Computational Downstream Analysis of High-Throughput RNA-Sequencing Data

The advent of RNA sequencing (RNA-seq) technology has significantly advanced transcriptome-related research. The availability of RNA-seq data has spurred computational biologists to develop algorithms that process this data in a statistically rigorous manner, yielding biologically meaningful results. Recent advancements in bioinformatics algorithms enable the extraction of gene expression, fusion, and pathway information as the most immediate results from RNA-seq data. The ongoing progress in computational biology further promises to expand the utility of RNA-seq data in transcriptome-based biological research. In this dissertation, we introduce a method to detect retained introns in RNA-seq data, with the aim of developing a vaccine against cancers harboring p53 mutations. We discuss our approaches to generating unique gene signatures to elucidate the role of sensory nerve interference in the anti-melanoma immune response and to study racial disparities in triple-negative breast cancer. We propose a clustering algorithm combined with statistical methods to analyze the heterogeneity in quadruple-negative breast cancer. Additionally, we conducted a benchmarking study to assess the resilience of machine learning classification algorithms on SARS-CoV-2 genome sequences, particularly those generated with long-read specific errors. In summary, this research provides novel methodologies for exploring RNA-seq data and their application to real-world biological research

SENTIGRADE: A SENTIMENT BASED USER PROFILING STRATEGY FOR PERSONALISATION

Nowadays, the availability of folksonomy data is increased to make importance for user profiling approaches to provide results of the retrieval data or personalized recommendation. The approach is used for detecting the preferences for users and can be able to understand the interest of the user in a better way. In this approach, the incorporation of information with numerous data which depends upon sentiment is implemented using a framework SentiGrade by User Profiles (UP) and Resource Profiles (RP) for user Personalized Search (PS). From the folksonomy data, the discovery of User Preference (UsP) is presented by a rigorous probabilistic framework and relevance method are proposed for obtaining Sentiment-Based Personalized (SBP) ranking. According to the evaluation of the approach, the proposed SBP search is compared with the existing method and uses the two datasets namely, Movielens and FMRS databases. The experimental outcome of the research proved the effectiveness of the framework and works well when compared to the existing method. Through user study, the evaluation of approaches and developed systems are made which shows that considering information such as relevance and probabilistic data in Web Personalization (WP) systems can able to offer better recommendations and provide much effective personalization services to users

Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches are more robust (and accurate) than others for specific embedding methods to certain adversarial attacks to the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics

Combined HER3-EGFR score in triple-negative breast cancer provides prognostic and predictive significance superior to individual biomarkers

© 2020, The Author(s). Epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 3 (HER3) have been investigated as triple-negative breast cancer (TNBC) biomarkers. Reduced EGFR levels can be compensated by increases in HER3; thus, assaying EGFR and HER3 together may improve prognostic value. In a multi-institutional cohort of 510 TNBC patients, we analyzed the impact of HER3, EGFR, or combined HER3-EGFR protein expression in pre-treatment samples on breast cancer-specific and distant metastasis-free survival (BCSS and DMFS, respectively). A subset of 60 TNBC samples were RNA-sequenced using massive parallel sequencing. The combined HER3-EGFR score outperformed individual HER3 and EGFR scores, with high HER3-EGFR score independently predicting worse BCSS (Hazard Ratio [HR] = 2.30, p = 0.006) and DMFS (HR = 1.78, p = 0.041, respectively). TNBCs with high HER3-EGFR scores exhibited significantly suppressed ATM signaling and differential expression of a network predicted to be controlled by low TXN activity, resulting in activation of EGFR, PARP1, and caspases and inhibition of p53 and NFκB. Nuclear PARP1 protein levels were higher in HER3-EGFR-high TNBCs based on immunohistochemistry (p = 0.036). Assessing HER3 and EGFR protein expression in combination may identify which adjuvant chemotherapy-treated TNBC patients have a higher risk of treatment resistance and may benefit from a dual HER3-EGFR inhibitor and a PARP1 inhibitor

Notch Signaling Pathway: An Emerging Therapeutic Target for African-American Triple Negative Breast Cancer Patients

The most fatal form of breast cancer, triple negative (TNBC), continues to challenge clinicians worldwide with its lack of reliable prognostic biomarkers and pharmacologically actionable treatment targets. In the US, this aggressive disease disproportionately afflicts African-American (AA) women at a rate 2-3 times higher than European-American (EA) women, thereby contributing to the observed higher mortality rates of AA BC patients.  In order to address the unmet clinical need for new and effective treatments for AA TNBCs, we describe herein a potentially actionable pathway that appears to be in overdrive in TNBCs of AA patients compared to EA TNBCs: the Notch signaling pathway. Notch signaling is implicated in multiple aspects of carcinogenesis and tumor progression including in regulation of proliferation, apoptosis, the biology of cancer stem cells, tumor angiogenesis and epithelial-to-mesenchymal transition. Our gene expression analyses uncover significant upregulation of Notch signaling as well as gene ontologies reflecting dysregulation of key processes regulated by Notch signaling among AA compared to EA TNBC patients. Furthermore, we present evidence suggesting that upregulated Notch signaling may predict poor prognosis in TNBC. Our findings thus suggest differences in Notch signaling among racially-distinct TNBC patients that may contribute to the more aggressive clinical behavior of TNBC in AAs. These observations also suggest that Notch signaling may be an attractive therapeutic target for high-risk AA TNBC patients.&nbsp

Studies on Operational and Plant Parameters Affecting the Deposition of Charged and Uncharged Spray Droplets on Cabbage Plant Canopy

5-12The existing method of pesticide application has resulted in wastage of chemicals and environmental pollution. Electrostatic spray charging system for impregnation of charge on spray droplets and its subsequent transfer to the precise plant target can mitigate the problems in conventional spraying. The operational parameters of spraying system and plant characteristics play an important role in deposition of spray droplets on plant canopy. In view of that, an experimental electrostatically spray charging system was developed to assess the deposition on plant surface. At electrode voltage (4kV), flow rate (250 ml/min) deposition was maximum for forward speed (1.0 km/h) and height of application (0.55 m) at charge to mass ratio of 1.53 mC/kg

Studies on Operational and Plant Parameters Affecting the Deposition of Charged and Uncharged Spray Droplets on Cabbage Plant Canopy

The existing method of pesticide application has resulted in wastage of chemicals and environmental pollution. Electrostatic spray charging system for impregnation of charge on spray droplets and its subsequent transfer to the precise plant target can mitigate the problems in conventional spraying. The operational parameters of spraying system and plant characteristics play an important role in deposition of spray droplets on plant canopy. In view of that, an experimental electrostatically spray charging system was developed to assess the deposition on plant surface. At electrode voltage (4kV), flow rate (250 ml/min) deposition was maximum for forward speed (1.0 km/h) and height of application (0.55 m) at charge to mass ratio of 1.53 mC/kg