Biology subjects

Sadasivan, H.

Publications and source records attributed to Sadasivan, H..

5 recordsLinked to original sources

bioRxiv2024

mm2-gb: GPU Accelerated Minimap2 for Long Read DNA Mapping

Long-read DNA sequencing is becoming increasingly popular for genetic diagnostics. Minimap2 is the state-of-the-art long-read aligner. However, Minimap2s chaining step is slow on the CPU and takes 40-68% of the time especially for long DNA reads. Prior works in accelerating Minimap2 either lose mapping accuracy, are closed source (and not updated) or deliver inconsistent speedups for longer reads. We introduce mm2-gb which accelerates the chaining step of Minimap2 on GPU without compromising mapping accuracy. In addition to intra- and inter-read parallelism exploited by prior works, mm2-gb exploits finer levels of parallelism by breaking down high latency large workloads into smaller independent segments that can be run in parallel and leverages several strategies for better workload balancing including split-kernels and prioritized scheduling of segments based on sorted size. We show that mm2-gb on an AMD Instinct MI210 GPU achieves 2.57-5.33x performance improvement on long nanopore reads (10kb-100kb), and up to 1.87x performance gain on super long reads (100kb-300kb) compared to SIMD accelerated mm2-fast. mm2-gb is open-sourced and available at https://github.com/Minimap2onGPU/mm2-gb.

genomics↗

bioRxiv2023

GPU-accelerated Dynamic Time Warping for SelectiveNanopore Sequencing

The design and supply of RT-PCR primers for accurate virus testing is a complex process. The MinION is a revolutionary portable nanopore DNA sequencer that may be used to sequence the whole genome of a target virus in a biological sample. Human samples have more than 99% of non-target host DNA and Read Until is a protocol that enables the MinION to selectively eject reads in real-time. However, the MinION does not have any in-built compute power to select non-target reads. SquiggleFilter is a prior work that identified the accuracy and throughput challenges in performing Read Until using the state-of-the-art solution and proposed a hardware-accelerated subsequence Dynamic Time Warping (sDTW) based programmable filter on an ASIC. However, SquiggleFilter does not work for genomes larger than 100Kb. We optimize SquiggleFilters sDTW algorithm onto the more commonly available GPUs. DTWax better uses tensor core pipes, 2X-SIMD FP16 computations and efficient data handling strategies using offline pre-processing, coalesced global memory loads, warp shuffles and shared memory buffering among other optimizations. DTWax enables Read Until and yields 1.92X sequencing speedup and 3.64X compute speedup: costup over a sequencing workflow that does not use Read Until.

bioinformatics↗

bioRxiv2022

Rapid Real-time Squiggle Classification for Read Until Using RawMap

ReadUntil enables Oxford Nanopore Technologys (ONT) sequencers to selectively sequence reads of target species in real-time. This enables efficient microbial enrichment for applications such as microbial abundance estimation and is particularly beneficial for metagenomic samples with a very high fraction of non-target reads (> 99% can be human reads). However, read-until requires a fast and accurate software filter that analyzes a short prefix of a read and determines if it belongs to a microbe of interest (target) or not. The baseline Read Until pipeline uses a deep neural network-based basecaller called Guppy and is slow and inaccurate for this task ([~]60% of bases sequenced are unclassified). We present RawMap, an efficient CPU-only microbial species-agnostic Read Until classifier for filtering non-target human reads in the squiggle space. RawMap uses a Support Vector Machine (SVM), which is trained to distinguish human from microbe using non-linear and non-stationary characteristics of ONTs squiggle output (continuous electrical signals). Compared to the baseline Read Until pipeline, RawMap is a 1327X faster classifier and significantly improves the sequencing time and cost, and compute time savings. We show that RawMap augmented pipelines reduce sequencing time and cost by [~]24% and computing cost by [~]22%. Additionally, since RawMap is agnostic to microbial species, it can also classify microbial species it is not trained on. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2. Availability and implementationSoftware is released with MIT License and available on GitHub: https://github.com/harisankarsadasivan/RawMap

bioinformatics↗

bioRxiv2022

Accelerating Minimap2 for accurate long read alignment on GPUs

Long read sequencing technology is becoming increasingly popular for Precision Medicine applications like Whole Genome Sequencing (WGS) and microbial abundance estimation. Minimap2 is the state-of-the-art aligner and mapper used by the leading long read sequencing technologies, today. However, Minimap2 on CPUs is very slow for long noisy reads. [~]60-70% of the run-time on a CPU comes from the highly sequential chaining step in Minimap2. On the other hand, most Point-of-Care computational workflows in long read sequencing use Graphics Processing Units (GPUs). We present minimap2-accelerated (mm2-ax), a heterogeneous design for sequence mapping and alignment where minimap2s compute intensive chaining step is sped up on the GPU and demonstrate its time and cost benefits. We extract better intra-read parallelism from chaining without loosing mapping accuracy by forward transforming Minimap2s chaining algorithm. Moreover, we better utilize the high memory available on modern cloud instances apart from better workload balancing, data locality and minimal branch divergence on the GPU. We show mm2-ax on an NVIDIA A100 GPU improves the chaining step with 5.41 - 2.57X speedup and 4.07 - 1.93X speedup : costup over the fastest version of Minimap2, mm2-fast, benchmarked on a Google Cloud Platform instance of 30 SIMD cores.

genomics↗

bioRxiv2020

Digitization of Electrocardiogram Using Bilateral Filtering

Electrocardiogram (ECG) is one of the most basic tools available for the detection of cardiovascular disease (CVD) which has evolved as one of the major causes of death in recent times, spanning countries. Conventionally, ECGs are printed on graph sheets which keep fading away with time. Hence, the preservation of ECG graph sheets is difficult. Digitization of printed ECG graph sheets is a solution to this problem. Digitization also helps in faster analysis and interpretation of ECG signals. Although the previous works have succeeded in smoothing ECG after extracting the signal from graph sheet, the cost amounts to loss of peak amplitude characteristics leading to wrong diagnosis. In this work, we propose a digitization technique to extract ECG signals from graph sheets. The proposed bilateral filter based method achieves a high degree of smoothness with all the peak characteristics intact when compared to other implementations using Butterworth filters. With this technique, it is now possible to create extensive databases similar to MIT-BIH from printed ECG graph sheets. A high level of automation is achieved, and results verified with over 60 ECGs and compared.

bioinformatics↗