Browsing by Author "Alser, Mohammed"

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Open Access
Can you really anonymize the donors of genomic data in today’s digital world?
(Springer, 2016-09) Alser, Mohammed; Almadhoun, Nour; Nouri, Azita; Alkan, Can; Ayday, Erman
The rapid progress in genome sequencing technologies leads to availability of high amounts of genomic data. Accelerating the pace of biomedical breakthroughs and discoveries necessitates not only collecting millions of genetic samples but also granting open access to genetic databases. However, one growing concern is the ability to protect the privacy of sensitive information and its owner. In this work, we survey a wide spectrum of cross-layer privacy breaching strategies to human genomic data (using both public genomic databases and other public non-genomic data). We outline the principles and outcomes of each technique, and assess its technological complexity and maturation. We then review potential privacy-preserving countermeasure mechanisms for each threat. © Springer International Publishing Switzerland 2016.
Open Access
GateKeeper-GPU: fast and accurate pre-alignment filtering in short read mapping
(IEEE, 2021-06-24) Bingöl, Zülal; Alser, Mohammed; Mutlu, Onur; Öztürk, Özcan; Alkan, Can
We introduce GateKeeper-GPU, a fast and accurate pre-alignment filter that efficiently reduces the need for expensive sequence alignment. GateKeeper-GPU improves the filtering accuracy of GateKeeper, and by exploiting the massive parallelism provided by GPU threads it concurrently examines numerous sequence pairs rapidly. GateKeeper-GPU is available at https://github.com/BilkentCompGen/GateKeeper-GPU. Please refer to the preprint at arXiv:2103.14978 for more information.
Open Access
Shouji: a fast and efficient pre-alignment filter for sequence alignment
(Oxford University Press, 2019) Alser, Mohammed; Hassan, H.; Kumar, A.; Mutlu, Onur; Alkan, Can
The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm. Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step.
Open Access
Technology dictates algorithms: recent developments in read alignment
(BioMed Central, 2021-08-26) Alser, Mohammed; Rotman, J.; Deshpande, D.; Taraszka, K.; Shi, H.; Baykal, P. I.; Yang, H. T.; Xue, V.; Knyazev, S.; Singer, B. D.; Balliu, B.; Koslicki, D.; Skums, P.; Zelikovsky, A.; Alkan, Can; Mutlu, Onur; Mangul, S.
Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today’s diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.