Recent advances in mass-spectrometry based proteomics software, tools and databases

Halder, Ankit; Verma, Ayushi; Biswas, Deeptarup; Srivastava, Sanjeeva

doi:10.1016/j.ddtec.2021.06.007

Cited by 32 publications

(24 citation statements)

References 75 publications

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“…Three proteins selected as the best disease classifiers were not "significant" i.e. p value < 0.05 with t-test and adjustment for multiple testing, highlighting the limitations of univariate analysis in biomarker identification (45). Biomarker discovery is a lengthy process, akin to the pharmaceutical pipeline (13).…”

Section: Discussionmentioning

confidence: 99%

Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection

Bharucha

Gangadharan

Kumar

et al. 2022

Preprint

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Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus, and leading cause of neurological infection in Asia and the Pacific, with recent emergence in multiple territories in Australia in 2022. Patients may experience devastating socioeconomic consequences; JEV infection (JE) predominantly affects children in poor rural areas, has a 20-30% case fatality rate, and 30-50% of survivors suffer long-term disability. JEV RNA is rarely detected in patient samples, and the standard diagnostic test is an anti-JEV IgM ELISA with sub-optimal specificity; there is no means of detection in more remote areas. We aimed to test the hypothesis that there is a diagnostic protein signature of JE in human cerebrospinal fluid (CSF), and contribute to understanding of the host response and predictors of outcome during infection. We retrospectively tested a cohort of 163 patients recruited as part of the Laos central nervous system infection study. Application of liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labelling, enabled a comparison of the CSF proteome in 68 JE patient vs 95 non-JE neurological infections. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. We incorporated univariate analysis of differential protein expression, network analysis and machine learning techniques to build a ten-protein diagnostic signature of JE with >99% diagnostic accuracy. Pathways related to JE infection included neuronal damage, anti-apoptosis, heat shock and unfolded protein responses, cell adhesion, macrophage and dendritic cell activation as well as a reduced acute inflammatory response, hepatotoxicity, activation of coagulation, extracellular matrix and actin regulation. We verified the results by performing DIA LC-MS/MS in 16 (10%) of the samples, demonstrating 87% accuracy using the same model. Ultimately, antibody-based validation will be required, in a larger group of patients, in different locations and in field settings, to refine the list to 2-3 proteins that could be harnessed in a rapid diagnostic test.

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Section: Discussionmentioning

confidence: 99%

Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection

Bharucha

Gangadharan

Kumar

et al. 2022

Preprint

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“…The detailed discussion on various omics and multi-omics pipelines is beyond the manuscript's scope. There are excellent reviews available on transcriptomic (Wadapurkar et al, 2021), proteomic (Halder et al, 2021), metagenomic (Yang et al, 2021), and metabolomic (Du et al, 2022) pipelines and their integration for multi-omics (Subramanian et al, 2020;Reel et al, 2021), which can be referred. GitHub (https://github.com/danielecook/ Awesome-Bioinformatics) and Biostars (https://www.biostars.…”

Section: Analysis Of Omics Datamentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

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“…The generation of ‘big’ data in the aforementioned fields naturally brings about technical challenges. Both theoretical and computational approaches that fit large datasets are needed, and indeed, much progress has been made in this regard over the last few years [ 22 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. Most of these approaches were developed for genomics, the field that generates the largest amount of data.…”

Section: Similarities and Differences Between Genomics And Behavior Datamentioning

confidence: 99%

“…However, recent advances in computational approaches in other fields of biology that regularly deal with large datasets, such as genomics, might be beneficial as well. While several fields in biology underwent technological advancement around the same time [ 20 , 21 , 22 ], standardized protocols and tools for genomic and proteomic analysis developed in a faster manner. This is partially due to the universality of their measured outputs (nucleotides and peptides) and their massive use in many biological systems, unlike tools for behavioral analysis that were developed separately for specific behavioral paradigms in each type of animal.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Bentzur

Alon

Shohat-Ophir

2022

IJMS

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Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.

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Recent advances in mass-spectrometry based proteomics software, tools and databases

Cited by 32 publications

References 75 publications

Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection

Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection

Applications of Omics Technology for Livestock Selection and Improvement

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

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