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The genomic basis of circadian and circalunar timing adaptations in a midge

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      Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

       K Livak,  T Schmittgen (2001)
      The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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 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

            Affiliations
            [1 ]Max F. Perutz Laboratories, University of Vienna, Campus Vienna Biocenter, Dr. Bohr-Gasse 9/4, 1030 Vienna, Austria
            [2 ]Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna and Medical University of Vienna, Dr. Bohr-Gasse 9, 1030 Vienna, Austria
            [3 ]Research Platform “Rhythms of Life,” University of Vienna, 1030 Vienna, Austria
            [4 ]Department of Biology, Chemistry, Pharmacy, Institute of Chemistry and Biochemistry, FU Berlin, 14195 Berlin, Germany
            [5 ]Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
            [6 ]Institute of Population Genetics, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Josef-Baumann-Gasse 1, 1210 Vienna, Austria
            [7 ]Dept. of Neurobiology, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria
            [8 ]Department of Entomology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745 Jena, Germany
            Author notes
            Correspondence and requests for materials: K.T-R. ( kristin.tessmar@ 123456mfpl.ac.at ) and T.S.K. ( kaiser@ 123456evolbio.mpg.de ).
            [9]

            current address: Max Planck Institute for Evolutionary Biology, August-Thienemann-Straße 2, 24306 Plön, Germany

            [10]

            current address: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211, USA

            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            17 October 2016
            21 November 2016
            01 December 2016
            21 May 2017
            : 540
            : 7631
            : 69-73
            27871090 5133387 10.1038/nature20151 EMS70245

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