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On 24 Meán Fómhair 2025 11:54:39 UTC,
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| f | 1 | { | f | 1 | { |
| 2 | "api_access_url": "", | 2 | "api_access_url": "", | ||
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| 5 | "author": null, | 5 | "author": null, | ||
| 6 | "author_email": null, | 6 | "author_email": null, | ||
| 7 | "citation": "O'Sullivan, O., Yap, M. (2025) MicrobiomeMilkMap | 7 | "citation": "O'Sullivan, O., Yap, M. (2025) MicrobiomeMilkMap | ||
| 8 | 2021-2022: Data underlying the publication \"Seasonal and geographical | 8 | 2021-2022: Data underlying the publication \"Seasonal and geographical | ||
| 9 | impact on the Irish raw milk microbiota correlates with chemical | 9 | impact on the Irish raw milk microbiota correlates with chemical | ||
| 10 | composition and climatic variables\". Teagasc Open Platform. ", | 10 | composition and climatic variables\". Teagasc Open Platform. ", | ||
| 11 | "commercial_data": "false", | 11 | "commercial_data": "false", | ||
| 12 | "conforms_to": "", | 12 | "conforms_to": "", | ||
| 13 | "contact_email": "orla.osullivan@teagasc.ie", | 13 | "contact_email": "orla.osullivan@teagasc.ie", | ||
| 14 | "contact_name": "Dr Orla O'Sullivan ", | 14 | "contact_name": "Dr Orla O'Sullivan ", | ||
| 15 | "creator_user_id": "68d970d0-ff26-4dfe-ab51-46dde87e3f23", | 15 | "creator_user_id": "68d970d0-ff26-4dfe-ab51-46dde87e3f23", | ||
| 16 | "data_creator": [ | 16 | "data_creator": [ | ||
| 17 | "Min Yap" | 17 | "Min Yap" | ||
| 18 | ], | 18 | ], | ||
| 19 | "description_english": "Season and location have previously been | 19 | "description_english": "Season and location have previously been | ||
| 20 | shown to be associated with differences in the microbiota of raw milk, | 20 | shown to be associated with differences in the microbiota of raw milk, | ||
| 21 | \r\nespecially in milk from pasture-based systems. Here we further | 21 | \r\nespecially in milk from pasture-based systems. Here we further | ||
| 22 | advance research in this area by examining \r\ndifferences in the raw | 22 | advance research in this area by examining \r\ndifferences in the raw | ||
| 23 | milk microbiota from several locations across Ireland over 12 months, | 23 | milk microbiota from several locations across Ireland over 12 months, | ||
| 24 | and by investigating \r\nmicrobiota associations with climatic | 24 | and by investigating \r\nmicrobiota associations with climatic | ||
| 25 | variables and chemical composition. Shotgun metagenomic sequencing was | 25 | variables and chemical composition. Shotgun metagenomic sequencing was | ||
| 26 | used \r\nto investigate the microbiota of raw milk collected from 9 | 26 | used \r\nto investigate the microbiota of raw milk collected from 9 | ||
| 27 | locations (n=241). Concurrent chemical analysis of the \r\nprotein, | 27 | locations (n=241). Concurrent chemical analysis of the \r\nprotein, | ||
| 28 | fat, lactose, total solids, nonprotein nitrogen (NPN) contents and | 28 | fat, lactose, total solids, nonprotein nitrogen (NPN) contents and | ||
| 29 | titratable acidity (TA) of the same \r\nraw milk were performed.", | 29 | titratable acidity (TA) of the same \r\nraw milk were performed.", | ||
| 30 | "description_translated": "", | 30 | "description_translated": "", | ||
| 31 | "doi": "", | 31 | "doi": "", | ||
| 32 | "equipment_used": "- Sample collection and preparation: Raw bovine | 32 | "equipment_used": "- Sample collection and preparation: Raw bovine | ||
| 33 | milk samples (200 ml) were collected from silos from 9 locations | 33 | milk samples (200 ml) were collected from silos from 9 locations | ||
| 34 | \r\nacross Ireland weekly from March 2021 to March 2022 (n=241). The | 34 | \r\nacross Ireland weekly from March 2021 to March 2022 (n=241). The | ||
| 35 | samples were collected over 2 days, transported under | 35 | samples were collected over 2 days, transported under | ||
| 36 | \r\nrefrigeration and stored at 4 degrees C, to mimic conditions of | 36 | \r\nrefrigeration and stored at 4 degrees C, to mimic conditions of | ||
| 37 | their storage in bulk tanks or silos, for a maximum of \r\n48h before | 37 | their storage in bulk tanks or silos, for a maximum of \r\n48h before | ||
| 38 | sample processing of all samples together. Samples were prepared as | 38 | sample processing of all samples together. Samples were prepared as | ||
| 39 | follows: 30 ml of the bovine milk \r\nsample was centrifuged at 4,500 | 39 | follows: 30 ml of the bovine milk \r\nsample was centrifuged at 4,500 | ||
| 40 | x g for 20 min at 4 degrees C. After centrifugation, the cream and | 40 | x g for 20 min at 4 degrees C. After centrifugation, the cream and | ||
| 41 | supernatant were \r\ndiscarded, and the pellets were subjected to two | 41 | supernatant were \r\ndiscarded, and the pellets were subjected to two | ||
| 42 | washing steps, whereby the pellets were resuspended in sterile\r\nPBS | 42 | washing steps, whereby the pellets were resuspended in sterile\r\nPBS | ||
| 43 | and centrifuged at 13,000 x g for 1 minute, after which the | 43 | and centrifuged at 13,000 x g for 1 minute, after which the | ||
| 44 | supernatant was discarded, and the pellet was \r\nstored at -20 | 44 | supernatant was discarded, and the pellet was \r\nstored at -20 | ||
| 45 | degrees C before DNA extraction. \r\n\r\n- DNA extraction: Samples | 45 | degrees C before DNA extraction. \r\n\r\n- DNA extraction: Samples | ||
| 46 | were subjected to DNA extraction using the MolYsis complete5 kit | 46 | were subjected to DNA extraction using the MolYsis complete5 kit | ||
| 47 | (Molzym GmBH & Co. KG, \r\nBremen, Germany), with 50 microlitres of | 47 | (Molzym GmBH & Co. KG, \r\nBremen, Germany), with 50 microlitres of | ||
| 48 | DNA eluted for downstream sequencing. The MolYsis kit was used to | 48 | DNA eluted for downstream sequencing. The MolYsis kit was used to | ||
| 49 | improve\r\nmicrobiota characterization by significantly enhancing the | 49 | improve\r\nmicrobiota characterization by significantly enhancing the | ||
| 50 | microbial sequencing depth of milk samples. gDNA was \r\nquantified | 50 | microbial sequencing depth of milk samples. gDNA was \r\nquantified | ||
| 51 | using the Qubit dsDNA HS assay kit (Invitrogen) and stored at -20 | 51 | using the Qubit dsDNA HS assay kit (Invitrogen) and stored at -20 | ||
| 52 | degrees C before library preparation.\r\n\r\n- Shotgun metagenomic | 52 | degrees C before library preparation.\r\n\r\n- Shotgun metagenomic | ||
| 53 | sequencing: 248 samples (241 samples and 7 controls) were prepared for | 53 | sequencing: 248 samples (241 samples and 7 controls) were prepared for | ||
| 54 | shotgun metagenomic \r\nsequencing according to Illumina Nextera XT | 54 | shotgun metagenomic \r\nsequencing according to Illumina Nextera XT | ||
| 55 | library preparation kit guidelines, using unique dual indexes for | 55 | library preparation kit guidelines, using unique dual indexes for | ||
| 56 | \r\nmultiplexing with the Nextera XT index kit (Illumina). Following | 56 | \r\nmultiplexing with the Nextera XT index kit (Illumina). Following | ||
| 57 | indexing and clean-up, samples were pooled to an \r\nequimolar | 57 | indexing and clean-up, samples were pooled to an \r\nequimolar | ||
| 58 | concentration of 1 nM. Samples were sequenced in two pools, the first | 58 | concentration of 1 nM. Samples were sequenced in two pools, the first | ||
| 59 | pool containing 98 samples on an \r\nIllumina NextSeq 550 sequencing | 59 | pool containing 98 samples on an \r\nIllumina NextSeq 550 sequencing | ||
| 60 | platform with a V2 kit, and the second containing 150 samples on an | 60 | platform with a V2 kit, and the second containing 150 samples on an | ||
| 61 | Illumina NextSeq \r\n2000 sequencing platform with a P3 chip, at the | 61 | Illumina NextSeq \r\n2000 sequencing platform with a P3 chip, at the | ||
| 62 | Teagasc DNA Sequencing Facility, using standard Illumina sequencing | 62 | Teagasc DNA Sequencing Facility, using standard Illumina sequencing | ||
| 63 | \r\nprotocols. \r\n\r\n- Bioinformatic processing: Default parameters | 63 | \r\nprotocols. \r\n\r\n- Bioinformatic processing: Default parameters | ||
| 64 | were applied for all the bioinformatic tools unless otherwise | 64 | were applied for all the bioinformatic tools unless otherwise | ||
| 65 | specified. \r\nQuality checks and adapter trimming were performed with | 65 | specified. \r\nQuality checks and adapter trimming were performed with | ||
| 66 | FastQC (0.11.8) and cutadapt (2.6) and host reads were aligned \r\nto | 66 | FastQC (0.11.8) and cutadapt (2.6) and host reads were aligned \r\nto | ||
| 67 | the bovine genome (Bos taurus) and removed with Bowtie2 (2.4.4). | 67 | the bovine genome (Bos taurus) and removed with Bowtie2 (2.4.4). | ||
| 68 | Taxonomic classification was performed with Kraken2 \r\n(2.0.7) (32) | 68 | Taxonomic classification was performed with Kraken2 \r\n(2.0.7) (32) | ||
| 69 | using the Genome Taxonomy Database (release 89) which contains | 69 | using the Genome Taxonomy Database (release 89) which contains | ||
| 70 | Bacteria and Archaea. SUPER-FOCUS was used \r\nto predict the | 70 | Bacteria and Archaea. SUPER-FOCUS was used \r\nto predict the | ||
| 71 | microbiological functional potential of shotgun reads, through the | 71 | microbiological functional potential of shotgun reads, through the | ||
| 72 | alignment of reads against a reduced \r\nSEED database using DIAMOND, | 72 | alignment of reads against a reduced \r\nSEED database using DIAMOND, | ||
| 73 | with results classified into subsystems (sets of protein families with | 73 | with results classified into subsystems (sets of protein families with | ||
| 74 | similar function). \r\nResistome analysis was done using Resistance | 74 | similar function). \r\nResistome analysis was done using Resistance | ||
| 75 | Gene Identifier (RGI 4.2.2), with the strict cut-off. Assembly of | 75 | Gene Identifier (RGI 4.2.2), with the strict cut-off. Assembly of | ||
| 76 | Metagenome \r\nAssembled Genomes (MAGs) was done using metaSPAdes | 76 | Metagenome \r\nAssembled Genomes (MAGs) was done using metaSPAdes | ||
| 77 | (3.13), followed by binning with MetaBAT2 (2.12.1) and quality | 77 | (3.13), followed by binning with MetaBAT2 (2.12.1) and quality | ||
| 78 | \r\nassessment with checkM (1.0.18). High-quality MAGs, of at least | 78 | \r\nassessment with checkM (1.0.18). High-quality MAGs, of at least | ||
| 79 | 90% completeness and less than 5% contamination were \r\nassigned | 79 | 90% completeness and less than 5% contamination were \r\nassigned | ||
| 80 | taxonomy with GTDB-tk (2.1.1).\r\n\r\n- Chemical analysis: The | 80 | taxonomy with GTDB-tk (2.1.1).\r\n\r\n- Chemical analysis: The | ||
| 81 | chemical composition of the 100 ml of milk samples was determined by | 81 | chemical composition of the 100 ml of milk samples was determined by | ||
| 82 | DPTC analytical \r\nstaff at the Technical Services lab at the Teagasc | 82 | DPTC analytical \r\nstaff at the Technical Services lab at the Teagasc | ||
| 83 | Food Research Centre. Kjeldahl analysis was used to determine | 83 | Food Research Centre. Kjeldahl analysis was used to determine | ||
| 84 | \r\nprotein and nonprotein nitrogen (NPN) contents. Rose Gottlieb | 84 | \r\nprotein and nonprotein nitrogen (NPN) contents. Rose Gottlieb | ||
| 85 | method was used to determine fat content, and the \r\nCEM SMART Trac | 85 | method was used to determine fat content, and the \r\nCEM SMART Trac | ||
| 86 | II (CEM, Matthews, NC, USA) was used to measure the total solids | 86 | II (CEM, Matthews, NC, USA) was used to measure the total solids | ||
| 87 | content. Polarimetry was used to \r\ndetermine the lactose content, | 87 | content. Polarimetry was used to \r\ndetermine the lactose content, | ||
| 88 | and titration was used to determine titratable acidity (TA) in raw | 88 | and titration was used to determine titratable acidity (TA) in raw | ||
| 89 | milk samples.\r\n\r\n- Climactic data: Monthly climate data for the | 89 | milk samples.\r\n\r\n- Climactic data: Monthly climate data for the | ||
| 90 | sampling locations relating to mean temperature (degrees C), total | 90 | sampling locations relating to mean temperature (degrees C), total | ||
| 91 | rainfall \r\n(mm), grass minimum temperature (degrees C), mean wind | 91 | rainfall \r\n(mm), grass minimum temperature (degrees C), mean wind | ||
| 92 | speed (knots) and sunshine duration (daily hours of sun) was retrieved | 92 | speed (knots) and sunshine duration (daily hours of sun) was retrieved | ||
| 93 | \r\nfrom the Irish Meteorological Service website (www.met.ie). The | 93 | \r\nfrom the Irish Meteorological Service website (www.met.ie). The | ||
| 94 | months of March, April and May were classified as \r\nSpring, June, | 94 | months of March, April and May were classified as \r\nSpring, June, | ||
| 95 | July and August as Summer, September, October and November as Autumn | 95 | July and August as Summer, September, October and November as Autumn | ||
| 96 | and December, January and \r\nFebruary as Winter.\r\n\r\n- Statistical | 96 | and December, January and \r\nFebruary as Winter.\r\n\r\n- Statistical | ||
| 97 | analysis: Statistical analysis and data visualization was performed in | 97 | analysis: Statistical analysis and data visualization was performed in | ||
| 98 | R (4.1.2). All data was cleaned, \r\nanalyzed and visualised in R with | 98 | R (4.1.2). All data was cleaned, \r\nanalyzed and visualised in R with | ||
| 99 | ggplot2, tidyverse and ggpubr packages (44, 45). Kruskal-Wallis and | 99 | ggplot2, tidyverse and ggpubr packages (44, 45). Kruskal-Wallis and | ||
| 100 | pairwise \r\nWilcoxon rank sum tests with Benjamini-Hochberg P-value | 100 | pairwise \r\nWilcoxon rank sum tests with Benjamini-Hochberg P-value | ||
| 101 | correction were used to compare sampling seasons and locations. | 101 | correction were used to compare sampling seasons and locations. | ||
| 102 | \r\nMicrobiota diversity analysis was performed with the vegan package | 102 | \r\nMicrobiota diversity analysis was performed with the vegan package | ||
| 103 | (46), and beta diversity was calculated as Bray-Curtis \r\nmetrics, | 103 | (46), and beta diversity was calculated as Bray-Curtis \r\nmetrics, | ||
| 104 | visualised in a principal coordinate analysis plot. The adonis | 104 | visualised in a principal coordinate analysis plot. The adonis | ||
| 105 | function from the vegan package was used to \r\ncalculate the | 105 | function from the vegan package was used to \r\ncalculate the | ||
| 106 | permutational analysis of variance (PERMANOVA) to determine | 106 | permutational analysis of variance (PERMANOVA) to determine | ||
| 107 | differences in composition of the community \r\nbetween groups of | 107 | differences in composition of the community \r\nbetween groups of | ||
| 108 | samples (number of permutations=999). Redundancy analysis was also | 108 | samples (number of permutations=999). Redundancy analysis was also | ||
| 109 | done with vegan and visualised \r\nusing the ggord package. The | 109 | done with vegan and visualised \r\nusing the ggord package. The | ||
| 110 | multiplatt function from the indicspecies package was used to identify | 110 | multiplatt function from the indicspecies package was used to identify | ||
| 111 | taxa that were \r\nsignificantly associated with particular seasons | 111 | taxa that were \r\nsignificantly associated with particular seasons | ||
| 112 | and sampling locations, by calculating Pearson's phi coefficient of | 112 | and sampling locations, by calculating Pearson's phi coefficient of | ||
| 113 | \r\nassociation and correcting for unequal group sizes using the | 113 | \r\nassociation and correcting for unequal group sizes using the | ||
| 114 | parameter r.g. Pearson's correlation was measured \r\nwith the R base | 114 | parameter r.g. Pearson's correlation was measured \r\nwith the R base | ||
| 115 | function, cor, and visualised using ggcorrplot.\r\n", | 115 | function, cor, and visualised using ggcorrplot.\r\n", | ||
| 116 | "funding_acknowledgment": "", | 116 | "funding_acknowledgment": "", | ||
| 117 | "geographic_coverage": "Ireland", | 117 | "geographic_coverage": "Ireland", | ||
| 118 | "groups": [], | 118 | "groups": [], | ||
| 119 | "high_value_data": "false", | 119 | "high_value_data": "false", | ||
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| 122 | "isopen": false, | 122 | "isopen": false, | ||
| 123 | "language": "en", | 123 | "language": "en", | ||
| 124 | "license_id": "cc-by-nc", | 124 | "license_id": "cc-by-nc", | ||
| 125 | "license_title": "CC-BY-NC", | 125 | "license_title": "CC-BY-NC", | ||
| 126 | "maintainer": null, | 126 | "maintainer": null, | ||
| 127 | "maintainer_email": null, | 127 | "maintainer_email": null, | ||
| 128 | "metadata_created": "2025-09-24T11:54:27.934616", | 128 | "metadata_created": "2025-09-24T11:54:27.934616", | ||
| n | 129 | "metadata_modified": "2025-09-24T11:54:27.934624", | n | 129 | "metadata_modified": "2025-09-24T11:54:39.659845", |
| 130 | "name": "microbiomemilkmap-2021-2022", | 130 | "name": "microbiomemilkmap-2021-2022", | ||
| 131 | "num_resources": 4, | 131 | "num_resources": 4, | ||
| 132 | "num_tags": 2, | 132 | "num_tags": 2, | ||
| 133 | "open_data": "Open data", | 133 | "open_data": "Open data", | ||
| 134 | "orcid_id": "https://orcid.org/0000-0002-4332-1109", | 134 | "orcid_id": "https://orcid.org/0000-0002-4332-1109", | ||
| 135 | "organization": { | 135 | "organization": { | ||
| 136 | "approval_status": "approved", | 136 | "approval_status": "approved", | ||
| 137 | "created": "2025-09-24T11:47:36.394831", | 137 | "created": "2025-09-24T11:47:36.394831", | ||
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| 140 | "image_url": "", | 140 | "image_url": "", | ||
| 141 | "is_organization": true, | 141 | "is_organization": true, | ||
| 142 | "name": "food-biosciences-research", | 142 | "name": "food-biosciences-research", | ||
| 143 | "state": "active", | 143 | "state": "active", | ||
| 144 | "title": "Food Biosciences Research", | 144 | "title": "Food Biosciences Research", | ||
| 145 | "type": "organization" | 145 | "type": "organization" | ||
| 146 | }, | 146 | }, | ||
| 147 | "owner_org": "4b6fc2b8-ef2a-4899-974a-0aa62c78f935", | 147 | "owner_org": "4b6fc2b8-ef2a-4899-974a-0aa62c78f935", | ||
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| 151 | "provenance": "", | 151 | "provenance": "", | ||
| 152 | "publish_to_public": "true", | 152 | "publish_to_public": "true", | ||
| 153 | "related_resource": [ | 153 | "related_resource": [ | ||
| 154 | "Yap, M., O'Sullivan, O., O'Toole, P.W., Sheehan, J.J., Fenelon, | 154 | "Yap, M., O'Sullivan, O., O'Toole, P.W., Sheehan, J.J., Fenelon, | ||
| 155 | M.A. and Cotter, P.D., (2024) Seasonal and geographical impact on the | 155 | M.A. and Cotter, P.D., (2024) Seasonal and geographical impact on the | ||
| 156 | Irish raw milk microbiota correlates with chemical composition and | 156 | Irish raw milk microbiota correlates with chemical composition and | ||
| 157 | climatic variables. mSystems, 9, e01290-23. | 157 | climatic variables. mSystems, 9, e01290-23. | ||
| 158 | https://doi.org/10.1128/msystems.01290-23" | 158 | https://doi.org/10.1128/msystems.01290-23" | ||
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| 274 | purposes only, and only so long as attribution is given to the | 274 | purposes only, and only so long as attribution is given to the | ||
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| 304 | microbiota correlates with chemical composition and climatic | 304 | microbiota correlates with chemical composition and climatic | ||
| 305 | variables\"", | 305 | variables\"", | ||
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| 309 | microbiota correlates with chemical composition and climatic | 309 | microbiota correlates with chemical composition and climatic | ||
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