Jack Chen, Associate Professor

1. Alignment: from small sequences to whole genome
2. Homology: paralog vs. ortholog
3. Synteny: chromosomal synteny vs. synteny block
4. Phylogeny: gene phylogeny vs. organismal phylogeny
5. Species & speciation

Genome is a linear molecular structure that carries genetic information. It consists of four nucleotides (A, T, C, and G). Genetic information is scattered in genome and is encoded in these four "letters" in different order and combination. One fundamental question is how genetic information is encoded in genome, which is sometimes called the "dark matter"?

A genome contains a large number of different types of parts: genes and regulatory elements, sometimes collectively called functional elements. Genes, in particulary protein-coding genes, are relatively straightforward to identify. In contrast, other types of functional elements like enhancers and insulators are harder to detect. Regulatory elements dictates gene expression. In particular, it determines where and when a gene is expressed. It also determines how much is gene is expressed.

Presently, an effective way to understand genome is comparative genomics, in which two or more genomes are compared for similarities and for differences. Comparative analysis can be down at the DNA level and at the protein level. Using bioinformatics programs, sequences are aligned and the alignments are examined for their evolutionary relationship. Are they homologous, or do they share common ancestor? Comparative analysis can also be done for genomes of different distances, ranging from genomes of different strains of a species to different species that are distanly related. Differences of genomes (i.e., genotypes) can therefore be linked to functional consequences, or phenotypes.

Understand why comparative genomics is important for studying G2P (genotype-to-phenotype) relationship, and how comparative genomics is carried out.

1. Primer: Comparative genomics (by Ross Hardison, 2003).
2. Review: Comparative genomics (by Web Miller et al., 2004).

1. First-generation
   • Sanger sequencing
2. Second-generation
   • 454 sequencing
   • Solexa sequencing
   • SOLiD sequencing
3. Third-generation
   • Nanopore sequencing

The ability to determine the sequence of nucleotides are ordered in a DNA molecule is an essential first step for understanding the composition and function of a genome. Over the last 40 years, many different methods were developed. Sanger sequencing method, which was invented by Fred Sanger who won his second Nobel Prize for it, was the key method used in the Human Genome Project. Second(or "next")-generation DNA sequencing methods were introduced a few years after the completion of the Human Genome Project. Because different sequencing methods produce reads with different lengths and can be either paired or single-ended, they can be used to address different questions. The third-generation sequencing methods are on the horizon for production use.

Popular DNA sequencing methods will be described in this lesson, primarily because sequence reads from each method are unique so that they need separate methods for handling and for analysis.

Master file format of each sequencing method and how they can be processed for further analysis.

1. Review: Applications of new sequencing technologies for transcriptome analysis (by Morozova and Marra, 2009)
2. Review: The potential and challenges of nanopore sequencing (by Branton et al., 2008)
3. File format: The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants (by Cock et al., 2009).

1. Alignment
   • Short sequence alignment
   • Whole genome alignment
2. Database searches (blast)
3. Homolog identification
   • Ortholog
   • Paralog
4. Synteny
   • Synteny block identification (orthoCluster)
   • Synteny breakpoint
5. Genome visualization
   • dotplot
   • Circos
   • Genome Browser & Synteny browser (gbrowse & gbrowse_syn)

Bioinformatics is an sister field of genomics and emerged almost simultaneously as genomics. The amount of information generated in large genome sequencing and related functional genomics projects can no longer be recorded in notebooks used comfortably by traditional biologists. Computers, including both hardware and software, are needed for genome information storage, management, retrieval, analysis, and report.

In this lecture, I will overview bioinformatics tools developed in the last 10 years or so for DNA alignment, for synteny identification, and for display using genome browsers.

Be comfortable with selecting computer programs appropriate for different conditions.

1. Bioinformatics: alive and kicking (by Stein, 2008)
2. BWA (by Li et al., 2010)
3. OrthoCluster (by Zeng et al., 2008) & OrthoClusterDB (by Ng et al., 2009)
4. gbrowse (by Lincoln Stein, et al., 2010)
5. Circos: An information aesthetic for comparative genomics (by Krzywinski et al., 2009)

1. Primary and clade-specific databases
   • NCBI genome resources
   • Ensembl genome resources
   • UCSC genome resources
2. Model organism databases (MODs)
   • Yeast: SGD
   • Arabidopsis: TAIR (TAIR funding crisis)
   • C. elegans: WormBase
   • Drosophila melanogaster: FlyBase
   • Rat: RGD

Sequence results of publicly funded genome projects are deposited in publicly accessible databases, which makes it convenient to data retrieval. In this lecture, three major public genome data resources will be described. Although data stored in these databases are essentially identical, each database has its own unique bioinformatics tools, which makes each of them useful for certain purposes.

Be familar with the architecture of each of the three public genome data resources and learn how to retrieve data effectively.

1. Touring Ensembl: A practical guide to genome browsing (by Spudich and Fernandez-Suarez, 2010)
2. Entrez Gene: gene-centered information at NCBI (by Ostel et al., 2007)

1. The Human Genome Project
   • The Public
   • The Private

1. Gene definition
   • Protein-coding gene
     • Exon
     • Intron
     • 5' & 3' UTRs
     • Promoter
     • Alternative splicing & isoform
   • Non-coding gene
     • tRNA
     • rRNA
     • microRNA
2. Gene prediction
   • Comparative prediction
   • Transcriptome-based prediction
3. Genes in operons
   • Prokaryote
   • Eukaryote
4. Gene duplication
5. Gene birth and death

Gene is a concept in motion. When Gregor Mendel first published his studies on pea plants in 1866, he did not know the term gene because it was not defined yet. The term was first coiled by Danish botanist Wilhelm Johanssen in 1909 while the physical basis of gene remained unknown at that time. In 1910, Thomas Morgan's work on fruitfilies shows that genes sit on chromosomes, leading to the idea of genes as beads on a string. In 1941, George Beadle and Edward Tatum introduced the concept that one gene makes an enzyme. Shortly after in 1944, Oswald Avery and colleagues found that genes are made of DNA. James Watson and Francis Crick in 1953 published the chemical structure of DNA, as well as the central dogma of molecular biology. The concept that gene is a contiguous segment of DNA was broken when Richard Roberts and Phillip Sharp discovered that genes can be split into segments, leading to the idea that one gene can make several proteins. Genes do not have to code for proteins. RNA genes include rRNA and tRNAs. Work by Victor Ambros and Gary Ruvkun on the nematode C. elegans lead to the discovery of the first microRNA gene in 1993.

The structure of gene is dynamic. In genome, new genes are born and existing genes can die. The birth and death of genes can be detected by comparing genomes of closely related organisms.

1. Origins, evolution and phenotypic impact of new genes (by Kaessmann, Genome Research, 2010)
2. What is a gene, post-ENCODE? History and updated definition (by Gerstein et al., Genome Research, 2007)
3. The origin of new genes: glimpses from the young and old (by Long et al., Nature Review Genetics, 2003)

1. Identification
2. Function

1. Transcription factor binding sites (TFBSs)
2. Promoter
3. Enhancer
4. Insulator
5. Finding motifs: ChIP-chip & ChIP-SEQ

1. Pilot project (1% of the human genome)
2. ENCODE: functional elements in humans
3. modENCODE: functional elements in D. melanogaster and C. elegans

1. Chromosomal synteny
2. Synteny blocks
   • Perfect synteny blocks
   • Imperfect synteny blocks
   • "Ultraconserved" synteny blocks
3. Synteny breakpoints

1. Deletion
2. Insertion
3. Inversion
4. Transposition
5. Translocation

1. Types of GVs
2. Formation of GVs
   • Duplication
   • Non-homologous recombination
3. GVs and disease conditions
4. Personalized genomics & medicine

1. New York Times: Adventures in Very Recent Evolution (Nichlas Wade, 2010)
2. New York Times: Scientists Cite Fastest Case of Human Evolution (by Nichlas Wade, 2010)

1. Types of SNPs
2. Density and genome distribution
3. Impact on genes
   • Coding regions
   • Regulatory regions
4. Haplotype
5. The HapMap Project
   • Phase 1
   • Phase 2
   • Phase 3

1. Comparative genomics hybridization (CGH)
2. Copy number variation (CNV)
   • Deletion
   • Duplication
3. Balanced rearrangement (BRE)
   • Inversion
   • Transposition and translocation

1. Copy Number Variation in Human Health, Disease, and Evolution (by Zhang et al., Annual Review, 2009)

1. Identification
2. Validation
3. Buffering of genetic variation

1. Initial sequence of the chimpanzee genome and comparison with the human genome
   Note: Although this paper describes differences between two species (human & chimpanzee), such kind of differences also exist between human individuals. It reported many human disease genes in the chimpanzee genome.
2. Principles for the Buffering of Genetic Variation (by Hartman et al., Science, 2001)

1. Genomewide Association Studies and Assessment of the Risk of Disease (by Manolio, NEJM, 2010)

1. Personalized genomes
   • James Watson
   • Craig Venter
   • Yan Huang ("An Asian")
   • A Korean
   • Desmond Tutu
2. The 1000 Genome Project

1. Gene family classification
2. Comparative gene family classification
3. Stable gene family (e.g., ABC transporters)
4. Dynamic gene family (e.g., chemosensory genes)

1. Classification of transcription factors
2. Example: RFX gene family
3. Example: RFX gene battery

1. Nature Review Focus: Horizontal gene transfer (2005)
2. Lateral gene transfer and the nature of bacterial innovation (by Ochman et al., Nature, 2000)
3. Lateral gene transfer between Archaea and Bacteria (by Nelson et al., Nature, 1999)

1. Carbon metabolism of intracellular bacterial pathogens and possible links to virulence (Eisenreich et al., Nature Reviews Microbiology, 2010)

1. Environmental
   • Hot spring
   • Ocean
   • Sludge
   • soil
2. Organismal
   • Gut
   • Skin
   • feces
   • Lung

1. Primer: Metagenomics

1. Human vs. animals
2. Human vs. chimpanzee
3. Human vs. Neandertal
4. Human vs. human

1. A Draft Sequence of the Neandertal Genome (by Green et al., Science, 2010)
2. An RNA gene expressed during cortical development evolved rapidly in humans (by Pollard et al., Nature, 2006) Evolution at two levels in humans and chimpanzees (by King and Wilson, Science, 1975) Note: This study was regarded as the first contribution to comaprative genomics (Sean Carool, PLoS Biology, 2005).

1. Dr. Katherine Pollard: What makes us human?

1. Ancestral state reconstruction
2. Comparative genomics
3. Molecular evolution
4. Species conservation
5. Vertebrate biology

This large-scale project was proposed in anticipation of a precipitous drop in costs and an increase in sequencing efficiency.

Be aware of this large-scale project and the resource that will be available for comparative genomics du ring and after the completion of this project.

1. Genome 10K: A Proposal to Obtain Who le-Genome Sequence for 10 000 Vertebrate Species (by Haussler et al., 2009)

Students will be divided into groups of two students. Each group will propose a comparative genomics project at the beginning of the course, which will be carried out duing this course. At the end of the course, each group will present their projects. One student will focus on background and motivation, while the second student on results and interpretation.