Sequencing a genome

Approximate Molecular Dynamics: New Algorithms with Applications in Protein Folding Author: Qun (Marc) Ma Predicting the 3D native structures of proteins from the known amino acid sequence, i.e., protein folding, has become pressing in structural genomics and computational biology. Though it is plausible to use molecular dynamics (MD) simulations to study the folding of proteins, the currently available methodologies are incapable of addressing the timescale problems. In this talk, I will describe the recent advances in the development of two new multiscale integrators that allow very large time steps (and thus ``approximate'' molecular dynamics)

Definition Determining the identity and order of nucleotides in the genetic material – usually DNA, sometimes RNA, of an organism

Basic problem Genomes are large (typically millions or billions of base pairs) Current technology can only reliably ‘read’ a short stretch – typically hundreds of base pairs

Elements of a solution Automation – over the past decade, the amount of hand-labor in the ‘reads’ has been steadily and dramatically reduced Assembly of the reads into sequences is an algorithmic and computational problem

A human drama There are competing methods of assembly The competing – public and private – sequencing teams used competing assembly methods

Assembly: Putting sequenced fragments of DNA into their correct chromosomal positions

BAC Bacterial artificial chromosome: bacterial DNA spliced with a medium- sized fragment of a genome (100 to 300 kb) to be amplified in bacteria and sequenced.

Contig Contiguous sequence of DNA created by assembling overlapping sequenced fragments of a chromosome (whether natural or artificial, as in BACs)

Cosmid DNA from a bacterial virus spliced with a small fragment of a genome (45 kb or less) to be amplified and sequenced

Directed sequencing Successively sequencing DNA from adjacent stretches of chromosome

Draft sequence Sequence with lower accuracy than a finished sequence; some segments are missing or in the wrong order or orientation

EST Expressed sequence tag: a unique stretch of DNA within a coding region of a gene; useful for identifying full- length genes and as a landmark for mapping

Exon Region of a gene’s DNA that encodes a portion of its protein; exons are interspersed with noncoding introns

Genome The entire chromosomal genetic material of an organism

Intron Region of a gene’s DNA that is not translated into a protein

Kilobase (kb) Unit of DNA equal to 1000 bases

Locus Chromosomal location of a gene or other piece of DNA

Megabase (mb) Unit of DNA equal to 1 million bases

PCR Polymerase chain reaction: a technique for amplifying a piece of DNA quickly and cheaply

Physical map A map of the locations of identifiable markers spaced along the chromosomes; a physical map may also be a set of overlapping clones

Plasmid Loop of bacterial DNA that replicates independently of the chromosomes; artificial plasmids can be inserted into bacteria to amplify DNA for sequencing

Regulatory region A segment of DNA that controls whether a gene will be expressed and to what degree

Repetitive DNA Sequences of varying lenths that occur in multiple copies in the genome; it represents much of the genome

Restriction enzyme An enzyme that cuts DNA at specific sequences of base pairs

RFLP Restriction fragment length polymorphism: genetic variation in the length of DNA fragments produced by restriction enzymes; useful as markers on maps

Scaffold A series of contigs that are in the right order but are not necessarily connected in one continuous stretch of sequence

Shotgun sequencing Breaking DNA into many small pieces, sequencing the pieces, and assembling the fragments

STS Sequence tagged site: a unique stretch of DNA whose location is known; serves as a landmark for mapping and assembly

YAC Yeast artificial chromosome: yeast DNA spliced with a large fragment of a genome (up to 1 mb) to be amplified in yeast cells and sequenced

Readings Myers, “Whole Genome DNA Sequencing,” Venter, et al, “The Sequence of the Human Genome,” Science, 16 Feb 2001, Vol. 291 No 5507, 1304 (parts 1 & 2) Waterston, Lander, Sulston, “On the sequencing of the human genome,” PNAS, March 19, 2002, Vol 99, no 6, Myers, et.al., “On the sequencing and assembly of the human genome,”

Hierarchical sequencing Create a high-level physical map, using ESTs and STSs Shred genome into overlapping clones Multiply clones in BACs ‘shotgun’ each clone Read each ‘shotgunned’ fragment Assemble the fragments

Physical map

Whole genome sequencing (WGS) Make multiple copies of the target Randomly ‘shotgun’ each target, discarding very big and very small pieces Read each fragment Reassemble the ‘reads’

Hierarchical v. whole-genome

The fragment assembly problem Aim: infer the target from the reads Difficulties – –Incomplete coverage. Leaves contigs separated by gaps of unknown size. –Sequencing errors. Rate increases with length of read. Less than some . –Unknown orientation. Don’t know whether to use read or its Watson-Crick complement.

Scaling and computational complexity Increasing size of target G. –1990 – 40kb (one cosmid) –1995 – 1.8 mb (H. Influenza) –2001 – 3,200 mb (H. sapiens)

The repeat problem Repeats –Bigger G means more repeats –Complex organisms have more repetitive elements –Small repeats may appear multiple times in a read –Long repeats may be bigger than reads (no unique region)

Gaps Read length L R hasn’t changed much  = L R /G gets steadily smaller Gaps ~ Re -  R (Waterman & Lander)

How deep must coverage be?

Double-barreled shotgun sequencing Choose longer fragments (say, 2 x L R ) Read both ends Such fragments probably span gaps This gives an approximate size of the gap This links contigs into scaffolds

Genomic results

HGSC v Celera results

To do or not to do? “The idea is gathering momentum. I shiver at the thought.” – David Baltimore, 1986 “If there is anything worth doing twice, it’s the human genome.” – David Haussler, 2000

Public or private? “This information is so important that it cannot be proprietary.” – C Thomas Caskey, 1987 “If a company behaves in what scientists believe is a socially responsible manner, they can’t make a profit.” – Robert Cook- Deegan, 1987

HW for Feb 19 Comment on these assertions words: –WLS – “Our analysis indicates that the Celera paper provides neither a meaningful test of the WGS approach nor an independent sequence of the human genome.” –Venter – “This conclusion is based on incorrect assumptions and flawed reasoning.” Lesk, Exercise 2.15, problem 2.3