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      Improved reference genome of Aedes aegypti informs arbovirus vector control

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      Nature
      Springer Nature America, Inc

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          Abstract

          Female Aedes aegypti mosquitoes infect >400 million people each year with dangerous viral pathogens including dengue, yellow fever, Zika, and chikungunya. Progress in understanding mosquito biology and developing tools to fight them has been slowed by the lack of a high-quality genome assembly. Here we combine diverse technologies to produce the dramatically improved, fully re-annotated AaegL5 genome assembly, and demonstrate how it accelerates mosquito science. We anchored physical and cytogenetic maps, doubled the number of known chemosensory ionotropic receptors that guide mosquitoes to human hosts and egg-laying sites, provided further insight into the size and composition of the elusive sex-determining M locus, and revealed copy-number variation among glutathione S-transferase genes important for insecticide resistance. Using high-resolution quantitative trait locus (QTL) and population genomic analyses, we mapped new candidates for dengue vector competence and insecticide resistance. AaegL5 will catalyse new biological insights and intervention strategies to fight this deadly disease vector.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Nature America, Inc
                0028-0836
                1476-4687
                November 14 2018
                Article
                10.1038/s41586-018-0692-z
                61acf592-0ade-4386-b671-cc477ca6c4d9
                © 2018

                http://www.springer.com/tdm

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