In silico gene prediction has proven to be prone to errors, especially regarding precise localization of start codons that spread in subsequent biological studies. Therefore, the high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and especially proteogenomics fields. The trimethoxyphenyl phosphonium (TMPP) labeling approach (N-TOP) is an efficient N-terminomic approach that allows the characterization of both N-terminal and internal peptides in a single experiment. Due to its permanent positive charge, TMPP labeling strongly affects MS/MS fragmentation resulting in unadapted scoring of TMPP-derivatized peptide spectra by classical search engines. This behavior has led to difficulties in validating TMPP-derivatized peptide identifications with usual score filtering and thus to low/underestimated numbers of identified N-termini. We present herein a new strategy (dN-TOP) that overwhelmed the previous limitation allowing a confident and automated N-terminal peptide validation thanks to a combined labeling with light and heavy TMPP reagents. We show how this double labeling allows increasing the number of validated N-terminal peptides. This strategy represents a considerable improvement to the well-established N-TOP method with an enhanced and accelerated data processing making it now fully compatible with high-throughput proteogenomics studies.