Browsing by Author "Singh, G."

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    BLEND: A fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis
    (Oxford University Press, 2023-01-10) Firtina, C.; Park, J.; Alser, M.; Kim, J. S.; Cali, D. S.; Shahroodi, T.; Ghiasi, N. M.; Singh, G.; Kanellopoulos, K.; Alkan, Can; Mutlu, O.
    Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either (i) increasing the use of the costly sequence alignment or (ii) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND (i) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and (ii) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4×-83.9× (on average 19.3×), has a lower memory footprint by 0.9×-14.1× (on average 3.8×), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8×-4.1× (on average 1.7×) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND. © 2023 The Author(s). Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
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    Hierarchical Electrospun Nanofibers for Energy Harvesting, Production and Environmental Remediation
    (Royal Society of Chemistry, 2014) Kumar, P. S.; Sundaramurthy, J.; Sundarrajan, S.; Babu, V. J.; Singh, G.; Allakhverdiev, S. I.; Ramakrishna, S.
    As the demand for energy is rapidly growing worldwide ahead of energy supply, there is an impulse need to develop alternative energy-harvesting technologies to sustain economic growth. Due to their unique optical and electrical properties, one-dimensional (1D) electrospun nanostructured materials are attractive for the construction of active energy harvesting devices such as photovoltaics, photocatalysts, hydrogen energy generators, and fuel cells. 1D nanostructures produced from electrospinning possess high chemical reactivity, high surface area, low density, as well as improved light absorption and dye adsorption compared to their bulk counterparts. So, research has been focused on the synthesis of 1D nanostructured fibers made from metal oxides, composites, dopants and surface modification. Furthermore, fine tuning these NFs has facilitated fast charge transfer and efficient charge separation for improved light absorption in photocatalytic and photovoltaic properties. The recent trend in exploring these electrospun nanostructures has been promising in-terms of reducing costs and enhancing the efficiency compared to conventional materials. This review article presents the synthesis of 1D nanostructured fibers made via electrospinning and their applications in photovoltaics, photocatalysis, hydrogen energy harvesting and fuel cells. The current challenges and future perspectives for electrospun nanomaterials are also reviewed.
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    SeGraM: A universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping
    (Association for Computing Machinery, 2020-06-11) Cali, D.Ş; Kanellopoulos, K.; Lindegger, J.; Bingöl, Zülal; Kalsi, G.S.; Zuo, Z.; Fırtına, Can; Cavlak, M.B.; Kim, J.; Ghiasi, N.M.; Singh, G.; Gómez-Luna, J.; Almadhoun Alserr, N.; Alser, M.; Subramoney, S.; Alkan, Can; Ghose, S.; Mutlu, O.
    A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e., sequence-to-sequence mapping). Recent works replace the linear reference sequence with a graph-based representation of the reference genome, which captures the genetic variations and diversity across many individuals in a population. Mapping reads to the graph-based reference genome (i.e., sequence-to-graph mapping) results in notable quality improvements in genome analysis. Unfortunately, while sequence-to-sequence mapping is well studied with many available tools and accelerators, sequence-to-graph mapping is a more difficult computational problem, with a much smaller number of practical software tools currently available. We analyze two state-of-the-art sequence-to-graph mapping tools and reveal four key issues. We find that there is a pressing need to have a specialized, high-performance, scalable, and low-cost algorithm/hardware co-design that alleviates bottlenecks in both the seeding and alignment steps of sequence-to-graph mapping. Since sequence-to-sequence mapping can be treated as a special case of sequence-to-graph mapping, we aim to design an accelerator that is efficient for both linear and graph-based read mapping. To this end, we propose SeGraM, a universal algorithm/hardware co-designed genomic mapping accelerator that can effectively and efficiently support both sequence-to-graph mapping and sequence-to-sequence mapping, for both short and long reads. To our knowledge, SeGraM is the first algorithm/hardware co-design for accelerating sequence-to-graph mapping. SeGraM consists of two main components: (1) MinSeed, the first minimizer-based seeding accelerator, which finds the candidate locations in a given genome graph; and (2) BitAlign, the first bitvector-based sequence-to-graph alignment accelerator, which performs alignment between a given read and the subgraph identified by MinSeed. We couple SeGraM with high-bandwidth memory to exploit low latency and highly-parallel memory access, which alleviates the memory bottleneck. We demonstrate that SeGraM provides significant improvements for multiple steps of the sequence-to-graph (i.e., S2G) and sequence-to-sequence (i.e., S2S) mapping pipelines. First, SeGraM outperforms state-of-the-art S2G mapping tools by 5.9×/3.9× and 106×/- 742× for long and short reads, respectively, while reducing power consumption by 4.1×/4.4× and 3.0×/3.2×. Second, BitAlign outperforms a state-of-the-art S2G alignment tool by 41×-539× and three S2S alignment accelerators by 1.2×-4.8×. We conclude that SeGraM is a high-performance and low-cost universal genomics mapping accelerator that efficiently supports both sequence-to-graph and sequence-to-sequence mapping pipelines.