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      Massively-parallel enrichment of minor alleles for mutational testing via low-depth duplex sequencing

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          Abstract

          The ability to assay large numbers of low-frequency mutations is useful in biomedicine, yet, the technical hurdles of sequencing multiple mutations at extremely high depth, with accuracy, limits their detection in clinical practice. Low-frequency mutations can typically be detected by increasing the sequencing depth, however this limits the number of loci that can be probed for simultaneously. Here, we report a technique to accurately track thousands of distinct mutations with minimal reads, termed MAESTRO (minor allele enriched sequencing through recognition oligonucleotides), which employs massively-parallel mutation enrichment to enable duplex sequencing to track up to 10,000 low-frequency mutations, yet requiring up to 100-fold less sequencing. We show that MAESTRO could inform the mutation validation of whole-exome sequencing and whole genome sequencing data from tumor samples, enable chimerism testing, and is suitable for the monitoring of minimal residual disease via liquid biopsies. MAESTRO may improve the breadth, depth, accuracy, and efficiency of sequencing-based mutational testing.

          Editorial summary:

          Massively-parallel mutation enrichment enables the tracking of up to 10,000 low-frequency mutations, via duplex sequencing, requiring up to 100-fold less sequencing depth.

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          Most cited references53

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          Is Open Access

          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Snakemake--a scalable bioinformatics workflow engine.

            Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames. http://snakemake.googlecode.com. johannes.koester@uni-due.de.
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              Current understanding of the human microbiome

              Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.
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                Author and article information

                Journal
                101696896
                45929
                Nat Biomed Eng
                Nat Biomed Eng
                Nature biomedical engineering
                2157-846X
                1 February 2022
                March 2022
                17 March 2022
                17 September 2022
                : 6
                : 3
                : 257-266
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [2 ]Vanderbilt University, Nashville, TN, USA
                [3 ]Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA, USA
                [4 ]Harvard Medical School, Boston, MA, USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
                [6 ]Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
                [7 ]Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
                Author notes
                [ꜛ]

                Co-first authors

                Author Contributions:

                G.G., E.N., J.H.B. designed the research, analyzed the data, and wrote the manuscript. T.B. and J.R. analyzed the data and created figures for the manuscript. S.C.R., D.S., K.X., R.L., F.Y., K.W.L. designed experiments and interpreted results. A.D.C., D.G.S., S.M.T., I.E.K., H.E.P. contributed clinical samples and interpretation of results. J.C.L., G.M.M., T.R.G., and V.A.A. designed the research, interpreted the results, and wrote the manuscript. All authors reviewed and approved the final version of the manuscript.

                [* ]Co-corresponding authors
                Article
                NIHMS1775951
                10.1038/s41551-022-00855-9
                9089460
                35301450
                515ef8c0-32d4-4b4b-9889-9f772a7c1943

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