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Integration of transcriptomic and proteomic data should reveal multi-layered regulatory processes governing cancer cell behaviors. Traditional correlation-based analyses have demonstrated limited ability to identify the post-transcriptional regulatory (PTR) processes that drive the non-linear relationship between transcript and protein abundances. In this work, we ideate an integrative approach to explore the variety of post-transcriptional mechanisms that dictate relationships between genes and corresponding proteins. The proposed workflow utilizes the intuitive technique of scatterplot diagnostics or scagnostics, to characterize and examine the diverse scatterplots built from transcript and protein abundances in a proteogenomic experiment. The workflow includes representing gene-protein relationships as scatterplots, clustering on geometric scagnostic features of these scatterplots, and finally identifying and grouping the potential gene-protein relationships according to their disposition to various PTR mechanisms. Our study verifies the efficacy of the implemented approach to excavate possible regulatory mechanisms by utilizing comprehensive tests on a synthetic dataset. We also propose a variety of 2D pattern-specific downstream analyses methodologies such as mixture modeling, and mapping miRNA post-transcriptional effects to explore each mechanism further. This work suggests that the proposed methodology has the potential for discovering and categorizing post-transcriptional regulatory mechanisms, manifesting in proteogenomic trends. These trends subsequently provide evidence for cancer specificity, miRNA targeting, and identification of regulation impacted by biological functionality and different types of degradation. (Supplementary Material - https://github.com/arunima2/PTRE_PSB_2020)

Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.

Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.

The emergence of the novel severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) Coronavirus resulted in a global pandemic due to its nature of rapid transmission and variable severities that facilitated its spread worldwide. Correspondingly, owing to advances in molecular technologies, information on this virus is generated at an unprecedented pace. Since the onset of the pandemic, multiple high-throughput “omics” analyses — including transcriptomics and proteomics of different viral infection models — have been made readily available to the research and wider community. The availability and ability to rapidly generate these data facilitate the deciphering of virus–host interactions during SARS-CoV-2 infection — thus enhancing understanding of the viral transmission, host susceptibility, pathogenesis, viral evolution, and disease complications. Such information is vital for eventual applications towards biomarker and treatment discovery against Coronavirus disease 2019 (COVID-19), and can serve as useful models for future pandemic responses.

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

De novo peptide sequencing that determines the amino acid sequence of a peptide via tandem mass spectrometry (MS/MS) has been increasingly used nowadays in proteomics for protein identification. Current de novo methods generally employ a graph theory which usually produces a large number of candidate sequences and causes heavy computational cost while trying to determine a sequence with less ambiguity. In this paper, we will present an efficient and effective de novo sequencing algorithm which greatly reduces the number of candidate sequences. By utilizing certain properties of b- and y-ion series in MS/MS spectrum, we first propose a reliable two-way parallel searching algorithm to filter out the peptide candidates which are further pruned by an intensity evidence based screening criterion. Then, the best candidate is singled out using a scoring function by consideration of total intensity evidence within certain local region. Results of our algorithm were compared with those of PEAKS, a well-known de novo sequencing software. Experimental results demonstrated the efficiency and potency of our approach.

Although impressive progress in the area of our understanding of molecular signaling in disease resistance of rice has been made recently, we still know relatively little about the molecular mechanisms of pathogen recognition and signal transduction leading to disease resistance. Increasing evidence indicates that Rac GTPase is an important molecular switch in disease resistance of rice. It activates the production of reactive oxygen species, defense gene expression, phytoalexin production, and lignin synthesis. Recent evidence suggests that it forms a protein complex with other factors involved in defense signaling. Two new technologies that are useful for the study of molecular signaling in defense responses in rice are discussed.

Proteomics has become a powerful technique to investigate cellular processes and network functions. This became possible as a result of major progress in the sensitivity of mass spectrometry instrumentation and data analysis software. As proteomics technologies are now becoming available to the wider scientific community, efforts are under way to identify complete proteomes. This information is used to improve genome annotation and to identify and confirm protein splice variants. Analysis of protein modifications and protein variants uses novel scoring and prediction tools independent of established protein databases. We discuss the proteomics tools and analysis pipelines that can be applied to rice in order to facilitate our understanding of rice genome structure and function.

MALDI-based Imaging Mass Spectrometry (IMS) is an analytical technique that provides the opportunity to study the spatial distribution of biomolecules including proteins and peptides in organic tissue. IMS measures a large collection of mass spectra spread out over an organic tissue section and retains the absolute spatial location of these measurements for analysis and imaging. The classical approach to IMS imaging, producing univariate ion images, is not well suited as a first step in a prospective study where no a priori molecular target mass can be formulated. The main reasons for this are the size and the multivariate nature of IMS data. In this paper we describe the use of principal component analysis as a multivariate pre-analysis tool, to identify the major spatial and mass-related trends in the data and to guide further analysis downstream. First, a conceptual overview of principal component analysis for IMS is given. Then, we demonstrate the approach on an IMS data set collected from a transversal section of the spinal cord of a standard control rat.

In this paper a technique to improve protein secondary structure prediction is proposed. The approach is based on the idea of combining the results of a set of prediction tools, choosing the most correct parts of each prediction. The correctness of the resulting prediction is measured referring to accuracy parameters used in several editions of CASP. Experimental evaluations validating the proposed approach are also reported.

Cancer is a proteomic disease. Though MALDI-TOF mass spectrometry allows direct measurement of the protein signature of tissue, blood, or their biological samples, and holds tremendous potential for disease diagnosis and treatment, key challenges remain in the processing of proteomic data. In this chapter, we will introduce a wavelet based mathematical framework and computational tools for proteomic data processing, feature selection, and statistical analysis in cancer study.

Mass spectrometry is an analytical technique which, as its name implies, “weighs” molecules, and thereby can define molecular identity, interactions and reactions. With this technique, molecules from a source are ionized (i.e. made to carry a formal charge, either negative or positive) and subsequently enter the mass analyzer where ions are separated according to their mass/charge ratio (m/z), and then a spectrum is recorded as ions reach the detector. Initially applications were limited to small molecules but nowadays even large macromolecular complexes are amenable for analysis.

To solve the inefficient bottleneck problems of the traditional relational databases in big data storage and access, this paper proposes an efficient approach to store big proteomic data based on NoSQL (Not only SQL) database. MongoDB, a typical software of NoSQL, is compared with MySQL in data storage and query performance. The experiment results demonstrate that the query and read speed of NoSQL database has been improved significantly as compared to the traditional relational database, especially for mass unstructured and semi-structured data.

Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer’s disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA.

Global proteomics research is currently hampered by the extremely complexity of the proteome and the absence of techniques like the polymerase chain reaction in genomics which enables multiplication of a single protein molecule. Since all the existing analytical technologies cannot overcome the detection limit and the dynamic concentration barrier, development of improved analytical technologies at nanoscale, ideally those that could recognize single protein molecule in the presence of high abundant of others, is a high priority for proteomics. In this chapter, we will show the state-of-the-art of nanoproteomics, i.e., the application of nanotechnologies to proteomics. Various nanomaterials including carbon nanomaterials, magnetic nanoparticles, silica nanoparticles, polymer and copolymer nanoparticles, metal and metal oxide nanoparticles have been used to improve sensitivity, specificity, and repeatability of proteomic analysis especially when the multidimensional separation system coupled with MALDI-TOF-MS is used. Among them, gold nanoparticles (GNPs) and carbon nanotubes (CNTs) are the two most important nanomaterials: while GNPs are frequently utilized for enzyme immobilization, high throughput bioassay, selection of target-peptides and target-protein, CNTs including single-walled carbon nanotubes (SWCNTs) and mutiple-walled carbon nanotubes (MWCNTs) have wide applications to electronic sensor, sensitive immunodetection, nanobiocatalysis, affinity probes, MALDI matrices, protein digestion, peptides enrichment and analysis. In perspectives, a deep understanding of the structures and property of nanomaterials and interdisciplinary applications of nanotechnology to proteomics will certainly be revolutionary and intellectually rewarding.

Marinobacter hydrocarbonoclasticus was shown to have a high tolerance to copper sulphate. A minimum inhibitory concentration of 1.6 mM was determined, thus classifying Marinobacter hydrocarbonoclasticus as copper resistant. A proteomic approach based on two-dimensional gel electrophoresis provided a preliminary analysis over the differential protein expression induced by 1 mM of copper in Marinobacter hydrocarbonoclasticus. The copper stress led to a global change in the proteomic profile of the periplasmic fraction of this bacterium, involving modulation of the expression of 53 out of the close to 500 proteins detected, when compared with control growths with trace copper concentration.

Heritiera littoralis Dryand were used for studying the molecular mechanisms of semi-mangroves on salt tolerance. We used two-dimensional gel electrophoresis to measure changes in the proteome in leaves of H. littoralis (the total salt content in soil of 1 g/kg (control), 10 g/kg, 19 g/kg) for 50 days. The salt treatment resulted in reductions of the seedling growth in height, Proline content, and SOD activity. There were 76 protein spots were up-regulated and 61 protein spots were down-regulated. Based on MALDI-TOFTOF analysis and NCBI database in searching the identified proteins of H. littoralis seedlings to high-salinity due to (1) Isoflavone reductase play a significant role in binding substrate; (2) The up-regulation of light-harvesting chlorophyll a-b binding protein likely helps maintain the integrity of the chloroplast protein complexes and the efficiency of photosynthesis; (3) The up-regulated of Cu-Zn SOD enzyme indicating that the antioxidative system may protect cells from oxidative damage.

Precision medicine focuses on developing treatments and preventative strategies tailored to an individual’s genomic profile, lifestyle, and environmental context. The Precision Medicine sessions at the Pacific Symposium on Biocomputing (PSB) have consistently spotlighted progress in this domain. Our 2025 manuscript collection features algorithmic innovations that integrate data across scales and diverse data modalities, presenting novel techniques to derive clinically relevant insights from molecular datasets. These studies highlight recent advances in technology and analytics and their application toward realizing the potential of precision medicine to enhance human health outcomes and extend lifespan.