Testing. J Mol Diagn. 2016 Jul;18(4):469-70. doi: 10.1016/j.jmoldx.2016.05.001. Epub 2016 May 25. PMID: 27236102.
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Showing 1 to 12 of 410 entries