Genomic Wave Whole Genome Sequencing is one of the world’s most ambitious sequencing projects ever undertaken and its initial data set should become available by Spring 2021.
In the second interwave, 204 real time RT-PCR-confirmed COVID-19 positive samples were selected for whole genome sequencing; most belonged to VOC Alpha or Delta families.
Deep learning
Deep learning algorithms offer significant advantages over traditional computer programs in terms of taking in diverse data inputs and producing useful outputs. Their wide range of input capabilities have revolutionized computer science by making accurate results more easily achievable than ever before.
Deep learning technology lies at the core of image recognition, natural language processing and other applications. Up until recently, however, its scope was restricted by data sets available and computing power. Today however, thanks to big data collection methods, cloud computing services and GPU acceleration GPUs accelerating GPUs making deep learning solutions more accessible than ever.
Deep learning technology is revolutionizing genomic analysis. Deep learning enables rapid genome sequencing at lower costs while also dramatically improving quality and speed of analysis. Variant calling is a central process in genomics which allows researchers to detect differences between an individual sample genome and an official reference genome; this information is crucial in diagnosing genetic diseases or discovering potential drug targets; GPU-accelerated variant callers like GATK allow researchers to quickly detect rare mutations with greater precision.
Deep neural networks consist of several layers that form an iterative function for every input. Each layer connects to its predecessor through weights, which represent linear combinations of neuron values from all preceding layers. The final output of each layer feeds directly into another one for processing; producing results specific to that particular input.
This process helps the model recognize patterns and identify similarities. For instance, a network can examine two digital portraits and detect similarities between them; additionally it can read text including alphanumeric codes, serial numbers and handwritten digits.
Deep learning technology has proven its worth in manufacturing by helping increase productivity and quality control. Its ability to detect defects can assist production standards while optimizing processes to avoid costly downtime, and reduce customer risk as it helps prevent defective goods reaching market. These factors all can result in enhanced brand loyalty and satisfaction ratings from customers.
Accelerated AI base calling
Genomic Wave Whole Genome Sequencing can significantly enhance disease detection and patient treatment. However, it requires specialized equipment and expertise, as well as producing large volumes of data that may overwhelm computational resources. It is therefore vital to develop fast computation and data science techniques.
In order to identify genomic variation with WGS, raw sequences must first be processed through a computer algorithm known as base calling. This step of sequence processing is called base calling and it plays a vital role in interpreting genomic data. While various variant callers exist, they can often be slow and error-prone; NVIDIA has collaborated with various groups on creating GPU-optimized sequence processing software which improves accuracy and speed when identifying variants.
An individual’s genome contains thousands of genes and millions of nucleotides. Each gene encodes various proteins involved in biological processes. Variations is due to genetic mutations or complex genomic rearrangements; detection of such changes is key for diagnosing and treating diseases like cancer and inflammation.
Genomic sequencing technology can identify mutations that cause genetic rearrangements. Unfortunately, processing the vast amounts of data generated is time and resource intensive and difficult to compare between samples; leading to false positives or inaccurate results that need correcting across sequencing platforms. One way of solving this problem would be the development of standard metrics and use of artificial DNA samples for result calibration across platforms.
At the core of genomic wave sequencing is transforming raw reads into an overlap-layout-consensus (OLC) graph – this representation of the genome that identifies which sequences overlap with each other – an intensive computational task requiring high performance computers; assembling small bacterial genomes takes 10 minutes with laptop computers but 48 hours for human genome assembly.
Reconstructing the OLC graph involved reconstructing its nodes to find their Hamiltonian path, an NP-complete problem which is extremely challenging. We used NVIDIA Tensor Core GPUs’ QUBO solver as an expedient way of speeding this computation up.
Secondary genomic analysis
Genomic and sequencing tests stand out among other diagnostic tools in their ability to uncover unexpected findings, like radiologists have to plan an MRI scan around certain sections of the body in order for incidentals to show up. Genome sequencing does not offer this same level of obscuring cells and organs from being detected.
As COVID-19 cases rise globally, scientists are turning to genome sequencing technologies in order to accelerate research and diagnosis. Such tools allow scientists to analyze large volumes of real-time data in real-time while uncovering previously unknown genetic information in real time; however, this process requires several computational algorithms. Researchers at Ontario Institute for Cancer Research are taking advantage of a new platform from Illumina to speed this up – it has already become part of their workflow!
The NovaSeq X Series is a next-generation sequencing system designed to manage complex projects that require high-throughput sequencing. It boasts several advantages over existing systems, including shorter read length and increased depth of coverage; plus shorter data handling and analysis times.
OICR researchers have sequenced the genomes of 450,000 UK Biobank participants using NovaSeq X Series sequencers – making this project one of the world’s largest whole genome sequencing programs ever undertaken. Funded by UKRI and various charities/industry partners, this massive undertaking was supported by UK government’s innovation and research agency as well as additional charity donations.
This year’s fifth wave of SARS-CoV-2 infections was dominated by the Omicron lineage, which has been associated with increased transmissibility and immunoevasion among unvaccinated individuals. It consists of five sublineages – BA.2.23, BA.2.34, BA.2.10 and BA.2.32 with BA.2.10 being the most prominent variant with its D614G spike mutation that increases fusogenicity. These variants have been linked with two viruses from Clade 20B (GISAID Accession ID EPI_ISL_8886132 56_Gabon).
The initial inter-wave of samples were sequenced for presence of Delta VOC using qPCR; 32 out of 48 positive samples belonged to Alpha and 2 were Delta. A phylogenetic tree constructed using sequences from this inter-wave revealed close phylogenetic alignment with other Delta VOC genomes.
NVIDIA Tensor Core GPUs
GPUs are an invaluable computing platform for AI and high performance computing (HPC), offering fast processing speeds, greater memory capacities and more cores than CPUs. GPUs also run deep learning applications more efficiently than their counterparts – it is essential to know the differences between CUDA cores and Tensor cores when selecting the appropriate GPU for your task.
The NVIDIA V100 GPU is the world’s most advanced data center GPU ever constructed to accelerate AI, HPC, and data science applications. Boasting NVIDIA’s fastest memory architecture and up to 32 processors on one GPU, it makes an excellent solution for AI training and inference that require additional compute power than traditional CPUs can offer.
DNA and RNA sequencing analysis is an integral component of genomics research, clinical trials, and next-generation sequencing instruments. These tools are used to analyze genetic variation found in patient DNA/RNA samples from various conditions such as cancer. For optimal accuracy of their tools, researchers require fast data processing capabilities such as those offered by NVIDIA’s V100 that boasts gigaFLOPs of Tensor Core computing power.
This allows users to complete genome sequencing faster, reducing costs and increasing accuracy of results. Furthermore, the V100 features NVIDIA RTX technology for accelerating matrix operations and providing higher resolution shaders; simultaneously performing multiple tasks allows the system to operate at peak performance without slowing down.
With NVIDIA’s CUDA cores, the V100 can deliver up to three times faster performance for trillion-parameter models than previous generations. By supporting FP32 and INT8 precisions, this GPU also speeds up HPC applications that require complex matrix multiplications.
The NVIDIA V100 can help genomics researchers answer life’s biggest questions through genomics research. It accelerates NGS to secondary genomics workflows, allowing scientists to quickly identify genetic mutations and discover treatment options for those living with cancer and other illnesses. Furthermore, its GPU acceleration supports NVIDIA GPU-accelerated tools like Genome Analysis Toolkit and DeepVariant which help uncover more complex variants.