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67 results for “ASV”
18S Amplicon sequence variants (ASVs) data of NEREA Augmented Observatory
<p>The quality of the Illumina paired-end V9-18S raw reads (FASTQ format, 2 X 150 PE) was checked using vsearch (vsearch --fastq_stats), then pre-processed with cutadapt and vsearch to remove primer sequences, trim low quality bases and unify mixed orientation reads produced in the ligation-based library preparation. Processed reads were then used to generate amplicon sequence variants (ASVs) using the DADA2 R library; the pipeline was adapted from the one described on the program website (https://benjjneb.github.io/dada2/tutorial.html); no further quality filtering was implemented at this stage, except for discarding all reads with ambiguities (parameter maxN=0 of function filterAndTrim). Filtered F and R reads were used to train the error model and then denoised by applying the trained error model to generate ASVs. Finally, F and R reads were merged and checked for chimeras; up to 9 mismatches were allowed for read merging (parameter maxMismatch=9 of function mergePairs). ASVs were then classified with BLAST against the PR2 v5.01 reference database, integrated with 1293 sequences from Gulf of Naples protist strains and fungi environmental sequences. Highest bit score matches with the best taxonomic resolution were then selected among the returned results.</p>
Full year Lake Okeechobee continuous survey by Nav2 Sail and Solar ASV (2021)
<p>This dataset was generated during an entire year of operations (2020-12-31 to 2021-01-01) of the Nav2 Sail and Solar ASV (Navocean, Inc.) Vela, to survey Lake Okeechobee as part of the HALO program.</p> <p>Vela was equipped with the following sensors: C/T (Aanderaa), ADCP (Nortek), BB3 backscatter (Wetlabs), dissolved O<sub>2</sub> (Aanderaa), and Fluorometric sensors (Turner Cyclops Integrator). The dataset includes variables from all sensors as well as navigation parameters.</p> <p>The individual logger files have been combined under one register per TIMESTAMP, and a 1st level of quality control has been applied.</p> <p>This dataset is used in the publication in preparation: Ruiz Xomchuk, V., Duncan, S. McFarland M and Beckler, J. [2023] Elucidating Lake Okeechobee Harmful Algal Bloom population tracking through long-duration monitoring with an Autonomous Surface Vehicle, to be submitted to <em><a href="https://link.springer.com/journal/10661">Environmental Monitoring and Assessment</a>.</em></p> <p>Within the HALO program, the following public outreach bulletins were prepared during Vela's deployment time as data became available:</p> <p><a href="https://www.researchgate.net/publication/363428927_HALO_Bulletin_06042021_Issue_No_1?_sg%5B0%5D=UM9l9-QGqxamO8xpbQH-rxQBXO_6vh2-_ktW3W1pZfFURVyGDXIG8UOfj7s8i6kQNjaknOk49wivWDc.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sg%5B1%5D=wNUbCn_MUeB274aHT_ulVKvA5bePd-eJubBUdtCc7rPFFFvKhDVGsYRqS1kXPw2sn822YdNN-SYLiYqnJ4onbOvkIXU.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sg%5B2%5D=yTeKUqjvhIDTxNvFmR_TNqKCOXtm874fmh7Q8XMOH6A-rFpFvQ1Jd34Ofe2DQe99uFExIVgIzU5M7j0AuQ.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sgd%5Bsr%5D=1&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwifX0">HALO Bulletin 06/04/2021. Issue No 1</a>. <strong>DOI</strong>: <strong>10.13140/RG.2.2.27803.44323</strong></p> <p><a href="https://www.researchgate.net/publication/363428696_HALO_Bulletin_06182021_Issue_No_2?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwiLCJwcmV2aW91c1BhZ2UiOiJwdWJsaWNhdGlvbkRldGFpbCJ9fQ">HALO Bulletin 06/18/2021 Issue No. 2</a>. <strong>DOI</strong>: <strong>10.13140/RG.2.2.21092.55688</strong></p> <p><a href="https://www.researchgate.net/publication/363429143_HALO_Bulletin_07022021_Issue_No_3?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwiLCJwcmV2aW91c1BhZ2UiOiJwdWJsaWNhdGlvbkRldGFpbCJ9fQ">HALO Bulletin 07/02/2021. Issue No. 3</a>. <strong>DOI</strong>: <strong>10.13140/RG.2.2.12703.94884</strong></p> <p><a href="https://www.researchgate.net/publication/363429190_HALO_Bulletin_07152021_Issue_No_4?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwiLCJwcmV2aW91c1BhZ2UiOiJwdWJsaWNhdGlvbkRldGFpbCJ9fQ">HALO Bulletin 07/15/2021. Issue No. 4.</a> <strong>DOI</strong>: <strong>10.13140/RG.2.2.34514.32969</strong></p> <p><a href="https://www.researchgate.net/publication/363429433_HALO_Bulletin_08142021_Issue_No_5?_sg%5B0%5D=8Wpu75qW5LBYwg9XfRUKk15XAeDig-A_M8o0mW1by7L6Z_ITPiRWK8qv-2PnmcfErq2O0APSfOyt_Vc.Buwj4Bzg2IklAZ4eDQi9CoiyDzGaVWP1z9aiTHuiA3bCQ37K6Fwb3W4QlOSaKpaLaiwlOt6OmHN29V45ZsEkPw&_sg%5B1%5D=5hfpRjo9FFlB60mBkYUWacFQC4rmoLbG7S5_fxPqpL7TnTFF5d4e3458SQcB0FQjbBsVYeP1FzkC17BwMP1Suia2jI4.Buwj4Bzg2IklAZ4eDQi9CoiyDzGaVWP1z9aiTHuiA3bCQ37K6Fwb3W4QlOSaKpaLaiwlOt6OmHN29V45ZsEkPw&_sg%5B2%5D=Q3r6lHpsJi7U2iu10STFQnVHSidCy2qJ4MWzcEa_PYBCD5ZQNS8IQ-TU6NtAc26bzv80-mLULIH21Dv-VQ.Buwj4Bzg2IklAZ4eDQi9CoiyDzGaVWP1z9aiTHuiA3bCQ37K6Fwb3W4QlOSaKpaLaiwlOt6OmHN29V45ZsEkPw&_sgd%5Bsr%5D=1&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwiLCJwcmV2aW91c1BhZ2UiOiJwdWJsaWNhdGlvbkRldGFpbCJ9fQ">HALO Bulletin 08/14/2021. Issue No. 5.</a><strong> DOI: 10.13140/RG.2.2.16059.39201</strong></p> <p><a href="https://www.researchgate.net/publication/363429865_HALO_Bulletin_08272021_Issue_No_6?_sg%5B0%5D=UM9l9-QGqxamO8xpbQH-rxQBXO_6vh2-_ktW3W1pZfFURVyGDXIG8UOfj7s8i6kQNjaknOk49wivWDc.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sg%5B1%5D=wNUbCn_MUeB274aHT_ulVKvA5bePd-eJubBUdtCc7rPFFFvKhDVGsYRqS1kXPw2sn822YdNN-SYLiYqnJ4onbOvkIXU.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sg%5B2%5D=yTeKUqjvhIDTxNvFmR_TNqKCOXtm874fmh7Q8XMOH6A-rFpFvQ1Jd34Ofe2DQe99uFExIVgIzU5M7j0AuQ.LADy19cdhT56FtHo3vUEH2P4rIxdRsSlFxhRAh5tansi1SZTkfASFYuMMoZFbFNrXvLZvxJV9MIZ4mTcXnGdIg&_sgd%5Bsr%5D=1&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwifX0">HALO Bulletin 08/27/2021. Issue No. 6.</a> <strong>DOI</strong>: <strong>10.13140/RG.2.2.26125.72164</strong></p> <p><a href="https://www.researchgate.net/publication/363430068_HALO_Bulletin_09242021_Issue_No_7?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHVibGljYXRpb25EZXRhaWwiLCJwcmV2aW91c1BhZ2UiOiJwdWJsaWNhdGlvbkRldGFpbCJ9fQ">HALO Bulletin 09/24/2021. Issue No. 7</a>: <strong>DOI</strong>: <strong>10.13140/RG.2.2.19414.83529</strong></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Identifying antibody escape mutations using the evolutionary capacity of trVLP-ASV
GEO Series GSE302087. Mesocricetus auratus. 24 samples. Type: Expression profiling by high throughput sequencing.
Screening for SA55 escape mutations using a DMS library based on trVLP-ASV
GEO Series GSE302090. Mesocricetus auratus. 3 samples. Type: Expression profiling by high throughput sequencing.
rDNA 18S V9 ASV tables (SWARM) for Tara Pacific Expedition
<p>This data is the result of the second analysis of the V9 18S rDNA metabarcoding sequencing data from the <em>Tara</em> Pacific expedition. The analysis was conducted using cutadapt, vsearch, swarm and lulu. Please refer to the readme file for more details.</p>
ASVs, Taxonomy & Metadata
<p>Amplicon sequence variants (ASVs) and associated taxonomy called using DADA2 (v1.6.0) and Silva 16S database v132, along with anonymized specimen metadata.</p> <p>File list:</p> <ul> <li><strong>Specimen_Table.csv</strong>: contains specimen type, subject id, and study day for each specimen collected. Some specimens have data on antibiotics administered that day in the "abx_b" column.</li> <li><strong>seqtab.rds</strong>: ASVs for each specimen (rownames correlate to specimen_id2 in specimen table)</li> <li><strong>taxa.rds</strong>: taxonomic assignments for each ASV in seqtab.rds</li> </ul>
Tara Oceans (2009-2013) rDNA 16S V4V5 ASV table (dada2)
<p>This repository contains a rDNA 16S V4V5 ASV table (TARA-Oceans_16S-V4V5_dada2_table.tsv.gz) for <em>Tara</em> Oceans (2009-2013).</p> <p>In the file <strong>TARA-Oceans_16S-V4V5_dada2_table.tsv.gz</strong>, each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>TARA_XXXXXXXXXX</strong> = number of reads in each of the 1,134 <em>Tara</em> Oceans samples.</p> <p>Detailed information about DNA extraction, PCR amplification and Illumina sequencing of metabarcodes, as well as subsequent sequence data cleaning and taxonomic assignment can be found in (Alberti et al. 2017). Briefly, DNA samples were amplified by PCR targeting the hypervariable region V4V5 (primer pair: 15F-Y 5′- GTGYCAGCMGCCGCGGTAA-3′ and 926R 5′- CCGYCAATTYMTTTRAGTTT-3′ 16S primers; (Parada et al. 2016)) of the 16S rRNA marker gene followed by the high-throughput sequencing of the amplicons. The details of PCR mixes, thermocycling and sequencing conditions are provided in Alberti et al. (2017).</p> <p>Resulting paired-end reads were mixed-oriented meaning that both R1 and R2 files are composed by a mix of forward and reverse reads. Paired-end reads were trimmed to remove PCR primer sequences using Cutadapt v2.7 (Martin, 2011) and dispatched into four files, 2 files for the classical orientation (forward reads in R1 and reverse reads in R2) and 2 others for the other orientation (reverse reads in R1 and forward reads in R2). Paired-end reads without both primers were filtered out using the option --discard-untrimmed. Forward and reverse reads were trimmed at position 215 and 190 for R1 and R2 files respectively. Reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 (Callahan et al., 2016). For each run and read orientation, error rates were defined using the function learnErrors() and denoised using the dada() function with pool = TRUE before being merged using mergePairs() with default parameters. Mixed orientated reads from the same sample and sequencing replicates were summed together. Remaining chimeras were removed using the function removeBimeraDenovo(). Scripts producing the ASV table are publicly available here: https://gitlab.univ-nantes.fr/combi-ls2n/taradada.</p> <p>ASVs were taxonomically assigned using IDTAXA (50% confidence threshold) (Murali, Bhargava, and Wright 2018) with SILVA v138.</p> <p>Alberti, A., Poulain, J., Engelen, S., Labadie, K., Romac, S., Ferrera, I., Albini, G., Aury, J.-M., Belser, C., Bertrand, A., Cruaud, C., Da Silva, C., Dossat, C., Gavory, F., Gas, S., Guy, J., Haquelle, M., Jacoby, E., Jaillon, O., Lemainque, A., Pelletier, E., Samson, G., Wessner, M., Genoscope Technical Team, Bazire, P., Beluche, O., Bertrand, L., Besnard-Gonnet, M., Bordelais, I., Boutard, M., Dubois, M., Dumont, C., Ettedgui, E., Fernandez, P., Garcia, E., Aiach, N.G., Guerin, T., Hamon, C., Brun, E., Lebled, S., Lenoble, P., Louesse, C., Mahieu, E., Mairey, B., Martins, N., Megret, C., Milani, C., Muanga, J., Orvain, C., Payen, E., Perroud, P., Petit, E., Robert, D., Ronsin, M., Vacherie, B., Acinas, S.G., Royo-Llonch, M., Cornejo-Castillo, F.M., Logares, R., Fernández-Gómez, B., Bowler, C., Cochrane, G., Amid, C., Hoopen, P.T., De Vargas, C., Grimsley, N., Desgranges, E., Kandels-Lewis, S., Ogata, H., Poulton, N., Sieracki, M.E., Stepanauskas, R., Sullivan, M.B., Brum, J.R., Duhaime, M.B., Poulos, B.T., Hurwitz, B.L., Tara Oceans Consortium Coordinators, Acinas, S.G., Bork, P., Boss, E., Bowler, C., De Vargas, C., Follows, M., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Jaillon, O., Kandels-Lewis, S., Karp-Boss, L., Karsenti, E., Not, F., Ogata, H., Pesant, S., Raes, J., Sardet, C., Sieracki, M.E., Speich, S., Stemmann, L., Sullivan, M.B., Sunagawa, S., Wincker, P., Pesant, S., Karsenti, E., Wincker, P., 2017. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci Data 4, 170093. <a href="https://doi.org/10.1038/sdata.2017.93">https://doi.org/10.1038/sdata.2017.93</a></p> <p>Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. <a href="https://doi.org/10.1038/nmeth.3869">https://doi.org/10.1038/nmeth.3869</a></p> <p>Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 17, 10. <a href="https://doi.org/10.14806/ej.17.1.200">https://doi.org/10.14806/ej.17.1.200</a></p> <p>Murali, A., Bhargava, A., Wright, E.S., 2018. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140. <a href="https://doi.org/10.1186/s40168-018-0521-5">https://doi.org/10.1186/s40168-018-0521-5</a></p>
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