The exploration and interpretation of large, complex datasets is vital to discovery in genomics. However, researchers now confront a fundamental limitation; unprecedented experiments are possible thanks to modern DNA sequencing technologies, yet existing genome arithmetic techniques for comparing and dissecting the resulting datasets are incapable of keeping pace with inexorable growth in dataset size and complexity. Genome arithmetic (GA) represents a powerful and widely used set of techniques that allow one to explore relationships among sets of genome features (e.g., a gene, sequence alignment, ChIP-seq peak, or anything that can be described with chromosome coordinates). GA is used for a broad spectrum of analyses including: the detection of intersecting/overlapping features (e.g., sequence alignments and exons), describing feature coverage among datasets, and the merging, subtraction, and complementation of feature datasets. GA functionality is used by all genome browsers and data visualization tools, and by analysis software such as GATK and SAMTOOLS. Owing to its power and flexibility, own BEDTOOLS software is extremely popular and is used in a broad range of complex genomic analyses. However, while GA is central to genomic analysis and discovery, the core algorithms employed by all existing tools are inherently incapable of keeping pace with the scale and diversity of modern genomic datasets. Restricted to these approaches, the present analytic bottleneck will become increasingly acute. Therefore, the overall objective of this proposal is to provide the genomics community with innovative new algorithms and software that keep pace with modern genomics experiments and facilitate future discoveries.
The Specific Aims are to: (1) Create an ecosystem and software that allows researchers to easily integrate diverse genome annotations and datasets into their research. We will develop new tools that make it easy and reproducible for researchers to collect datasets germane to a given experiment. (2) Dramatically expand the utility, flexibility, and performance of BEDTOOLS. We will devise and implement new algorithms for scalable and flexible analysis of large-scale genome datasets. (3) Develop a workbench for visualizing and quantifying the biological significance of relationships among genomic datasets. We will leverage the technologies from Aims 1 and 2 to develop a comprehensive statistical and visualization workbench for the R statistical package that will allow researchers to detect biological relationships among genome datasets. The proposed research will devise entirely new, scalable approaches for genome arithmetic. This will provide the community with powerful new techniques for exploring and interpreting genomics experiments and give tool developers robust approaches for software development and improvement.
New discoveries in genomics depend in large part upon the exploration of many large experimental datasets and diverse genome annotations. Unfortunately, researchers are facing a fundamental analysis constraint caused by the fact that existing 'genome arithmetic' analysis techniques are incapable of scaling to the size and complexity of modern genomics experiments. We therefore propose to devise innovative analysis techniques that will provide the genomics research community with scalable, reliable tools for exploring and interpreting tomorrow's genomics experiments.
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| Belyeu, Jonathan R; Nicholas, Thomas J; Pedersen, Brent S et al. (2018) SV-plaudit: A cloud-based framework for manually curating thousands of structural variants. Gigascience 7: |
| Layer, Ryan M; Pedersen, Brent S; DiSera, Tonya et al. (2018) GIGGLE: a search engine for large-scale integrated genome analysis. Nat Methods 15:123-126 |
| Pedersen, Brent S; Quinlan, Aaron R (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34:867-868 |
| Pedersen, Brent S; Collins, Ryan L; Talkowski, Michael E et al. (2017) Indexcov: fast coverage quality control for whole-genome sequencing. Gigascience 6:1-6 |
| Layer, Ryan M; Quinlan, Aaron R (2017) A parallel algorithm for N-way interval set intersection. Proc IEEE Inst Electr Electron Eng 105:542-551 |
| Pedersen, Brent S; Quinlan, Aaron R (2017) cyvcf2: fast, flexible variant analysis with Python. Bioinformatics 33:1867-1869 |
| Pedersen, Brent S; Quinlan, Aaron R (2017) Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy. Am J Hum Genet 100:406-413 |
| Eilbeck, Karen; Quinlan, Aaron; Yandell, Mark (2017) Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet 18:599-612 |
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