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      Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking

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          Abstract

          While lung cancer risk among smokers is dependent on smoking dose, it remains unknown if this increased risk reflects an increased rate of somatic mutation accumulation in normal lung cells. Here we applied single-cell whole genome sequencing of proximal bronchial basal cells from 33 subjects aged between 11 and 86 years with smoking histories varying from never smoking to 116 pack years. We found an increase in the frequency of single-nucleotide variants and small insertions and deletions with chronological age in never-smokers with mutation frequencies significantly elevated among smokers. When plotted against smoking pack-years, mutations followed the linear increase in cancer risk only until about 23 pack years, after which no further increase in mutation frequency was observed, pointing towards individual selection for mutation avoidance. Known lung cancer-defined mutation signatures tracked with both age and smoking. No significant enrichment for somatic mutations in lung cancer driver genes was observed.

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          Most cited references65

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          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fitting Linear Mixed-Effects Models Usinglme4

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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                22 December 2022
                April 2022
                11 April 2022
                17 January 2023
                : 54
                : 4
                : 492-498
                Affiliations
                [1. ]Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461.
                [2. ]Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461.
                [3. ]Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY 10461.
                [4. ]Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
                [5. ]Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technologies, Voronezh 394000, Russia.
                [6. ]Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455.
                [7. ]Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455.
                [8. ]Biology Department, Touro College; Pulmonary Medicine, Pulmonary Medicine, Albert Einstein College of Medicine
                Author notes
                [*]

                These authors contributed equally to this work.

                Author contributions

                J.V., A.Y.M., S.D.S. conceived this study and designed the experiments. S.D.S., M.S., T.S., Y. P., C. S., and A. S. provided clinical, procedural, and specimen-specific study expertise and logistics. Z.H. performed the experiments. Z.H., J.V., A.Y.M., S.S. and KY analyzed the data. Z.H., and J.V. wrote the manuscript.

                Article
                NIHMS1781710
                10.1038/s41588-022-01035-w
                9844147
                35410377
                62d780b6-db28-4d44-92df-f50d3e5c8323

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                Genetics
                Genetics

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