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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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K-mer Based Error Modeling in Illumina Reads
Chaisson’s paper.
