AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results

Levitsky, Lev I.; Bubis, Julia A.; Gorshkov, Mikhail V.; Tarasova, Irina A.

doi:10.1016/j.jprot.2021.104350

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…This implies that HOMS-TC can identify biologically relevant PTMs through the OMS. Note that HOMS-TC does not determine modification identities, but they can be obtained from the precursor mass difference using post-processing tools like PTM-Shepherd ( Geiszler et al 2021 ) and AA_stat ( Levitsky et al 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Accelerating open modification spectral library searching on tensor core in high-dimensional space

Kang

Bittremieux

et al. 2023

Bioinformatics

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Motivation Driven by technological advances, the throughput and cost of mass spectrometry proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications, will remain unknown. Open modification searching is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of mass spectrometry proteomics datasets. Results We propose an open modification searching algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering post-translational modifications. We accelerate HOMS-TC on NVIDIA’s Tensor Core Units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31 × faster on average than alternative search engines and provides comparable accuracy to competing search tools. Availability HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

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Section: Resultsmentioning

confidence: 99%

Accelerating open modification spectral library searching on tensor core in high-dimensional space

Kang

Bittremieux

et al. 2023

Bioinformatics

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“…For LysC digestion, the cleavage rule was set to LysC and up to two miscleavages were allowed. Search parameters for Confetti data were the same, except that the fixed modification on cysteine was set to N -ethylmaleimide (this decision was motivated by previous analysis of modifications with AA_stat). , Also, for each protease, its corresponding rule was specified, and the number of allowed miscleavages was set as follows: two for ArgC, four for AspN, six for chymotrypsin, four for GluC, two for LysC, and two for trypsin. For elastase, nonspecific cleavage was used.…”

Section: Experimental Proceduresmentioning

confidence: 99%

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

et al. 2022

Self Cite

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Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.

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Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

Levitsky

Kuznetsova

Kliuchnikova

et al. 2022

Preprint

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Mass spectrometry-based proteome analysis usually implies matching mass spectra of proteolytic peptides to amino acid sequences predicted from nucleic acid sequences. At the same time, due to the stochastic nature of the method when it comes to proteome-wide analysis, in which only a fraction of peptides are selected for sequencing, the completeness of protein sequence identification is undermined. Likewise, the reliability of peptide variant identification in proteogenomic studies is suffering. We propose a way to interpret shotgun proteomics results, specifically in data-dependent acquisition mode, as protein sequence coverage by multiple reads, just as it is done in the field of nucleic acid sequencing for the calling of single nucleotide variants. Multiple reads for each position in a sequence could be provided by overlapping distinct peptides, thus, confirming the presence of certain amino acid residues in the overlapping stretch with much lower false discovery rate than conventional 1%. The source of overlapping distinct peptides are, first, miscleaved tryptic peptides in combination with their properly cleaved counterparts, and, second, peptides generated by several proteases with different specificities after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease proteomic datasets and our own data generated for HEK-293 cell line digests obtained using trypsin, LysC and GluC proteases. From 5000 to 8000 protein groups are identified for each digest corresponding to up to 30% of the whole proteome coverage. Most of this coverage was provided by a single read, while up to 7% of the observed protein sequences were covered two-fold and more. The proteogenomic analysis of HEK-293 cell line revealed 36 peptide variants associated with SNP, seven of which were supported by multiple reads. The efficiency of the multiple reads approach depends strongly on the depth of proteome analysis, the digesting features such as the level of miscleavages, and will increase with the number of different proteases used in parallel proteome digestion.

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AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results

Cited by 3 publications

References 49 publications

Accelerating open modification spectral library searching on tensor core in high-dimensional space

Accelerating open modification spectral library searching on tensor core in high-dimensional space

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

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