Erykah Kaveri Ephrem Monteiro.

I am a Master's student in Molecular Biosciences (Bioinformatics) student at the University of Bath, with a strong practical experience gained from a comprehensive internship at Biocon Pharma Limited, and growing expertise in computational biology, large-scale data analysis, and bioinformatics pipeline development and proficient analytical skills and a solid foundation in research. I am also learning Git for version control and sharing bioinformatics projects on GitHub. My projects demonstrate proficiency in Unix, Python, R, and genomic sequence data, applying these skills to data processing, statistical analysis, and visualization. I am eager and committed to leveraging innovative tools for genomic research. Passionate about continuous learning and applying academic knowledge to real-world challenges, with a focus on advancing research and development in the field.

• Short-term course on MLT (Medical Laboratory Technology) organized by the Departments of Genetics and Biochemistry, Jyoti Nivas College Autonomous during the year 2020-2021.
• Attended two days workshop on 'Molecular Techniques' jointly organized by the Departments of Genetics and Biotechnology, Jyoti Nivas College Autonomous and Azyme Biosciences Pvt. Ltd. Bengaluru in the year 2019.
• Attended a one day workshop in Cell Kraft Biotech Pvt. Ltd. Bengaluru organized by the Department of Genetics, Jyoti Nivas College Autonomous in the year 2022.
• Short-term course on Basic Bioinformatics organized by the Department of Biotechnology, Jyoti Nivas College Autonomous during the year 2021-2022.
• Short-term course on Basic German Language (A1 Level) organized by Jyoti Nivas College Autonomous during the year 2020-2021.
• Successfully completed six months of internship, from September 2022 to March 2023 at Biocon Pharma Limited, Bangalore-560100, under the department of Generic Formulations and Quality Control Microbiology.
• Completed a four months course in Medical Coding, from November 2023 to February 2024, at FIDES Medical Coding Academy (organized by the Salesian Sisters of Don Bosco, Auxilium school campus), sponsored by Tech Mahindra SMART Foundation, Bangalore-560049.

Optimization of Rolling Average Analysis for Better Classification of Cell Cycle Stages Using Cell Tracking and its Future Directions and Experimental approaches by Integrating Autophagy Cell Tracking as a Novel Approach:
[University Of Bath], [2024-2025]

• Developed Python piplines with the help of research guide and compared methodologies for cell cycle classification using rolling average analysis and autophagic markers.
• Utilized Fiji/ImageJ and FUCCI to generate and analyse cellular tracking data.
• Recap of the benefits of rolling average analysis as a powerful tool for improving the accuracy of cell cycle classification by leveraging temporal trends.
• Emphasis on the role of bioinformatics in enhancing the robustness of cell tracking methods.
• Highlighting on how incorporating autophagy cell tracking into future research can provide novel insights into the interplay between metabolic stress and cell cycle regulation.
• Stressing on the fact that, as experimental and computational techniques continue to evolve, these interdisciplinary approaches will significantly enhance our understanding of cellular dynamics in health and disease.
• Applied statistical techniques to improve classification accuracy and enhance data interpretation.

Bacterial Autophagy Evasion and Its Genomic Basis - Autophagy-Driven Selection in certain Bacterial Genome:

 [University Of Bath], [2024-2025]

• To investigated the role of autophagy in bacterial genomics and molecular evolution. Highlighting bacterial strategies for autophagy evasion, compares bacteria that fail to evade autophagy, and proposes genomic approaches for identifying novel autophagy-related genes. A comparative study of these bacterial genomes, focusing on BLAST and HGT analyses, could provide new insights into bacterial pathogenesis and potential therapeutic targets.
• To analyse genomic sequences using Python and R to identify evolutionary patterns influenced by autophagy.
• To conduct comparative genomics studies to explore bacterial adaptation mechanisms. The area of interest is bacterial autophagy evasion or subversion.
• The hypothesis broadly is that there are some genes responsible for this phenotype that have not been identified. These genes will either be specific to the bacteria capable of autophagy evasion as a group or individually. In addition, these genes may be horizontally transferred.
• Two experimental approaches are possible and may be complementary.
• Carry out an all-versus-all BLAST analysis of autophagy-evasion bacteria against a range of other bacteria to find genes specific to this group (at various levels of coverage). These genes will be compared to known pathogenicity genes and any previously unidentified genes will be analysed for possible function. These genes will also be investigated for signals of HGT.
• Carry out a HGT scan to detect possibly transferred genes. Develop a method of identifying potential HGT regions, compare to genome annotations to obtain a list of candidate genes, compared to known pathogenicity genes and any previously unidentified genes will be analysed for possible function.

Postgraduate-Academic Programming Projects:

• Developed DNA nucleotide counting algorithm processing 1000+ sequences with 99% accuracy
• Created RNA transcription tool handling sequences up to 1000bp
• Implemented DNA complementary strand generator with error handling
• Built GC content calculator with optimised performance
• Designed motif finding algorithm for pattern matching

Variant Calling Pipeline

• Developed an automated variant calling pipeline using Conda/SAMtools, implementing sequence alignment and quality control metrics for genomic data processing.

Shiga Toxin E. coli Report

• Used R and Plotly to create interactive heatmaps of Shiga-Toxigenic Escherichia coli (STEC) cases, incorporating statistical calculations (mean, median, standard deviation) to highlight the significance of results.
• Designed a bar chart to visualise the distribution of Shiga toxin (STX) and phage types (PT), highlighting the most prevalent categories for each year (2014-2019) and excluding unclassified data for accuracy.
• Conducted in-depth analysis and evaluation of data representation, ensuring the visualisations were appropriate for subsequent statistical analysis and helped identify meaningful patterns in the dataset.