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      Genetic modification of spikelet arrangement in wheat increases grain number without significantly affecting grain weight

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          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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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

              Heng Li (2011)
              Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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                Author and article information

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                Journal
                Molecular Genetics and Genomics
                Mol Genet Genomics
                Springer Science and Business Media LLC
                1617-4615
                1617-4623
                April 2019
                December 27 2018
                April 2019
                : 294
                : 2
                : 457-468
                Article
                10.1007/s00438-018-1523-5
                30591960
                31b708a9-bc0e-44ff-865b-5bd01d3229fa
                © 2019

                https://creativecommons.org/licenses/by/4.0

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