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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) Distribution of exclusivity scores of antibodies. (b) PSM coverage along positions of each cluster. Positions of CDR1, CDR2 and CDR3 shown in gray as determined for a single cluster. Coverage is normalized for shared peptides using their exclusivity scores. (c) Origin of identified peptides. For each identified peptide, a representative cluster sequence was used to determine from which reference segment it originated; V, D or J. Each peptide is classified as V-, D- or J-peptide depending on whether it overlaps with segments marked as V, D or J regions for the heavy chain sequence (peptides spanning more than one region, e.g. V and J, are classified as both V-peptides and J-peptides)
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btv238-F4: (a) Distribution of exclusivity scores of antibodies. (b) PSM coverage along positions of each cluster. Positions of CDR1, CDR2 and CDR3 shown in gray as determined for a single cluster. Coverage is normalized for shared peptides using their exclusivity scores. (c) Origin of identified peptides. For each identified peptide, a representative cluster sequence was used to determine from which reference segment it originated; V, D or J. Each peptide is classified as V-, D- or J-peptide depending on whether it overlaps with segments marked as V, D or J regions for the heavy chain sequence (peptides spanning more than one region, e.g. V and J, are classified as both V-peptides and J-peptides)

Mentions: Table 2 shows the number of identified peptides and PSMs. Modified peptides are considered identical to those without modifications should their sequences be the same; and hence are not counted when considering unique peptides. Note the large number of peptides identified only with modifications (i.e. unmodified versions of these peptides were not identified) suggesting that future immunoproteogenomics searches should include search for post-translational modification (PTMs). Overall, we identify nearly 13% of all spectra when performing restrictive PTM searches at 1% FDR. The number of identified peptides is further boosted when employing a multi-layer strategy, noted by the ‘layer’ column in the table. Blind modification search was performed on the trypsin dataset only (since MODa is not designed for spectral datasets generated with other digestive enzymes). MODa identified 3334 PSMs with modifications, corresponding to 970 peptide IDs; 815 of which were identified only by the blind modification search. It brings the total percentage of identified spectra to % at 1% FDR [Cheung et al.., (2012) identified 6% of spectra at 2% FDR]. See Appendix I on specific modifications found by our blind search. Figure 4c shows the breakdown of the origin of each identified peptide.Fig. 4.


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) Distribution of exclusivity scores of antibodies. (b) PSM coverage along positions of each cluster. Positions of CDR1, CDR2 and CDR3 shown in gray as determined for a single cluster. Coverage is normalized for shared peptides using their exclusivity scores. (c) Origin of identified peptides. For each identified peptide, a representative cluster sequence was used to determine from which reference segment it originated; V, D or J. Each peptide is classified as V-, D- or J-peptide depending on whether it overlaps with segments marked as V, D or J regions for the heavy chain sequence (peptides spanning more than one region, e.g. V and J, are classified as both V-peptides and J-peptides)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv238-F4: (a) Distribution of exclusivity scores of antibodies. (b) PSM coverage along positions of each cluster. Positions of CDR1, CDR2 and CDR3 shown in gray as determined for a single cluster. Coverage is normalized for shared peptides using their exclusivity scores. (c) Origin of identified peptides. For each identified peptide, a representative cluster sequence was used to determine from which reference segment it originated; V, D or J. Each peptide is classified as V-, D- or J-peptide depending on whether it overlaps with segments marked as V, D or J regions for the heavy chain sequence (peptides spanning more than one region, e.g. V and J, are classified as both V-peptides and J-peptides)
Mentions: Table 2 shows the number of identified peptides and PSMs. Modified peptides are considered identical to those without modifications should their sequences be the same; and hence are not counted when considering unique peptides. Note the large number of peptides identified only with modifications (i.e. unmodified versions of these peptides were not identified) suggesting that future immunoproteogenomics searches should include search for post-translational modification (PTMs). Overall, we identify nearly 13% of all spectra when performing restrictive PTM searches at 1% FDR. The number of identified peptides is further boosted when employing a multi-layer strategy, noted by the ‘layer’ column in the table. Blind modification search was performed on the trypsin dataset only (since MODa is not designed for spectral datasets generated with other digestive enzymes). MODa identified 3334 PSMs with modifications, corresponding to 970 peptide IDs; 815 of which were identified only by the blind modification search. It brings the total percentage of identified spectra to % at 1% FDR [Cheung et al.., (2012) identified 6% of spectra at 2% FDR]. See Appendix I on specific modifications found by our blind search. Figure 4c shows the breakdown of the origin of each identified peptide.Fig. 4.

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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