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> Anatomy > Cell Component > Ribosomes

Ribosomes

Ribosomes are cellular organelles responsible for the synthesis of proteins, a fundamental process in all living organisms.
These complex macromolecular structures are composed of ribosomal RNA and proteins, and serve as the site of translation, where the genetic code is decoded and amino acids are assembled into polypeptides.
Ribosomes can be found in the cytoplasm or associated with the endoplasmic reticulum, and their structure and function has been extensively studied to unravel the mechanics of protein synthesis.
Understanding the role of ribosomes is crucial for advancements in fields such as cell biology, genetics, and pharmacology.
Furthering ribosome research with tools like PubCompare.ai can lead to improved experimental protocols and enhanced reproducibility, empowering scientists to unlock new discoveries.

Most cited protocols related to «Ribosomes»

1
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
2
Benchmarking Taxonomy Classification Pipelines
Representative sequences for all analyses (mock community, cross-validated, and novel taxa) were classified taxonomically using the following taxonomy classifiers and setting sweeps:

q2-feature-classifier multinomial naive Bayes classifier. Varied k-mer length in {4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 32} and confidence threshold in {0, 0.5, 0.7, 0.9, 0.92, 0.94, 0.96, 0.98, 1}.

BLAST+ [9 (link)] local sequence alignment followed by consensus taxonomy classification implemented in q2-feature-classifier. Varied max accepts from 1 to 100; percent identity from 0.80 to 0.99; and minimum consensus from 0.51 to 0.99. See description below.

VSEARCH [10 (link)] global sequence alignment followed by consensus taxonomy classification implemented in q2-feature-classifier. Varied max accepts from 1 to 100; percent identity from 0.80 to 0.99; and minimum consensus from 0.51 to 0.99. See description below.

Ribosomal Database Project (RDP) naïve Bayesian classifier [11 (link)] (QIIME1 wrapper), with confidence thresholds between 0.0 and 1.0 in steps of 0.1.

Legacy BLAST [15 (link)] (QIIME1 wrapper) varying e-value thresholds from 1e-9 to 1000.

SortMeRNA [13 (link)] (QIIME1 wrapper) varying minimum consensus fraction from 0.51 to 0.99; similarity from 0.8 to 0.9; max accepts from 1 to 10; and coverage from 0.8 to 0.9.

UCLUST [12 (link)] (QIIME1 wrapper) varying minimum consensus fraction from 0.51 to 0.99; similarity from 0.8 to 0.9; and max accepts from 1 to 10.

With the exception of the UCLUST classifier, we have only benchmarked the performance of open-source, free, marker-gene-agnostic classifiers, i.e., those that can be trained/aligned on a reference database of any marker gene. Hence, we excluded classifiers that can only assign taxonomy to a particular marker gene (e.g., only bacterial 16S rRNA genes) and those that rely on specialized or unavailable reference databases and cannot be trained on other databases, effectively restricting their use for other marker genes and custom databases.
Classification of bacterial/archaeal 16S rRNA gene sequences was made using the Greengenes (13_8 release) [5 (link)] reference sequence database preclustered at 99% ID, with amplicons for the domain of interest extracted using primers 27F/1492R [27 (link)], 515F/806R [28 (link)], or 27F/534R [29 (link)] with q2-feature-classifier’s extract_reads method. Classification of fungal ITS sequences was made using the UNITE database (version 7.1 QIIME developer release) [31 (link)] preclustered at 99% ID. For the cross validation and novel taxon classification tests, we prefiltered to remove sequences with incomplete or ambiguous taxonomies (containing the substrings ‘unknown,’ ‘unidentified,’ or ‘_sp’ or terminating at any level with ‘__’).
The notebooks detailing taxonomy classification sweeps of mock communities are available at https://github.com/caporaso-lab/tax-credit-data/tree/0.1.0/ipynb/mock-community. Cross-validated read classification sweeps are available at https://github.com/caporaso-lab/tax-credit-data/blob/0.1.0/ipynb/cross-validated/taxonomy-assignment.ipynb. Novel taxon classification sweeps are available at https://github.com/caporaso-lab/tax-credit-data/blob/0.1.0/ipynb/novel-taxa/taxonomy-assignment.ipynb.
Bokulich N.A., Kaehler B.D., Rideout J.R., Dillon M., Bolyen E., Knight R., Huttley G.A, & Gregory Caporaso J. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6, 90.
Publication 2018
Archaea Bacteria Credit Assignment Genes Genes, Archaeal Genes, Bacterial Genetic Markers Oligonucleotide Primers Ribosomes RNA, Ribosomal, 16S Sequence Alignment Sequence Analysis Trees Unite resin
3
Automated RNA Integrity Assessment
The aim of this step is to define and extract informative features from the electropherograms. For this purpose, each electropherogram is divided into the following nine adjacent segments covering the entire electropherogram: a pre-region, a marker-region, a 5S-region, a fast-region, an 18S-region, an inter-region, a 28S-region, a precursor-region and a post-region (Fig. 8). The subdivision is based on the peak table and the fragment table as computed by Agilent's Expert Software.
Each of these segments can then be considered separately yielding a number of local features. Several iterations of generating and evaluating features showed that it is sufficient to extract a set of specific features reflecting statistical properties, such as average and maximum heights, areas and their ratios as well as signal-to-noise ratios to cover for the information contained in the different regions of an electropherogram.
In addition, several global features were extracted, i.e. features that span several segments. Among these, the average and maximum height, areas and their ratios, total RNA ratio and the 28S area ratio are the most important features. Both have been used as criteria for RNA integrity assessment in the past. The total RNA ratio is the ratio of the area of the ribosomal bands to the total area of the electropherogram, whereas 28S area ratio measures only the fraction of 28S-fragment. This set of features extracted from the electropherograms and their manually assigned RNA integrity categories form the knowledge base for the following steps of the algorithm. Additional file 2 contains a complete description of the set of features.
Schroeder A., Mueller O., Stocker S., Salowsky R., Leiber M., Gassmann M., Lightfoot S., Menzel W., Granzow M, & Ragg T. (2006). The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology, 7, 3.
Publication 2006
Ribosomes
4
Taxonomic Analysis of Microbiome Datasets
454 SFF files and barcode dictionaries were downloaded and run through the same pipeline. Conservative Operational Taxonomic Units (OTUs) were constructed by pooling together the sequences from all samples, then clustered using DNAclust10 (link) with default parameters (99% identity clusters) to ensure that the definition of an OTU is consistent across all samples. To obtain taxonomic identification, a representative sequence from each OTU was aligned to Ribosomal Database (rdp.cme.msu.edu, release 10.4) using Blastn with long word length (-W 100) in order to only detect nearly-identical sequences. Sequences without a nearly-identical match to RDP were marked as having “no match” and assigned an OTU identifier. The resulting data was organized into a collection of tables at many different taxonomic levels containing each taxonomic group as a row and each sample as a column. These tables formed the substrate for the statistical analyses described. This process was performed for the human microbiome project and the human lung microbiome datasets. After removing OTUs present in less than 5 samples, the HMP dataset consisted of 23,685 OTUs, whereas the human lung microbiome consisted of 2,365 OTUs. We explored the effect of ambiguosly assigned reads (sequences that have good matches to two or more OTUs) by running DNAclust in ‘non-overlapping’ mode – a mode that ensures high separation between clusters and eliminates ambiguous reads. We also ran the HMP dataset using this option and confirmed all results shown in the paper (Supplementary Figs. 14A–B, 15A–B). We provide further discussion of the ambiguity of mapping reads to OTUs in the supplementary material.
Paulson J.N., Stine O.C., Bravo H.C, & Pop M. (2013). Robust methods for differential abundance analysis in marker gene surveys. Nature methods, 10(12), 1200-1202.
Publication 2013
Figs Human Microbiome Lung Ribosomes
5
Taxonomic Analysis of Microbiome Datasets
454 SFF files and barcode dictionaries were downloaded and run through the same pipeline. Conservative Operational Taxonomic Units (OTUs) were constructed by pooling together the sequences from all samples, then clustered using DNAclust10 (link) with default parameters (99% identity clusters) to ensure that the definition of an OTU is consistent across all samples. To obtain taxonomic identification, a representative sequence from each OTU was aligned to Ribosomal Database (rdp.cme.msu.edu, release 10.4) using Blastn with long word length (-W 100) in order to only detect nearly-identical sequences. Sequences without a nearly-identical match to RDP were marked as having “no match” and assigned an OTU identifier. The resulting data was organized into a collection of tables at many different taxonomic levels containing each taxonomic group as a row and each sample as a column. These tables formed the substrate for the statistical analyses described. This process was performed for the human microbiome project and the human lung microbiome datasets. After removing OTUs present in less than 5 samples, the HMP dataset consisted of 23,685 OTUs, whereas the human lung microbiome consisted of 2,365 OTUs. We explored the effect of ambiguosly assigned reads (sequences that have good matches to two or more OTUs) by running DNAclust in ‘non-overlapping’ mode – a mode that ensures high separation between clusters and eliminates ambiguous reads. We also ran the HMP dataset using this option and confirmed all results shown in the paper (Supplementary Figs. 14A–B, 15A–B). We provide further discussion of the ambiguity of mapping reads to OTUs in the supplementary material.
Paulson J.N., Stine O.C., Bravo H.C, & Pop M. (2013). Robust methods for differential abundance analysis in marker gene surveys. Nature methods, 10(12), 1200-1202.
Publication 2013
Figs Human Microbiome Lung Ribosomes

Most recents protocols related to «Ribosomes»

1
Transgenic Zebrafish Line Generation
Not available on PMC !

Example 1

Adult fish were raised and maintained as described in [28] and in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals by University of Southern California, where the protocol was approved by the Institutional Animal Care and Use Committee (IACUC) (Permit Number: 12007 USC). Transgenic FlipTrap Gt(desm-citrine)ct122a/+ line was obtained from a previously described screen in the lab [23], Tg(kdrl:eGFP)s843 line [24] was provided by the Stainier lab, and Tg(ubiq:membrane-Cerulean-2a-H2B-tdTomato) line was generated by injecting a construct containing tol2 transposable elements flanking the ubiquitin promoter, coding sequence for membrane localized cerulean, a short sequence encoding the ribosome-skipping peptide of Thosea asigna virus (2a) followed by H2B-tdTomato. Upon crossing appropriate adult lines, the embryos obtained were raised in Egg Water (about 60 μg/ml of Instant Ocean and about 75 μg/ml of CaSO4 in Milli-Q water) at about 28.5° C. with addition of about 0.003% (w/v) 1-phenyl-2-thiourea (PTU) about 18 hpf to reduce pigment formation [28].

US11869176B2. Hyperspectral imaging system (2024-01-09). UNIVERSITY OF SOUTHERN CALIFORNIA [US]. Inventors: Wen Shi [US], Eun Sang Koo [US], Scott E. Fraser [US], Francesco Cutrale [US].
Patent 2024
Adult Animals, Laboratory Animals, Transgenic DNA Transposable Elements Embryo Fishes Institutional Animal Care and Use Committees LINE-1 Elements Open Reading Frames Peptides Phenylthiourea Pigmentation Ribosomes tdTomato Tissue, Membrane Ubiquitin Virus Zebrafish
2
Retroviral Vectors for Gene Modification
Not available on PMC !

Example 2

Another example of a suitable vector is a retroviral vector. Retroviruses are RNA viruses that contain an RNA genome. The gag, pol, and env genes are flanked by long terminal repeat (LTR) sequences (or their corresponding proteins). The 5′ and 3′ LTR sequences promote transcription and polyadenylation of mRNAs.

The retroviral vector may provide a regulable transactivating element, an internal ribosome reentry site (IRES), a selection marker, and a target heterologous gene operated by a regulable promoter.

Alternatively, multiple sequences may be expressed under the control of multiple promoters. Finally, the retroviral vector may contain cis-acting sequences necessary for reverse transcription and integration. Upon infection, the RNA is reverse transcribed to DNA that integrates efficiently into the host genome. The recombinant retrovirus of this invention is genetically modified in such a way that some of the retroviral, infectious genes of the native virus have been removed and in certain instances replaced instead with a target nucleic acid sequence for genetic modification of the cell. The sequences may be exogenous DNA or RNA, in its natural or altered form.

US11859168B2. Electroporation, developmentally-activated cells, pluripotent-like cells, cell reprogramming and regenerative medicine (2024-01-02). None [US]. Inventors: Christopher B. Reid [US], Melissa Braga [US], Lilian N. Santamaria [US], Ashley N. Wickrema [US], Marinne D. Wickrema [US], Anthony Hao Dinh [US], Yaman Eksioglu [US], Mat Hoang Ho [US].
Patent 2024
Base Sequence Cells Cloning Vectors Electroporation Gene Editing Genes Genes, env Genes, Viral Genome Infection Internal Ribosome Entry Sites Long Terminal Repeat Polyadenylation Proteins Retroviridae Retroviridae Infections Reverse Transcription Ribosomes RNA, Messenger RNA Viruses Transcription, Genetic
3
Transcriptional Analysis of RELA Mutant CD4+ T Cells
CD4+ T cells transduced with either pTRIP-SFFV-GFP or pTRIP-SFFV-GFP-RELA or pTRIP-SFFV-GFP-RELA K5R and RNA was isolated 4 d after transduction. The analysis was performed by GenoSplice. Analysis of sequencing data quality, reads repartition (e.g., for potential ribosomal contamination), inner distance size estimation, genebody coverage, and strand-specificity of the library were performed using FastQC v0.11.2, Picard-Tools v1.119, Samtools v1.0, and RSeQC v2.3.9. Reads were mapped using STAR v2.7.5a on the human hg38 genome assembly and read count was performed using featureCount from SubRead v1.5.0. Gene expression was estimated as described previously (Paillet et al., 2021 (link)) using Human FAST DB v2022_1 annotations. Only genes expressed in at least one of the two compared conditions were analyzed further. Genes were considered as expressed if their FPKM value was greater than the FPKM of 96% of the intergenic regions (background). Analysis at the gene level was performed using DESeq2 using experiment ID in the DESeq2 GLM model. Genes were considered differentially expressed for fold changes ≥1.5 and P values ≤0.05. Pathway analyses were performed using WebGestalt v0.4.4 merging results from upregulated and downregulated genes only, as well as all regulated genes. Pathways and networks were considered significant with P values ≤0.05. The results of this analysis were compared to those from Zhao et al. (2015) (link) corresponding to the GSE68329 GEO dataset ID. Regulated genes from Zhao et al. (2015) (link) were retrieved using GEO2R using adjusted P value ≤0.05 and fold change ≥ 1.5. Gene expression data have been deposited at GEO (accession no. GSE182647).
Jeremiah N., Ferran H., Antoniadou K., De Azevedo K., Nikolic J., Maurin M., Benaroch P, & Manel N. (2023). RELA tunes innate-like interferon I/III responses in human T cells. The Journal of Experimental Medicine, 220(5), e20220666.
Publication 2023
CD4 Positive T Lymphocytes DNA Library Gene Expression Genes Genome, Human Homo sapiens Intergenic Region RELA protein, human Ribosomes Sequence Analysis Spleen Focus-Forming Virus
4
Phylogenetic Analysis of Streptococcus ASVs
To examine relatedness between ASV sequences affiliated with the genus Streptococcus and others, including pathogenic, Streptococcus spp., a phylogenetic tree was calculated. This was done by alignment of all ASV sequences affiliated with the genus Streptococcus found in this study to the Arb-Silva small subunit ribosomal RNA database. The ACT engine of arb-silva web tools3 was used for alignment, and the 5 closest relatives from the databases for each ASV was curated along with ASV sequences. The alignment of ASV and relative sequences were used to calculate a phylogenetic tree (FastTree algorithm) in the ACT engine. The tree was visualized in FigTree.
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.
Publication 2023
Pathogenicity Protein Subunits Ribosomes Sequence Alignment Streptococcus Trees
5
Single-cell RNA-seq data preprocessing
Cell Ranger pipeline (V.3.0.0) was used for demultiplexing, mapping to the human genome (GRCh38) and counting UMIs for individual cells. Cells were filtered out if they had less than 500 unique molecular identifiers (UMIs), and if more than 20 or 15% of their transcripts were ribosomal or mitochondrial genes, respectively. In each placenta, cells from the two infection conditions with more abundant yields were down‐sampled in order to achieve roughly equivalent cell numbers between conditions for analysis. We normalized the expression to transcripts per median (TPM) and log transformed the expression. Each placenta was analyzed separately.
Kuperwaser F., Avital G., Vaz M.J., Noble K.N., Dammann A.N., Randis T.M., Aronoff D.M., Ratner A.J, & Yanai I. (2023). Host inflammatory dynamics reveal placental immune modulation by Group B Streptococcus during pregnancy. Molecular Systems Biology, 19(3), e11021.
Publication 2023
Cells Genes, Mitochondrial Homo sapiens Infection Placenta Ribosomes

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1
Trizol reagent by Thermo Fisher Scientific
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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Rneasy mini kit by Qiagen
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More about "Ribosomes"

Ribosomes are the cellular organelles responsible for the synthesis of proteins, a fundamental process in all living organisms.
These complex macromolecular structures, composed of ribosomal RNA (rRNA) and proteins, serve as the site of translation where the genetic code is decoded and amino acids are assembled into polypeptides.
Ribosomes can be found in the cytoplasm or associated with the endoplasmic reticulum, and their structure and function have been extensively studied to unravel the mechanics of protein synthesis.
Understanding the role of ribosomes is crucial for advancements in fields such as cell biology, genetics, and pharmacology.
Furthering ribosome research with tools like PubCompare.ai can lead to improved experimental protocols and enhanced reproducibility, empowering scientists to unlock new discoveries.
PubCompare.ai is the leading AI-driven platform for reproducible protocols, allowing researchers to easily locate protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products for their research.
In the context of ribosome research, common techniques and tools used include TRIzol reagent for RNA extraction, RNeasy Mini Kit for purification, and platforms like MiSeq and HiSeq 2500 for sequencing.
The Agilent 2100 Bioanalyzer is often used for quality control of RNA samples, while cDNA synthesis can be performed using kits like High-Capacity cDNA Reverse Transcription Kit or IScript cDNA synthesis kit.
Real-time PCR analysis can be carried out on instruments such as the StepOnePlus Real-Time PCR System.
By leveraging these technologies and integrating them with the power of PubCompare.ai, scientists can streamline their ribosome research, optimize experimental protocols, and enhance the reproducibility of their findings, ultimately driving new discoveries in fields like cell biology, genetics, and pharmacology.