Edward C. Thayer

Single-molecule DNA sequencing of a viral genome.

The full promise of human genomics will be realized only when the genomes of thousands of individuals can be sequenced for comparative analysis. A reference sequence enables the use of short read length. We report an amplification-free method for determining the nucleotide sequence of more than 280,000 individual DNA molecules simultaneously. A DNA polymerase adds labeled nucleotides to surface-immobilized primer-template duplexes in stepwise fashion, and the asynchronous growth of individual DNA molecules was monitored by fluorescence imaging. Read lengths of >25 bases and equivalent phred software program quality scores approaching 30 were achieved. We used this method to sequence the M13 virus to an average depth of >150× and with 100% coverage; thus, we resequenced the M13 genome with high-sensitivity mutation detection. This demonstrates a strategy for high-throughput low-cost resequencing.

Quantification of the yeast transcriptome by single-molecule sequencing

We present single-molecule sequencing digital gene expression (smsDGE), a high-throughput, amplification-free method for accurate quantification of the full range of cellular polyadenylated RNA transcripts using a Helicos Genetic Analysis system. smsDGE involves a reverse-transcription and polyA-tailing sample preparation procedure followed by sequencing that generates a single read per transcript. We applied smsDGE to the transcriptome of Saccharomyces cerevisiae strain DBY746, using 6 of the available 50 channels in a single sequencing run, yielding on average 12 million aligned reads per channel. Using spiked-in RNA, accurate quantitative measurements were obtained over four orders of magnitude. High correlation was demonstrated across independent flow-cell channels, instrument runs and sample preparations. Transcript counting in smsDGE is highly efficient due to the representation of each transcript molecule by a single read. This efficiency, coupled with the high throughput enabled by the single-molecule sequencing platform, provides an alternative method for expression profiling.

Error Tolerant Indexing and Alignment of Short Reads with Covering Template Families

The rapid adoption of high-throughput next generation sequence data in biological research is presenting a major challenge for sequence alignment tools—specifically, the efficient alignment of vast amounts of short reads to large references in the presence of differences arising from sequencing errors and biological sequence variations. To address this challenge, we developed a short read aligner for high-throughput sequencer data that is tolerant of errors or mutations of all types—namely, substitutions, deletions, and insertions. The aligner utilizes a multi-stage approach in which template-based indexing is used to identify candidate regions for alignment with dynamic programming. A template is a pair of gapped seeds, with one used with the read and one used with the reference. In this article, we focus on the development of template families that yield error-tolerant indexing up to a given error-budget. A general algorithm for finding those families is presented, and a recursive construction that creates families with higher error tolerance from ones with a lower error tolerance is developed.

An algorithmic approach to multiple complete digest mapping

Multiple Complete Digest (MCD) mapping is a method of determining the locations of restriction sites along a target DNA molecule. The resulting restriction map has many potential applications in DNA sequencing and genetics. In this work, we present a heuristic algorithm for fragment identification, a key step in the process of constructing an MCD map. Given measurements of the restriction fragment sizes from one or more complete digestions of each clone in a clone library covering the molecule to be mapped, the algorithm identifies groups of restriction fragments on different clones that correspond to the same region of the target DNA. Once these groups are correctly determined the desired map can be constructed by solving a system of simple linear inequalities. We demonstrate the effectiveness of our algorithm on real data provided by the Genome Center at the University of Washington.

Detection of protein coding sequences using a mixture model for local protein amino acid sequence.

Locating protein coding regions in genomic DNA is a critical step in accessing the information generated by large scale sequencing projects. Current methods for gene detection depend on statistical measures of content differences between coding and noncoding DNA in addition to the recognition of promoters, splice sites, and other regulatory sites. Here we explore the potential value of recurrent amino acid sequence patterns 3-19 amino acids in length as a content statistic for use in gene finding approaches. A finite mixture model incorporating these patterns can partially discriminate protein sequences which have no (detectable) known homologs from randomized versions of these sequences, and from short (< or = 50 amino acids) non-coding segments extracted from the S. cerevisiea genome. The mixture model derived scores for a collection of human exons were not correlated with the GENSCAN scores, suggesting that the addition of our protein pattern recognition module to current gene recognition programs may improve their performance.

An algorithmic approach to multiple complete digest mapping.

Multiple Complete Digest (MCD) mapping is a method of determining the locations of restriction sites along a target DNA molecule. The resulting restriction map has many potential applications in DNA sequencing and genetics. In this work, we present a heuristic algorithm for fragment identification, a key step in the process of constructing an MCD map. Given measurements of the restriction fragment sizes from one or more complete digestions of each clone in a clone library covering the molecule to be mapped, the algorithm identifies groups of restriction fragments on different clones that correspond to the same region of the target DNA. Once these groups are correctly determined the desired map can be constructed by solving a system of simple linear inequalities. We demonstrate the effectiveness of our algorithm on real data provided by the Genome Center at the University of Washington.