- 1. NGS Genome and RNAseq Assembly - A Hands on Primer
- 1.1 Introduction
- 1.2 Chromosomes
- 1.3 Typical Assembled Genomes
- 1.4 Shotgun Assembly Approach
- 1.5 Technological versus Algorithmic Limitations
- 1.6 Iterative Approach and the Role of Planning
- 1.7 Our Data
- 2. Sequencing Technologies
- 2.1 Illumina Reads
- 2.2 K-mer Based Error Modeling in Illumina Reads
- 2.3 PCR Amplification Errors in Mate-pairs
- 2.4 ABI SOLiD Reads
- 2.6 454 Reads
- 2.7 PacBio Reads
- 2.8 Error Distribution in PacBio Reads
- 3. Preanalysis of Reads
- 3.1 K-mer Distribution
- 3.2 Genome Size Estimation
- 3.3 Deviation from Ideal - Nonuniform Coverage
- 3.4 Deviation from Ideal - Polymorphism
- 3.5 Deviation from Ideal - Sequencing Error
- 3.6 Deviation from Ideal - PCR Amplification
- 3.7 K-mer Counting Algorithms
- 3.8 Optimal K for Genome Assembly
- 4. Contig Assembly
- 4.1 Model of a Genome
- 4.2 De Bruijn Graph Construction
- 4.3 Removing Tips and Bubbles
- 4.4 De Bruijn Graph Reduction
- 4.5 Multi-kmer Assembly
- 5. Scaffolding
- 5.1 PE Reads
- 5.2 Mate Pair Reads
- 5.3 Other Evidences - PacBio
- 5.4 Other Evidences - Synteny
- 5.5 Other Evidences - RNAseq
- 6. Detecting Assembly Errors
- 6.1 Incorrect de Bruijn Graph in Low Coverage Regions
- 6.2 Scaffolding Error
- 7. RNAseq
- 7.1 De Novo Assembly of RNAseq
- 7.2 K-mer Distribution
- 7.3 De Bruijn Graph
- 7.4 Alternative Splice Forms
- 7.5 Generating Final Output
- 7.6 Statistical Analysis of RNASeq
- 8 References
- 8.1 References
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