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12 results for “primer design”
A fast machine-learning-guided primer design pipeline for selective whole genome amplification
<p>Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales – precisely the scales at which these processes occur – microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluates strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime from weeks to minutes. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluated the novel swga2.0 pipeline by designing primers sets that successfully amplify <em>Prevotella melaninogenica</em>, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.</p>
KuafuPrimer: Machine learning facilitates the design of 16S rRNA gene primers with minimal bias in bacterial communities
<p>KuafuPrimer is a machine learning-aided method that learns community characteristics from several samples to design 16S rRNA gene primers with minimal bias for microbial communities. It is built on <strong>Python 3.9.0</strong>, <strong>Pytorch 1.12.0</strong>. Here are some large size files required to run KuafuPrimer, and users need to download and put them in correct directories before running the program.</p> <ol> <li>Silva_ref_data.zip: processed files of silva dataset that should be put in <code>Model_data/Silva_ref_data/</code>.</li> <li>DeepAnno16_publicated_model.zip: parameters of the trained DeepAnno16 model that should be put in <code>Model_data/DeepAnno16_publicated_model/</code> .</li> </ol> <p>For more information, please refer to https://github.com/zhanghaoyu9931/KuafuPrimer.</p>
A fast machine-learning-guided primer design pipeline for selective whole genome amplification
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Data from: Quantitative PCR primer design affects quantification of dsRNA-mediated gene knockdown
RNA interference (RNAi) is a powerful tool for studying functions of candidate genes in both model and non-model organisms and a promising technique for therapeutic applications. Successful application of this technique relies on the accuracy and reliability of methods used to quantify gene knockdown. With the limitation in the availability of antibodies for detecting proteins, quantitative PCR (qPCR) remains the preferred method for quantifying target gene knockdown after dsRNA treatment . We evaluated how qPCR primer binding site and target gene expression levels affect quantification of intact mRNA transcripts following dsRNA-mediated RNAi. The use of primer pairs targeting the mRNA sequence within the dsRNA target region failed to reveal a significant decrease in target mRNA transcripts for genes with low expression levels, but not for a highly expressed gene. By contrast, significant knockdown was detected in all cases with primer pairs targeting the mRNA sequence extending beyond the dsRNA target region, regardless of the expression levels of the target gene. Our results suggest that at least for genes with low expression levels, quantifying the efficiency of dsRNA-mediated RNAi with primers amplifying sequences completely contained in the dsRNA target region should be avoided due to the risk of false negative results. Instead, primer pairs extending beyond the dsRNA target region of the mRNA transcript sequences should be used for accurate and reliable quantification of silencing efficiency.
Data from: Scrimer: designing primers from transcriptome data
With the rise of next-generation sequencing methods, it has become increasingly possible to obtain genomewide sequence data even for nonmodel species. Such data are often used for the development of single nucleotide polymorphism (SNP) markers, which can subsequently be screened in a larger population sample using a variety of genotyping techniques. Many of these techniques require appropriate locus-specific PCR and genotyping primers. Currently, there is no publicly available software for the automated design of suitable PCR and genotyping primers from next-generation sequence data. Here we present a pipeline called Scrimer that automates multiple steps, including adaptor removal, read mapping, selection of SNPs and multiple primer design from transcriptome data. The designed primers can be used in conjunction with several widely used genotyping methods such as SNaPshot or MALDI-TOF genotyping. Scrimer is composed of several reusable modules and an interactive bash workflow that connects these modules. Even the basic steps are presented, so the workflow can be executed in a step-by-step manner. The use of standard formats throughout the pipeline allows data from various sources to be plugged in, as well as easy inspection of intermediate results with visualization tools of the user's choice.
Primers designed for red deer microsatellite loci
<p>Primers designed for microsatellite loci in the red deer genome</p>
Supplementary information for GIL: A python package for designing custom indexing primers
<p>Supplementary information for GIL: A python package for designing custom indexing primers. Final_Indexes.zip contains the output from GIL run with default parameters. The first plate of generated indexing primers were ordered and used to create 44 Illumina sequencing libraries. BCL files from a MiSeq Nano run of the 44 libraries were successfully demultiplexed with bcl2fastq using the sample sheets generated by GIL. Analysis of sequences that were not demultiplexed successfully to determine deletion frequency in index sequences can be found in demultiplexing_tests.zip, along with relevant fastq and stats files.</p>
Data from: Quantitative PCR primer design affects quantification of dsRNA-mediated gene knockdown
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Data from: Scrimer: designing primers from transcriptome data
Open the record for dataset details and reuse information.
COI sequences used in primer design
<p>COI sequences used for designing specific primers for <em>Garra cambodgiensis</em></p>
Quantitative Mammalian Transcriptomics using Designed Primer-based Amplification
GEO Series GSE45474. Mus musculus. 33 samples. Type: Expression profiling by high throughput sequencing.
5C-ID: Increased resolution Chromosome-Conformation-CaptureCarbon-Copy with in situ 3C and double alternating primer design
GEO Series GSE114121. Mus musculus. 7 samples. Type: Other.
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OpenNeuro
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