Preliminary Search Engine for Open Protein Identification

2012 ◽  
Author(s):  
Wenli Zhang ◽  
Hao Chi ◽  
Yuanzheng Lu ◽  
Yuqing Huang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification

scholarly journals A comprehensive and scalable database search system for metaproteomics

2016 ◽  
Author(s):  
Sandip Chatterjee ◽  
Gregory S. Stupp ◽  
Sung Kyu (Robin) Park ◽  
Jean-Christophe Ducom ◽  
John R. Yates ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
High Throughput Sequencing ◽  
Shotgun Proteomics ◽  
Identification Accuracy ◽  
Sequencing Data ◽  
Protein Database ◽  
Healthy Human ◽  
Genomic Libraries ◽  
Sequence Databases

AbstractBackgroundMass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations.ResultsOur approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed “Blazmass”) to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy, and allowing for a more in-depth characterization of the functional landscape of the samples.ConclusionsThe combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomics search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

Comparison of search engine contributions in protein mass fingerprinting for protein identification

2007 ◽  
Vol 12 (2) ◽  
pp. 125-130 ◽  
Author(s):  
Won-A Joo ◽  
Jeong-Bok Lee ◽  
Mira Park ◽  
Jae-Won Lee ◽  
Hyun-Jung Kim ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
Protein Mass

Parallel FPGA Search Engine for Protein Identification

2010 ◽  
pp. 313-335
Author(s):  
Daniel Coca ◽  
Istvan Bogdan ◽  
Robert Beynon
Keyword(s):  
Search Engine ◽  
Protein Identification

pTop 1.0: A High-Accuracy and High-Efficiency Search Engine for Intact Protein Identification

2016 ◽  
Vol 88 (6) ◽  
pp. 3082-3090 ◽  
Author(s):  
Rui-Xiang Sun ◽  
Lan Luo ◽  
Long Wu ◽  
Rui-Min Wang ◽  
Wen-Feng Zeng ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
High Efficiency ◽  
High Accuracy ◽  
Intact Protein

scholarly journals PepTiger: Search Engine for Error-Tolerant Protein Identification from de Novo Sequences

2007 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Irina Fedulova ◽  
Zheng Ouyang ◽  
Charles Buck ◽  
Xiang Zhang
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
De Novo

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics

2018 ◽  
Vol 17 (7) ◽  
pp. 2249-2255 ◽  
Author(s):  
Lev I. Levitsky ◽  
Mark V. Ivanov ◽  
Anna A. Lobas ◽  
Julia A. Bubis ◽  
Irina A. Tarasova ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
Shotgun Proteomics

Who owns a personal home page?

2003 ◽  
Vol 62 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Astrid Schütz ◽  
Franz Machilek
Keyword(s):  
Search Engine ◽  
Home Page ◽  
Sampling Strategies ◽  
Home Pages ◽  
Personal Home Pages ◽  
Age And Sex

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.

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