Browsing by Author "Dash, Sajal"

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    Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations
    Dash, Sajal; Kinney, N.A.; Varghese, Ronnie; Garner, Harold R.; Feng, Wu-chun; Anandakrishnan, Ramu (Nature Publishing Group, 2019-01-30)
    Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89–92%) and 93% specificity (95% CI = 91–94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies. © 2019, The Author(s).
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    Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design
    Dash, Sajal (Virginia Tech, 2020-08-20)
    Experimental and observational data emerging from various scientific domains necessitate fast, accurate, and low-cost analysis of the data. While exploring the landscape of big data analytics, multiple challenges arise from three characteristics of big data: the volume, the variety, and the velocity. High volume and velocity of the data warrant a large amount of storage, memory, and compute power while a large variety of data demands cognition across domains. Addressing domain-intrinsic properties of data can help us analyze the data efficiently through the frugal use of high-performance computing (HPC) resources. In this thesis, we present our exploration of the data analytics landscape with domain-aware approximate and incremental algorithm design. We propose three guidelines targeting three properties of big data for domain-aware big data analytics: (1) explore geometric and domain-specific properties of high dimensional data for succinct representation, which addresses the volume property, (2) design domain-aware algorithms through mapping of domain problems to computational problems, which addresses the variety property, and (3) leverage incremental arrival of data through incremental analysis and invention of problem-specific merging methodologies, which addresses the velocity property. We demonstrate these three guidelines through the solution approaches of three representative domain problems. We present Claret, a fast and portable parallel weighted multi-dimensional scaling (WMDS) tool, to demonstrate the application of the first guideline. It combines algorithmic concepts extended from the stochastic force-based multi-dimensional scaling (SF-MDS) and Glimmer. Claret computes approximate weighted Euclidean distances by combining a novel data mapping called stretching and Johnson Lindestrauss' lemma to reduce the complexity of WMDS from O(f(n)d) to O(f(n) log d). In demonstrating the second guideline, we map the problem of identifying multi-hit combinations of genetic mutations responsible for cancers to weighted set cover (WSC) problem by leveraging the semantics of cancer genomic data obtained from cancer biology. Solving the mapped WSC with an approximate algorithm, we identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples. To identify three- and four-hits, which require orders of magnitude larger computational power, we have scaled out the WSC algorithm on a hundred nodes of Summit supercomputer. In demonstrating the third guideline, we developed a tool iBLAST to perform an incremental sequence similarity search. Developing new statistics to combine search results over time makes incremental analysis feasible. iBLAST performs (1+δ)/δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We also explored various approaches to mitigate catastrophic forgetting in incremental training of deep learning models.
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    iBLAST: Incremental BLAST of new sequences via automated e-value correction
    Dash, Sajal; Rahman, S. R.; Hines, H. M.; Feng, Wu-chun (PLoS, 2021-04-01)
    Search results from local alignment search tools use statistical scores that are sensitive to the size of the database to report the quality of the result. For example, NCBI BLAST reports the best matches using similarity scores and expect values (i.e., e-values) calculated against the database size. Given the astronomical growth in genomics data throughout a genomic research investigation, sequence databases grow as new sequences are continuously being added to these databases. As a consequence, the results (e.g., best hits) and associated statistics (e.g., e-values) for a specific set of queries may change over the course of a genomic investigation. Thus, to update the results of a previously conducted BLAST search to find the best matches on an updated database, scientists must currently rerun the BLAST search against the entire updated database, which translates into irrecoverable and, in turn, wasted execution time, money, and computational resources. To address this issue, we devise a novel and efficient method to redeem past BLAST searches by introducing iBLAST. iBLAST leverages previous BLAST search results to conduct the same query search but only on the incremental (i.e., newly added) part of the database, recomputes the associated critical statistics such as e-values, and combines these results to produce updated search results. Our experimental results and fidelity analyses show that iBLAST delivers search results that are identical to NCBI BLAST at a substantially reduced computational cost, i.e., iBLAST performs (1 + δ)/δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We then present three different use cases to demonstrate that iBLAST can enable efficient biological discovery at a much faster speed with a substantially reduced computational cost.
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    Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU
    Al Hajri, Qais; Dash, Sajal; Feng, Wu-chun; Garner, Harold R.; Anandakrishnan, Ramu (Nature Publishing Group, 2020-02-06)
    Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.
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    Scaling out a combinatorial algorithm for discovering carcinogenic gene combinations to thousands of GPUs
    Dash, Sajal; Al-Hajri, Qais; Feng, Wu-chun; Garner, Harold R.; Anandakrishnan, Ramu (IEEE, 2021-05-01)
    Cancer is a leading cause of death in the US, second only to heart disease. It is primarily a result of a combination of an estimated two-nine genetic mutations (multi-hit combinations). Although a body of research has identified hundreds of cancer-causing genetic mutations, we don't know the specific combination of mutations responsible for specific instances of cancer for most cancer types. An approximate algorithm for solving the weighted set cover problem was previously adapted to identify combinations of genes with mutations that may be responsible for individual instances of cancer. However, the algorithm's computational requirement scales exponentially with the number of genes, making it impractical for identifying more than three-hit combinations, even after the algorithm was parallelized and scaled up to a V100 GPU. Since most cancers have been estimated to require more than three hits, we scaled out the algorithm to identify combinations of four or more hits using 1000 nodes (6000 V100 GPUs with ≈ 48× 106 processing cores) on the Summit supercomputer at Oak Ridge National Laboratory. Efficiently scaling out the algorithm required a series of algorithmic innovations and optimizations for balancing an exponentially divergent workload across processors and for minimizing memory latency and inter-node communication. We achieved an average strong scaling efficiency of 90.14% (80.96%-97.96% for 200 to 1000 nodes), compared to a 100 node run, with 84.18% scaling efficiency for 1000 nodes. With experimental validation, the multi-hit combinations identified here could provide further insight into the etiology of different cancer subtypes and provide a rational basis for targeted combination therapy.