A <i>Scientist Guerilla Fighter</i> in the Frontiers of Bioinformatics—In Memory of Bailin Hao

Yu, Jun

doi:10.1016/j.gpb.2018.11.001

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…, modeling the genomes to the CVs, calculating the DM from the CVs, and inferring a phylogenetic tree based on the DM. In the CVTree software, the classical CVTree CV generative model and algorithm were named as Hao model and Hao method, respectively, to honor Prof. Bailin Hao [27] . And in the Hao method, the genome sequences were cut into small k-strings.…”

Section: Algorithms and Implementationsmentioning

confidence: 99%

CVTree: A Parallel Alignment-Free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

Zuo

2021

Genomics, Proteomics &Amp; Bioinformatics

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Composition Vector Tree (CVTree) is an alignment-free algorithm to infer phylogenetic relationships from genome sequences. It has been successfully applied to study phylogeny and taxonomy of viruses, prokaryotes, and fungi based on the whole genomes, as well as chloroplast genomes, mitochondrial genomes, and metagenomes. Here we presented the standalone software for the CVTree algorithm. In the software, an extensible parallel workflow for the CVTree algorithm was designed. Based on the workflow, new alignment-free methods were also implemented. And by examining the phylogeny and taxonomy of 13,903 prokaryotes based on 16S rRNA sequences, we showed that CVTree software is an efficient and effective tool for studying phylogeny and taxonomy based on genome sequences. The code of CVTree software can be available at https://github.com/ghzuo/cvtree.

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Section: Algorithms and Implementationsmentioning

confidence: 99%

CVTree: A Parallel Alignment-Free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

Zuo

2021

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

show abstract

“…There are three steps for the algorithm, i.e., modeling the genomes to the composition vectors, calculating the dissimilarity matrix from the composition vectors, and inferring phylogenetic tree based on the dissimilarity matrix. In the CVTree software, the classical CVTree composition vector generative model and algorithm were named as Hao model and Hao method to honor Prof. Bailin Hao [27]. And in the Hao method, the genome sequences were cut into small k-strings.…”

Section: Scheme Of Cvtreementioning

confidence: 99%

CVTree: A Parallel Alignment-free Phylogeny and Taxonomy Tool based on Composition Vectors of Genomes

Zuo

2021

Preprint

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CVTree is an alignment-free algorithm to infer phylogenetic relationships from genome sequences. It had been successfully applied to study phylogeny and taxonomy of viruses, prokaryotes, and fungi based on the whole genomes, as well as chloroplasts, mitochondria, and metagenomes. Here we presented the standalone software for the CVTree algorithm. In the software, a parallel workflow for the CVTree algorithm was designed. Based on the workflow, new alignment-free methods were also implemented. And by examining the phylogeny and taxonomy of 13903 prokaryotes based on 16S rRNA sequences, we showed that CVTree software is an efficient and effective tool for the studying of phylogeny and taxonomy based on genome sequences.

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Enhancing DNA Sequencing Workflow with AI-Driven Analytics

Aravind Ayyagiri,

Anshika Aggarwal,

Shalu Jain

2024

JRPS

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The rapid advancements in DNA sequencing technologies have revolutionized genomics, enabling a deeper understanding of genetic information and its implications in various fields such as medicine, agriculture, and evolutionary biology. However, the exponential increase in sequencing data presents significant challenges in terms of data management, analysis, and interpretation. Traditional methods often fall short in handling the complexity and volume of data generated, necessitating the integration of advanced technologies like Artificial Intelligence (AI) to optimize the DNA sequencing workflow. AI-driven analytics offer transformative potential in enhancing DNA sequencing workflows by automating data processing, improving accuracy, and accelerating the pace of discovery. This abstract explores how AI can be integrated into various stages of the DNA sequencing process, including data preprocessing, alignment, variant calling, and downstream analysis. The integration of AI algorithms, such as machine learning and deep learning models, can streamline these processes by reducing manual intervention and minimizing errors. For instance, AI can enhance base calling accuracy, identify rare variants, and predict phenotypic outcomes with higher precision than traditional methods. The AI-driven approach in DNA sequencing is particularly beneficial in handling the challenges posed by next-generation sequencing (NGS) technologies. These technologies generate massive amounts of data that require efficient processing and interpretation. AI algorithms can be trained on large datasets to recognize patterns and anomalies that may be overlooked by human analysts. This capability is crucial in identifying novel mutations, understanding complex gene interactions, and drawing meaningful conclusions from vast genomic datasets.

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A Scientist Guerilla Fighter in the Frontiers of Bioinformatics—In Memory of Bailin Hao

Cited by 3 publications

References 13 publications

CVTree: A Parallel Alignment-Free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

CVTree: A Parallel Alignment-Free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

CVTree: A Parallel Alignment-free Phylogeny and Taxonomy Tool based on Composition Vectors of Genomes

Enhancing DNA Sequencing Workflow with AI-Driven Analytics

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