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Asseri AH, Islam MR, Alghamdi RM, Altayb HN. Identification of natural antimicrobial peptides mimetic to inhibit Ca 2+ influx DDX3X activity for blocking dengue viral infectivity. J Bioenerg Biomembr 2024; 56:125-139. [PMID: 38095733 DOI: 10.1007/s10863-023-09996-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/16/2023] [Indexed: 04/06/2024]
Abstract
Viruses are microscopic biological entities that can quickly invade and multiply in a living organism. Each year, over 36,000 people die and nearly 400 million are infected with the dengue virus (DENV). Despite dengue being an endemic disease, no targeted and effective antiviral peptide resource is available against the dengue species. Antiviral peptides (AVPs) have shown tremendous ability to fight against different viruses. Accelerating antiviral drug discovery is crucial, particularly for RNA viruses. DDX3X, a vital cell component, supports viral translation and interacts with TRPV4, regulating viral RNA metabolism and infectivity. Its diverse signaling pathway makes it a potential therapeutic target. Our study focuses on inhibiting viral RNA translation by blocking the activity of the target gene and the TRPV4-mediated Ca2+ cation channel. Six major proteins from camel milk were first extracted and split with the enzyme pepsin. The antiviral properties were then analyzed using online bioinformatics programs, including AVPpred, Meta-iAVP, AMPfun, and ENNAVIA. The stability of the complex was assessed using MD simulation, MM/GBSA, and principal component analysis. Cytotoxicity evaluations were conducted using COPid and ToxinPred. The top ten AVPs, determined by optimal scores, were selected and saved for docking studies with the GalaxyPepDock tools. Bioinformatics analyses revealed that the peptides had very short hydrogen bond distances (1.8 to 3.6 Å) near the active site of the target protein. Approximately 76% of the peptide residues were 5-11 amino acids long. Additionally, the identified peptide candidates exhibited desirable properties for potential therapeutic agents, including a net positive charge, moderate toxicity, hydrophilicity, and selectivity. In conclusion, this computational study provides promising insights for discovering peptide-based therapeutic agents against DENV.
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Affiliation(s)
- Amer H Asseri
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Rashedul Islam
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Advanced Biological Invention Centre (Bioinventics), Rajshahi, 6204, Bangladesh
| | - Reem M Alghamdi
- Department of Radiology, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Xiang T, Li T, Li J, Li X, Wang J. Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs. FASEB J 2023; 37:e22961. [PMID: 37178007 DOI: 10.1096/fj.202300245r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/30/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023]
Abstract
Genomic prediction, which is based on solving linear mixed-model (LMM) equations, is the most popular method for predicting breeding values or phenotypic performance for economic traits in livestock. With the need to further improve the performance of genomic prediction, nonlinear methods have been considered as an alternative and promising approach. The excellent ability to predict phenotypes in animal husbandry has been demonstrated by machine learning (ML) approaches, which have been rapidly developed. To investigate the feasibility and reliability of implementing genomic prediction using nonlinear models, the performances of genomic predictions for pig productive traits using the linear genomic selection model and nonlinear machine learning models were compared. Then, to reduce the high-dimensional features of genome sequence data, different machine learning algorithms, including the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and convolutional neural network (CNN) algorithms, were used to perform genomic feature selection as well as genomic prediction on reduced feature genome data. All of the analyses were processed on two real pig datasets: the published PIC pig dataset and a dataset comprising data from a national pig nucleus herd in Chifeng, North China. Overall, the accuracies of predicted phenotypic performance for traits T1, T2, T3 and T5 in the PIC dataset and average daily gain (ADG) in the Chifeng dataset were higher using the ML methods than the LMM method, while those for trait T4 in the PIC dataset and total number of piglets born (TNB) in the Chifeng dataset were slightly lower using the ML methods than the LMM method. Among all the different ML algorithms, SVM was the most appropriate for genomic prediction. For the genomic feature selection experiment, the most stable and most accurate results across different algorithms were achieved using XGBoost in combination with the SVM algorithm. Through feature selection, the number of genomic markers can be reduced to 1 in 20, while the predictive performance on some traits can even be improved compared to using the full genome data. Finally, we developed a new tool that can be used to execute combined XGBoost and SVM algorithms to realize genomic feature selection and phenotypic prediction.
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Affiliation(s)
- Tao Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Tao Li
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Jielin Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xin Li
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Jia Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
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Giordano S, Takeda S, Donadon M, Saiki H, Brunelli L, Pastorelli R, Cimino M, Soldani C, Franceschini B, Di Tommaso L, Lleo A, Yoshimura K, Nakajima H, Torzilli G, Davoli E. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence. Liver Int 2020; 40:3117-3124. [PMID: 32662575 PMCID: PMC7754124 DOI: 10.1111/liv.14604] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/17/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS Complete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra-operative assessment of tumour resection margins are time-consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS-based system for rapid and objective liver cancer identification and classification. METHODS A large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non-tumours) and 96 patients with mass-forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non-tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated. RESULTS The overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non-tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non-tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity. CONCLUSIONS The herein reported MS-based system, combined with AI, permits liver cancer identification with high accuracy. Its bench-top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision-making process in real-time with the ultimate aim of improving cancer patient cure.
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Affiliation(s)
- Silvia Giordano
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly,Present address:
Shimadzu Italia SrlMilanItaly
| | - Sen Takeda
- Department of Anatomy and Cell BiologyUniversity of Yamanashi Faculty of MedicineChuoJapan
| | - Matteo Donadon
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | | | - Laura Brunelli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Roberta Pastorelli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Matteo Cimino
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Cristiana Soldani
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Barbara Franceschini
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Luca Di Tommaso
- Department of PathologyHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Ana Lleo
- Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly,Department of Internal MedicineHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Kentaro Yoshimura
- Department of Anatomy and Cell BiologyUniversity of Yamanashi Faculty of MedicineChuoJapan
| | | | - Guido Torzilli
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Enrico Davoli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. Int J Mol Sci 2020; 21:ijms21082873. [PMID: 32326049 PMCID: PMC7216093 DOI: 10.3390/ijms21082873] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/16/2020] [Accepted: 04/18/2020] [Indexed: 01/15/2023] Open
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
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Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J. Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES. Biosystems 2018; 169-170:5-12. [DOI: 10.1016/j.biosystems.2018.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/10/2018] [Accepted: 05/14/2018] [Indexed: 11/24/2022]
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Maudsley S, Devanarayan V, Martin B, Geerts H. Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy. Alzheimers Dement 2018; 14:961-975. [DOI: 10.1016/j.jalz.2018.01.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 10/03/2017] [Accepted: 01/18/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Stuart Maudsley
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
- VIB Center for Molecular NeurologyAntwerpBelgium
| | | | - Bronwen Martin
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
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Silvestre DD, Zoppis I, Brambilla F, Bellettato V, Mauri G, Mauri P. Availability of MudPIT data for classification of biological samples. J Clin Bioinforma 2013; 3:1. [PMID: 23317455 PMCID: PMC3563498 DOI: 10.1186/2043-9113-3-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 01/07/2013] [Indexed: 01/18/2023] Open
Abstract
Background Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins. Results Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software. Conclusions These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.
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Affiliation(s)
- Dario Di Silvestre
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Italo Zoppis
- Department of Informatics, Systems and Communication, Viale Sarca 336, University of Milano-Bicocca, Milan, Italy
| | - Francesca Brambilla
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Valeria Bellettato
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, Viale Sarca 336, University of Milano-Bicocca, Milan, Italy
| | - Pierluigi Mauri
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
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Wang X, Brunetti P, Mauri PL. Processing of Mass Spectrometry Data in Clinical Applications. BIOINFORMATICS OF HUMAN PROTEOMICS 2012; 3. [PMCID: PMC7123949 DOI: 10.1007/978-94-007-5811-7_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.
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Affiliation(s)
- Xiangdong Wang
- , Medicine, Biomedical Research Center, Fudan University Zhongshan Hospital, Shang Hai, China, People's Republic
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Yadav AK, Kumar D, Dash D. Learning from decoys to improve the sensitivity and specificity of proteomics database search results. PLoS One 2012. [PMID: 23189209 PMCID: PMC3506577 DOI: 10.1371/journal.pone.0050651] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.
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Affiliation(s)
- Amit Kumar Yadav
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Dhirendra Kumar
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Debasis Dash
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
- * E-mail:
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