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FCER2 protein, human

FCER2, also known as CD23, is a low-affinity immunoglobulin E (IgE) receptor that plays a key role in the regulation of IgE-mediated immune responses.
It is expressed on the surface of B lymphocytes, monocytes, and other cell types.
FCER2 is involved in the activation and differentiation of B cells, as well as the modulation of IgE production.
This protein has been implicated in various allergic and inflammatory conditions, making it an important target for research and therapeutic interventions.
Undestanding the biology and fuction of FCER2 can provide valuable insights into the pathogenesis of IgE-mediated disorders and help develop more effective strategies for their management.

Most cited protocols related to «FCER2 protein, human»

1
Comparative Sequence Analysis Software Benchmarking
Cited 1960 times
Software versions used: SAM 3.5 (Jul 2005) [37] (link), NCBI BLAST+ 2.2.24+ (Aug 2010) [3] (link), FASTA 36.3.3 (Feb 2011) [40] , WU-BLAST 2.0MP-WashU (May 2006), HMMER 2.3.2 (Oct 2003), and HMMER 3.0 (Mar 2010).
Example sequence alignments and profile HMMs were sampled from Seed alignments and profiles in Pfam 24 [11] (link). Example target sequences were sampled from UniProt version 2011_03 [43] (link). One experiment that characterized roundoff error used older versions, Pfam 22 and UniProt 7.0.
, & Eddy S.R. (2011). Accelerated Profile HMM Searches. PLoS Computational Biology, 7(10), e1002195.
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Publication 2011
FCER2 protein, human Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Sequence Alignment
2
16S rDNA Amplicon and Metagenome Preprocessing
Cited 1001 times
Unassembled sequence reads from both SSU rRNA gene PCR amplicons (pyrotags) and metagenome sequencing were preprocessed (quality control and alignment) by the bioinformatics pipeline of the SILVA project (20 (link)). Briefly, reads shorter than 200 nt or with more than 2% of ambiguities or more than 2% of homopolymers were removed. Remaining reads from amplicons and metagenomes were aligned against the SSU rDNA seed of the SILVA database release 108 (www.arb-silva.de/documentation/background/release-108/) (20 (link)) using SINA (26 (link)). Unaligned reads were not considered in downstream analysis to eliminate non 16S rDNA sequences.
Remaining PCR amplicons were separated based on the presence of aligned nucleotides at E. coli positions of the respective primer binding sites instead of searching for the primer sequences itself. This strategy is robust against sequencing errors within the primer signatures or incomplete primer signatures. This separation strategy works because the amplicon size of one primer pair is significant longer, with overhangs on both 3′ and 5′ site, compared with the amplicon of the second primer pair. With this approach the need for barcoding during combined sequencing of 16S pyrotags derived from different PCR reactions on the same PTP lane was avoided. FASTA files for each primer pair of the separated samples are available online at www.arb-silva.de/download/archive/primer_evaluation.
Reads of the filtered and separated 16S pyrotag datasets as well as metagenomes were dereplicated, clustered and classified on a sample by sample basis. Dereplication (identification of identical reads ignoring overhangs) was done with cd-hit-est of the cd-hit package 3.1.2 (www.bioinformatics.org/cd-hit) using an identity criterion of 1.00 and a wordsize of 8. Remaining sequences were clustered again with cd-hit-est using an identity criterion of 0.98 (wordsize 8). The longest read of each cluster was used as a reference for taxonomic classification, which was done using a local BLAST search against the SILVA SSURef 108 NR dataset (www.arb-silva.de/projects/ssu-ref-nr/) using blast-2.2.22+ (http://blast.ncbi.nlm.nih.gov/Blast.cgi) with default settings. The full SILVA taxonomic path of the best BLAST hit was assigned to the reads if the value for (percentage of sequence identity + percentage of alignment coverage)/2 was at least 93. In the final step, the taxonomic path of each cluster reference read was mapped to the additional reads within the corresponding cluster plus the corresponding replicates (as identified in the previous analysis step) to finally obtain (semi-) quantitative information (number of individual reads representing a taxonomic path). Raw output data are available in the Supplementary Material in Supplementary Tables S48–S50.
Klindworth A., Pruesse E., Schweer T., Peplies J., Quast C., Horn M, & Glöckner F.O. (2012). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research, 41(1), e1.
Publication 2012
Binding Sites DNA, Ribosomal Escherichia coli FCER2 protein, human Metagenome Nucleotides Oligonucleotide Primers Ribosomal RNA Genes Sequence Alignment SULT1E1 protein, human
3
Protein Function Annotation Using BLAST and eggNOG-mapper
Cited 636 times
BLAST searches were performed using NCBI-BLAST 2.3.0 with an E-value threshold of 0.001, 20 threads and unlimited number of hits. The whole set of protein sequences in eggNOG v4.5 was used as target database (http://eggnogdb.embl.de/download/eggnog_4.5/eggnog4.proteins.core_periphery.fa.gz). While annotating query sequences, self hits were excluded both from BLAST and eggNOG-mapper hits to avoid circular annotations. No taxonomic restrictions were applied when transferring GO terms from BLAST or eggNOG-mapper hits (automatic taxonomic adjustment was manually disabled). eggNOG-mapper was called with the following parameters: “–m hmmer –-tax_scope NOG-–target_orthologs all –-go_evidence experimental –-excluded_taxa [self_taxid]-–cpu 20”. The same eggNOG v4.5 Gene Ontology annotations were used both for BLAST and eggNOG-mapper.
Huerta-Cepas J., Forslund K., Coelho L.P., Szklarczyk D., Jensen L.J., von Mering C, & Bork P. (2017). Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper. Molecular Biology and Evolution, 34(8), 2115-2122.
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Publication 2017
Amino Acid Sequence FCER2 protein, human Gene Annotation Proteins SET protein, human
4
Comparative Genomics for GO Annotation
Cited 373 times

Glycyrrhiza uralensis was used as an example to introduce the comparative genomic approach to predict the GO annotation. We use BLAST-2.2.19 (-e 1e-3 -m 8) to compare the protein sequences of G. uralensis (34 (link)) and Arabidopsis thaliana (35 (link)). Then, the orthologous pairs with A. thaliana were determined, as was the corresponding GO annotation. Additionally, Blast2GO has a comprehensive bioinformatics platform that can automatically predict GO annotation based on the sequence information and a powerful remote annotation background (36 (link)). After the BLAST, interpro and mapping analyses steps in the Blast2GO application, we can export the GO annotation file and update agriGO v2.0. The InterPro (37 (link)) ID and Pfam (38 (link)) accessions have internal connections with the GO. Thus, we can map the GO terms to candidate genes using information on specific domains.
Tian T., Liu Y., Yan H., You Q., Yi X., Du Z., Xu W, & Su Z. (2017). agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Research, 45(Web Server issue), W122-W129.
Publication 2017
Amino Acid Sequence Arabidopsis thalianas Base Sequence FCER2 protein, human Genes Glycyrrhiza uralensis
5
PASTEC: Modular TE Consensus Classifier
Cited 92 times
PASTEC was developed in the REPET package [7] . In this context, we used PASTEC to classify the consensus TE sequences found de novo in a genome. PASTEC uses several features of TEs to classify TE consensus sequences. It searches for structural evidence and sequence similarities stored in a MySQL database obtained during a preprocessing step. The structural features considered are TE length, presence of a LTR (long terminal repeat) or TIR (terminal inverted repeat) detected with a custom-built tool (with a minimum length of 10 bp, a minimum identity of 80%, the taking into account of reciprocal orientations of terminal repeats and a maximal length of 7000 bp), the presence of SSRs (simple sequence repeats detected with the tandem repeat finder (TRF) tool [8] (link)), the polyA tail and an ORF (open reading frame). The blastx and tblastx routines are used to search for similarities to known TEs in Repbase Update, and the hmmer3 package [9] to search against a HMM profile databases (TE-specific or not), after translation in all six frames. Sequence similarities are also identified by blastn searches against known rDNA sequences, known host genes and known helitron ends. The databanks used are preprocessed and formatted. The Repbase Update for PASTEC can be downloaded from http://www.girinst.org/repbase/index.html, whereas the HMM profile databank formatted for PASTEC is available from the REPET download directory (http://urgi.versailles.inra.fr/download/repet/).
PASTEC classifies TEs by testing all classifications from Wicker's hierarchical TE classification system. Each possible classification is weighted according to the available evidence, with respect to the classification considered. TEs are currently classified to class and order level. PASTEC can also determine whether a TE is complete on the basis of four criteria: sequence coverage for known TEs, profile coverage, presence of terminal repeats for certain classes, presence of a polyA or SSR tail for LINEs and SINEs, and the length of the TEs with respect to expectations for the class concerned.
We designed PASTEC as a modular multi-agent classifier. The system is composed of four types of agents: retrievers, classifiers, filter agents, and a super-agent (Figure 1). The retriever agents retrieve the pre-computed analysis results stored in the MySQL database. They act on the requests of the classifier or filter agents, filtering, formatting and supplying the results. The classifier and filter agents are specialized to recognize a particular category. For example, the LTR agent can determine only whether the TE is a LTR or not. The classifier and filter agents act on the request of the super-agent, deciding whether they can classify the TE or not. For example, the LTR agent decides whether the consensus TE is a LTR on the basis of the following evidence: presence of the ENV (envelope protein) profile (a condition sufficient for classification), the presence of INT (integrase), RT (reverse transcriptase), GAG (capsid protein), AP (aspartate proteinase) and RH (RNase H) profiles together with the detection of a LTR (long terminal repeat), a blast match with the sequence of a known LTR retrotransposon. The super-agent resolves classification conflicts and formats the output file. It resolves conflicts by using a confidence index normalized to 100. For example, the LTR agent calculates a confidence index with the following rules: presence of ENV profiles (+2 because this condition is sufficient for classification), presence of a long terminal repeat and an INT, GAG, RT, RH or AP profile (+1 for each profile combined with the long terminal repeat), +1 for each profile (ENV, AP, RT, RH and GAG) found in the same frame in the same ORF. If the consensus matches at least one known LTR retrotransposon, the LTR agent adds +2 for each type of blast (blastx or tblastx) at the confidence index. Finally, the length of the TE is taken into account because we add +1 if the TE without the long terminal repeat is between 4000 and 15000 bp in length, and we decrease the confidence index by 1 if the TE without the long terminal repeat is less than 1000 bp or more than 15000 bp long. The super-agent uses the maximum confidence index defined for each classifier agent to normalize the confidence index for each classification to 100 and then compare the different classifications. Advanced users can edit all decisions rules and maximum confidence indices in the Decision_rules.yaml file.
The output can be read by humans and is biologist-friendly. A single line specifies the name of the TE, its length, status, class, order, completeness, confidence index and all the features characterizing it. A status of “potential chimeric” or “OK” is assigned to the TE. If the TE is not considered to be “OK” then users must apply their own expertise. A TE is declared “potential chimeric” when at least two classifications are possible. In this case, PASTEC chooses the best status based on the available evidence, or does not classify the TE if no decision is possible. In this last case, all possible classifications are given (separated by a pipe symbol “|”). We present an example of PASTEC output in table S1. PASTEC output is a tabular file, with the columns from left to right indicating the name of the TE, its length, the orientation of the sequence, chimeric/non-chimeric status (OK indicating that the element is not potentially chimeric), class (class I in this case), order. In the first line of the example provided, the TE is a LTR. We presume that the element is complete because we have no evidence to suggest that it is incomplete, and the confidence index is 71/100. The last column summarizes all the evidence found: coding sequence evidence, such as the results of tblastX queries against the Repbase database (TE_BLRtx evidence), blastX queries against the Repbase database (TE_BLRx evidence) and profiles. A blast match is taken account if coverage exceeds 5%, and a profile is taken into account if its coverage exceeds 20% (these parameters can be edited in the configuration file). For each item of coding sequence evidence, the coverage of the subject is specified. The structural evidence is also detailed: >4000 bp indicates that TE length without terminal repeats is between 4000 and 15000 bp, the next item of information presented in the comments columns is the presence of terminal repeats: we have a LTR in this case, with an LTR length of 433 bp; two long ORFs have been identified, the last of which contains four profiles in the same frame and is up to 3000 bp long. Other evidence provided for this example includes the partial match with a Drosophila melanogaster gene (coverage 16.55% and the TE contains 18% SSRs). The super-agent determines whether a TE is complete based on whether it is sufficiently long, whether the expected terminal repeats or polyA tail are present, whether blast match coverage exceeds 30% and profile coverage exceeds 75%. The second line of the example corresponds to a potentially chimeric TE, for which human expertise is required.
Hoede C., Arnoux S., Moisset M., Chaumier T., Inizan O., Jamilloux V, & Quesneville H. (2014). PASTEC: An Automatic Transposable Element Classification Tool. PLoS ONE, 9(5), e91929.
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Publication 2014
Aspartate Capsid Proteins Chimera Consensus Sequence DNA, Ribosomal Drosophila melanogaster FCER2 protein, human Gene Products, env Genes Genome Homo sapiens Integrase Open Reading Frames Peptide Hydrolases Poly(A) Tail Poly A Reading Frames Retrotransposons Ribonuclease H RNA-Directed DNA Polymerase Short Interspersed Nucleotide Elements Short Tandem Repeat Tail Tandem Repeat Sequences Terminal Repeat Sequences

Most recents protocols related to «FCER2 protein, human»

1
Comprehensive Functional Annotation of Transcriptome
When comparing the open-reading frames in the transcriptomic data with their genomic counterparts, we failed to observe obvious spliceosomal introns. Therefore, Prodigal v. 2.6.3 (Hyatt et al. 2010 (link)), a bacterial gene prediction tool, was used to predict gene models and proteins for both P. canceri genome assemblies. TransDecoder v.5.3.0 (https://github.com/TransDecoder/TransDecoder) was used to identify candidate coding regions from all transcriptome assemblies generated in this study and the published transcriptome of M. mackini (Burki et al. 2013 (link)). Functional annotation of the predicted proteins was performed based on the following strategy. The predicted proteome was used as a query against the NCBI nr database (May 2020) to retrieve the top scoring hits (BLAST suite v. 2.9.0+). Interpro (IPR) domains were assigned using Interproscan v.5.30-69.0 (Jones et al. 2014 (link)). The online version of eggNOG-mapper v2 (Huerta-Cepas et al. 2017 (link)) was used for orthology assignments of the predicted proteins and K numbers were assigned on the GhostKoala web server (https://www.genome.jp/kegg/tool/map_pathway.html). The subcellular localization of each protein was determined with targetP v.2 (Almagro Armenteros et al. 2019 ) searching the non-plant organism group, MitoFates with fungal settings and DeepLoc-1.0 with default settings (Almagro Armenteros et al. 2017 (link)).
Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.
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Publication 2023
FCER2 protein, human Gene Expression Profiling Genes, Bacterial Genome Introns Plants Protein Annotation Proteins Proteome Spliceosomes Transcriptome
2
Identifying Mitochondrial Proteins in Protists
Several searching strategies were employed to identify putative mitochondrial proteins in P. canceri and M. mackini (Burki et al. 2013 (link)). The predicted proteomes of P. canceri and M. mackini were inspected for the presence of proteins encoding MRO-localized proteins using the functional annotation and subcellular localization determined in the previous section. The output of eggNOG-mapper and Interproscan were additionally searched for any components of the protein import machinery or mitochondrial carrier family proteins. Moreover, the predicted mitochondrial proteomes from Pygsuia biforma (Stairs et al. 2014 (link)), Blastocystis sp. (Abrahamian et al. 2017 ) and B. motovehiculus (Gawryluk et al. 2016 (link)) were used as query sequences against the predicted proteins from P. canceri and M. mackini using BLAST v.2.1.8 (Altschul et al. 1990 (link)). Any protein that was predicted to be mitochondrial related based on at least one software tool used above or had one mitochondrial subject sequence retrieved in the top 100 BLAST hits was further investigated for completeness, annotation and mitosomal provenance. First, the gene model's completeness was assessed by manually examining the query coverage to similar sequences via BLAST. Those P. canceri predicted proteins that did not match to any sequence with BLAST or only aligned with hypothetical proteins were not examined but can be found in supplementary Table S1A and C, Supplementary Material online. Those predicted protein sequences with a methionine that align with the starting methionine of subject sequences were annotated as “complete“. In case that some but not all components of a certain metabolic pathway were predicted to be present in P. canceri, the putative missing genes were further searched in the metagenomic and metatranscriptomic predicted proteomes. If these searches proved negative, the raw reads of each library were further investigated with Phylomagnet (Schön et al. 2020 (link)). This program employs a gene centric approach to retrieve and assemble genes of interest directly from a raw read library. None of the investigated genes could be recovered from the raw reads.
Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.
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Publication 2023
Amino Acid Sequence Blastocystis Carrier Proteins cDNA Library FCER2 protein, human Genes Metagenome Methionine Mitochondria Mitochondrial Proteins OCA2 protein, human Proteins Proteome
3
Phylogenetic Validation of Mitochondrial Proteins
To confirm the taxonomic identity of the putative mitochondrial-related protein identified in P. canceri and eliminate the possibility of residual contamination, maximum-likelihood phylogenetic trees were constructed (supplementary fig. S5, Supplementary Material online). Except for the mitochondrial ABC transporter gene (atm1), the phylogenetic analysis workflow was performed as follows. All mitochondrial-related proteins identified in P. canceri were queried against the NCBI nr database (August, 2020) with BLAST v.2.1.9 (Altschul et al. 1990 (link)) using the BLASTP algorithm. The top 5,000 hits with an e-value less than 1e-10 (or 1e-5 if few hits were identified) were retrieved and clustered at 90% identity with CD-HIT v.4.8.1 (Edgar 2010 (link)). The predicted proteomes of M. mackini and C. pagurus were searched with BLASTP to retrieve homologous proteins. Lastly, a reciprocal BLASTP in all P. canceri predicted proteoms was performed. The sequences were aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-auto). The alignments were trimmed of ambiguous sites with (trimAL v.1.4.1 (Capella-Gutierrez et al. 2009 (link)), -automated1). The amino acid substitution model was determined with IQ-TREE2.1.6.5 using the default settings (Kalyaanamoorthy et al. 2017 (link)). Phylogenies and 1,000 ultrafast bootstrap trees with 1,000 SH-aLRT replicates were constructed with IQ-TREE2 v.1.6.5 (Minh et al. 2013 (link)). These initial phylogenies were visualized in FigTree v.1.4.4 and manually pruned to reduce the number of taxa. The reduced data set was aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-linsi). Removal of ambiguous sites, evaluation of amino acid substitution models, and phylogenetic reconstruction proceeded as above. For the putative atm1 transporter, a Hidden Markov Model profile for orthologous group KOG0057 (retrieved from EggNOG 5.0.0 (Huerta-Cepas et al. 2019 (link)) database) was used to retrieve the protein models of P. canceri and M. mackini using the default settings of with hmmsearch. The resulting hits were used as queries against the NCBI nr database (August 2020) as described above. This dataset was supplemented with atm1 sequences reported previously (Freibert et al. 2017 (link)). The proteins were aligned with hmmalign from HMMER v.3.2.1 (http://hmmer.org/) and the Atm1 phylogeny was performed as described above.
Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.
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Publication 2023
Amino Acid Substitution ATP-Binding Cassette Transporters FCER2 protein, human Genes, Mitochondrial Membrane Transport Proteins Mitochondrial Proteins Pagurus Proteins Proteome Trees
4
Genome Annotation of QG10 Using Integrated Bioinformatics
The RNA of QG10 were isolated from the mixed tissues (leaves, culms, roots, and panicles) following the manufacturer’s protocol (Wang et al., 2023 (link)). We then performed the sequencing on the Illumina HiSeq 2500 platform according to the manufacturer’s instructions. The repetitive sequence of the genome based on the principle of structure prediction and de-novo prediction was constructed with the software package LTR_FINDER v1.05 (Xu and Wang, 2007 (link)) and the software package RepeatScout v1.0.5 (Price et al., 2005 (link)). The PASTE Classifier was used to classify the database (Hoede et al., 2014 (link)). Then it was merged with the database of Repbase as the final repeat sequence database (Jurka et al., 2005 (link)). And then the software package RepeatMasker v4.0.6 was used to predict the repeat sequence of the QG10 genome based on the constructed repetitive sequence database (Tarailo‐Graovac and Chen, 2009 (link)). the software packages Genscan (Burge and Karlin, 1997 (link)), Augustus v2.4 (Stanke and Waack, 2003 (link)), GlimmerHMM v3.0.4 (Majoros et al., 2004 (link)), GeneID v1.4 (Alioto et al., 2018 (link)), and SNAP (Korf, 2004 (link)) were used for de-novo prediction. The software package GeMoMa v1.3.1 was used for prediction based on homologous species (Keilwagen et al., 2016 (link); Keilwagen et al., 2018 (link)). The software packages Hisat v2.0.4 (Kim et al., 2015 (link)) and Stringtie v1.2.3 (Pertea et al., 2015 (link)) were used for assembly based on reference transcripts, and the software packages TransDecoder v2.0 and GeneMarkS-T v5.1 (Tang et al., 2015 (link)) were used for gene prediction. The software package PASA v2.0.2 was used to predict Unigene sequences without reference based on transcriptome data (Campbell et al., 2006 (link)). Finally, the software package EVM v1.1.1 (Haas et al., 2008 (link)) was used to integrate the prediction results obtained by the above three methods. The predicted gene sequences were compared with NR, KOG, GO, KEGG, TrEMBL, and other functional databases by the software package BLAST v2.2.31 (-evalue 1e-5) (Altschul et al., 1990 (link)) to perform KEGG pathway, KOG function, GO function and other genes functional annotation analysis.
Jiang S., Zhang X., Yang X., Liu C., Wang L., Ma B., Miao Y., Hu J., Tan K., Wang Y., Jiang H, & Wang J. (2023). A chromosome-level genome assembly of an early matured aromatic Japonica rice variety Qigeng10 to accelerate rice breeding for high grain quality in Northeast China. Frontiers in Plant Science, 14, 1134308.
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Publication 2023
FCER2 protein, human Gene Annotation Genes Genome Paste Plant Roots Repetitive Region Tissues Transcriptome
5
Comparative Genomic Analysis of Rice Cultivars
The whole-genome assemblies sequences of QG10 were compared with the rice reference genome sequence (Oryza_sativa_MSU7 version) using the software package MUMmer v3 (Kurtz et al., 2004 (link)). According to the results from the software package MUMmer, the sequence variations and SVs were further re-called using the software package BLAST. The synteny/inversion comparison were analysis by using GenomeSyn_Win.v1 (Zhou et al., 2022 (link)). At the site of each sequence variant, the genotypic information for QG10, Nipponbare, and the elite variety having important genes was called according to the results of the one-to-one alignments. The allelic information of sequence variants was detected based on gff files from the Oryza_sativa_MSU7 version. The software packages ClustalW v1.8.3(Thompson et al., 1994 (link)) and BLAST v2.2.31 were used for re-detected the sequence variations and detailed haplotype analyses for the well-characterized genes in rice (Zhao et al., 2018b (link)).
Jiang S., Zhang X., Yang X., Liu C., Wang L., Ma B., Miao Y., Hu J., Tan K., Wang Y., Jiang H, & Wang J. (2023). A chromosome-level genome assembly of an early matured aromatic Japonica rice variety Qigeng10 to accelerate rice breeding for high grain quality in Northeast China. Frontiers in Plant Science, 14, 1134308.
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Publication 2023
Alleles FCER2 protein, human Genes Genetic Diversity Genome Genotype Haplotypes Inversion, Chromosome Oryza sativa Synteny

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More about "FCER2 protein, human"

FCER2, also known as CD23, is a crucial player in the regulation of IgE-mediated immune responses.
This low-affinity immunoglobulin E (IgE) receptor is expressed on the surface of B lymphocytes, monocytes, and other cell types, and it plays a key role in the activation and differentiation of B cells, as well as the modulation of IgE production.
Understanding the biology and function of FCER2 can provide valuable insights into the pathogenesis of IgE-mediated disorders, such as allergies and inflammatory conditions, making it an important target for research and therapeutic interventions.
Researchers can leverage powerful AI-driven tools like PubCompare.ai to optimize their FCER2 protein research.
These tools can help locate the best protocols from literature, pre-prints, and patents, using advanced comparison capabilities.
By maximizing research efficiency and finding the right products for their experiments, scientists can take their FCER2 studies to the next level.
For example, techniques like HiSeq 2500, HiSeq 2000, and HiSeq 4000 sequencing platforms can be used to analyze the genetic and transcriptomic profiles of FCER2-related cells and pathways.
The QIAquick Gel Extraction Kit and ABI 3730XL DNA sequencer can be employed for purification and sequencing of FCER2-associated DNA and RNA samples.
Additionally, flow cytometry with CD41-PE-Cy7 and Tie2-PE antibodies can be used to phenotype and quantify FCER2-expressing cell populations.
By incorporating these cutting-edge techniques and tools, researchers can gain a deeper understanding of the FCER2 protein and its role in IgE-mediated disorders.
This knowledge can then be leveraged to develop more effective strategies for the management and treatment of these conditions, ultimately improving the lives of patients.