1 code implementation • 9 Dec 2022 • Meryem Banu Cavlak, Gagandeep Singh, Mohammed Alser, Can Firtina, Joël Lindegger, Mohammad Sadrosadati, Nika Mansouri Ghiasi, Can Alkan, Onur Mutlu
However, for many applications, the majority of reads do no match the reference genome of interest (i. e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation.
1 code implementation • 9 Aug 2022 • Zuher Jahshan, Can Alkan, Leonid Yavits
We are attempting to alleviate the computational bottleneck by modifying and applying Vision Transformer, a recently developed neural network model for image recognition, to taxonomic classification and placement of viral genomes, such as SARS-CoV-2.
1 code implementation • 17 Jan 2022 • Jeremie S. Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Can Alkan, Onur Mutlu
Also available in Bioconda at: https://anaconda. org/bioconda/fastremap-bio.
1 code implementation • 16 Dec 2021 • Can Firtina, Jisung Park, Mohammed Alser, Jeremie S. Kim, Damla Senol Cali, Taha Shahroodi, Nika Mansouri Ghiasi, Gagandeep Singh, Konstantinos Kanellopoulos, Can Alkan, Onur Mutlu
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.
1 code implementation • 27 Mar 2021 • Zülal Bingöl, Mohammed Alser, Onur Mutlu, Ozcan Ozturk, Can Alkan
At the last step of short read mapping, the candidate locations of the reads on the reference genome are verified to compute their differences from the corresponding reference segments using sequence alignment algorithms.
2 code implementations • 16 Sep 2020 • Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Nori, Allison Scibisz, Sreenivas Subramoney, Can Alkan, Saugata Ghose, Onur Mutlu
Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data.
Hardware Architecture Genomics
2 code implementations • 28 Feb 2020 • Mohammed Alser, Jeremy Rotman, Kodi Taraszka, Huwenbo Shi, Pelin Icer Baykal, Harry Taegyun Yang, Victor Xue, Sergey Knyazev, Benjamin D. Singer, Brunilda Balliu, David Koslicki, Pavel Skums, Alex Zelikovsky, Can Alkan, Onur Mutlu, Serghei Mangul
We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques.
1 code implementation • 18 Dec 2019 • Jeremie S. Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Mohammed Alser, Nastaran Hajinazar, Can Alkan, Onur Mutlu
There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i. e., remapping).
1 code implementation • 20 Oct 2019 • Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu
The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout.
1 code implementation • 12 Feb 2019 • Can Firtina, Jeremie S. Kim, Mohammed Alser, Damla Senol Cali, A. Ercument Cicek, Can Alkan, Onur Mutlu
Our experiments with real read sets demonstrate that Apollo is the only algorithm that 1) uses reads from any sequencing technology within a single run and 2) scales well to polish large assemblies without splitting the assembly into multiple parts.
1 code implementation • 2 Nov 2017 • Jeremie S. Kim, Damla Senol Cali, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, Onur Mutlu
State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i. e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i. e., sequence alignment) to determine the origin of the read.
1 code implementation • 6 Apr 2016 • Mohammed Alser, Hasan Hassan, Hongyi Xin, Oğuz Ergin, Onur Mutlu, Can Alkan
The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10.