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Qin et al. reply.

Nature

Qin N, Le Chatelier E, Guo J, Prifti E, Li L, Ehrlich SD.
PMID: 26381989
Nature. 2015 Sep 17;525(7569):E2-3. doi: 10.1038/nature14852.

No abstract available.

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Qin N, Le Chatelier E, Guo J, et al. Qin et al. reply. Nature. 2015;525(7569):E2-3doi: 10.1038/nature14852.
Qin, N., Le Chatelier, E., Guo, J., Prifti, E., Li, L., & Ehrlich, S. D. (2015). Qin et al. reply. Nature, 525(7569), E2-3. https://doi.org/10.1038/nature14852
Qin, Nan, et al. "Qin et al. reply." Nature vol. 525,7569 (2015): E2-3. doi: https://doi.org/10.1038/nature14852
Qin N, Le Chatelier E, Guo J, Prifti E, Li L, Ehrlich SD. Qin et al. reply. Nature. 2015 Sep 17;525(7569):E2-3. doi: 10.1038/nature14852. PMID: 26381989.
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Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification.

PloS one

Pongor LS, Vera R, Ligeti B.
PMID: 25077800
PLoS One. 2014 Jul 31;9(7):e103441. doi: 10.1371/journal.pone.0103441. eCollection 2014.

Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result,...

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Pongor LS, Vera R, Ligeti B. Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PLoS One. 2014;9(7):e103441doi: 10.1371/journal.pone.0103441.
Pongor, L. S., Vera, R., & Ligeti, B. (2014). Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PloS one, 9(7), e103441. https://doi.org/10.1371/journal.pone.0103441
Pongor, Lőrinc S, et al. "Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification." PloS one vol. 9,7 (2014): e103441. doi: https://doi.org/10.1371/journal.pone.0103441
Pongor LS, Vera R, Ligeti B. Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PLoS One. 2014 Jul 31;9(7):e103441. doi: 10.1371/journal.pone.0103441. eCollection 2014. PMID: 25077800; PMCID: PMC4117525.
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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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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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Up-close-and-personal with the human microbiome.

Environmental microbiology reports

Berry D.
PMID: 30431239
Environ Microbiol Rep. 2019 Feb;11(1):17-19. doi: 10.1111/1758-2229.12709. Epub 2018 Nov 15.

No abstract available.

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Berry D. Up-close-and-personal with the human microbiome. Environ Microbiol Rep. 2018;11(1):17-19doi: 10.1111/1758-2229.12709.
Berry, D. (2019). Up-close-and-personal with the human microbiome. Environmental microbiology reports, 11(1), 17-19. https://doi.org/10.1111/1758-2229.12709
Berry, David. "Up-close-and-personal with the human microbiome." Environmental microbiology reports vol. 11,1 (2019): 17-19. doi: https://doi.org/10.1111/1758-2229.12709
Berry D. Up-close-and-personal with the human microbiome. Environ Microbiol Rep. 2019 Feb;11(1):17-19. doi: 10.1111/1758-2229.12709. Epub 2018 Nov 15. PMID: 30431239.
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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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Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities.

BMC bioinformatics

Dacey DP, Chain FJJ.
PMID: 34641782
BMC Bioinformatics. 2021 Oct 12;22(1):493. doi: 10.1186/s12859-021-04410-2.

BACKGROUND: Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap....

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Dacey DP, Chain FJJ. Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities. BMC Bioinformatics. 2021;22(1):493doi: 10.1186/s12859-021-04410-2.
Dacey, D. P., & Chain, F. J. J. (2021). Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities. BMC bioinformatics, 22(1), 493. https://doi.org/10.1186/s12859-021-04410-2
Dacey, Daniel P, and Chain, Frédéric J J. "Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities." BMC bioinformatics vol. 22,1 (2021): 493. doi: https://doi.org/10.1186/s12859-021-04410-2
Dacey DP, Chain FJJ. Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities. BMC Bioinformatics. 2021 Oct 12;22(1):493. doi: 10.1186/s12859-021-04410-2. PMID: 34641782; PMCID: PMC8507205.
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Melding microbiome and nutritional science with early child development.

Nature medicine

Chen RY, Mostafa I, Hibberd MC, Das S, Lynn HM, Webber DM, Mahfuz M, Barratt MJ, Ahmed T, Gordon JI.
PMID: 34326550
Nat Med. 2021 Sep;27(9):1503-1506. doi: 10.1038/s41591-021-01451-1.

No abstract available.

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Chen RY, Mostafa I, Hibberd MC, et al. Melding microbiome and nutritional science with early child development. Nat Med. 2021;27(9):1503-1506doi: 10.1038/s41591-021-01451-1.
Chen, R. Y., Mostafa, I., Hibberd, M. C., Das, S., Lynn, H. M., Webber, D. M., Mahfuz, M., Barratt, M. J., Ahmed, T., & Gordon, J. I. (2021). Melding microbiome and nutritional science with early child development. Nature medicine, 27(9), 1503-1506. https://doi.org/10.1038/s41591-021-01451-1
Chen, Robert Y, et al. "Melding microbiome and nutritional science with early child development." Nature medicine vol. 27,9 (2021): 1503-1506. doi: https://doi.org/10.1038/s41591-021-01451-1
Chen RY, Mostafa I, Hibberd MC, Das S, Lynn HM, Webber DM, Mahfuz M, Barratt MJ, Ahmed T, Gordon JI. Melding microbiome and nutritional science with early child development. Nat Med. 2021 Sep;27(9):1503-1506. doi: 10.1038/s41591-021-01451-1. PMID: 34326550; PMCID: PMC8713503.
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ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework.

F1000Research

Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD.
PMID: 34484688
F1000Res. 2021 Feb 11;10:103. doi: 10.12688/f1000research.28608.2. eCollection 2021.

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies,...

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Mehta S, Crane M, Leith E, et al. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res. 2021;10:103doi: 10.12688/f1000research.28608.2.
Mehta, S., Crane, M., Leith, E., Batut, B., Hiltemann, S., Arntzen, M. �. �., Kunath, B. J., Pope, P. B., Delogu, F., Sajulga, R., Kumar, P., Johnson, J. E., Griffin, T. J., & Jagtap, P. D. (2021). ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Research, 10103. https://doi.org/10.12688/f1000research.28608.2
Mehta, Subina, et al. "ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework." F1000Research vol. 10 (2021): 103. doi: https://doi.org/10.12688/f1000research.28608.2
Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res. 2021 Feb 11;10:103. doi: 10.12688/f1000research.28608.2. eCollection 2021. PMID: 34484688; PMCID: PMC8383124.
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A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

PloS one

Budinich M, Bourdon J, Larhlimi A, Eveillard D.
PMID: 28187207
PLoS One. 2017 Feb 10;12(2):e0171744. doi: 10.1371/journal.pone.0171744. eCollection 2017.

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates...

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Budinich M, Bourdon J, Larhlimi A, et al. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PLoS One. 2017;12(2):e0171744doi: 10.1371/journal.pone.0171744.
Budinich, M., Bourdon, J., Larhlimi, A., & Eveillard, D. (2017). A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PloS one, 12(2), e0171744. https://doi.org/10.1371/journal.pone.0171744
Budinich, Marko, et al. "A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems." PloS one vol. 12,2 (2017): e0171744. doi: https://doi.org/10.1371/journal.pone.0171744
Budinich M, Bourdon J, Larhlimi A, Eveillard D. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PLoS One. 2017 Feb 10;12(2):e0171744. doi: 10.1371/journal.pone.0171744. eCollection 2017. PMID: 28187207; PMCID: PMC5302800.
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Recent Advancements in Intestinal Microbiota Analyses: A Review for Non-Microbiologists.

Current medical science

Feng XW, Ding WP, Xiong LY, Guo L, Sun JM, Xiao P.
PMID: 30536055
Curr Med Sci. 2018 Dec;38(6):949-961. doi: 10.1007/s11596-018-1969-z. Epub 2018 Dec 07.

Microbial constituents naturally inhabiting the gastrointestinal tract may influence the homeostasis of the gut environment. The presence or overabundance of some bacterial taxa has been reported to be associated with complex diseases, and the metabolites of certain bacteria may...

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Feng XW, Ding WP, Xiong LY, et al. Recent Advancements in Intestinal Microbiota Analyses: A Review for Non-Microbiologists. Curr Med Sci. 2018;38(6):949-961doi: 10.1007/s11596-018-1969-z.
Feng, X. W., Ding, W. P., Xiong, L. Y., Guo, L., Sun, J. M., & Xiao, P. (2018). Recent Advancements in Intestinal Microbiota Analyses: A Review for Non-Microbiologists. Current medical science, 38(6), 949-961. https://doi.org/10.1007/s11596-018-1969-z
Feng, Xiao-Wei, et al. "Recent Advancements in Intestinal Microbiota Analyses: A Review for Non-Microbiologists." Current medical science vol. 38,6 (2018): 949-961. doi: https://doi.org/10.1007/s11596-018-1969-z
Feng XW, Ding WP, Xiong LY, Guo L, Sun JM, Xiao P. Recent Advancements in Intestinal Microbiota Analyses: A Review for Non-Microbiologists. Curr Med Sci. 2018 Dec;38(6):949-961. doi: 10.1007/s11596-018-1969-z. Epub 2018 Dec 07. PMID: 30536055.
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Showing 1 to 12 of 34 entries