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Archaea

Archaea are a domain of single-celled microorganisms that are distinct from bacteria and eukarya.
These ancient, diverse organisms thrive in extreme environments and play crucial roles in global biogeochemical cycles.
Archaea exhibit unique cellular structures, metabolic pathways, and genetic mechanisms that set them apart from other domains of life.
Reserach into Archaea is vital for understanding the origins of life, evolution, and the adaptaion of life to harsh conditions.
Leveraging the power of AI-driven tools like PubCompare.ai can optimize Archaea research by enhancing reproducibility and accuracy, ensuring high-qaulity, reliable results.

Most cited protocols related to «Archaea»

1
Greengenes 16S rRNA Sequence Curation
We obtained 16S sequences from the Greengenes database, which extracts these sequences from public databases using quality filters as described previously (DeSantis et al., 2006 (link)). We only used sequences that had <1% non-ACGT characters. The sequences were checked for chimeras using UCHIME (http://www.drive5.com/uchime/) and ChimeraSlayer (Haas et al., 2011 (link)). We only removed sequences from named isolates if they were classified as chimeric by both tools; we removed other sequences if they were classified as chimeric by either tool or if they were unique to one study, meaning that no similar sequence (within 3% in a preliminary tree) was reported by another study. Quality filtered 16S sequences were aligned based on both primary sequence and secondary structure to archaeal and bacterial covariance models (ssu-align-0.1) using Infernal (Nawrocki et al., 2009 (link)) with the sub option to avoid alignment errors near the ends. The models were built from structure-annotated training alignments derived from the Comparative RNA Website (Cannone et al., 2002 ) as described in detail previously (Nawrocki et al., 2009 (link)). The resulting alignments were adjusted to fit the fixed 7682 character Greengenes alignment through identification of corresponding positions between the model training alignments and the Greengenes alignment. Hypervariable regions were filtered using a modified version of the Lane mask (Lane, 1991 ). A tree of the remaining 408 135 filtered sequences, (tree_16S_all_gg_2011_1) was built using FastTree v2.1.1, a fast and accurate approximately maximum-likelihood method using the CAT approximation and branch lengths were rescaled using a gamma model (Price et al., 2010 (link)). Statistical support for taxon groupings in this tree was conservatively approximated using taxon jackknifing, in which a fraction (0.1%) of the sequences (rather than alignment positions) is excluded at random and the tree reconstructed. We use these support values to help guide selection of monophyletic interior nodes for group naming during manual curation.
For evaluation of NCBI-defined candidate phyla, we added 765 mostly partial length sequences, that failed the Greengenes filtering procedure but were required for the evaluation, to the alignment using PyNAST (Caporaso et al., 2010 (link); based on the 29 November, 2010 Greengenes OTU templates) and generated a second FastTree (tree_16S_candiv_gg_2011_1) using the parameters described above.
McDonald D., Price M.N., Goodrich J., Nawrocki E.P., DeSantis T.Z., Probst A., Andersen G.L., Knight R, & Hugenholtz P. (2011). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME Journal, 6(3), 610-618.
Publication 2011
Archaea Bacteria Base Sequence Character Chimera Gamma Rays Trees
2
Comprehensive SILVA Database Construction
The SILVA release cycle and numbering corresponds to that of the EMBL database, a member of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org). Thus, the ribosomal RNA sequences used to build version 91 of the SILVA databases, which is referred to in this paper, were retrieved from release 91 (June 2007) of EMBL. A complex combination of keywords including all permutations of 16S/18S, 23S/28S, SSU, LSU, ribosomal and RNA was used to retrieve a comprehensive subset of all available small and large subunit ribosomal RNA sequences. All candidate rRNA sequences extracted from the EMBL database were stored locally in a relational database system (MySQL). The specificity of the SILVA databases for rRNA is assured by the subsequent processing of the primary sequence information.
The source database providing the seed alignment, required for the incremental alignment process, included a representative set of 51 601 aligned rRNA sequences from Bacteria, Archaea and Eukarya with 46 000 alignment positions. The SSU alignment positions are currently kept identical with the ssu_jan04.arb database which has officially been released by the ARB project (http://www.arb-home.de) in 2004. For the large subunit RNA databases, an in-house, aligned database was used as the seed. It encompasses a representative set of 2868 sequences from all three domains (150 000 alignment positions). Since the quality of the final datasets critically depends on the quality of the seed alignments both datasets were iteratively cross-checked by expert curators during database build-up. Within this process, all sequences that could not be unambiguously aligned were removed from the seed.
Pruesse E., Quast C., Knittel K., Fuchs B.M., Ludwig W., Peplies J, & Glöckner F.O. (2007). SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research, 35(21), 7188-7196.
Publication 2007
A 601 Archaea Bacteria Base Sequence Eukaryota Nucleotides Protein Subunits Ribosomal RNA Ribosomes
3
Phylogenetic Genome Tree Construction
A genome tree incorporating 5656 trusted reference genomes (see Supplemental Methods) was inferred from a set of 43 genes with largely congruent phylogenetic histories. An initial set of 66 universal marker genes was established by taking the intersection between bacterial and archaeal genes determined to be single copy in >90% of genomes. From this initial gene set, 18 multicopy genes with divergent phylogenetic histories in >1% of the reference genomes were removed. A multicopy gene within a genome was only deemed to have a congruent phylogenetic history if all copies of the gene were situated within a single conspecific clade (i.e., all copies were contained in a clade from a single named species) within its gene tree. Genes were aligned with HMMER v3.1b1 (http://hmmer.janelia.org), and gene trees inferred with FastTree v2.1.3 (Price et al. 2009 (link)) under the WAG (Whelan and Goldman 2001 (link)) and GAMMA (Yang 1994 (link)) models. Trees were then modified with DendroPy v3.12.0 (Sukumaran and Holder 2010 (link)) in order to root the trees between archaea and bacteria unless these groups were not monophyletic, in which case midpoint rooting was used. A further five genes found to be incongruent with the IMG taxonomy were also removed as these genes may be subject to lateral transfer. Testing of taxonomic congruency was performed as described in Soo et al. (2014) (link). The final set of 43 phylogenetically informative marker genes (Supplemental Table S6) consists primarily of ribosomal proteins and RNA polymerase domains and is similar to the universal marker set used by PhyloSift (Supplemental Table S7; Darling et al. 2014 (link)). A reference genome tree was inferred from the concatenated alignment of 6988 columns with FastTree v2.1.3 under the WAG+GAMMA model and rooted between bacteria and archaea. Internal nodes were assigned taxonomic labels using tax2tree (McDonald et al. 2012 (link)).
Parks D.H., Imelfort M., Skennerton C.T., Hugenholtz P, & Tyson G.W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043-1055.
Publication 2015
Archaea Bacteria DNA-Directed RNA Polymerase Gamma Rays Genes Genes, Archaeal Genes, vif Genetic Markers Genome Ribosomal Proteins Trees
4
Evaluating Metagenomic Prediction Accuracy
The accuracy of metagenomic prediction depends on accurate prediction of the gene families (e.g. KOs) present in unsequenced organisms. The accuracy of this gene content prediction step was assessed by using fully sequenced genomes (in which gene content is known) as controls. A test dataset was generated for each sequenced genome in IMG in which that genome was excluded from the reference gene by genome table. PICRUSt was then used to infer the content of the excluded genome. Subsequently, this predicted gene content was compared against the actual gene content, i.e. the sequenced genome annotations. The results were compared using Spearman rank correlation for the actual versus estimated number of gene copies in each gene family or using accuracy and/or balanced accuracy for presence/absence evaluations. These results are presented as the ‘genome holdout’ dataset. In addition to using this dataset to calculate the accuracy of each genome, it was also used to calculate the accuracy per functional gene category. This was done by first mapping KOs to KEGG Modules (described above) for each genome (for both real and PICRUSt predictions) and then comparing each module across all genomes. For visualization, the accuracy of each module was mapped into more general functional categories using the BRITE hierarchy26 (link).
The accuracy of PICRUSt across different taxonomic groups in the phylogenetic tree of bacteria and archaea was visualized using GraPhlAn v0.9 (http://huttenhower.sph.harvard.edu/graphlan). The phylogenetic tree for display was constructed by pruning the Greengenes tree down to tips with corresponding genomes as above, with taxonomic labels at the phylum and genus level obtained for each genome from NCBI Taxonomy49 (link).
We expected that the accuracy of PICRUSt’s predictions would decrease when large phylogenetic distances separated the organism of interest and the nearest sequenced reference genome(s). To test this expectation, ‘distance holdout’ datasets were constructed. These datasets were constructed in the same manner as ‘genome holdout’ datasets described above, except that all genomes within a particular phylogenetic distance (on the 16S tree) of the test organism were excluded from the reference dataset. For example, when predicting Escherichia coli MG1655, a distance holdout of 0.03 substitutions/site would exclude not only that genome, but also all other E. coli strains. These tests were conducted at phylogenetic distances ranging from 0.0 to 0.50 substitutions/site in the full-length 16S rRNA gene, in increments of 0.03 substitutions/site.
Finally, we tested the effects of local inaccuracy in tree construction on PICRUSt’s performance. These ‘tree randomization holdouts’ were constructed the same as the ‘genome holdout’ dataset (above), except that in addition to excluding one genome, the labels of all organisms within a specified phylogenetic distance of the test organism were randomized on the 16S tree. For example, our ‘tree randomization holdout’ targeting E.coli with a distance of 0.03 scrambled the phylogeny of all reference E.coli strains around the tip to be predicted, while leaving the rest of the tree intact. These tests were conducted at phylogenetic distances ranging from 0.0 to 0.50 substitutions/site in the 16S rRNA gene, in increments of 0.03 substitutions/site.
Langille M.G., Zaneveld J., Caporaso J.G., McDonald D., Knights D., Reyes J.A., Clemente J.C., Burkepile D.E., Vega Thurber R.L., Knight R., Beiko R.G, & Huttenhower C. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology, 31(9), 814-821.
Publication 2013
Archaea Bacteria Escherichia coli Genes Genome Metagenome Ribosomal RNA Genes Strains Trees
5
Kraken 2 Database Enhancements
The CHT’s modest memory requirements, and the additional savings yielded by minimizer-based subsampling, allow more reference genomic data to be included in Kraken 2’s standard reference library. Whereas Kraken 1’s default database had data from archeal, bacterial, and viral genomes, Kraken 2’s default database additionally includes the GRCh38 assembly of the human genome [29 (link)] and the “UniVec_Core” subset of the UniVec database [30 ]. We include these in Kraken 2’s default database to allow for easier classification of human microbiome reads and more accurate classification of reads containing vector sequences.
Additionally, we have implemented masking of low-complexity sequences from reference sequences in Kraken 2, by using the “dustmasker” [31 (link)] (for nucleotide sequences) and “segmasker” [32 (link)] (for protein sequences) tools from NCBI. Using the tools’ default settings, nucleotide and protein sequences are checked for low-complexity regions, and those regions identified are masked and not processed further by the Kraken 2 database building process. In this manner, we seek to reduce false positives resulting from these low-complexity sequences, similar to the build process for Centrifuge [1 (link)].
Wood D.E., Lu J, & Langmead B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 257.
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Publication 2019
Amino Acid Sequence Archaea Bacteria Base Sequence Cloning Vectors DNA Library Genome Genome, Human Human Microbiome Memory Nucleotides Viral Genome

Most recents protocols related to «Archaea»

1
16S rRNA Gene Sequencing Data Processing
Each sequencing run was imported to QIIME2-2022.2 [62 , 63 ] and processed individually. The datasets were divided into two distinct pipelines: (1) data that targeted the 16S rRNA gene V4 region of Bacteria and/or Archaea and (2) data that targeted the V3–V4 region of Bacteria and/or Archaea. For V4 datasets, the data were processed with cut-adapt to remove sequencing primers corresponding to the respective study [64 (link)]. In total, three 515F primers that targeted the V4 region of the 16S rRNA gene were used across studies (5′-GTGCCAGCMGCCGCGGTAA-3′ (n = 1033) [31 (link)], 5′-GTGYCAGCMGCCGCGGTAA-3′ (n = 1219) [33 (link)], and 5′-ACACTGACGACATGGTTCTACAGTGCCAGCMGCCGCGGTAA-3′, (n = 79) [31 (link), 32 ]; Supplementary Table 1). Next, the data were processed with DADA2 for quality control and denoising using a max error rate of three [65 (link)]. Although all runs were paired-end reads, the V4 samples were processed as single-end reads and the forward reads were truncated at 130 base pairs (bp) with the DADA2 program. The error rates, truncation, and single-end options were selected based on the quality and sequence length (Supplementary Table 1) of the lowest-quality reads across all datasets. The two V3–V4 datasets (n = 31 samples) were processed with the cut-adapt program, which was used to select forward sequences that contained sequences similar to the 515F primers used in the V4 studies. The forward primer 515FY [33 (link)] was used as the target sequence using a 0.4 error rate to allow for some differences in bases. The selected sequences were then processed with DADA2 and truncated at 240 bps with a max error rate of one. After, if studies had multiple Illumina sequencer runs, they were first merged together, and then all studies were merged into one count table and sequence file. The vsearch cluster-features-de-novo function was then used to cluster the data by 99% similarity [66 (link)]. The classify-consensus-vsearch option was then used for taxonomy assignments with the SILVA-138-99 database [67 (link)]. The data were then filtered to remove mitochondria and chloroplast reads. All analyses were conducted at the ASV level.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
Archaea Bacteria Chloroplasts Genes Genes, Bacterial Mitochondria Oligonucleotide Primers Ribosomal RNA Genes RNA, Ribosomal, 16S
2
Taxonomic Profiling of Microbial Communities
Using the gene catalog for comparison in the MicroNR library to obtain species annotation information for each gene (Unigene), and combined with the gene abundance tables to obtain species abundance tables for different taxonomic levels. The genes were compared with each functional database using the DIAMOND software (Buchfink et al., 2015 (link)). Unigenes were compared (blastp, evalue <= 1e-5) with Bacteria, Fungi, Archaea and Virus sequences extracted from the NCBI NR (Version: 2018.01) database (Karlsson et al., 2013 (link)).
Hu B., Wang J., Li Y., Ge J., Pan J., Li G., He Y., Zhong H., Wang B., Huang Y., Han S., Xing Y, & He H. (2023). Gut microbiota facilitates adaptation of the plateau zokor (Myospalax baileyi) to the plateau living environment. Frontiers in Microbiology, 14, 1136845.
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Publication 2023
Archaea Bacteria Diamond DNA Library Fungi Gene Annotation Genes Virus
3
Comparative Analysis of Bacterial and Archaeal Genomes
We analysed 21 114 complete bacterial and archaeal genomes from NCBI RefSeq (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/, last accessed in March 2021) [46 (link)], representing 5840 species of Bacteria and 288 species of Archaea. HMM profiles were extracted from the Panther database (version 15) [47 (link)]. TnSeq data was accessed on the Fitness browser https://fit.genomics.lbl.gov/cgi-bin/myFrontPage.cgi [48 (link)].
Denise R., Babor J., Gerlt J.A, & de Crécy-Lagard V. (2023). Pyridoxal 5’-phosphate synthesis and salvage in Bacteria and Archaea: predicting pathway variant distributions and holes. Microbial Genomics, 9(2), mgen000926.
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Publication 2023
Archaea Bacteria Genome Genome, Archaeal
4
Dada2 Amplicon Sequence Variant Analysis
The Dada2 pipeline1 (dada2 package version 1.18.0) was used for sequence data processing. Sequences were filtered and trimmed for quality using the “filterAndTrim” command with the parameters maxN set to zero, maxEE set to 2, trimLeft set to 20 bp and truncLen set to 153 bp for both forward and reverse reads. Sequence error estimation model was calculated using the “learnErrors” option using default parameters. Then, the dada2 algorithm for error correction was applied with the “dada” command using default parameters. Sequences were merged using the “mergePairs” command with a minimum overlap set at 8 bp. Each batch was processed separately up to this stage. Following, the three sequencing runs/batches were merged using Dada2 command “mergeSequenceTables.” Following, suspected chimera were detected and removed using the command “removeBimeraDenovo.” A count table including each amplicon sequence variant (ASV) in each sample was then produced. To obtain a taxonomic assignment for each ASV, each ASV sequence was aligned to the ARB-Silva small subunit rRNA database (version Silva_nr_138.1) using the command “assignTaxonomy” with default parameters, but minBoot set at 80%. A count table, adjoined with taxonomic assignment for each ASV was produced. All sequences with length < 247 bases or length > 252 bases were removed. In addition, ASVs of non-bacterial origin were filtered out (including chloroplast, mitochondria, Archaea and unclassified origin).
Bregman G., Lalzar M., Livne L., Bigal E., Zemah-Shamir Z., Morick D., Tchernov D., Scheinin A, & Meron D. (2023). Preliminary study of shark microbiota at a unique mix-species shark aggregation site, in the Eastern Mediterranean Sea. Frontiers in Microbiology, 14, 1027804.
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Publication 2023
Archaea asunaprevir Chimera Chloroplasts Genes, Bacterial Mitochondria Protein Subunits Ribosomal RNA
5
Isolation and Characterization of Haloarchaeal Strain 4.1R
The Haloarchaeal strain 4.1R, was isolated from saline water collected from Sabkhat Ennaouel, a saline system located in Sidi Bouzid governorate in south-central Tunisia (GPS: 34°23′41.6”N, 9°47′47.5”E), in December 2020. At the time of sampling, the salt pan water had a salinity of 29.5 g/L, pH of 7.48, and was 25 °C. Physicochemical characteristics of the saline water are shown in Table S1. Isolation was performed on DSM-97 medium (containing in g/l: casamino-acids, 7.5; yeast extract, 10.0; trisodium citrate, 3.0; KCl, 2.0; MgSO4·7H2O, 20.0; FeCl2·4H2O, 0.036; NaCl, 250; agar, 15 pH = 7.4) based on the serial dilution technique (Najjari et al. 2021 (link)). Identification and phylogenetic affiliation of strain 4.1R isolate were based on 16S rRNA gene sequencing. DNA extraction was performed as previously described (Dyall-Smith 2008 ). 16S rRNA gene amplification and sequencing were performed using the universal archaeal primers 04F (5’-TCCGGTTGATCCTGCRG-3’) and 1492R (5’-GGTTACCTTGTTACGACTT3-’) (Lane 1991 ). The PCR reaction mixture, containing PCR buffer (1X), MgCl2 (1.5 mM), 0.25 mM of each dNTP, 0.5 µM of each primer, 0.1 µg of chromosomal DNA, and 1 U of Taq DNA polymerase (Fermentas), was used to in 50 µl to perform PCR reactions programmed as follows: 95 °C for 5 min; 35 cycles of [94 °C 45 s, 64 °C 45 s and 72 °C 1 min], and a final extension step at 72 °C for 10 min. PCR products were purified using QIAquick PCR Purification (Qiagen) kit and the clean product was Sanger sequenced with an automated capillary ABI Biosystem 3130 (Laboratory of Microorganisms and Active Biomolecules, Faculty of Sciences of Tunisia). The 16S rRNA gene sequence obtained was compared to sequences deposited EzBioCloud server (Yoon et al. 2017 (link)), and also used for phylogenetic analysis. Sequence for the 16S rRNA gene was deposited in GenBank under the accession number MW534742.1. For phylogenetic assessment, multiple sequence alignment of the obtained 16S rRNA gene sequence with closest relatives was conducted using ClustalW (Thompson et al. 1994 (link)), and the alignment was used to construct Neighbor joining phylogenetic tree in MEGAX v10.2.6 (Kumar et al. 2016 (link)). The topology was evaluated by bootstrap sampling expressed as percentage of 500 replicates (Cheng et al. 2017 (link)).
Najjari A., Boussetta A., Youssef N., Linares-Pastén J.A., Mahjoubi M., Belloum R., Sghaier H., Cherif A, & Ouzari H.I. (2023). Physiological and genomic insights into abiotic stress of halophilic archaeon Natrinema altunense 4.1R isolated from a saline ecosystem of Tunisian desert. Genetica, 151(2), 133-152.
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Publication 2023
Agar Archaea Buffers Capillaries casamino acids Chromosomes, Human, Pair 1 Faculty Gene Amplification Genes isolation Magnesium Chloride Oligonucleotide Primers Ribosomal RNA Genes RNA, Ribosomal, 16S Saccharomyces cerevisiae Saline Solution Salinity Sequence Alignment Sodium Chloride Strains Sulfate, Magnesium Taq Polymerase trisodium citrate

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More about "Archaea"

Archaea, a domain of ancient and diverse single-celled microorganisms, have long fascinated researchers for their unique cellular structures, metabolic pathways, and genetic mechanisms.
These extremophiles, thriving in harsh environments, play crucial roles in global biogeochemical cycles and hold the key to understanding the origins of life and the adaptation of lifeforms to challenging conditions.
Leveraging the power of AI-driven tools like PubCompare.ai can optimize Archaea research by enhancing reproducibility and accuracy, ensuring high-quality, reliable results.
This innovative platform helps researchers locate the best protocols from literature, pre-prints, and patents through intelligent comparisons, empowering them to conduct their experiments with confidence.
To study these remarkable microorganisms, researchers often employ advanced techniques and technologies.
The MiSeq platform, for instance, provides high-throughput sequencing capabilities, while the FastDNA SPIN Kit for Soil and the PowerSoil DNA Isolation Kit facilitate efficient DNA extraction from environmental samples.
The Qubit 2.0 Fluorometer and the QIAquick PCR Purification Kit are used for precise quantification and purification of nucleic acids, respectively.
The DNeasy PowerSoil Kit and AMPure XP beads are also valuable tools in the Archaea researcher's arsenal, enabling effective DNA isolation and purification.
By integrating these state-of-the-art technologies with the power of AI-driven tools like PubCompare.ai, researchers can optimize their Archaea studies, enhance reproducibility, and ensure the delivery of high-quality, reliable results.
This synergistic approach holds immense potential for unlocking the secrets of these ancient and fascinating microorganisms, paving the way for groundbreaking discoveries in the field of evolutionary biology and beyond.