A fast coarse filtering method for peptide identification by mass spectrometry

Ramakrishnan, S.; Mao, Rui; Nakorchevskiy, Aleksey A.; Prince, John T.; Willard, Willard S.; Xu, Wei; Marcotte, Edward M.; Miranker, Daniel P.

doi:10.1093/bioinformatics/btl118

Cited by 38 publications

(37 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This coarse filter reduces search time, which would otherwise be quite significant in high-throughput analysis. This type of filter analysis has produced results consistent with other industry programs including SEQUEST [55].…”

Section: Protein Profiling Using Ms and Microarrayssupporting

confidence: 71%

See 1 more Smart Citation

Bayesian methods for proteomics

et al. 2007

View full text Add to dashboard Cite

Biological and medical data have been growing exponentially over the past several years [1,2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4][5][6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the background mathematics behind the Bayesian methodology: from parameter estimation to Bayesian networks. The article then goes on to discuss how emerging Bayesian approaches have already been successfully applied to research across proteomics, a field for which Bayesian methods are particularly well suited [7][8][9]. After reviewing the literature on the subject of Bayesian methods in biological contexts, the article discusses some of the recent applications in proteomics and emerging directions in the field.

show abstract

Section: Protein Profiling Using Ms and Microarrayssupporting

confidence: 71%

“…This means a large amount calculations and comparisons are needed in its analysis. Ramakrishnan et al recently explored the method of analysis using a coarse, but lossless, filter [55]. This coarse filter reduces search time, which would otherwise be quite significant in high-throughput analysis.…”

Section: Protein Profiling Using Ms and Microarraysmentioning

confidence: 99%

Bayesian methods for proteomics

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Using this heuristic we can avoid 94.5% of the unnecessary similarity computations (i.e., computing the similarity between pairs of spectra from different peptides) while overlooking only 2% of the pairs of spectra from the same peptides. The reduction in running time required for similarity computations achieved with this heuristic is in par with the reductions reported for metric space indexing [14] or local sensitive hashing [15]. Additional analysis of the heuristics we use is given in the supplemental material.…”

Section: Clustering Algorithmmentioning

confidence: 87%

Clustering Millions of Tandem Mass Spectra

et al. 2007

View full text Add to dashboard Cite

SummaryTandem mass spectrometry (MS/MS) experiments often generate redundant datasets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/ MS database searches. We present an efficient clustering approach for analyzing large MS/MS datasets (over ten million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular non-clustered searches. Our open source software MS-Clustering is available for download at

show abstract

“…21,22 These approaches promise even larger speedups at the expense of failing to retrieve a small fraction of the candidate peptides.…”

Section: Discussionmentioning

confidence: 99%

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

et al. 2008

View full text Add to dashboard Cite

Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program SEQUEST. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.

show abstract

A fast coarse filtering method for peptide identification by mass spectrometry

Cited by 38 publications

References 16 publications

Bayesian methods for proteomics

Bayesian methods for proteomics

Clustering Millions of Tandem Mass Spectra

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

Contact Info

Product

Resources

About