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A microarray-based high throughput molecular marker genotyping method: the tagged microarray marker (TAM) approach.

ABSTRACT: A microarray-based method has been developed for scoring thousands of DNAs for a co-dominant molecular marker on a glass slide. The approach was developed to detect insertional polymorphism of transposons and works well with single nucleotide polymorphism (SNP) markers. Biotin- terminated allele-specific PCR products are spotted unpurified onto streptavidin-coated glass slides and visualised by hybridisation of fluorescent detector oligonucleotides to tags attached to the allele- specific PCR primers. Two tagged primer oligonucleotides are used per locus and each tag is detected by hybridisation to a concatameric DNA probe labelled with multiple fluorochromes.

SUBMITTER: Flavell AJ

PROVIDER: S-EPMC206481 | biostudies-literature | 2003 Oct

REPOSITORIES: biostudies-literature

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A microarray-based high throughput molecular marker genotyping method: the tagged microarray marker (TAM) approach.

Flavell Andrew J AJ Bolshakov Viacheslav N VN Booth Allan A Jing Runchun R Russell Joanne J Ellis T H Noel TH Isaac Peter P

Nucleic acids research 20031001 19

A microarray-based method has been developed for scoring thousands of DNAs for a co-dominant molecular marker on a glass slide. The approach was developed to detect insertional polymorphism of transposons and works well with single nucleotide polymorphism (SNP) markers. Biotin- terminated allele-specific PCR products are spotted unpurified onto streptavidin-coated glass slides and visualised by hybridisation of fluorescent detector oligonucleotides to tags attached to the allele- specific PCR pr ...[more]

PMID: 14500843

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Project description:BackgroundHigh throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.ResultsWe introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.ConclusionsAccording to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Project description:The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines (Triticum aestivum, CDC Stanley x CDC Landmark), wheat-barley (T. aestivum x Hordeum vulgare) and wheat-wheatgrass (Triticum durum x Thinopyrum intermedium) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

Dataset Information

A microarray-based high throughput molecular marker genotyping method: the tagged microarray marker (TAM) approach.

Publications

A microarray-based high throughput molecular marker genotyping method: the tagged microarray marker (TAM) approach.

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