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nterrupting two SSRs in a compound microsatellite was 100 bp. Primer pairs flanking each SSR loci were designed using the Primer3 program, SNPs in the cDNA sequences between WI1983G and WI1983H were identified with PolyBayes, To eliminate errors introduced by PCR amplification BIO GSK-3 inhibitor during the cDNA synthesis step and homopolymer errors introduced by the 454 pyrosequenc ing technology, and to distinguish true BIO GSK-3 inhibitor SNPs from allele differences, we further filtered the PolyBayes results and only kept SNPs meeting all the following criteria. 1 at least 2× coverage at the potential SNP site for each culti var. 2 not an indel site surrounded by long stretch homopolyers. 3 no same bases at the potential SNP site between the two cultivars. Understanding the genetic basis of adaptive changes has been a major goal of evolutionary biology.
So far, com plete, comprehensive analyses have been possible only in microorganisms, The advent of a new genera tion of massively parallel DNA sequencing technologies brings the promise of rapid progress in understanding the genetic basis of adaptation also in more complex organisms, including mammals, The marriage of large scale selection Dynasore experiments with new sequencing technologies Haematopoiesis appears to be a prospective research strategy to this end. Even now, whole genome resequencing in most non model eukaryotes, possessing complex genomes, is not a viable option, due to challenges with assembly in the presence of large amounts of repetitive sequences, and it is unclear whether the situation will improve in the near future, Therefore, researchers have turned to tran scriptome analysis as a powerful and universal tool for identification of both variation at the gene expression level and sequence polymorphisms in coding regions.
Deep coverage transcriptome sequencing Dynasore enables the developmental stage and or tissue specific analysis of the abundance of transcripts as well as detec tion of sequence variants, Thus, a comprehensive characterization of the transcriptional differences between selection regimes in terms of single nucleotide polymorphisms, splicing variants, transcription start sites and at the level of transcription of individual genes is possible. The design and feasibility of RNAseq experiments, however, depend on the availability of the reference genome to which the short reads from RNAseq experiments are aligned.
If the reference genome is BIO GSK-3 inhibitor not available, which is the case for the majority of non model eukaryotes, the lack of genomic resources may be cir cumvented by employing a two step strategy. i assemble the transcriptome de novo, and then ii use the assembly as a reference to Dynasore align the short reads from RNAseq experiments. If the initial assembly is performed BIO GSK-3 inhibitor on sequences derived from multiple individuals, the detection of sequence differences between individuals or populations can be also performed at this stage. Theoretically, producing both de novo assembly and obtaining information about the levels of transcription would be possible in a single step, although currently available technologies impose serious constrains on such experiments.
Technology offering long reads does not provide enough coverage for detailed expression profiling, while assembling Dynasore short reads provided in large amounts by Illu mina and ABI SOLiD has been notoriously difficult. Thus, de novo assembly using 454 technology or a combi nation of 454 and shorter read technologies, followed by expression profiling using short reads seems a reason able approach. When selecting the organ and or developmental stage for transcriptome characterization one encoun ters a tradeoff between maximizing the number of dis tinct transcripts and maximizing coverage of individual transcripts, the two determinants of transcriptome com pleteness. This tradeoff is likely to remain even when cDNA normalization is used to limit the variation in abundance of transcripts from various genes, simply because the expression of many genes is spatially or tem porally r