Limits...
IgRepertoireConstructor: a novel algorithm for antibody repertoire construction and immunoproteogenomics analysis.

Safonova Y, Bonissone S, Kurpilyansky E, Starostina E, Lapidus A, Stinson J, DePalatis L, Sandoval W, Lill J, Pevzner PA - Bioinformatics (2015)

Bottom Line: Therefore, the protein database required for the interpretation of spectra from circulating antibodies is custom for each individual.Although such a database can be constructed via NGS, the reads generated by NGS are error-prone and even a single nucleotide error precludes identification of a peptide by the standard proteomics tools.IgRepertoireConstructor is open source and freely available as a C++ and Python program running on all Unix-compatible platforms.

View Article: PubMed Central - PubMed

Affiliation: Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.

Show MeSH
Construction of the antibody repertoire based on the decomposition of the Bounded Hamming Graph into dense subgraphs. (Top left) The adjacency matrix of the Bounded Hamming Graph shown in Figure 2a. Each element in the matrix corresponds to a pair of vertices x and y and is colored green if the edge (x, y) is presented in the graph. (Top right) Decomposition of the Bounded Hamming Graph into dense subgraphs (highlighted by different colors). Edges connecting vertices from different dense subgraph are colored in grey. (Bottom left) The adjacency matrix with edges corresponding to SHM-triggering patterns RGYW/WRCY highlighted in orange. (Bottom right) The final decomposition of the Bounded Hamming Graph takes into account the multiple alignment of reads corresponding to the same subgraph in the decomposition and breaks the large yellow subgraph (top right subfigure) into two smaller subgraphs highlighted in yellow and blue. The multiple alignment of ‘yellow’ and ‘blue’ reads from these smaller subgraphs is shown on the right (limited to positions 52–100). Note that all ‘yellow’ reads are similar to each other and all ‘blue’ reads are similar to each other (the differences are highlighted in red and likely represent sequencing errors). However, there exists a systematic difference (C/G mismatch within RGYW pattern in CDR1 region) between ‘yellow’ and ‘blue’ reads that allows IgRepertoireConstructor to split the large yellow subgraph in top right subfigure
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4542777&req=5

btv238-F3: Construction of the antibody repertoire based on the decomposition of the Bounded Hamming Graph into dense subgraphs. (Top left) The adjacency matrix of the Bounded Hamming Graph shown in Figure 2a. Each element in the matrix corresponds to a pair of vertices x and y and is colored green if the edge (x, y) is presented in the graph. (Top right) Decomposition of the Bounded Hamming Graph into dense subgraphs (highlighted by different colors). Edges connecting vertices from different dense subgraph are colored in grey. (Bottom left) The adjacency matrix with edges corresponding to SHM-triggering patterns RGYW/WRCY highlighted in orange. (Bottom right) The final decomposition of the Bounded Hamming Graph takes into account the multiple alignment of reads corresponding to the same subgraph in the decomposition and breaks the large yellow subgraph (top right subfigure) into two smaller subgraphs highlighted in yellow and blue. The multiple alignment of ‘yellow’ and ‘blue’ reads from these smaller subgraphs is shown on the right (limited to positions 52–100). Note that all ‘yellow’ reads are similar to each other and all ‘blue’ reads are similar to each other (the differences are highlighted in red and likely represent sequencing errors). However, there exists a systematic difference (C/G mismatch within RGYW pattern in CDR1 region) between ‘yellow’ and ‘blue’ reads that allows IgRepertoireConstructor to split the large yellow subgraph in top right subfigure

Mentions: (a) A connected component with 107 vertices and 1426 edges in the Bounded Hamming graph with τ = 3 (fill-in is 0.25). The sizes of vertices are proportional to their degrees. (b) Clusters constructed as result of vertex decomposition of the Bounded Hamming Graph. Vertices of the same colors define the dense subgraphs in the decomposition [the colors are coordinated with Fig. 3 (bottom right)]. IgRepertoireConstructor constructs 42 clusters but 35 of them are trivial, i.e. are induced by a single read. Sizes and edge fill-ins (in brackets) of the remaining seven non-trivial clusters are: 2 (1.0), 3 (1.0), 6 (1.0), 8 (1.0), 12 (1.0), 18 (0.9) and 23 (0.9)


IgRepertoireConstructor: a novel algorithm for antibody repertoire construction and immunoproteogenomics analysis.

Safonova Y, Bonissone S, Kurpilyansky E, Starostina E, Lapidus A, Stinson J, DePalatis L, Sandoval W, Lill J, Pevzner PA - Bioinformatics (2015)

Construction of the antibody repertoire based on the decomposition of the Bounded Hamming Graph into dense subgraphs. (Top left) The adjacency matrix of the Bounded Hamming Graph shown in Figure 2a. Each element in the matrix corresponds to a pair of vertices x and y and is colored green if the edge (x, y) is presented in the graph. (Top right) Decomposition of the Bounded Hamming Graph into dense subgraphs (highlighted by different colors). Edges connecting vertices from different dense subgraph are colored in grey. (Bottom left) The adjacency matrix with edges corresponding to SHM-triggering patterns RGYW/WRCY highlighted in orange. (Bottom right) The final decomposition of the Bounded Hamming Graph takes into account the multiple alignment of reads corresponding to the same subgraph in the decomposition and breaks the large yellow subgraph (top right subfigure) into two smaller subgraphs highlighted in yellow and blue. The multiple alignment of ‘yellow’ and ‘blue’ reads from these smaller subgraphs is shown on the right (limited to positions 52–100). Note that all ‘yellow’ reads are similar to each other and all ‘blue’ reads are similar to each other (the differences are highlighted in red and likely represent sequencing errors). However, there exists a systematic difference (C/G mismatch within RGYW pattern in CDR1 region) between ‘yellow’ and ‘blue’ reads that allows IgRepertoireConstructor to split the large yellow subgraph in top right subfigure
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542777&req=5

btv238-F3: Construction of the antibody repertoire based on the decomposition of the Bounded Hamming Graph into dense subgraphs. (Top left) The adjacency matrix of the Bounded Hamming Graph shown in Figure 2a. Each element in the matrix corresponds to a pair of vertices x and y and is colored green if the edge (x, y) is presented in the graph. (Top right) Decomposition of the Bounded Hamming Graph into dense subgraphs (highlighted by different colors). Edges connecting vertices from different dense subgraph are colored in grey. (Bottom left) The adjacency matrix with edges corresponding to SHM-triggering patterns RGYW/WRCY highlighted in orange. (Bottom right) The final decomposition of the Bounded Hamming Graph takes into account the multiple alignment of reads corresponding to the same subgraph in the decomposition and breaks the large yellow subgraph (top right subfigure) into two smaller subgraphs highlighted in yellow and blue. The multiple alignment of ‘yellow’ and ‘blue’ reads from these smaller subgraphs is shown on the right (limited to positions 52–100). Note that all ‘yellow’ reads are similar to each other and all ‘blue’ reads are similar to each other (the differences are highlighted in red and likely represent sequencing errors). However, there exists a systematic difference (C/G mismatch within RGYW pattern in CDR1 region) between ‘yellow’ and ‘blue’ reads that allows IgRepertoireConstructor to split the large yellow subgraph in top right subfigure
Mentions: (a) A connected component with 107 vertices and 1426 edges in the Bounded Hamming graph with τ = 3 (fill-in is 0.25). The sizes of vertices are proportional to their degrees. (b) Clusters constructed as result of vertex decomposition of the Bounded Hamming Graph. Vertices of the same colors define the dense subgraphs in the decomposition [the colors are coordinated with Fig. 3 (bottom right)]. IgRepertoireConstructor constructs 42 clusters but 35 of them are trivial, i.e. are induced by a single read. Sizes and edge fill-ins (in brackets) of the remaining seven non-trivial clusters are: 2 (1.0), 3 (1.0), 6 (1.0), 8 (1.0), 12 (1.0), 18 (0.9) and 23 (0.9)

Bottom Line: Therefore, the protein database required for the interpretation of spectra from circulating antibodies is custom for each individual.Although such a database can be constructed via NGS, the reads generated by NGS are error-prone and even a single nucleotide error precludes identification of a peptide by the standard proteomics tools.IgRepertoireConstructor is open source and freely available as a C++ and Python program running on all Unix-compatible platforms.

View Article: PubMed Central - PubMed

Affiliation: Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.

Show MeSH