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Locus-patterned sequence oriented enrichment for multi-dimensional gene analysis.

ABSTRACT: Multi-dimensional gene analysis provides in-depth insights into gene sequence, locus variations and molecular abundance, whereas it is vulnerable to the perturbation of complex reaction networks and always compromises on the discrimination of analogous sequences. Here, we present a sequence oriented enrichment method patterned by the prescribed locus without crosstalk between concurrent reactions. Energetically favourable structures of nucleic acid probes are theoretically derived and oriented to a specific gene locus. We designed a pair of universal probes for multiple conserved loci to avoid side reactions from undesired interactions among increased probe sets. Furthermore, competitive probes were customized to sink analogues for differentiating the reaction equilibrium and kinetics of sequence enrichment from the target, so variant loci can be synchronously identified with nucleotide-level resolution. Thus, the gene locus guides sequence enrichment and combinatorial signals to create unique codes, which provides access to multidimensional and precise information for gene decoding.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC6844269 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Locus-patterned sequence oriented enrichment for multi-dimensional gene analysis.

Zhao Yue Y Fang Xiaoxing X Chen Feng F Bai Min M Fan Chunhai C Zhao Yongxi Y

Chemical science 20190722 36

Multi-dimensional gene analysis provides in-depth insights into gene sequence, locus variations and molecular abundance, whereas it is vulnerable to the perturbation of complex reaction networks and always compromises on the discrimination of analogous sequences. Here, we present a sequence oriented enrichment method patterned by the prescribed locus without crosstalk between concurrent reactions. Energetically favourable structures of nucleic acid probes are theoretically derived and oriented t ...[more]

PMID: 31803421

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Project description:Multi-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context.We present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles.SRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.

Dataset Information

Locus-patterned sequence oriented enrichment for multi-dimensional gene analysis.

Publications

Locus-patterned sequence oriented enrichment for multi-dimensional gene analysis.

Similar Datasets

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