scholarly journals 567: Optimization of methods of interrogating large proteomic data sets for disease progression prediction in CF

2021 ◽  
Vol 20 ◽  
pp. S268
Author(s):  
E. Gecili ◽  
Y. Cheng ◽  
M. Siefert ◽  
E. Skala ◽  
A. Ziady ◽  
...  
Keyword(s):  
Disease Progression ◽  
Data Sets ◽  
Proteomic Data ◽  
Progression Prediction

Proteomics: Opportunities and Challenges

2011 ◽  
Vol 3 (4) ◽  
pp. 1165-1172 ◽  
Author(s):  
Parag A Pathade ◽  
Vinod A Bairagi ◽  
Yogesh S. Ahire ◽  
Neela M Bhatia
Keyword(s):  
Biomedical Research ◽  
Therapy Monitoring ◽  
Dynamic State ◽  
Data Sets ◽  
Protein Markers ◽  
Drug Target Discovery ◽  
Proteomic Data ◽  
Cell Tissue ◽  
A Cell ◽  
Analytical Tools

‘‘Proteomics’’, is the emerging technology leading to high-throughput identification and understanding of proteins. Proteomics is the protein equivalent of genomics and has captured the imagination of biomolecular scientists, worldwide. Because proteome reveals more accurately the dynamic state of a cell, tissue, or organism, much is expected from proteomics to indicate better disease markers for diagnosis and therapy monitoring. Proteomics is expected to play a major role in biomedical research, and it will have a significant impact on the development of diagnostics and therapeutics for cancer, heart ailments and infectious diseases, in future. Proteomics research leads to the identification of new protein markers for diagnostic purposes and novel molecular targets for drug discovery.  Though the potential is great, many challenges and issues remain to be solved, such as gene expression, peptides, generation of low abundant proteins, analytical tools, drug target discovery and cost. A systematic and efficient analysis of vast genomic and proteomic data sets is a major challenge for researchers, today. Nevertheless, proteomics is the groundwork for constructing and extracting useful comprehension to biomedical research. This review article covers some opportunities and challenges offered by proteomics.   

scholarly journals Probabilistic data integration identifies reliable gametocyte-specific proteins and transcripts in malaria parasites

2017 ◽  
Author(s):  
Lisette Meerstein-Kessel ◽  
Robin van der Lee ◽  
Will Stone ◽  
Kjerstin Lanke ◽  
David A Baker ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
Data Sets ◽  
Asexual Blood Stage ◽  
Systematic Analysis ◽  
Humoral Immune ◽  
Proteomic Data ◽  
Genome Wide ◽  
Field Samples ◽  
Specific Proteins ◽  
Specific Antigens

AbstractPlasmodium gametocytes are the sexual forms of the malaria parasite essential for transmission to mosquitoes. To better understand how gametocytes differ from asexual blood-stage parasites, we performed a systematic analysis of available ‘omics data for P. falciparum and other Plasmodium species. 18 transcriptomic and proteomic data sets were evaluated for the presence of curated “gold standards” of 41 gametocyte-specific versus 46 non-gametocyte genes and integrated using Bayesian probabilities, resulting in gametocyte-specificity scores for all P. falciparum genes.To illustrate the utility of the gametocyte score, we explored newly predicted gametocyte-specific genes as potential biomarkers of gametocyte carriage and exposure. We analyzed the humoral immune response in field samples against 30 novel gametocyte-specific antigens and found five antigens to be differentially recognized by gametocyte carriers as compared to malaria-infected individuals without detectable gametocytes. We also validated the gametocyte-specificity of 15 identified gametocyte transcripts on culture material and samples from naturally infected individuals, resulting in eight transcripts that were >1000-fold higher expressed in gametocytes compared to asexual parasites and whose transcript abundance allowed gametocyte detection in naturally infected individuals. Our integrated genome-wide gametocyte-specificity scores provide a comprehensive resource to identify targets and monitor P. falciparum gametocytemia.

scholarly journals Alzheimer's disease progression and risk factors: A standardized comparison between six large data sets

2019 ◽  
Vol 5 (1) ◽  
pp. 515-523
Author(s):  
Stephanie Evans ◽  
Kevin McRae‐McKee ◽  
Christoforos Hadjichrysanthou ◽  
Mei Mei Wong ◽  
David Ames ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

scholarly journals A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

2015 ◽  
Vol 14 (9) ◽  
pp. 2394-2404 ◽  
Author(s):  
Mikhail M. Savitski ◽  
Mathias Wilhelm ◽  
Hannes Hahne ◽  
Bernhard Kuster ◽  
Marcus Bantscheff
Keyword(s):  
False Discovery Rate ◽  
Data Sets ◽  
Rate Estimation ◽  
Proteomic Data ◽  
False Discovery ◽  
False Discovery Rate Estimation

scholarly journals Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies

2016 ◽  
Vol 15s4 ◽  
pp. CIN.S40301
Author(s):  
Nguyen Phuoc Long ◽  
Wun Jun Lee ◽  
Nguyen Truong Huy ◽  
Seul Ji Lee ◽  
Jeong Hill Park ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Microarray Data ◽  
Cancer Metastasis ◽  
Meta Analysis ◽  
Data Sets ◽  
Proteomic Data ◽  
Colorectal Cancer Metastasis ◽  
Microarray Studies ◽  
Potential Biomarkers

Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Two microarray data sets included found 21 significant genes. Of these significant genes, ALDOA, IL8 (CXCL8), and PARP4 had strong potential as prognostic candidates. LAMB2, MCM7, CXCL23A, SERPINA3, ABCA3, ALDH3A2, and POLR2I also have potential. Other candidates were more controversial, possibly because of the biologic heterogeneity of tumor cells, which is a major obstacle to predicting metastasis. In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation.

scholarly journals Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering

2015 ◽  
Vol 14 ◽  
pp. CIN.S33076 ◽  
Author(s):  
Kevin K. Mcdade ◽  
Uma Chandran ◽  
Roger S. Day
Keyword(s):  
Data Quality ◽  
Research Question ◽  
Quality Data ◽  
Data Sets ◽  
Expression Data ◽  
Test Bed ◽  
Filtering Method ◽  
Proteomic Data ◽  
Combination Strategies ◽  
Test Beds

Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.

scholarly journals PTMphinder: an R package for PTM site localization and motif extraction from proteomic datasets

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7046 ◽  
Author(s):  
Jacob M. Wozniak ◽  
David J. Gonzalez
Keyword(s):  
R Package ◽  
Data Sets ◽  
Amino Acid Residues ◽  
Post Translational Modifications ◽  
Specific Amino Acid ◽  
Proteomic Data ◽  
Wide Range ◽  
Site Localization ◽  
Programming Knowledge ◽  
Beta Testing

Background Mass-spectrometry-based proteomics is a prominent field of study that allows for the unbiased quantification of thousands of proteins from a particular sample. A key advantage of these techniques is the ability to detect protein post-translational modifications (PTMs) and localize them to specific amino acid residues. These approaches have led to many significant findings in a wide range of biological disciplines, from developmental biology to cancer and infectious diseases. However, there is a current lack of tools available to connect raw PTM site information to biologically meaningful results in a high-throughput manner. Furthermore, many of the available tools require significant programming knowledge to implement. Results The R package PTMphinder was designed to enable researchers, particularly those with minimal programming background, to thoroughly analyze PTMs in proteomic data sets. The package contains three functions: parseDB, phindPTMs and extractBackground. Together, these functions allow users to reformat proteome databases for easier analysis, localize PTMs within full proteins, extract motifs surrounding the identified sites and create proteome-specific motif backgrounds for statistical purposes. Beta-testing of this R package has demonstrated its simplicity and ease of integration with existing tools. Conclusion PTMphinder empowers researchers to fully analyze and interpret PTMs derived from proteomic data. This package is simple enough for researchers with limited programming experience to understand and implement. The data produced from this package can inform subsequent research by itself and also be used in conjunction with other tools, such as motif-x, for further analysis.

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