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optimized iterative de bruijn graph assembly pipeline  (SourceForge net)

 
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    SourceForge net optimized iterative de bruijn graph assembly pipeline
    A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a <t>de</t> <t>Bruijn</t> graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.
    Optimized Iterative De Bruijn Graph Assembly Pipeline, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/optimized iterative de bruijn graph assembly pipeline/product/SourceForge net
    Average 90 stars, based on 1 article reviews
    optimized iterative de bruijn graph assembly pipeline - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Conservation of Gene Cassettes among Diverse Viruses of the Human Gut"

    Article Title: Conservation of Gene Cassettes among Diverse Viruses of the Human Gut

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0042342

    A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a de Bruijn graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.
    Figure Legend Snippet: A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a de Bruijn graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.

    Techniques Used: Produced, Construct, Sequencing



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    optimized iterative de bruijn graph assembly pipeline  (SourceForge net)
    90
    SourceForge net optimized iterative de bruijn graph assembly pipeline
    A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a <t>de</t> <t>Bruijn</t> graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.
    Optimized Iterative De Bruijn Graph Assembly Pipeline, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/optimized iterative de bruijn graph assembly pipeline/product/SourceForge net
    Average 90 stars, based on 1 article reviews
    optimized iterative de bruijn graph assembly pipeline - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

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    A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a de Bruijn graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.

    Journal: PLoS ONE

    Article Title: Conservation of Gene Cassettes among Diverse Viruses of the Human Gut

    doi: 10.1371/journal.pone.0042342

    Figure Lengend Snippet: A) Shotgun sequences are produced from two different genomes (shown in blue and red at the top). Those sequences are used to construct a de Bruijn graph, where nodes are formed by all possible sequences of length k-1 (in this case 4 bases), which are connected by edges of length k (5 bases). Since there are no 4mers shared between these two example genomes, the resulting de Bruijn subgraphs are separate. B) Nucleotide polymorphisms are better resolved by short kmers. We consider a mixture of four genomes, each with three polymorphic positions separated by 25 bp. The identity at each polymorphic position is represented by either blue or red to indicate different nucleotides. At all other positions the genomes are identical. The de Bruijn graph that is constructed from this mixture of genomes using a kmer of 23 is shown on the left, where three independent bubbles form around each polymorphic position. The equivalent graph at k = 27 is shown on the right, where three independent sets of bubbles overlap, forming a more complex and suboptimal graph structure. C) Short regions of similarity are better resolved by long kmers. We consider a mixture of two genomes which are entirely different except for a 25 bp region of sequence identity (shown in black). The de Bruijn graph that is constructed from this mixture at k = 23 is shown on the left, where the two resulting subgraphs intersect at the 23mer of similarity. The de Bruijn graph at k = 27 is shown on the right, where the two resulting subgraphs (corresponding to the two genomes) do not intersect, since they have no 26mer in common. The examples in B and C together illustrate how different kmers can be optimal for assembling graphs with different types of polymorphisms.

    Article Snippet: Here we first describe the basic steps of the optimized iterative de Bruijn graph assembly pipeline (available at https://sourceforge.net/projects/optitdba/ ), and then describe the implementation of each step in more detail.

    Techniques: Produced, Construct, Sequencing