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> Phenomena > Natural Phenomenon or Process > Species Specificity

Species Specificity

Species Specificity refers to the degree of specificity or selectivity of a substance, process, or response to a particular species of organism.
This concept is important in biomedical research, as the behaviour and response of different species can vary significantly, impacting the applicability and translatability of research findings.
Factors such as genetic, physiological, and biochemical differences between species can contribute to species specificity.
Considering species specificity is crucial for designing robust and reproducible research protocols, selecting appropriate animal models, and interpreting experimental results with accuracy.
Identifying and accounting for species-specific factors can help improve the reliability and clinical relevance of biomedical investigations.

Most cited protocols related to «Species Specificity»

1
Gene Ontology Semantic Similarity Analysis with REVIGO
REVIGO is a server-side Java web application running on a Glassfish 3 application server. For data visualization, REVIGO relies on Google Motion Chart for scatterplots, Cytoscape Web [12] (link) for graphs and DrasticTreemap for treemaps. For multidimensional scaling, the MSDJ library [19] is used.
For calculation of semantic similarity measures between GO terms, REVIGO relies on pre-computed information content (IC) for the GO terms. The IC is calculated as a negative logarithm of the GO term's relative frequency in a reference database – the EBI GOA database [20] (link) – which annotates all UniProt entries with GO terms. The user may optionally decide to select the database with one of the 11 species-specific GOA subsets for common model organisms, in order to fine-tune the calculation of semantic distances (which rely on IC) for the problem at hand. If the particular organism is not offered in REVIGO, the closest available organism or the default UniProt database should generally be adequate replacements, assuming that the relative frequencies of gene functions in the user's genome are not far from the ones in the selected genome, or in case UniProt was selected, from the overall trends in the genomic databases.
REVIGO supports four semantic similarity measures based on the concept of the “most informative common ancestor”: Lin's, Resnik's, Jiang and Conrath's measures, and the SimRel measure [8] (link). These and other measures and the role of the IC in their calculation are reviewed in [12] (link). The employed semantic similarity measures are quite robust with regard to future changes in the EBI GOA database due to new or updated annotations, as they don't rely on the GO annotations of each particular gene, but only on the terms' overall IC, which is expected to change little with time. Therefore, an aggressive update schedule is not necessary for REVIGO, and the underlying Gene Ontology and the EBI GOA database will normally be updated on a yearly basis, and more frequently in case of a large-scale release of new GO terms by the GO Consortium.
REVIGO also has a facility for integration with Web servers/software which produce lists of GO categories, typically by testing for statistically significant enrichment of a variable in GO terms; see Introduction for several examples. Owners of such Web servers can use a HTTP POST request to pre-populate REVIGO's input form with output of their server; please refer to the online instructions for technical details.
REVIGO is freely available from http://revigo.irb.hr/. Any modern internet browser with Adobe Flash capabilities is sufficient to access the server; additionaly, client-side Java is required if Cytoscape [13] (link) is invoked via Java Web Start.
Supek F., Bošnjak M., Škunca N, & Šmuc T. (2011). REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. PLoS ONE, 6(7), e21800.
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Publication 2011
DNA Library Gene Annotation Genome Surgical Replantation
2
Accurate Species Tree Rooting for Orthologs
The rooted species tree is required in order to identify the correct out-group in each orthogroup tree, as correct gene tree rooting is critical for the orthology assessment from that tree [22 (link)]. Since orthogroups can potentially contain any subset of the species in the analysis, it is not sufficient to simply know the out-group for the complete species set. Instead, the complete rooted species tree is required. If the user knows the rooted species tree for the set of species being analyzed, then it is recommended to specify this tree manually at the command line to remove the possibility of species tree inference error. Such a tree can be provided as a Newick format text file. In the event that a species tree is not provided (or not known), then OrthoFinder automatically infers it.
Sets of one-to-one orthologs that are present in all species are often used for species tree inference; however, in real-world large-scale analyses, these can be rare [33 ]. A new algorithm, Species Tree from All Genes (STAG), was developed to allow species tree inference even for species sets with few or no complete sets of one-to-one orthologs present in all species [33 ]. Without this algorithm, species tree inference could fail if there were no sets of one-to-one orthologs present in all species. STAG infers the species tree using the most closely related genes within single-copy or multi-copy orthogroups. In benchmark tests, STAG [24 (link)] had higher accuracy than other leading methods for species tree inference, including maximum likelihood species tree inference from concatenated alignments of protein sequences, ASTRAL [38 (link)] and NJst [39 (link)].
The Species Tree Root Inference from Duplication Events (STRIDE) algorithm [22 (link)] is used to root the species tree in OrthoFinder. STRIDE was developed to enable the rooting of the species tree using only information available in the set of gene trees. STRIDE does this by identifying the set of well-supported in-group gene duplication events in the complete set of unrooted orthogroup trees, and using these events to infer a probability distribution over an unrooted STAG species tree for the location of its root. Similarly to STAG, STRIDE has been shown to identify the correct root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of time scales and taxonomic groups [22 (link)]. In some cases, it is possible that there could be few duplications within the gene trees, and so STRIDE will not be able to identify the root of the species tree, or will only be able to exclude the root from clades in which gene duplication events are observed. In this case, ortholog inference should still not be significantly impacted since the rooting of the gene tree only affects ortholog inference in cases where gene duplication events are present [22 (link)]. This makes the STRIDE approach particularly suited to gene tree rooting for ortholog inference.
Emms D.M, & Kelly S. (2019). OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biology, 20, 238.
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Publication 2019
Aster Plant Gene Duplication Genes Genes, vif Plant Roots Proteins Sequence Alignment Species Specificity Trees Wakerobin
3
Benchmark of Computational Mutation Prediction
A collection of five human mutation datasets from online databases and the literature were downloaded and used in this study (Table 1). First, inherited disease-causing AASs annotated as DMs (damaging mutations) in the Human Gene Mutation Database [Stenson et al., 2009 (link)] (HGMD—November 2011; http://www.hgmd.org) and inherited putative functionally neutral AASs in the UniProt database [Apweiler et al., 2004 (link)] (UniProt—November 2011; http://www.uniprot.org/docs/humsavar) were downloaded and used to calculate the pathogenicity weights implemented in our weighted/species-specific method. Next, we obtained two human mutation datasets to assess the performance of FATHMM against the performance of other computational prediction algorithms previously reported in the literature: the VariBench database (VariBench—November 2011; http://bioinf.uta.fi/VariBench) used in a comprehensive review [Thusberg et al., 2011 (link)] of nine other computational prediction methods [Adzhubei et al., 2010 (link); Bao et al., 2005 (link); Bromberg and Rost, 2007 (link); Calabrese et al., 2009 (link); Capriotti et al., 2006 (link); Li et al., 2009 (link); Mort et al., 2010 (link); Ng and Henikoff, 2001 (link); Ramensky et al., 2002 (link); Thomas et al., 2003 (link)] and 267 AASs in four cancer-associated genes (BRCA1, MSH2, MLH1, and TP53) used in a recent review [Hicks et al., 2011 (link)] of four alternative computational prediction algorithms [Adzhubei et al., 2010 (link); Ng and Henikoff, 2001 (link); Reva et al., 2011 (link); Tavtigian et al., 2006 (link)]. Finally, we downloaded a human mutation dataset consisting of disease-associated and putative functionally neutral AASs from the SwissVar portal [Mottaz et al., 2010 (link)] (SwissVar—February 2011; http://swissvar.expasy.org) and performed an independent benchmark of FATHMM against eight other computational prediction algorithms [Adzhubei et al., 2010 (link); Calabrese et al., 2009 (link); Capriotti et al., 2006 (link); Ferrer-Costa et al., 2004 (link); Li et al., 2009 (link); Mort et al., 2010 (link); Ng and Henikoff, 2001 (link); Ramensky et al., 2002 (link); Thomas et al., 2003 (link)].
Shihab H.A., Gough J., Cooper D.N., Stenson P.D., Barker G.L., Edwards K.J., Day I.N, & Gaunt T.R. (2012). Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Human Mutation, 34(1), 57-65.
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Publication 2012
BRCA1 protein, human Gene, Cancer Homo sapiens MLH1 protein, human Mutation Oncogenes Pathogenicity Ribs STK35 protein, human TP53 protein, human
4
ResFinder 4.0: Comprehensive AMR Database
ResFinder 4.0 contains four databases including AMR genes (ResFinder), chromosomal gene mutations mediating AMR (PointFinder), translation of genotypes into phenotypes and species-specific panels for in silico antibiograms. The databases of ResFinder15 (link) and PointFinder16 (link) were reviewed by experts and, when necessary, entries were removed or added. Furthermore, the PointFinder database was extended to include chromosomal gene mutations leading to ampicillin resistance in Enterococcus faecium, ciprofloxacin resistance in E. faecium and Enterococcus faecalis, and resistance to cefoxitin, chloramphenicol, ciprofloxacin, fusidic acid, linezolid, mupirocin, quinupristin–dalfopristin, rifampicin and trimethoprim in Staphylococcus aureus. The genotype-to-phenotype tables were created by experts, by using additional databases (www.bldb.eu for β-lactam resistance genes,18 (link)  http://faculty.washington.edu/marilynr/ for tetracycline as well as macrolide, lincosamide, streptogramin and oxazolidinone resistance genes) and by performing extensive literature searches. In the genotype-to-phenotype tables, the ResFinder and PointFinder entries have been associated with an AMR phenotype both at the antimicrobial class and at the antimicrobial compound level. A selection of antimicrobial compounds within each class was made to include antimicrobial agents important for clinical and surveillance purposes for the different bacterial species included (Table S1, available as Supplementary data at JAC Online). The genotype-to-phenotype tables also include: (i) the PubMed ID of relevant literature describing the respective AMR determinants and phenotypes, when available; (ii) the mechanism of resistance by which each AMR determinant functions; and (iii) notes, which may contain different information such as warnings on variable expression levels (inducible resistance, cryptic genes in some species, etc.), structural and functional information, and alternative nomenclature.
Bortolaia V., Kaas R.S., Ruppe E., Roberts M.C., Schwarz S., Cattoir V., Philippon A., Allesoe R.L., Rebelo A.R., Florensa A.F., Fagelhauer L., Chakraborty T., Neumann B., Werner G., Bender J.K., Stingl K., Nguyen M., Coppens J., Xavier B.B., Malhotra-Kumar S., Westh H., Pinholt M., Anjum M.F., Duggett N.A., Kempf I., Nykäsenoja S., Olkkola S., Wieczorek K., Amaro A., Clemente L., Mossong J., Losch S., Ragimbeau C., Lund O, & Aarestrup F.M. (2020). ResFinder 4.0 for predictions of phenotypes from genotypes. Journal of Antimicrobial Chemotherapy, 75(12), 3491-3500.
Publication 2020
Antibiogram Bacteria Cefoxitin CFC1 protein, human Chloramphenicol Chromosomes Ciprofloxacin Enterococcus faecalis Enterococcus faecium Faculty fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Fusidic Acid Genes Genotype Lactams Lincosamides Linezolid Macrolides Microbicides Mupirocin Mutation Oxazolidinones Phenotype quinupristin-dalfopristin Rifampin Staphylococcus aureus Streptogramins Tetracycline Trimethoprim
5
Genome Annotation Pipeline (PGAP) at NCBI
The PGAP pipeline is designed to annotate both complete genomes and draft genomes comprising multiple contigs. PGAP is deeply integrated into NCBI infrastructure and processes, and uses a modular software framework, GPipe, developed at NCBI for execution of all annotation tasks, from fetching of raw and curated data from public repositories (the Sequence and Assembly databases) through sequence alignment and model-based gene prediction, to submission of annotated genomic data to public NCBI databases.
On input, PGAP accepts an assembly (either draft or complete) with a predefined NCBI Taxonomy ID that defines the genetic code of the organism. PGAP also accepts a predetermined clade identifier, matching the genome in question to a species-specific clade. Clade IDs are computed using a series of 23 universal ribosomal protein markers and are independent of taxonomy. In the absence of a clade ID, we can infer the ID from taxonomy in the majority of cases. The clade ID determines the realm of core proteins used as the target protein set. PGAP annotation of a new genomic sequence can be requested at the time of submission to GenBank. Taxonomic and clade identifiers are determined outside of the annotation pipeline, and are influenced by GenBank curatorial decisions. The clade-dependent sets of protein clusters as well as sets of curated structural ribosomal RNAs (5S, 16S and 23S) are generated and maintained outside of PGAP. More details on the PGAP workflow are provided below.
Tatusova T., DiCuccio M., Badretdin A., Chetvernin V., Nawrocki E.P., Zaslavsky L., Lomsadze A., Pruitt K.D., Borodovsky M, & Ostell J. (2016). NCBI prokaryotic genome annotation pipeline. Nucleic Acids Research, 44(14), 6614-6624.
Publication 2016
Genes Genetic Code Genome Proteins Protein Targeting, Cellular ribosomal A-protein Ribosomal RNA Sequence Alignment SET protein, human

Most recents protocols related to «Species Specificity»

1
Protein Extraction and Immunoblotting Methodology
Cell lysate preparation, SDS-PAGE, and Western blotting were carried out according to standard protocol. Proteins were harvested in cell lysis buffer supplemented with proteinase inhibitor cocktail (P8340; Sigma-Aldrich) and phosphatase inhibitor cocktail 2 (P5726; Sigma-Aldrich). Antigen detection was performed using antibodies directed against c-Src (rabbit anti-mouse/human antibody; #2109; Cell Signaling), Ctsk (mouse anti-mouse/human antibody; sc-48353; Santa Cruz), Rho (mouse anti-mouse/human antibody; #05-778; Millipore), galectin-3 (mouse anti-mouse/human antibody; ab2785; Abcam; epitopes mapped within the N-terminal region), Lrp1 (mouse anti-mouse antibody; MABN1796; Millipore), Mmp9 (rabbit anti-mouse antibody; ab38898; Abcam), Mmp14 (rabbit anti-mouse antibody; ab53712; Abcam), OXPHOS (rabbit anti-mouse antibody; ab110413; Abcam), vinculin (mouse anti-mouse antibody; V9131; Sigma-Aldrich), β3 integrin (rabbit anti-mouse antibody; #4702; Cell Signaling), or β-actin (rabbit anti-mouse antibody; #4970; Cell Signaling). Goat anti-rabbit IgG horseradish peroxidase (#65-6120; Thermofisher Scientific) or goat anti-mouse IgG horseradish peroxidase (#32430; Thermofisher Scientific) were used as secondary antibody. Bound primary antibodies (diluted to 1:1,000) were detected with horseradish peroxidase–conjugated species-specific secondary antibodies (Santa Cruz; diluted to 1:2,000) using the Super Signal Pico system (Thermo Fisher Scientific).
For immunoprecipitation analysis, cells were solubilized in IP Lysis Buffer (#87788; Thermo Fisher Scientific) supplemented with complete protease inhibitor cocktail (Roche). Immunoprecipitation was performed by incubation with a mouse monoclonal IgG (#5415; Cell Signaling) or anti–galectin-3 antibody (sc-32790; Santa Cruz) followed by the addition of Protein A/G Magnetic Beads (#88803; Thermo Fisher Scientific). Immune complexes were separated by electrophoresis followed by blotting with antibodies directed against Lrp1 (MABN1796; Millipore) and galectin-3 (ab2785; Abcam).
Zhu L., Tang Y., Li X.Y., Kerk S.A., Lyssiotis C.A., Sun X., Wang Z., Cho J.S., Ma J, & Weiss S.J. (2023). Proteolytic regulation of a galectin-3/Lrp1 axis controls osteoclast-mediated bone resorption. The Journal of Cell Biology, 222(4), e202206121.
Publication 2023
Actins anti-IgG Antibodies Antibodies, Anti-Idiotypic Antigens Buffers Cells Complex, Immune CTSK protein, human Electrophoresis Epitopes G-substrate Galectin 3 Goat GTP-Binding Proteins Homo sapiens Horseradish Peroxidase Immunoglobulins Immunoprecipitation Integrin beta3 MMP9 protein, human MMP14 protein, human Mus Protease Inhibitors protein phosphatase inhibitor-2 Proteins Rabbits SDS-PAGE Staphylococcal Protein A Vinculin
2
Immunoblotting Neuronal Cytoplasmic Proteins
Cytoplasmic lysates from the neuronal cultures (10 µg) were electrophoresed on 10% Criterion TGX gels (Bio-Rad) and transferred to PVDF membranes. Non-specific binding was blocked using 5% milk in TBST for 1 h at room temperature (RT). Membranes were incubated overnight at 4oC with primary antibodies for pTDP-43 (1:1000, # 66318, Proteintech) and TDP-43 (1:1000, # 67345, Proteintech). After washing in TBST, membranes were incubated in species-specific HRP-conjugated secondary antibodies for 1 h at RT. Membranes were scanned with a Bio-Rad ChemiDoc Imager and optical density was determined using Image Lab software (V6.0.1, Bio-Rad).
Ohene-Nyako M., Nass S.R., Richard H.T., Lukande R., Nicol M.R., McRae M., Knapp P.E, & Hauser K.F. (2023). Casein Kinase 2 Mediates HIV- and Opioid-Induced Pathologic Phosphorylation of TAR DNA Binding Protein 43 in the Basal Ganglia. ASN NEURO, 15, 17590914231158218.
Publication 2023
Antibodies Cytoplasm Gels Milk, Cow's Neurons polyvinylidene fluoride protein TDP-43, human Tissue, Membrane Vision
3
Extraction and Analysis of Striatal Proteins
Cytoplasmic and nuclear lysates were obtained from striatal samples using NE-PER nuclear and cytoplasmic extraction reagents (#78833, Thermo Fisher) per the manufacturer's instructions. Briefly, striatal tissues were washed with neutral pH phosphate-buffered saline (PBS) and centrifuged at 500   ×   g for 5 min. After discarding the supernatant, samples were homogenized with a Dounce homogenizer in ice-cold CER I buffer, vortexed for 15 s, and incubated on ice for 10 min. A 1×  solution of HALT protease and phosphatase inhibitor cocktail (Thermo Fisher) was added to all lysis reagents. Ice-cold CER II reagent was added to the homogenate, after which samples were vortexed and centrifuged at 16,000   ×   g for 5 min. The supernatant (containing the cytoplasmic fraction) was aliquoted and stored at −80oC. To obtain the nuclear fraction, the remaining pellet was resuspended in ice-cold NER solution, vortexed for 15 s every 10 min for a total of 40 min, then centrifuged at 16,000   ×   g for 10 min. Nuclear extracts were aliquoted and stored at −80oC. Protein concentration in lysates was determined by the bicinchoninic acid (BCA) method. 30 µg samples were electrophoresed on 10% Criterion TGX gels (Bio-Rad, Hercules, CA) and transferred to PVDF membranes. Non-specific binding sites on membranes were blocked for 1 h with Intercept blocking buffer (LI-COR, Lincoln, NE) for all kinase blots or 5% non-fat dry milk in Tris-buffered saline/Tween-20 (TBST) for pTDP-43/TDP-43 blots. Membranes were then incubated overnight in primary antibodies directed against pTDP-43 (Ser409/410) (1:1000, # 66318, Proteintech), CK2 (1:1,000, #10992, Proteintech), CK1δ (1:1,000, #14388, Proteintech), and inositol-triphosphate 3-kinase B (ITPKB) (1:1000, #12816, Proteintech) followed by incubation in species-specific HRP-conjugated secondary antibodies (for pTDP-43/TDP-43) or fluorescent-labeled secondary antibodies (for CK2, CK1δ, or ITPKB). For cytoplasmic extracts, GAPDH (# A303-878A; 1:2000; Bethyl Laboratories Inc., Montgomery, TX) was used to assess loading while LI-COR Total Protein Stain (# 92611011, LI-COR) was used to assess loading in nuclear extracts. Images of blots were obtained using a ChemiDoc Imager (Bio-Rad) and western blotting bands were analyzed with the Image Lab software (V6.0.1, Bio-Rad).
Ohene-Nyako M., Nass S.R., Richard H.T., Lukande R., Nicol M.R., McRae M., Knapp P.E, & Hauser K.F. (2023). Casein Kinase 2 Mediates HIV- and Opioid-Induced Pathologic Phosphorylation of TAR DNA Binding Protein 43 in the Basal Ganglia. ASN NEURO, 15, 17590914231158218.
Publication 2023
Antibodies bicinchoninic acid Binding Sites Cardiac Arrest Cold Temperature Cytoplasm Fluorescent Antibody Technique GAPDH protein, human Gels Inositol Milk, Cow's Peptide Hydrolases Phosphates Phosphoric Monoester Hydrolases Phosphotransferases polyvinylidene fluoride Proteins protein TDP-43, human Saline Solution Stains Striatum, Corpus Tissue, Membrane Tissues triphosphate Tween 20
4
Quantitative Immunofluorescence Analysis of TDP-43 Pathology in HIV-Associated Neurodegeneration
Paraffin-embedded tissues from the posterior basal ganglia (primarily, the caudate and putamen) of HIV+   and seronegative individuals were dewaxed and rehydrated in xylene (100%; 3   ×   10 min), ethanol (100%; 2   ×   10 min, 95%; 2   ×   5 min, 70%; 2   ×   5 min, 50%; 2   ×   5 min), and dH2O (2   ×   5 min) prior to immunofluorescence assay. Snap-frozen tissues were embedded in O.C.T compound, sectioned, and postfixed (with ice-cold 4% paraformaldehyde for 20 min) prior to use. All tissues (12–18 μm-thick) were heated (at 50% microwave power for 3 min) in Tris-based antigen unmasking solution (pH 9.0, # H3301, Vector Laboratories, Burlingame, CA), followed by permeabilization in a neutral pH phosphate-buffered saline (PBS) containing 0.25% Triton X 100. Tissue sections were incubated (2 h) in Animal-Free Blocker® and Diluent solution (# SP-5035-100, Vector Laboratories) and then incubated overnight (at 4oC) in primary antibodies for the 32 kDa dopamine- and cAMP-regulated neuronal phosphoprotein (DARPP-32) (1:200, # sc271111 AF647, Santa Cruz, Dallas, TX), TDP-43 (1:100, # 67345, Proteintech, Rosemont, IL), phospho-TDP-43 Ser409/410 (pTDP-43) (1:100, # 66318, Proteintech), CK2 (1:100, #10992, Proteintech), and CK1δ (1:100, #14388, Proteintech) followed by a 1 h incubation in species-specific secondary antibodies. Autofluorescence in tissue sections was eliminated using ReadyProbes Tissue Autofluorescence Quenching Kit (#R37630, Thermo Fisher, Waltham, MA) per the manufacturer's instructions. Mean fluorescence/pixel intensity values for pTDP-43, TDP-43, CK2, and CK1δ (corresponding to the level of immunostaining) were acquired in optical sections using confocal microscopy and measured in the cytoplasm and nuclear compartments using CellProfilerTM software (V 6.1) (Broad Institute, Cambridge, MA) (see supplementary Figure 1 for details on the CellProfilerTM workflow). At least 300 Hoechst+   cells were analyzed for each subject.
Ohene-Nyako M., Nass S.R., Richard H.T., Lukande R., Nicol M.R., McRae M., Knapp P.E, & Hauser K.F. (2023). Casein Kinase 2 Mediates HIV- and Opioid-Induced Pathologic Phosphorylation of TAR DNA Binding Protein 43 in the Basal Ganglia. ASN NEURO, 15, 17590914231158218.
Publication 2023
Alexa Fluor 647 Animals Antibodies Antigens Basal Ganglia Cells Cloning Vectors Cold Temperature Cytoplasm Dopamine Dopamine and cAMP-Regulated Phosphoprotein 32 Ethanol Fluorescence Freezing Immunofluorescence Microscopy, Confocal Microwaves Neurons Paraffin Embedding paraform Phosphates Phosphoproteins protein TDP-43, human Putamen Saline Solution Tissues Triton X-100 Tromethamine Xylene
5
ITS1-5.8S-ITS2 Amplification Protocol
The genomic DNA templates and aforementioned universal, genotype- and species-specific primers were used in PCR assays using a PCR instrument (Bio-Rad, United States) and Taq PCR reagent kit (Invitrogen) to amplify the ITS1-5.8S-ITS2 segments using the following touch-down protocol: (1) 95°C for 5 min; (2) 36 cycles of 95°C for 30 sec, annealing temperature for 30 sec (the annealing temperature was initially set to 70°C and decreased stepwise by 0.3°C in each cycle), and 72°C for 1 min; (3) 72°C for 10 min and final incubation at 4°C [26 , 27 ]. The PCR amplicons were examined by agarose gel electrophoresis and sequencing.
Li Y.L., Li X.Z., Yao Y.S., Wu Z.M., Gao L., Tan N.Z., Luo Z.Q., Xie W.D., Wu J.Y, & Zhu J.S. (2023). Differential coexistence of multiple genotypes of Ophiocordyceps sinensis in the stromata, ascocarps and ascospores of natural Cordyceps sinensis. PLOS ONE, 18(3), e0270776.
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Publication 2023
Biological Assay Electrophoresis, Agar Gel Genome Genotype Lanugo Oligonucleotide Primers

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More about "Species Specificity"

Exploring Species Specificity: Enhancing Research Reliability and Translational Potential Species specificity is a crucial concept in biomedical research, referring to the degree of selectivity or selectivity of a substance, process, or response to a particular organism.
Understanding species-specific factors, such as genetic, physiological, and biochemical differences, is essential for designing robust and reproducible research protocols, selecting appropriate animal models, and accurately interpreting experimental results.
When conducting biomedical investigations, it's important to consider the impact of species-specific factors, as the behavior and response of different species can vary significantly.
This variability can impact the applicability and translatability of research findings, making it crucial to identify and account for these factors.
Optimizing your research protocols with tools like PubCompare.ai, an AI-powered platform, can enhance reproducibility and accuracy by analyzing species specificity.
This platform allows you to easily locate the most relevant protocols from literature, pre-prints, and patents, and use AI-driven comparisons to identify the best protocols and products for your research needs.
Factors such as PVDF membranes, Alexa Fluor 488, QIAamp DNA Mini Kit, Nitrocellulose membranes, DAPI, Vectashield, Triton X-100, and Bovine serum albumin can also play a role in species-specific responses and should be considered when designing your experiments.
By understanding and accounting for species specificity, you can improve your experimental design, get more reliable results, and enhance the clinical relevance of your biomedical investigations.
With the right tools and knowledge, you can optimize your research protocols and advance scientific understanding in a more effective and reproducible manner.