Using de Bruijn Graphs for Short-read Assembly
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Using de Bruijn Graphs for Short-read Assembly
About the Book
About the Author
Table of Contents
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1. Introduction
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Assembly procedure requires an understanding of sequencing technologies, algorithms and statistics
- Sequencing technologies
- Algorithms
- Statistics
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Why should biologists learn about assembly algorithms?
- Software tools change, but the algorithms remain more stable
- Knowledge of algorithms helps in experimental design
- Understanding algorithms helps in purchasing adequate computing hardware
- Improving assembly quality and interpreting results better
- RNA-seq in non-model organisms
- Using NGS technologies to solve novel problems
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Description of this book
- Chapters
- Algebraic notations are avoided as much as possible
- Living electronic book
- Core concepts are reinforced through repetition
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Regarding the exercises
- Code - Pandora’s Toolbox for Bioinformatics
- Data - E. coli and Electrophorus
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Other online resources for learning
- Introductory resources
- Intermediate Resources
- Advanced resources
- Acknowledgements for this book
- Further readings
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Assembly procedure requires an understanding of sequencing technologies, algorithms and statistics
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2. The Genome Assembly Problem
- Shotgun sequencing and assembly of genomes
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Is the problem solvable?
- Lander-Waterman statistics for read length and coverage
- Log(N) estimate for random sequences
- Ukkonen’s condition
- Optimum read length and coverage in presence of repeats
- Reconstruction with paired end read
- Summary
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Greedy, overlap-layout-consensus and de Bruijn graph-based algorithms
- Definitions
- Greedy algorithm
- Overlap-layout-consensus algorithm
- de Bruijn graph-based algorithm
- A short historical overview of using de Bruijn graphs in genome assembly
- Advantages and disadvantages of using de Bruijn graphs for assembly
- Further readings
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3. De Bruijn Graph of the Genome and a Simple Assembler
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De Bruijn Graph of a known genome
- De Bruijn graph of a small sequence
- Double-stranded nature of the genome
- De Bruijn graph in repetitive regions
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Properties of de Bruijn Graph
- The graph structure is unique for a given set of kmers
- Irreversible
- Impact of changing kmer size
- Generalized definition of de Bruijn graph
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Viewing de Bruijn graphs
- Graphview
- Online method - Alex Hadik
- Ray cloud browser
- FASTG Viewer BANDAGE
- De Bruijn Graph of the E. coli genome
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Genome assembly from de Bruijn graphs in the absense of noise
- Relationship between de Bruijn graph of short reads and the underlying genome
- Loss of read coherence during de Bruijn graph construction
- Varying k-mer sizes to compensate for the loss of read coherence
- Memory requirement and k-mer distribution of perfect library
- Summary
- Further readings
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De Bruijn Graph of a known genome
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4. Experimental Considerations
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Evolution of sequencing technologies
- Sanger sequencing
- 454 pyrosequencing
- Illumina dye sequencing
- ABI SOLiD sequencing
- Ion semiconductor sequencing
- PacBio single molecule real time sequencing
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Sources of artifacts
- Random errors - substitutions and insertion-deletions
- Homopolymer error
- Quality drop near 3’ ends of reads
- Coverage bias in AT-rich or GC-rich regions
- Distance between the read pairs is variable in mate pairs
- Mate pair inversion
- Duplication due to PCR amplification
- Inherent noise of PacBio reads
- Diploid genome
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Paired-end and mate-pair reads
- Paired end
- Mate pairs
- Insert size
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Data formats
- FASTA and FASTQ
- BAM and SAM Alignments
- CIGAR
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Detailed description of ABI SOLiD color space data
- How to convert sequences to color space?
- How do we compute reverse complement in color space?
- Simple sequences
- SNP
- Advantage of color space
- Disadvantage
- Is conversion to nucleotide space prudent?
- Pseudo-basespace
- Error correction
- de Bruijn Graph of SOLiD Reads
- Summary of what we learned so far
- Further readings
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Evolution of sequencing technologies
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5. Genome Assembly from Noisy Reads with Uneven Coverage
- Errors lead to high RAM usage for de Bruijn graph-based assemblers
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Impact of sequencing errors on the structure of de Bruijn graphs
- Tips
- Bubbles
- Crosslinks
- Tips and bubbles can appear due to read errors or polymorphism
- Impact of uneven coverage
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Full conceptual picture of short read assembly using de Bruijn graph
- Effect of changing the coverage cutoff parameter
- Effect of changing the k-mer size
- Further readings
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6. Assembling Transcriptomes, Metagenomes and Heterozygous Genomes
- General approach for solving complex assembly problems related to short reads
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Using de Bruijn graphs to assembles heterozygous genomes
- Impact of haplotype differences on de Bruijn Graph structure
- Coverage
- Assembly method
- Separating phases after assembly
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Using de Bruijn graphs for transcriptome (RNA-seq) assembly
- De Bruijn graph structure of transcriptomic libraries
- K-mer coverage
- Assembly method
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Using de Bruijn graphs for metagenome assembly
- De Bruijn graph structure for metagenomic libraries
- Coverage
- Assembly method
- Further readings
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7. Faster, Better and Cheaper
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Computer science concepts for advanced work
- Architecture of modern computer
- Conference analogy
- Processing Elements - CPU, GPU and FPGA
- Algorithm and data structure
- Disk-based algorithms and Hadoop
- Parallel code, compare-and-swap, shared memory and queues
- Hashing-related concepts
- Alignment-related concepts
- Other algorithmic concepts related to bioinformatics
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Data structures for efficient storage and processing of de-Bruijn graphs
- An elaborate data structure for de Bruijn graph
- Using simplified k-mers - ABySS
- Using edge-based data structure - Conway and Bromage
- Using Sparse de Bruijn Graph - Sparse-assembler
- Using Bloom filter - Minia
- Using Perfect hash - Meraculous
- Using Minimizer - BCALM
- Using Succint Data Structure
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Efficient algorithms for counting k-mers
- Meryl
- Suffix array - Tallymer
- Bloom filter - BFcounter and scTurtle
- Lock-free hash table - Jellyfish
- Disk-based - DSK, KMC and KAnalyze
- Minimizer-based - MSPKmerCounter, KMC2 and DSK2
- Approximate counting - khmer
- Efficient algorithms for read error correction
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Improving assembly by changing k-mer size for de Bruin graph
- Optimal k-mer-size for dBG construction
- Merging read pairs to increase effective read length
- Combining assemblies from multiple k-mers - IDBA and SPAdes
- Variable order k-mer
- String Graph Assembler for Short Reads
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Scaffolding
- Hierarchical scaffolding - SOAPdenovo
- Rectangular Graph - SPAdes
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Repeat resolution
- SOAPdenovo
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SPAdes
- Hyperconnected k-mers
- Further readings
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Computer science concepts for advanced work
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8. In Depth Discussion of Three de Bruijn Graph-based Assemblers
- Where to get them
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Genome assembler - SOAPdenovo
- History
- How to run
- Features
- Details of algorithm
- Details of code
- SOAPdenovo-trans Transcriptome Assembler
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Genome Assembler - SPAdes
- History
- How to run
- Features of SPAdes
- Details of algorithm
- Details of code
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Transcriptome assembler - Trinity
- History
- How to run
- Details of algorithm
- Details of code
- Further readings
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9. References
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1. Pre-NGS genome assemblers
- Base-calling and error detection
- Assemblers
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2. NGS genome assemblers
- non de Bruijn, k-mer based
- de Bruijn graph-based assemblers
- Applications
- Comparison
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3. Exomes, transcriptomes, metagenomes and highly polymorphic genomes
- Transcriptome assemblers
- Metagenomes
- Polymorphic genomes
- Targeted assembly
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4. Faster, better, cheaper
- k-mer counting
- Storage
- Error correction
- Hadoop
- Hardware accelerators
- String graph assembler
- Scaffolding
- Repeats
- 6. Reviews and forecasts
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1. Pre-NGS genome assemblers
Other books by this author
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