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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.

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(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)
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btv238-F2: (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)

Mentions: In reality, the large connected components of the Bounded Hamming Graph often have a more complex structure. Given a connected component with m edges and n vertices, we define its edge fill-in as the ratio of the number its edges (m) to the maximal possible number of edges in the graph on n vertices []. Figure 2a presents a connected component of the Bounded Hamming graph with edge fill-in 0.25 (τ = 3). The lion’s share of large connected components in the Bounded Hamming Graph (i.e. components with more than 100 vertices) have similar structures characterized by small edge fill-ins; the average edge fill-in for large components is 0.32 (Supplementary Fig. A4). Additional analysis of the connected components reveals that the nearly all of them (98.6%) consist of dense (complete or nearly complete) subgraphs connected by very few edges. Most vertices in these dense subgraphs correspond to error-prone reads derived from a single antibody or from highly similar antibodies differing from each other by a small number of SHMs.Fig. 2.


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)

(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)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4542777&req=5

btv238-F2: (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)
Mentions: In reality, the large connected components of the Bounded Hamming Graph often have a more complex structure. Given a connected component with m edges and n vertices, we define its edge fill-in as the ratio of the number its edges (m) to the maximal possible number of edges in the graph on n vertices []. Figure 2a presents a connected component of the Bounded Hamming graph with edge fill-in 0.25 (τ = 3). The lion’s share of large connected components in the Bounded Hamming Graph (i.e. components with more than 100 vertices) have similar structures characterized by small edge fill-ins; the average edge fill-in for large components is 0.32 (Supplementary Fig. A4). Additional analysis of the connected components reveals that the nearly all of them (98.6%) consist of dense (complete or nearly complete) subgraphs connected by very few edges. Most vertices in these dense subgraphs correspond to error-prone reads derived from a single antibody or from highly similar antibodies differing from each other by a small number of SHMs.Fig. 2.

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.

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