ArticlemAbs
Improved sequence variant analysis strategy by automated false positive removal.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Sequence Variant Analysis During Cell Line Selection via High-Throughput Peptide Mapping.Advances in experimental medicine and biology · 2026Article
- Diagnostic utility of N-terminal TMPP labels for unambiguous identification of clipped sites in therapeutic proteins.Scientific reports · 2023Article
- A Highly Efficient Workflow for Detecting and Identifying Sequence Variants in Therapeutic Proteins with a High Resolution LC-MS/MS Method.Molecules (Basel, Switzerland) · 2023Article
- Characterization of light chain c-terminal extension sequence variant in one bispecific antibody.Frontiers in chemistry · 2022Article
- Identification, characterization and control of a sequence variant in monoclonal antibody drug product: a case study.Scientific reports · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequence variant analysis (SVA) is critical in therapeutic protein development because it ensures the absence of genetic mutations of a production clone or high-level misincorporations during cell culture. While software for searching sequence variants from mass spectrometry data are available, effectively distinguishing true positives from a large number of false positives in the reported hits or identifications found in the error tolerant search mode is a challenge. This verification process must be done manually and can take several days or even weeks to accomplish. We report here the use of a Perl-based script to evaluate every identified hit to remove the false positives from the search results of PepFinder™ (also known as MassAnalyzer) based on orthogonal criteria. Our data show that the false positives from PepFinder™ output were reduced ∼4-fold without loss of accuracy in the detection of true identifications, representing a more than 70% reduction in time compared with the manual data verification process.
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Registered trials
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