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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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MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering.

Bioinformatics (Oxford, England)

Su CH, Hsu MT, Wang TY, Chiang S, Cheng JH, Weng FC, Kao CY, Wang D, Tsai HK.
PMID: 21697124
Bioinformatics. 2011 Aug 15;27(16):2298-9. doi: 10.1093/bioinformatics/btr376. Epub 2011 Jun 22.

SUMMARY: MetaABC is a metagenomic platform that integrates several binning tools coupled with methods for removing artifacts, analyzing unassigned reads and controlling sampling biases. It allows users to arrive at a better interpretation via series of distinct combinations of...

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Su CH, Hsu MT, Wang TY, et al. MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering. Bioinformatics. 2011;27(16):2298-9doi: 10.1093/bioinformatics/btr376.
Su, C. H., Hsu, M. T., Wang, T. Y., Chiang, S., Cheng, J. H., Weng, F. C., Kao, C. Y., Wang, D., & Tsai, H. K. (2011). MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering. Bioinformatics (Oxford, England), 27(16), 2298-9. https://doi.org/10.1093/bioinformatics/btr376
Su, Chien-Hao, et al. "MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering." Bioinformatics (Oxford, England) vol. 27,16 (2011): 2298-9. doi: https://doi.org/10.1093/bioinformatics/btr376
Su CH, Hsu MT, Wang TY, Chiang S, Cheng JH, Weng FC, Kao CY, Wang D, Tsai HK. MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering. Bioinformatics. 2011 Aug 15;27(16):2298-9. doi: 10.1093/bioinformatics/btr376. Epub 2011 Jun 22. PMID: 21697124.
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High throughput sequencing methods and analysis for microbiome research.

Journal of microbiological methods

Di Bella JM, Bao Y, Gloor GB, Burton JP, Reid G.
PMID: 24029734
J Microbiol Methods. 2013 Dec;95(3):401-14. doi: 10.1016/j.mimet.2013.08.011. Epub 2013 Sep 09.

High-throughput sequencing technology is rapidly improving in quality, speed and cost. It is therefore becoming more widely used to study whole communities of prokaryotes in many niches. This review discusses these techniques, including nucleic acid extraction from different environments,...

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Di Bella JM, Bao Y, Gloor GB, et al. High throughput sequencing methods and analysis for microbiome research. J Microbiol Methods. 2013;95(3):401-14doi: 10.1016/j.mimet.2013.08.011.
Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P., & Reid, G. (2013). High throughput sequencing methods and analysis for microbiome research. Journal of microbiological methods, 95(3), 401-14. https://doi.org/10.1016/j.mimet.2013.08.011
Di Bella, Julia M, et al. "High throughput sequencing methods and analysis for microbiome research." Journal of microbiological methods vol. 95,3 (2013): 401-14. doi: https://doi.org/10.1016/j.mimet.2013.08.011
Di Bella JM, Bao Y, Gloor GB, Burton JP, Reid G. High throughput sequencing methods and analysis for microbiome research. J Microbiol Methods. 2013 Dec;95(3):401-14. doi: 10.1016/j.mimet.2013.08.011. Epub 2013 Sep 09. PMID: 24029734.
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Metagenomics: unrestricted access to microbial communities.

Virulence

Allan E.
PMID: 24521706
Virulence. 2014 Apr 01;5(3):397-8. doi: 10.4161/viru.28057. Epub 2014 Feb 12.

No abstract available.

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Allan E. Metagenomics: unrestricted access to microbial communities. Virulence. 2014;5(3):397-8doi: 10.4161/viru.28057.
Allan, E. (2014). Metagenomics: unrestricted access to microbial communities. Virulence, 5(3), 397-8. https://doi.org/10.4161/viru.28057
Allan, Elaine. "Metagenomics: unrestricted access to microbial communities." Virulence vol. 5,3 (2014): 397-8. doi: https://doi.org/10.4161/viru.28057
Allan E. Metagenomics: unrestricted access to microbial communities. Virulence. 2014 Apr 01;5(3):397-8. doi: 10.4161/viru.28057. Epub 2014 Feb 12. PMID: 24521706; PMCID: PMC3979867.
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Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes.

Bioinformatics (Oxford, England)

Desai DK, Schunck H, Löser JW, Laroche J.
PMID: 23303511
Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09.

MOTIVATION: The sheer scale of the metagenomic and metatranscriptomic datasets that are now available warrants the development of automated protocols for organizing, annotating and comparing the samples in terms of their metabolic profiles. We describe a user-friendly java program...

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Desai DK, Schunck H, Löser JW, et al. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013;29(6):790-1doi: 10.1093/bioinformatics/bts721.
Desai, D. K., Schunck, H., Löser, J. W., & Laroche, J. (2013). Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics (Oxford, England), 29(6), 790-1. https://doi.org/10.1093/bioinformatics/bts721
Desai, Dhwani K, et al. "Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes." Bioinformatics (Oxford, England) vol. 29,6 (2013): 790-1. doi: https://doi.org/10.1093/bioinformatics/bts721
Desai DK, Schunck H, Löser JW, Laroche J. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09. PMID: 23303511.
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Metagenomic systems biology and metabolic modeling of the human microbiome: from species composition to community assembly rules.

Gut microbes

Levy R, Borenstein E.
PMID: 24637600
Gut Microbes. 2014 Mar-Apr;5(2):265-70. doi: 10.4161/gmic.28261. Epub 2014 Feb 20.

The human microbiome is a key contributor to health and development. Yet little is known about the ecological forces that are at play in defining the composition of such host-associated communities. Metagenomics-based studies have uncovered clear patterns of community...

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Levy R, Borenstein E. Metagenomic systems biology and metabolic modeling of the human microbiome: from species composition to community assembly rules. Gut Microbes. 2014;5(2):265-70doi: 10.4161/gmic.28261.
Levy, R., & Borenstein, E. (2014). Metagenomic systems biology and metabolic modeling of the human microbiome: from species composition to community assembly rules. Gut microbes, 5(2), 265-70. https://doi.org/10.4161/gmic.28261
Levy, Roie, and Borenstein, Elhanan. "Metagenomic systems biology and metabolic modeling of the human microbiome: from species composition to community assembly rules." Gut microbes vol. 5,2 (2014): 265-70. doi: https://doi.org/10.4161/gmic.28261
Levy R, Borenstein E. Metagenomic systems biology and metabolic modeling of the human microbiome: from species composition to community assembly rules. Gut Microbes. 2014 Mar-Apr;5(2):265-70. doi: 10.4161/gmic.28261. Epub 2014 Feb 20. PMID: 24637600; PMCID: PMC4063856.
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'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets.

The ISME journal

Kuntal BK, Chandrakar P, Sadhu S, Mande SS.
PMID: 30287886
ISME J. 2019 Feb;13(2):442-454. doi: 10.1038/s41396-018-0291-x. Epub 2018 Oct 04.

The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify...

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Kuntal BK, Chandrakar P, Sadhu S, et al. 'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets. ISME J. 2018;13(2):442-454doi: 10.1038/s41396-018-0291-x.
Kuntal, B. K., Chandrakar, P., Sadhu, S., & Mande, S. S. (2019). 'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets. The ISME journal, 13(2), 442-454. https://doi.org/10.1038/s41396-018-0291-x
Kuntal, Bhusan K, et al. "'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets." The ISME journal vol. 13,2 (2019): 442-454. doi: https://doi.org/10.1038/s41396-018-0291-x
Kuntal BK, Chandrakar P, Sadhu S, Mande SS. 'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets. ISME J. 2019 Feb;13(2):442-454. doi: 10.1038/s41396-018-0291-x. Epub 2018 Oct 04. PMID: 30287886; PMCID: PMC6331612.
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bioBakery: a meta'omic analysis environment.

Bioinformatics (Oxford, England)

McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, Segata N, Huttenhower C.
PMID: 29194469
Bioinformatics. 2018 Apr 01;34(7):1235-1237. doi: 10.1093/bioinformatics/btx754.

SUMMARY: bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured...

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McIver LJ, Abu-Ali G, Franzosa EA, et al. bioBakery: a meta'omic analysis environment. Bioinformatics. 2018;34(7):1235-1237doi: 10.1093/bioinformatics/btx754.
McIver, L. J., Abu-Ali, G., Franzosa, E. A., Schwager, R., Morgan, X. C., Waldron, L., Segata, N., & Huttenhower, C. (2018). bioBakery: a meta'omic analysis environment. Bioinformatics (Oxford, England), 34(7), 1235-1237. https://doi.org/10.1093/bioinformatics/btx754
McIver, Lauren J, et al. "bioBakery: a meta'omic analysis environment." Bioinformatics (Oxford, England) vol. 34,7 (2018): 1235-1237. doi: https://doi.org/10.1093/bioinformatics/btx754
McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, Segata N, Huttenhower C. bioBakery: a meta'omic analysis environment. Bioinformatics. 2018 Apr 01;34(7):1235-1237. doi: 10.1093/bioinformatics/btx754. PMID: 29194469; PMCID: PMC6030947.
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MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression.

BMC bioinformatics

Kim M, Zhang X, Ligo JG, Farnoud F, Veeravalli VV, Milenkovic O.
PMID: 26895947
BMC Bioinformatics. 2016 Feb 19;17:94. doi: 10.1186/s12859-016-0932-x.

BACKGROUND: Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and...

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Kim M, Zhang X, Ligo JG, et al. MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC Bioinformatics. 2016;17:94doi: 10.1186/s12859-016-0932-x.
Kim, M., Zhang, X., Ligo, J. G., Farnoud, F., Veeravalli, V. V., & Milenkovic, O. (2016). MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC bioinformatics, 1794. https://doi.org/10.1186/s12859-016-0932-x
Kim, Minji, et al. "MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression." BMC bioinformatics vol. 17 (2016): 94. doi: https://doi.org/10.1186/s12859-016-0932-x
Kim M, Zhang X, Ligo JG, Farnoud F, Veeravalli VV, Milenkovic O. MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression. BMC Bioinformatics. 2016 Feb 19;17:94. doi: 10.1186/s12859-016-0932-x. PMID: 26895947; PMCID: PMC4759986.
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ASAR: visual analysis of metagenomes in R.

Bioinformatics (Oxford, England)

Orakov AN, Sakenova NK, Sorokin A, Goryanin II.
PMID: 29211828
Bioinformatics. 2018 Apr 15;34(8):1404-1405. doi: 10.1093/bioinformatics/btx775.

MOTIVATION: Functional and taxonomic analyses are critical steps in understanding interspecific interactions within microbial communities. Currently, such analyses are run separately, which complicates interpretation of results. Here we present the ASAR interactive tool for simultaneous analysis of metagenomic data...

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Orakov AN, Sakenova NK, Sorokin A, et al. ASAR: visual analysis of metagenomes in R. Bioinformatics. 2018;34(8):1404-1405doi: 10.1093/bioinformatics/btx775.
Orakov, A. N., Sakenova, N. K., Sorokin, A., & Goryanin, I. I. (2018). ASAR: visual analysis of metagenomes in R. Bioinformatics (Oxford, England), 34(8), 1404-1405. https://doi.org/10.1093/bioinformatics/btx775
Orakov, Askarbek N, et al. "ASAR: visual analysis of metagenomes in R." Bioinformatics (Oxford, England) vol. 34,8 (2018): 1404-1405. doi: https://doi.org/10.1093/bioinformatics/btx775
Orakov AN, Sakenova NK, Sorokin A, Goryanin II. ASAR: visual analysis of metagenomes in R. Bioinformatics. 2018 Apr 15;34(8):1404-1405. doi: 10.1093/bioinformatics/btx775. PMID: 29211828; PMCID: PMC5905653.
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WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data.

BMC genomics

Devlin JC, Battaglia T, Blaser MJ, Ruggles KV.
PMID: 29940835
BMC Genomics. 2018 Jun 25;19(1):493. doi: 10.1186/s12864-018-4870-z.

BACKGROUND: Exploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process. Although tools for this purpose exist for 16S ribosomal...

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Devlin JC, Battaglia T, Blaser MJ, et al. WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data. BMC Genomics. 2018;19(1):493doi: 10.1186/s12864-018-4870-z.
Devlin, J. C., Battaglia, T., Blaser, M. J., & Ruggles, K. V. (2018). WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data. BMC genomics, 19(1), 493. https://doi.org/10.1186/s12864-018-4870-z
Devlin, Joseph C, et al. "WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data." BMC genomics vol. 19,1 (2018): 493. doi: https://doi.org/10.1186/s12864-018-4870-z
Devlin JC, Battaglia T, Blaser MJ, Ruggles KV. WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data. BMC Genomics. 2018 Jun 25;19(1):493. doi: 10.1186/s12864-018-4870-z. PMID: 29940835; PMCID: PMC6019711.
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Improved metagenomic analysis with Kraken 2.

Genome biology

Wood DE, Lu J, Langmead B.
PMID: 31779668
Genome Biol. 2019 Nov 28;20(1):257. doi: 10.1186/s13059-019-1891-0.

Although Kraken's k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of...

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Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257doi: 10.1186/s13059-019-1891-0.
Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome biology, 20(1), 257. https://doi.org/10.1186/s13059-019-1891-0
Wood, Derrick E, et al. "Improved metagenomic analysis with Kraken 2." Genome biology vol. 20,1 (2019): 257. doi: https://doi.org/10.1186/s13059-019-1891-0
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019 Nov 28;20(1):257. doi: 10.1186/s13059-019-1891-0. PMID: 31779668; PMCID: PMC6883579.
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Showing 1 to 12 of 60 entries