Biology subjects

Narayanasamy, S.

Publications and source records attributed to Narayanasamy, S..

12 recordsLinked to original sources

bioRxiv2026

Molecular characterization of pathogen-induced SlCBL1-2, a calcium sensor, reveals the structural basis of calcium sensing in tomato

Calcium (Ca2+) is a universal second messenger that orchestrates plant growth, development, and stress responses. Plant calcineurin B-like (CBL) proteins sense and relay Ca2+; signals in response to pathogens to activate downstream defense signalling pathways. Early blight disease, caused by the necrotrophic fungal pathogen Alternaria solani, affects tomato crops and severely limits their productivity. In the present study, we found that the tolerant tomato cultivar (Arka Rakshak) restricts A. solani infection and disease progression compared to the susceptible cultivar (PKM-1). Comparative transcriptome and qRT-PCR analysis of A. solani-infected tomato leaves showed that SlCBL1-2 (Solyc08g007160.3), a Ca2+; sensor, is up-regulated in the tolerant cultivar, both relative to its own uninfected control and the infected susceptible cultivar, whereas it was not significantly induced in the uninfected susceptible cultivar. Protein sequence analysis indicates that SlCBL1-2 contains four EF-hand motifs, an N-terminal N-myristoylation/S-acylation module and a C-terminal FPSF motif, and it is predicted to interact with several CBL-interacting serine/threonine protein kinases (CIPKs). However, the evolutionary analysis, conformational transitions upon binding to Ca2+, and the Ca2+-binding affinity of SlCBL1-2 have not been previously characterized. Molecular Dynamics (MD) simulation of the AlphaFold structure of SlCBL1-2 indicates a well-folded structure and binding of two Ca2+ ions at EF-hands 3 and 4. Further, far-UV circular dichroism and tryptophan fluorescence spectroscopy of recombinant SlCBL1-2 indicate a natively folded structure in the absence of Ca2+ and undergo minimal secondary and tertiary structural changes upon Ca2+-binding, in contrast to several Ca2+-binding proteins. Of note, SlCBL1-2 binds Ca2+ with apparent KD values of 526 {+/-} 113 {micro}M and 28.7 {+/-} 9.47 {micro}M, as evidenced by isothermal titration calorimetry. Together, our study provides an experimental description of the Ca2+-binding properties of tomato CBL protein and defines the molecular basis by which pathogen-induced SlCBL1-2 may act as a Ca2+ sensor in defense signalling.

plant biology↗

bioRxiv2024

A rapid, inexpensive, culture-free, universal bacterial identification system using internal transcribed spacer targeting primers: a proof-of-principle study

Techniques for bacterial detection and identification can be characterized as either untargeted and taxonomically broad or targeted and taxonomically narrow. Untargeted techniques (e.g., culture and sequencing) are time-consuming and/or expensive while targeted techniques (polymerase chain reaction; PCR) can be faster and less expensive but require a strong pre-test suspicion of the potential organism to choose the correct test. We have developed a universal bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The platform utilizes a unique universal polymerase chain reaction (PCR) primer set that targets the internal transcribed spacer (ITS) regions between conserved bacterial genes, creating a distinguishable electrophoretic pattern for each bacterial species. Bioinformatic simulation demonstrates that at least 45 commonly isolated pathogenic species can be uniquely identified from a single set of PCR primers using this approach. We experimentally confirmed these predictions on seven representative human pathogens, including gram-negatives and gram-positives, aerobes and anaerobes, and spore formers. Without a priori knowledge of the organism, this system can rapidly identify the unique pattern generated by multiple species in a single reaction. Using quantitative PCR, the system can also determine the corresponding concentration of the organism in question. We also show that the primers are resilient to human DNA contamination at physiologic concentrations, eliminating the need for complex and time intensive extraction methods. Proof-of-principle testing on actual clinical specimens demonstrate that this assay can identify more than twice the species as current multiplex PCR assays (e.g., BioFire) using only one universal primer pair in a single PCR reaction, providing results in <3 hours for <$20 without bioinformatic turnaround time. SIGNIFICANCE STATEMENTThis paper describes a novel bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The assay covers more than twice the species as the leading multiplex PCR assays but uses only one universal primer pair in a single PCR reaction. It is resilient to human genomic contamination precluding the need for timely or costly methods to clear human DNA. This approach represents a major advancement in the decades-long struggle to rapidly and accurately identify bacteria.

bioengineering↗

bioRxiv2024

SECRET-GWAS: Confidential Computing for Population-Scale GWAS

Genomic data from a single institution lacks global diversity representation, especially for rare variants and diseases. Confidential computing can enable collaborative GWAS without compromising privacy or accuracy, however, due to limited secure memory space and performance overheads previous solutions fail to support widely used regression methods. We present SECRET-GWAS: a rapid, privacy-preserving, population-scale, collaborative GWAS tool. We discuss several system optimizations, including streaming, batching, data parallelization, and reducing trusted hardware overheads to efficiently scale linear and logistic regression to over a thousand processor cores on an Intel SGX-based cloud platform. In addition, we protect SECRET-GWAS against several hardware side-channel attacks, including Spectre, using data-oblivious code transformations and optimized speculative load hardening. SECRET-GWAS is an open-source tool and works with the widely used Hail genomic analysis framework. Our experiments on Azures Confidential Computing platform demonstrate that SECRET-GWAS enables multivariate linear and logistic regression GWAS queries on population-scale datasets (one million patients, four million SNPs, 12 covariates) from ten independent sources in just 4.5 and 29 minutes, respectively.

bioinformatics↗

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↗

bioRxiv2024

Jointly benchmarking small and structural variant calls with vcfdist

In this work, we extend vcfdist to be the first variant call benchmarking tool to jointly evaluate phased single-nucleotide polymorphisms (SNPs), small insertions/deletions (INDELs), and structural variants (SVs) for the whole genome. First, we find that a joint evaluation of small and structural variants uniformly reduces measured errors for SNPs (-28.9%), INDELs (-19.3%), and SVs (-52.4%) across three datasets. Next, we correct a common flaw in phasing evaluations, reducing measured flip errors by over 50%. Lastly, we show that vcfdist is more accurate than previously published works and on par with the newest approaches, but with improved result interpretability.

bioinformatics↗

bioRxiv2023

vcfdist: Accurately benchmarking phased small variant calls in human genomes

AbstractAccurately benchmarking small variant calling accuracy is critical for the continued improvement of human whole genome sequencing. In this work, we show that current variant calling evaluations are biased towards certain variant representations and may misrepresent the relative performance of different variant calling pipelines. We propose solutions, first exploring the affine gap parameter design space for complex variant representation and suggesting a standard. Next, we present our tool "vcfdist" and demonstrate the importance of enforcing local phasing for evaluation accuracy. We then introduce the notion of partial credit for mostly-correct calls and present an algorithm for clustering dependent variants. Lastly, we motivate using alignment distance metrics to supplement precision-recall curves for understanding variant calling performance. We evaluate the performance of 64 phased "Truth Challenge V2" submissions and show that vcfdist improves measured (SNP, INDEL) performance consistency across variant representations from R2 = (0.14542, 0.97243) for baseline vcfeval to (0.99999, 0.99996) for vcfdist.

bioinformatics↗

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↗

bioRxiv2022

nPoRe: n-Polymer Realigner for improved pileup variant calling

Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now regularly exceeds 99.5%, INDEL recall at relatively high coverages (85x) remains below 80% for standard R9.4.1 flow cells [22, 23, 31]. Current nanopore variant callers work in two stages: an efficient pileup-based method identifies candidates of interest, and then a more expensive full-alignment model provides the final variant calls. Most false negative INDELs are lost during the first (pileup-based) step, particularly in low-complexity repeated regions. We show that read phasing and realignment can recover a significant portion of INDELs lost during this stage. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeated n-polymer sequences such as homopolymers (n = 1) and tandem repeats (2 [≤] n [≤] 6). On our dataset with 60.6x coverage, haplotype phasing improves INDEL recall in all evaluated high confidence regions from 63.76% to 70.66% and then nPoRe realignment improves it further to 73.04%, with no loss of precision.

bioinformatics↗

bioRxiv2021

Ultra-rapid somatic variant detection via real-time threshold sequencing

Molecular markers are becoming increasingly important for cancer diagnosis, proper clinical trial enrollment, and even surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform. In this work, we present Threshold Sequencing, a methodology for designing protocols for targeted variant detection on real-time sequencers with a minimal time to result. Threshold Sequencing analytically identifies a time-optimal threshold to stop target amplification and begin sequencing. To further reduce diagnostic time, we explore targeted Loop-mediated Isothermal Amplification (LAMP) and design a LAMP-specific bioinformatics tool--LAMPrey--to process sequenced LAMP product. LAMPreys concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Coupled with time-optimized DNA extraction and library preparation, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

bioinformatics↗

bioRxiv2020

Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

MotivationRead alignment is a time-consuming step in genome sequence analysis. In the read alignment software BWA-MEM and the recently published faster version BWA-MEM2, the seeding step is a major bottleneck, for instance, contributing 38% to the overall execution time in BWA-MEM2 when aligning single-end whole human genome reads from the Platinum Genomes dataset. This is because both BWA-MEM and BWA-MEM2 use a compressed index structure called the FMD-Index, which results in high memory bandwidth requirements for seeding, primarily due to its character-by-character processing of reads. ResultsWe propose a memory bandwidth-aware data structure for maximal-exact-match seeding called Enumerated Radix Tree (ERT). ERT trades off memory capacity to improve seeding performance ([~]60 GB index for human genome). Together with optimizations to the seeding algorithm and mate-rescue step, ERT when integrated into BWA-MEM2 speeds up overall read alignment by 1.28x and provides up to 2.1x higher seeding performance while guaranteeing identical output to the original software. Furthermore, we prototype an FPGA implementation of ERT on Amazon EC2 F1 cloud and observe 1.6x higher seeding throughput over a 48-thread optimized CPU-ERT implementation. Availability and implementationhttps://github.com/arun-sub/bwa-mem2 Contactarunsub@umich.edu, reetudas@umich.edu

bioinformatics↗