Decision Support Systems 31 405-428 A flexible knowledge by Man Leung Wong

By Man Leung Wong

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1 Experimental design considerations (I) Project phase Sample Sequencing strategy Bioinformatic analyses Efficient reporting Recommendations 1. Reduce expected genetic variability of the sample by using minimal number of inbreed individuals if possible 1. Determine early size, heterozygosity, and repetitive structure of the target genome 2. Consider recent experiences in similar organisms 3. Consider contiguity and coverage needed to address the specific questions 4. Combine throughput with long-range approach (FOSMIDS, longer read technology) 1.

Consider contiguity and coverage needed to address the specific questions 4. Combine throughput with long-range approach (FOSMIDS, longer read technology) 1. Engage expert collaborators from the beginning 2. Survey state-of-the-art methodology 3. , high heterozygosity) 1. Deposit all possible data (raw reads, assemblies, annotations) in public repositories 2. Link data to publication 3. Report standard quality parameters for assembly and annotation 4. 2 Experimental design considerations (II) Project phase Sample Sequencing strategy Bioinformatic analyses Efficient reporting Recommendations 1.

Longer fragments are not amplified as well by bridging PCR on the Illumina flow cell, so smaller fragments need to be removed by column purification if longer (>500 bp) fragment libraries are to be sequenced. 6 T. S. Alioto Decision Tree for Good Sequencing Strategy Selection 1000000 233 116 0 500000 frequency 1500000 2000000 The most important aspects that anticipate the difficulty of an assembly in a de novo genome sequencing project is the complexity of the target genome, in terms of size, repeat structure, and level of heterozygosity.

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