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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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GeNePi: a graphics processing unit enhanced next-generation bioinformatics pipeline for whole-genome sequencing

Stefano Marangoni1,2, Federica Furia1,2, Debora Charrance1,2

  • 1Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP3VdA, Via Lavoratori - Vittime del Col du Mont 28, 11100 Aosta, Italy.

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|January 25, 2026
PubMed
Summary

GeNePi is a new bioinformatic pipeline that efficiently analyzes whole-genome sequencing (WGS) data. It uses GPU acceleration for high-performance variant discovery, making WGS analysis scalable for research and clinical use.

Keywords:
GPU-accelerated algorithmNextflowNvidia Clara Parabricksbioinformatics pipelinegenomic variantsnext-generation sequencingwhole-genome sequencing analyses

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) has advanced genome biology, enabling rapid whole-genome sequencing (WGS).
  • High-throughput NGS generates complex data, necessitating efficient computational analysis pipelines.
  • Existing tools may lack the scalability or comprehensive variant detection required for large-scale WGS studies.

Purpose of the Study:

  • To develop and present GeNePi, a modular bioinformatic pipeline for efficient and accurate WGS short paired-end read analysis.
  • To integrate GPU-accelerated algorithms for high-performance genomic variant discovery.
  • To provide a comprehensive framework for detecting diverse genomic variants in WGS data.

Main Methods:

  • GeNePi is built on the Nextflow framework, utilizing NVIDIA Clara Parabricks for GPU-accelerated algorithms.
  • The pipeline automates the detection of single-nucleotide variants, small insertions/deletions, copy number variants (CNVs), and structural variants.
  • It incorporates tools like HaplotypeCaller, CNVkit, Manta, Lumpy, BreakDancer, CNVnator, and MELT for comprehensive variant characterization.

Main Results:

  • Benchmarking on synthetic and real datasets demonstrated high accuracy and performance.
  • GeNePi's performance is comparable to state-of-the-art tools such as the Genome Analysis ToolKit (GATK).
  • The pipeline offers a scalable solution for comprehensive WGS analysis, supporting multiple workflow configurations.

Conclusions:

  • GeNePi provides an efficient, accurate, and scalable solution for WGS data analysis.
  • Its comprehensive variant detection capabilities make it valuable for large-scale research and clinical applications.
  • GeNePi represents a significant advancement towards establishing robust computational infrastructure for genomic medicine.