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Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance., Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization., Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

We analyzed the whole-genome sequences of a family of four, consisting of two siblings and their parents. Family-based sequencing allowed us to delineate recombination sites precisely, identify 70% of the sequencing errors (resulting in > 99.999% accuracy), and identify very rare single-nucleotide polymorphisms. We also directly estimated a human intergeneration mutation rate of approximately 1.1 x 10(-8) per position per haploid genome. Both offspring in this family have two recessive disorders: Miller syndrome, for which the gene was concurrently identified, and primary ciliary dyskinesia, for which causative genes have been previously identified. Family-based genome analysis enabled us to narrow the candidate genes for both of these Mendelian disorders to only four. Our results demonstrate the value of complete genome sequencing in families.

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent "genomic deserts."

The parking strategy is an iterative approach to DNA sequencing. Each iteration consists of sequencing a novel portion of target DNA that does not overlap any previously sequenced region. Subject to the constraint of no overlap, each new region is chosen randomly. A parking strategy is often ideal in the early stages of a project for rapidly generating unique data. As a project progresses, parking becomes progressively more expensive and eventually prohibitive. We present a mathematical model with a generalization to allow for overlaps. This model predicts multiple parameters, including progress, costs, and the distribution of gap sizes left by a parking strategy. The highly fragmented nature of the gaps left after an initial parking strategy may make it difficult to finish a project efficiently. Therefore, in addition to our parking model, we model gap closing by walking. Our gap-closing model is generalizable to many other strategies. Our discussion includes modified parking strategies and hybrids with other strategies. A hybrid parking strategy has been employed for portions of the Human Genome Project.

The BAC-end sequencing, or sequence-tagged-connector (STC), approach to genome sequencing involves sequencing the ends of BAC inserts to scatter sequence tags (STCs) randomly across the genome. Once any BAC or other large segment of DNA is sequenced to completion by conventional shotgun approaches, these STC tags can be used to identify a minimum tiling path of BAC clones overlapping the nucleation sequence for sequence extension. Here, we explore the properties of STC-sequencing strategies within a mathematical model of a random target with homologous repeats and imperfect sequencing technology to understand the consequences of varying various parameters on the incidence of problem clones and the cost of the sequencing project. Problem clones are defined as clones for which either (A) there is no identifiable overlapping STC to extend the sequence in a particular direction or (B) the identified STC with minimum overlap comes from a nonoverlapping clone, either owing to random false matches or repeat-family homology. Based on the minimum overlap, we estimate the number of clones to be entirely sequenced and, then, using cost estimates, identify the decision rule (the degree of sequence similarity required before a match is declared between an STC and a clone) to minimize overall sequencing cost. A method to optimize the overlap decision rule is highly desirable, because both the total cost and the number of problem clones are shown to be highly sensitive to this choice. For a target of 3 Gb containing approximately 800 Mb of repeats with 85%-90% identity, we expect <10 problem clones with 15 times coverage by 150-kb clones. We derive the optimal redundancy and insert sizes of clone libraries for sequencing genomes of various sizes, from microbial to human. We estimate that establishing the resource of STCs as a means of identifying minimally overlapping clones represents only 1%-3% of the total cost of sequencing the human genome, and, up to a point of diminishing returns, a larger STC resource is associated with a smaller total sequencing cost.

Random subcloning strategies are commonly employed for analyzing pieces of DNA that are too large for direct analysis. Such strategies are applicable to gene finding, physical mapping, and DNA sequencing. Random subcloning refers to the generation of many small, directly analyzable fragments of DNA that represent random fragments of a larger whole, such as a genome. Following analysis of these fragments, a map or sequence of the original target may be reconstructed. Mathematical modeling is useful in planning such strategies and in providing a reference for their evaluation, both during execution and following completion. The statistical theory necessary for constructing these models has been developed independently over the last century. This paper brings this theory together into a statistical model for random subcloning strategies. This mathematical model retains its utility even at high subclone redundancies, which are necessary for project completion. The discussion here centers on shotgun sequencing, a random subcloning strategy envisioned as the method of choice for sequencing the human genome.

Strategies for large-scale genomic DNA sequencing currently require physical mapping, followed by detailed mapping, and finally sequencing. The level of mapping detail determines the amount of effort, or sequence redundancy, required to finish a project. Current strategies attempt to find a balance between mapping and sequencing efforts. One such approach is to employ strategies that use sequence data to build physical maps. Such maps alleviate the need for prior mapping and reduce the final required sequence redundancy. To this end, the utility of correlating pairs of sequence data derived from both ends of subcloned templates is well recognized. However, optimal strategies employing such pairwise data have not been established. In the present work, we simulate and analyze the parameters of pairwise sequencing projects including template length, sequence read length, and total sequence redundancy. One pairwise strategy based on sequencing both ends of plasmid subclones is recommended and illustrated with raw data simulations. We find that pairwise strategies are effective with both small (cosmid) and large (megaYAC) targets and produce ordered sequence data with a high level of mapping completeness. They are ideal for finescale mapping and gene finding and as initial steps for either a high- or a low-redundancy sequencing effort. Such strategies are highly automatable.