E. coli BW25141 (rrnB3 DElacZ4787 DEphoBR580 hsdR514 DE(araBAD)567 DE(rhaBAD)568 galU95 DEendA9::FRT DEuidA3::pir(wt) recA1 rph-1) was used for maintenance of the template plasmid pKD13 (GenBank™ Accession number AY048744). pKD46 (GenBank™ Accession number AY048746; Datsenko and Wanner, 2000 (link)) was made by PCR amplification of the Red recombinase genes from phage λ and cloning into pKD16, a derivative of INT-ts (Haldimann and Wanner, 2001 (link)) carrying araC and araBp from pBAD18 (Guzman et al, 1995 (link)).
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Physiology
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Genetic Function
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Gene Amplification
Gene Amplification
Gene amplification is a process in molecular biology where specific DNA sequebces are replicated to produce multiple copies.
This technique is commonly used in gene expression studies, genetic engineering, and diagnostic applications.
By increasing the number of target genes, gene amplification enhances the sensitivity and reliability of downstream analyses, such as PCR, sequencing, and hybridization assays.
Optimizing gene amplification protocols is crucial for reproducible and accurate results in a variety of research and clinical settings.
PubCompare.ai offers an AI-powered platform to help researchers identify the most effective methods and products for their gene amplification needs, streamlining workflows and delivering reliable outcomes.
This technique is commonly used in gene expression studies, genetic engineering, and diagnostic applications.
By increasing the number of target genes, gene amplification enhances the sensitivity and reliability of downstream analyses, such as PCR, sequencing, and hybridization assays.
Optimizing gene amplification protocols is crucial for reproducible and accurate results in a variety of research and clinical settings.
PubCompare.ai offers an AI-powered platform to help researchers identify the most effective methods and products for their gene amplification needs, streamlining workflows and delivering reliable outcomes.
Most cited protocols related to «Gene Amplification»
E. coli BW25141 (rrnB3 DElacZ4787 DEphoBR580 hsdR514 DE(araBAD)567 DE(rhaBAD)568 galU95 DEendA9::FRT DEuidA3::pir(wt) recA1 rph-1) was used for maintenance of the template plasmid pKD13 (GenBank™ Accession number AY048744). pKD46 (GenBank™ Accession number AY048746; Datsenko and Wanner, 2000 (link)) was made by PCR amplification of the Red recombinase genes from phage λ and cloning into pKD16, a derivative of INT-ts (Haldimann and Wanner, 2001 (link)) carrying araC and araBp from pBAD18 (Guzman et al, 1995 (link)).
Ara-C
Bacteriophages
Escherichia coli
Gene Amplification
Plasmids
Recombinase
Cells were treated with 9 concentrations (2-fold dilutions) of drug for 72 hours before measuring cell number relative to controls. A MANOVA was used to examine how drug IC50 and slope values associate with tissue type, the mutation status of 64 cancer genes (including gene amplifications and homozygous deletions), rearrangements and MSI. The elastic net utilised the same genomic datasets as the MANOVA and also incorporated additional copy number data from a total of 426 cancer genes, transcriptional profiles, and tissue type to identify feature associated with drug response as measured by cell line IC50.
Cell Lines
Cells
Gene, Cancer
Gene Amplification
Gene Deletion
Gene Rearrangement
Genome
Histocompatibility Testing
Homozygote
Mutation
Pharmaceutical Preparations
Technique, Dilution
Transcription, Genetic
Copy Number Polymorphism
Gene Amplification
Gene Deletion
Gene Expression
Genes
Genes, vif
Neoplasms
Oncogenes
To identify the candidate primer sequences for the PCR amplification of prokaryotic 16S rRNA genes, such genes from genome-sequenced strains were used as references because they have been accurately sequenced, are full-length genes, have well-defined taxonomic information. Bacterial and archaeal genomic sequences were obtained from the NCBI Genome Database (ftp://ftp.ncbi.nih.gov/genbank/genomes/Bacteria/ , accessed on 11 November 2013) in November 2008. The 16S rRNA gene sequences in each strain were identified by RNAmmer.25 (link) Then, one 16S rRNA gene sequence per species was randomly chosen because slight sequence differences exist among the 16S rRNA genes from strains of the same species,26 (link) and among the gene copies within a genome.27 (link) A total of 531 16S rRNA gene sequences were chosen. Their taxonomic information was obtained from the NCBI Taxonomy Database (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/ , accessed on 11 November 2013). A multiple sequence alignment of the 531 16S rRNA gene sequences was constructed using MAFFT version 6.713 with default parameters.28 (link)To find out the candidate sequences described above, highly conserved regions identified in the reference alignment were chosen as follows. Generally, the primer lengths for the PCR-amplification of 16S rRNA genes are more than 15 nt;9 (link) therefore, we used a sliding window of 15 nt with a step size of 1 nt across the reference alignment. For each window, we calculated the frequency of each 15-nt sequence with one mismatch allowed. The 15-nt sequences that included gaps were also considered when calculating the frequencies. The consensus sequence for each window was defined as the 15-nt sequence that was found most frequently within one mismatch among strains. The coverage rate for a consensus sequence in each phylum was defined as the percentage of matched sequences among genome-sequenced strains within one mismatch.
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Bacteria
Consensus Sequence
Gene Amplification
Genes
Genes, vif
Genome
Genome, Archaeal
Oligonucleotide Primers
Prokaryotic Cells
Ribosomal RNA Genes
RNA, Ribosomal, 16S
Sequence Alignment
Strains
All study TMAs were based on a series of 547 gastric cancer core biopsy specimens assembled and provided by Prof. Kreipe (Hannover). All individual patients gave written informed consent for biological studies at their initial presentation. All samples were obtained from surgery performed for diagnostic and/or therapeutic purposes and were used according to German ethical regulations. The study followed the guidelines of the Declaration of Helsinki and patient identity of the pathological specimens remained anonymous in the context of this study.
In a first step, a 30-core TMA set was pre-selected that provided a representative of all tumor types (intestinal, mixed, and diffuse) according to Lauren classification as well as different HER2 expression and amplification levels. Core size was 0.4 cm and each represented a different tumor sample. HER2 assessment was performed using different commercial assays according to the manufacturers’ instructions at the different participating sites (n = 8). IHC immunostaining was conducted using HercepTest™ (Dako Denmark A/S, Glostrup, Denmark) and/or the PATHWAY® HER2/neu (4B5) antibody (Ventana Medical Systems SA, Illkirch, France). HER2 amplification was determined by FISH assays, using either HER2 FISH pharmDX™ (Dako Denmark A/S) or PathVysion® (Abbott Laboratories, Des Plaines, IL, USA). Automated bright-field dual-color silver in situ hybridization (SISH) assay (BDISH; Inform™, Ventana Medical Systems SA) was used to determine gene amplification at three of the participating sites [26 (link)]. Evaluation was performed according to the modified gastric cancer testing protocol [11 (link)] taking incomplete basolateral or only lateral staining into account. As TMA cores were tested analogous to biopsies the 10% cut-off was recorded but not regarded for the final scoring (i.e., 1+, 2+, and 3+). FISH and BDISH were performed according to the manufacturers’ recommendations with ratios above 2.0 being considered amplified.
In a second step, the complete TMA sample series of 547 tumor cores was used to determine inter-observer variation of HER2 expression (staining intensity and area stained) scoring independent of inter-laboratory staining variation. Thus, TMAs used for evaluation were already IHC stained using the 4B5 antibody (Ventana Medical Systems SA) at the Hannover laboratory that supplied samples.
Data for the first 30-core TMA set were presented and discussed at a 2-day consensus meeting conducted at the Institute of Pathology, Charite, Berlin (27/28 March 2009). After a consensus was reached about specific issues concerning HER2 scoring by IHC in gastric cancer, the second full set of 547 cores were evaluated independently by six German pathologists (GB, MD, HH, JR, SA, AW). The complete 547 TMA set was scanned (Provito GmbH, Berlin, Germany) and provided as virtual slides to the panelists. By use of this data set, all cases that resulted in discordant IHC scores between observers were then individually discussed at a separate meeting in Düsseldorf (10 June 2009) to determine the most reproducible practical guideline for HER2 testing in gastric cancer. Statistical analyses were performed using the statistical program R version 1.9.1 and Microsoft® Excel®. Kappa statistics was calculated according to the method of Conger (1980) by package “irr” of program R [33 (link)]. In order validate these guidelines in routine practice a series of n = 447 prospective diagnostic gastric cancer samples have been tested at five different participating sites throughout Germany which comprised either a biopsy tissue block or one representative tissue block of resection specimen. Thereby, four sites followed the algorithm as proposed by EMEA with IHC (4B5, Ventana) being used first and one center applied both IHC and ISH (BDISH, Ventana) to all n = 152 specimens at their site.
In a first step, a 30-core TMA set was pre-selected that provided a representative of all tumor types (intestinal, mixed, and diffuse) according to Lauren classification as well as different HER2 expression and amplification levels. Core size was 0.4 cm and each represented a different tumor sample. HER2 assessment was performed using different commercial assays according to the manufacturers’ instructions at the different participating sites (n = 8). IHC immunostaining was conducted using HercepTest™ (Dako Denmark A/S, Glostrup, Denmark) and/or the PATHWAY® HER2/neu (4B5) antibody (Ventana Medical Systems SA, Illkirch, France). HER2 amplification was determined by FISH assays, using either HER2 FISH pharmDX™ (Dako Denmark A/S) or PathVysion® (Abbott Laboratories, Des Plaines, IL, USA). Automated bright-field dual-color silver in situ hybridization (SISH) assay (BDISH; Inform™, Ventana Medical Systems SA) was used to determine gene amplification at three of the participating sites [26 (link)]. Evaluation was performed according to the modified gastric cancer testing protocol [11 (link)] taking incomplete basolateral or only lateral staining into account. As TMA cores were tested analogous to biopsies the 10% cut-off was recorded but not regarded for the final scoring (i.e., 1+, 2+, and 3+). FISH and BDISH were performed according to the manufacturers’ recommendations with ratios above 2.0 being considered amplified.
In a second step, the complete TMA sample series of 547 tumor cores was used to determine inter-observer variation of HER2 expression (staining intensity and area stained) scoring independent of inter-laboratory staining variation. Thus, TMAs used for evaluation were already IHC stained using the 4B5 antibody (Ventana Medical Systems SA) at the Hannover laboratory that supplied samples.
Data for the first 30-core TMA set were presented and discussed at a 2-day consensus meeting conducted at the Institute of Pathology, Charite, Berlin (27/28 March 2009). After a consensus was reached about specific issues concerning HER2 scoring by IHC in gastric cancer, the second full set of 547 cores were evaluated independently by six German pathologists (GB, MD, HH, JR, SA, AW). The complete 547 TMA set was scanned (Provito GmbH, Berlin, Germany) and provided as virtual slides to the panelists. By use of this data set, all cases that resulted in discordant IHC scores between observers were then individually discussed at a separate meeting in Düsseldorf (10 June 2009) to determine the most reproducible practical guideline for HER2 testing in gastric cancer. Statistical analyses were performed using the statistical program R version 1.9.1 and Microsoft® Excel®. Kappa statistics was calculated according to the method of Conger (1980) by package “irr” of program R [33 (link)]. In order validate these guidelines in routine practice a series of n = 447 prospective diagnostic gastric cancer samples have been tested at five different participating sites throughout Germany which comprised either a biopsy tissue block or one representative tissue block of resection specimen. Thereby, four sites followed the algorithm as proposed by EMEA with IHC (4B5, Ventana) being used first and one center applied both IHC and ISH (BDISH, Ventana) to all n = 152 specimens at their site.
Biological Assay
Biopharmaceuticals
Biopsy
Core Needle Biopsy
Diagnosis
ERBB2 protein, human
Fishes
Gastric Cancer
Gene Amplification
Immunoglobulins
In Situ Hybridization
Intestines
Neoplasms
Operative Surgical Procedures
Pathologists
Patients
Silver
Therapeutics
Tissues
Most recents protocols related to «Gene Amplification»
To find associations between TF targeting and promoter methylation status and copy number variation status, we selected 76 melanoma CCLE cell lines and we computed the significance of associations using ANOVA as implemented in the Python package statsmodels v0.13.2 [96 ]. Since we were mostly interested in finding strong associations and prominent regulatory hallmarks of melanoma, we discretized the input data by considering a gene to be amplified if it had more than three copies and to be deleted if both copies are lost. For promoter methylation data, promoters were defined in CCLE as the 1kb region downstream of the gene’s transcriptional start site (TSS). We defined hypermethylated promoter sites as those having methylation status with a z-score greater than three and we defined hypomethylated sites as those having methylation status with a z-score less than negative three; we considered a gene to be amplified if it had evidence of more than three copies in the genome and to be deleted if both copies are lost. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, for a given gene at least three cell lines had a hypomethylation in that gene’s promoter) and corrected for multiple testing using a false discovery rate of less than 25% following the Benjamini-Hochberg procedure [97 ].
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S5 ) as the explanatory variable.
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
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Biological Assay
Cell Lines
Cell Survival
Copy Number Polymorphism
Gene Amplification
Gene Deletion
Genes
Genome
Melanoma
Methylation
Neoplasm Metastasis
Neoplasms
neuro-oncological ventral antigen 2, human
Pharmaceutical Preparations
Promoter, Genetic
Python
Reconstructive Surgical Procedures
regorafenib
Transcription Initiation Site
As detailed above, RNA and DNA were extracted in parallel and RNA was subsequently reverse transcribed. DNA and cDNA extracts were used as templates for PCR-based amplification for 16S rRNA gene/transcript V4 amplicon generation using the following primers: 515F-ACACTGACGACATGGTTCTACAGTGCCAGCMGCCGCGGTAA and 806R-TACGGTAGCAGAGACTTGGTCTGGACTACNVGGGTWTCTAAT, and thermocycling program: 94°C for 3 min, followed for 32 × [ 94°C for 45s, 50°C for 60s, 72°C for 90s], 72°C for 10 min, and a 4°C hold. Amplicon sequencing was performed on an Illumina MiSeq platform. Sequence analyses were performed using the DADA2 package [63 (link)] implemented in R. Briefly, forward and reverse reads were trimmed with the filterAndTrim() command using the following parameters: trimLeft = c(20,20), maxEE = c(2,2), phix = TRUE, multithread = TRUE, minLen = 120, followed by error assessments and independent forward and reverse read de-replication. Sequencing errors were removed using the dada() command and error-free forward and reverse reads were merged using the mergePairs() command, specifying overhand trimming and a minimum overlap of 120 base pairs. The resulting amplicon sequence variants (ASVs) were assigned taxonomy by alignment against the SILVA 132 database [64 (link)]. ASV count tables and taxonomy assignments were merged into an S4 object for diversity analysis and summary visualization using vegan in phyloseq [65 (link)].
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DNA, Complementary
DNA Replication
Gene Amplification
Genes
Genetic Diversity
Oligonucleotide Primers
RNA, Ribosomal, 16S
Vegan
Individuals were grouped as follows. Group 1 (five D. reticulatum fed a lab diet for 7 days); Group 2 (five D. reticulatum fed a lab diet for 14 days); Group 3 (five D. reticulatum fed a lab diet and infected on Day 7 with P. hermaphrodita with feces collected 7 days postinfection—14 days in total); Group 4 (three A. valentianus fed a lab diet for 7 days); Group 5 (three A. valentianus fed a lab diet for 14 days); Group 6 (three A. valentianus infected with P. hermaphrodita with feces collected 7 days postinfection—14 days in total). Feces were collected from each slug for DNA extraction.
DNA was extracted from feces using DNeasy PowerSoil Pro Kit (Qiagen) following the manufacturer's instructions. The presence of bacterial DNA was checked after extractions using PCR amplification of the hypervariable regions of the 16S rRNA gene. This was carried out using the primers 27f (5′‐AGAGTTTGATCMTGGCTCAG‐3′) and 1492r (5′‐TACGGYTACCTTGTTACGACTT‐3′) (Lane, 1991 ) with the following thermocycler conditions: 3 min at 95°C followed by 35 cycles of 15 s at 95°C, 30 s at 55°C, 1.5 min at 72°C, and a final step of 8 min at 72°C. Amplicons were visualized using agarose gel electrophoresis to confirm that PCRs had worked; in all cases, bands of the correct size were present, and no amplification of bacterial DNA could be seen in the extraction negative control or the PCR negative control.
DNA samples were sent for 16S rRNA metagenomic sequencing (Novogene). The V4 hypervariable region of the 16S rRNA gene was amplified using the primers 515F (5′‐GTGCCAGCMGCCGCGGTAA‐3′) and 806R (5′‐GGACTACHVGGGTWTCTAAT‐3′). All PCR reactions were carried out with Phusion® High‐Fidelity PCR Master Mix (New England Biolabs). Sequencing libraries were generated with NEBNext® UltraTM DNA Library Prep Kit for Illumina and quantified via Qubit and Q‐PCR. Libraries were sequenced on an Illumina NovaSeq. 6000 platform to generate 2 × 250 bp paired‐end reads.
Analysis of the raw reads occurred at Novogene using the following method. Paired‐end reads were merged using FLASH (V1.2.7) (Magoč and Salzberg, 2011 (link)). Quality filtering on the raw tags was performed under specific filtering conditions to obtain high‐quality clean tags according to the QIIME (V1.7.0) (Caporaso et al., 2010 (link)). The tags were compared with the reference database (SILVA database) using the UCHIME algorithm (Edgar et al., 2011 (link)) to detect chimera sequences. Detected chimera sequences were then removed to obtain Effective Tags. All Effective Tags were processed by UPARSE software (v7.0.1090) (Edgar, 2013 (link)). Sequences with ≥97% similarity were assigned to the same Operational Taxonomic Units (OTUs).
For each OTU, QIIME (Version 1.7.0) in the Mothur method was performed against the SSU rRNA database of SILVA Database for species annotation at each taxonomic rank (Threshold:0.8~1) (Quast et al., 2012 (link)). MUSCLE (Version 3.8.31) (Edgar, 2004 (link)) was used to obtain the phylogenetic relationship of all OTUs.
OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the least sequences. OTUs were analyzed for Alpha diversity (Wilcoxon test function) and Beta diversity (AMOVA—Analysis of Molecular Variance) to obtain richness and evenness information in samples. AMOVA was also used to compare the taxonomic compositions of infected and noninfected slugs in weighted PCoA. Analysis of Alpha and Beta diversity were all performed on the normalized data and calculated with QIIME (Version 1.7.0). Significant intragroup variation is detected via MetaStats based on their abundance.
DNA was extracted from feces using DNeasy PowerSoil Pro Kit (Qiagen) following the manufacturer's instructions. The presence of bacterial DNA was checked after extractions using PCR amplification of the hypervariable regions of the 16S rRNA gene. This was carried out using the primers 27f (5′‐AGAGTTTGATCMTGGCTCAG‐3′) and 1492r (5′‐TACGGYTACCTTGTTACGACTT‐3′) (Lane, 1991 ) with the following thermocycler conditions: 3 min at 95°C followed by 35 cycles of 15 s at 95°C, 30 s at 55°C, 1.5 min at 72°C, and a final step of 8 min at 72°C. Amplicons were visualized using agarose gel electrophoresis to confirm that PCRs had worked; in all cases, bands of the correct size were present, and no amplification of bacterial DNA could be seen in the extraction negative control or the PCR negative control.
DNA samples were sent for 16S rRNA metagenomic sequencing (Novogene). The V4 hypervariable region of the 16S rRNA gene was amplified using the primers 515F (5′‐GTGCCAGCMGCCGCGGTAA‐3′) and 806R (5′‐GGACTACHVGGGTWTCTAAT‐3′). All PCR reactions were carried out with Phusion® High‐Fidelity PCR Master Mix (New England Biolabs). Sequencing libraries were generated with NEBNext® UltraTM DNA Library Prep Kit for Illumina and quantified via Qubit and Q‐PCR. Libraries were sequenced on an Illumina NovaSeq. 6000 platform to generate 2 × 250 bp paired‐end reads.
Analysis of the raw reads occurred at Novogene using the following method. Paired‐end reads were merged using FLASH (V1.2.7) (Magoč and Salzberg, 2011 (link)). Quality filtering on the raw tags was performed under specific filtering conditions to obtain high‐quality clean tags according to the QIIME (V1.7.0) (Caporaso et al., 2010 (link)). The tags were compared with the reference database (SILVA database) using the UCHIME algorithm (Edgar et al., 2011 (link)) to detect chimera sequences. Detected chimera sequences were then removed to obtain Effective Tags. All Effective Tags were processed by UPARSE software (v7.0.1090) (Edgar, 2013 (link)). Sequences with ≥97% similarity were assigned to the same Operational Taxonomic Units (OTUs).
For each OTU, QIIME (Version 1.7.0) in the Mothur method was performed against the SSU rRNA database of SILVA Database for species annotation at each taxonomic rank (Threshold:0.8~1) (Quast et al., 2012 (link)). MUSCLE (Version 3.8.31) (Edgar, 2004 (link)) was used to obtain the phylogenetic relationship of all OTUs.
OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the least sequences. OTUs were analyzed for Alpha diversity (Wilcoxon test function) and Beta diversity (AMOVA—Analysis of Molecular Variance) to obtain richness and evenness information in samples. AMOVA was also used to compare the taxonomic compositions of infected and noninfected slugs in weighted PCoA. Analysis of Alpha and Beta diversity were all performed on the normalized data and calculated with QIIME (Version 1.7.0). Significant intragroup variation is detected via MetaStats based on their abundance.
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Chimera
Diet
DNA, Bacterial
DNA Library
Electrophoresis, Agar Gel
Feces
Gastrointestinal Microbiome
Gene Amplification
Metagenome
Muscle Tissue
Oligonucleotide Primers
Ribosomal RNA
Ribosomal RNA Genes
RNA, Ribosomal, 16S
Slugs
Vision
Approximately 5 g of the middle section of the feces was collected and immediately frozen and stored at ‒80°C. The samples were transported on dry ice to Shenzhen Micro Health Gene Technology Co., Ltd. for high-throughput sequencing. MoBio's PowerSoil® DNA Isolation Kit was used to extract bacterial DNA from fecal samples. Amplification of the V3 – V4 region of the 16S rRNA gene in DNA was performed by polymerase chain reaction (PCR). Amplified samples were sequenced using the Illumina MiSeq high-throughput sequencing platform.
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DNA, Bacterial
Dry Ice
Feces
Freezing
Gene Amplification
Genes
isolation
Polymerase Chain Reaction
RNA, Ribosomal, 16S
Technology, Health Care
Fresh bacterial colonies were used for Genomic DNA (gDNA) extraction using the Invisorb Spin Universal Kit (Stratec Molecular, Berlin, Germany) following the protocol suggested by the fabricant and were stored at −20°C until further use. Molecular confirmation of Salmonella isolates was done by amplification of a fragment of invA gene (accession number M90846.1) by endpoint PCR.
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Bacteria
Gene Amplification
Genome
Salmonella
Top products related to «Gene Amplification»
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The StepOnePlus Real-Time PCR System is a compact, flexible, and easy-to-use instrument designed for real-time PCR analysis. It can be used to detect and quantify nucleic acid sequences.
More about "Gene Amplification"
Gene amplification is a fundamental technique in molecular biology and genetics, where specific DNA sequences are replicated to produce multiple copies.
This process is widely utilized in gene expression studies, genetic engineering, and diagnostic applications.
By increasing the number of target genes, gene amplification enhances the sensitivity and reliability of downstream analyses, such as PCR (Polymerase Chain Reaction), DNA sequencing, and hybridization assays.
Optimizing gene amplification protocols is crucial for reproducible and accurate results in various research and clinical settings.
Researchers often rely on reagents like TRIzol and RNeasy Mini Kit for RNA extraction, MiSeq platform for DNA sequencing, and tools like NanoDrop 2000 for nucleic acid quantification.
Reverse transcription kits, such as High-Capacity cDNA Reverse Transcription Kit and PrimeScript RT reagent kit, are commonly used to generate cDNA from RNA templates.
To enhanec the sensitivity and reliability of gene amplification experiments, researchers can utilize AI-powered platforms like PubCompare.ai.
This intelligent tool helps identify the most effective methods and products from literature, preprints, and patents, streamlining workflows and delivering reliable outcomes.
By leveraging PubCompare.ai, researchers can optimize their gene amplification protocols, improving reproducibility and accuracy in their studies.
Furthermore, DNA extraction kits like the QIAamp DNA Mini Kit are often employed to purify genetic material for downstream applications.
The StepOnePlus Real-Time PCR System is another commonly used instrument for quantitative gene expression analysis, complementing the gene amplification process.
In summary, gene amplification is a crutial technique in molecular biology, with numerous applications in research and diagnostics.
By understanding the relevant tools, reagents, and platforms, researchers can optimize their gene amplification workflows, enhancing the reproducibility and accuracy of their findings.
The integration of AI-powered solutions, such as PubCompare.ai, further streamlines this process, empowering researchers to deliver reliable and impactful results.
This process is widely utilized in gene expression studies, genetic engineering, and diagnostic applications.
By increasing the number of target genes, gene amplification enhances the sensitivity and reliability of downstream analyses, such as PCR (Polymerase Chain Reaction), DNA sequencing, and hybridization assays.
Optimizing gene amplification protocols is crucial for reproducible and accurate results in various research and clinical settings.
Researchers often rely on reagents like TRIzol and RNeasy Mini Kit for RNA extraction, MiSeq platform for DNA sequencing, and tools like NanoDrop 2000 for nucleic acid quantification.
Reverse transcription kits, such as High-Capacity cDNA Reverse Transcription Kit and PrimeScript RT reagent kit, are commonly used to generate cDNA from RNA templates.
To enhanec the sensitivity and reliability of gene amplification experiments, researchers can utilize AI-powered platforms like PubCompare.ai.
This intelligent tool helps identify the most effective methods and products from literature, preprints, and patents, streamlining workflows and delivering reliable outcomes.
By leveraging PubCompare.ai, researchers can optimize their gene amplification protocols, improving reproducibility and accuracy in their studies.
Furthermore, DNA extraction kits like the QIAamp DNA Mini Kit are often employed to purify genetic material for downstream applications.
The StepOnePlus Real-Time PCR System is another commonly used instrument for quantitative gene expression analysis, complementing the gene amplification process.
In summary, gene amplification is a crutial technique in molecular biology, with numerous applications in research and diagnostics.
By understanding the relevant tools, reagents, and platforms, researchers can optimize their gene amplification workflows, enhancing the reproducibility and accuracy of their findings.
The integration of AI-powered solutions, such as PubCompare.ai, further streamlines this process, empowering researchers to deliver reliable and impactful results.