Biology subjects

Firtina, C.

Publications and source records attributed to Firtina, C..

7 recordsLinked to original sources

bioRxiv2025

RawBench: A Comprehensive Benchmarking Framework for Raw Nanopore Signal Analysis Techniques

Nanopore sequencing technologies continue to advance rapidly, offering critical benefits such as real-time analysis, the ability to sequence extremely long DNA fragments (up to millions of bases in a single read), and the option to selectively stop sequencing a molecule before completion. Traditionally, the raw electrical signals generated during sequencing are converted into DNA sequences through a process called basecalling, which typically relies on large neural network models. While accurate, these models are computationally intensive and often require high-end GPUs to process the vast volume of raw signal data. This presents a significant challenge for real-time processing, particularly on edge devices with limited computational resources, ultimately restricting the scalability and deployment of nanopore sequencing in resourceconstrained settings. Raw signal analysis has emerged as a promising alternative to these resource-intensive approaches. While attempts have been made to benchmark conventional basecalling methods, existing evaluation frameworks 1) overlook raw signal analysis techniques, 2) lack the flexibility to accommodate new raw signal analysis tools easily, and 3) fail to include the latest improvements in nanopore datasets. Our goal is to provide an extensible benchmarking framework that enables designing and comparing new methods for raw signal analysis. To this end, we introduce RawBench, the first flexible framework for evaluating raw nanopore signal analysis techniques. RawBench provides modular evaluation of three core pipeline components: 1) reference genome encoding (using different pore models), 2) signal encoding (through various segmentation methods), and 3) representation matching (via different data structures). We extensively evaluate raw signal analysis techniques in terms of 1) quality and performance for read mapping, quality and performance for read classification, and 3) quality of raw signal analysis-assisted basecalling. Our evaluations show that raw signal analysis can achieve competitive quality while significantly reducing resource requirements, particularly in settings where real-time processing or edge deployment is necessary. CCS ConceptsComputing methodologies [->] Bioinformatics; Evaluation methodologies; * Applied computing [->] Computational genomics. ACM Reference FormatFurkan Eris, Ulysse McConnell, Can Firtina, and Onur Mutlu. 2025. RawBench: A Comprehensive Benchmarking Framework for Raw Nanopore Signal Analysis Techniques. In Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB 25), October 11-15, 2025, Philadelphia, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3765612.3767302

genomics↗

bioRxiv2023

RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8x and 3.4x better average throughput and 2) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.

bioinformatics↗

bioRxiv2022

TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. 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. To overcome this issue, we propose TargetCall, the first fast and widely-applicable pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCalls key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall filters out all off-target reads before basecalling; and the highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance of the state-of-the-art basecaller by 3.31 x while maintaining high (98.88%) sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, sensitivity, and generality compared to prior works. We freely open-source TargetCall to aid future research in pre-basecalling filtering at https://github.com/CMU-SAFARI/TargetCall.

bioinformatics↗

bioRxiv2022

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

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 1) increasing the use of the costly sequence alignment or 2) 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 1) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and 2) 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 2.4x-83.9x (on average 19.3x), has a lower memory footprint by 0.9x-14.1 x (on average 3.8x), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is fasterby 0.8x-4.1x (on average 1.7x) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

bioinformatics↗

bioRxiv2022

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

genomics↗

bioRxiv2021

AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4x. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants. Code AvailabilityAirLift source code and readme describing how to reproduce our results are available at https://github.com/CMU-SAFARI/AirLift.

bioinformatics↗

bioRxiv2017

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

MotivationChoosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies.\n\nResultsWe designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platforms error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.\n\nAvailabilityHercules source code is available at https://github.com/BilkentCompGen/Hercules

bioinformatics↗