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Methods in Enzymology 2019Aptamers are small, functional nucleic acids that bind a variety of targets, often with high specificity and affinity. Genomic aptamers constitute the ligand-binding... (Review)
Review
Aptamers are small, functional nucleic acids that bind a variety of targets, often with high specificity and affinity. Genomic aptamers constitute the ligand-binding domains of riboswitches, whereas synthetic aptamers find applications as diagnostic and therapeutic tools, and as ligand-binding domains of regulatory RNAs in synthetic biology. Discovery and characterization of aptamers has been limited by a lack of high-throughput approaches that uncover the target-binding domains and the biochemical properties of individual sequences. With the advent of high-throughput sequencing, large-scale analysis of in vitro selected populations of aptamers (and catalytic nucleic acids, such as ribozymes and DNAzmes) became possible. In recent years the development of new experimental approaches and software tools has led to significant streamlining of the selection-pool analysis. This article provides an overview of post-selection data analysis and describes high-throughput methods that facilitate rapid discovery and biochemical characterization of aptamers.
Topics: Animals; Aptamers, Nucleotide; High-Throughput Nucleotide Sequencing; Humans; Nucleic Acids; SELEX Aptamer Technique; Software
PubMed: 31128787
DOI: 10.1016/bs.mie.2019.02.009 -
Journal of Experimental Zoology. Part... Nov 2012
Topics: Biological Evolution; Developmental Biology; Gene Regulatory Networks; Genes, Developmental; High-Throughput Nucleotide Sequencing; Systems Biology
PubMed: 22791647
DOI: 10.1002/jez.b.22463 -
Nature Reviews. Genetics Apr 2018Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the... (Review)
Review
Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the chance to diagnose rare inherited diseases or inform the targeting of therapies. New genomic diagnostic tests compete with traditional approaches to diagnosis, including the genetic testing of single genes and other clinical strategies, for finite health-care budgets. In this context, decision analytic model-based cost-effectiveness analysis is a useful method to help evaluate the costs versus consequences of introducing new health-care interventions. This Perspective presents key methodological, technical, practical and organizational challenges that must be considered by decision-makers responsible for the allocation of health-care resources to obtain robust and timely information about the relative cost-effectiveness of the increasing numbers of emerging genomic tests.
Topics: Cost-Benefit Analysis; Genetic Testing; Genomics; High-Throughput Nucleotide Sequencing; Humans
PubMed: 29353875
DOI: 10.1038/nrg.2017.108 -
Science China. Life Sciences Feb 2013
Topics: Computational Biology; Epigenesis, Genetic; Genome, Human; High-Throughput Nucleotide Sequencing; Humans; Precision Medicine; Sequence Analysis, RNA
PubMed: 23393024
DOI: 10.1007/s11427-013-4436-x -
Molecular Ecology Feb 2022The sequencing depth required to genotype autopolyploid populations is a very controversial topic. Different studies have adopted variable depth values without a clear...
The sequencing depth required to genotype autopolyploid populations is a very controversial topic. Different studies have adopted variable depth values without a clear guide on the optimal sequencing depth value. Many studies suggest high depth thresholds for different ploidies that may not be practical and substantially increase the overall genotyping cost for different projects. However, such conservative thresholds may not be required to achieve the most common research goals. In fact, some recent reports in the field of quantitative genetics found that much lower sequencing depth thresholds could achieve the same accuracy as high depth thresholds. In this manuscript, I discuss when researchers need to use stringent sequencing depth thresholds and when they can use more relaxed ones. I support my argument by calculating the probabilities of sampling different homologues at a given sequencing depth. I also discuss the uses and the uncertainty in calculating a continuous allelic dosage as the proportion of sequencing reads that hold the alternative allele, which is becoming a common method now in quantitative genetics to replace discrete dosage estimation.
Topics: Alleles; Genotype; High-Throughput Nucleotide Sequencing; Polymorphism, Single Nucleotide
PubMed: 34875138
DOI: 10.1111/mec.16313 -
Briefings in Bioinformatics Jan 2021The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed... (Review)
Review
The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.
Topics: Animals; High-Throughput Nucleotide Sequencing; Humans; Metagenomics; Microbiota
PubMed: 32577746
DOI: 10.1093/bib/bbaa121 -
Nature Methods Mar 2012
Topics: DNA; High-Throughput Nucleotide Sequencing
PubMed: 22453909
DOI: 10.1038/nmeth.1934 -
Value in Health : the Journal of the... Sep 2018
Topics: High-Throughput Nucleotide Sequencing; Humans
PubMed: 30224105
DOI: 10.1016/j.jval.2018.06.012 -
Annual Review of Microbiology Sep 2020Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we... (Review)
Review
Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we highlight the benefits of using metagenomics as well as the breadth of conclusions that can be made using currently available analytical tools, such as greater resolution of species and strains across phyla and functional content, while highlighting challenges of metagenomic data analysis. Major challenges remain in annotating function, given the dearth of functional databases for environmental bacteria compared to model organisms, and the technical difficulties of metagenome assembly and phasing in heterogeneous environmental samples. In the future, improvements and innovation in technology and methodology will lead to lowered costs. Data integration using multiple technological platforms will lead to a better understanding of how to harness metagenomes. Subsequently, we will be able not only to characterize complex microbiomes but also to manipulate communities to achieve prosperous outcomes for health, agriculture, and environmental sustainability.
Topics: Bacteria; Computational Biology; High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Microbiota
PubMed: 32603623
DOI: 10.1146/annurev-micro-012520-072314 -
Trends in Genetics : TIG Feb 2015Science is defined in part by an honest exposition of the uncertainties that arise in measurements and propagate through calculations and inferences, so that the... (Review)
Review
Science is defined in part by an honest exposition of the uncertainties that arise in measurements and propagate through calculations and inferences, so that the reliabilities of its conclusions are made apparent. The recent rapid development of high-throughput DNA sequencing technologies has dramatically increased the number of measurements made at the biochemical and molecular level. These data come from many different DNA-sequencing technologies, each with their own platform-specific errors and biases, which vary widely. Several statistical studies have tried to measure error rates for basic determinations, but there are no general schemes to project these uncertainties so as to assess the surety of the conclusions drawn about genetic, epigenetic, and more general biological questions. We review here the state of uncertainty quantification in DNA sequencing applications, describe sources of error, and propose methods that can be used for accounting and propagating these errors and their uncertainties through subsequent calculations.
Topics: Base Sequence; High-Throughput Nucleotide Sequencing; Humans; Models, Statistical; Sequence Analysis, DNA; Uncertainty
PubMed: 25579994
DOI: 10.1016/j.tig.2014.12.002