Zahra Abedi

Dear Hiring Committee,

This is Zahra Abedi, a post-doc fellow in computational biology, and bioinformatics. With a strong background in computational biology and bioinformatics, coupled with extensive experience in genomic and transcriptomic data analysis, I am excited about the prospect of contributing to your innovative work in developing immunotherapies against cancer and other diseases. I did my Ph.D. in Biometrical Genetics, which I was doing mostly Statistical genetic analysis on "Biometrical and molecular analysis of sulfur effect on soybean genotypes under salt stress". In 2019, I joined LBB and started from scratch learning about bioinformatics, network analysis, non-coding RNA discovery, co-expression-based biomarker development in cancer, python programing, machine learning, and then deep in to microbiome analysis. Beside learning part, I am also helping other Ph.D. students in the Lab to learn analysis protocols and writing manuscripts. I have published several articles and have a few other manuscripts currently under review. In 2022, I joined the university of Luxembourg as a postdoctoral researcher in computational biology. I was actively involved in a project focused on inflammation, working with various omics data types, including metabolomics, microbiome, immunomics, flow cytometric data, proteomics, single-cell RNA-seq, ChIP-sequencing, and integrating them to develop a machine learning model for patient classification.

My expertise extends to the analysis of genetic and epigenetic datasets. I am well-versed in chromatin immunoprecipitation-sequencing, RNA-sequencing (with skills in single-cell NGS analysis), ATAC-sequencing, RNA sequencing analysis, Next Generation Sequencing (NGS), Genome Wide Association Study (GWAS), microbiome and metagenome, whole Exome Sequencing analysis, and flow cytometric data. Additionally, I bring a solid foundation in statistical analysis, enhancing my ability to derive meaningful insights from complex biological data. My proficiency includes bulk- and single-cell RNA-seq data analysis, utilizing platforms such as Illumina NovaSeq, NextSeq, and MiSeq. I am adept at developing reproducible analysis pipelines and reports, and I bring extensive experience with tools like STAR, kallisto, salmon, GATK, GSEA, and pathway enrichment tools. My programming experience in R and Python, along with hands-on experience in a Linux HPC environment and version control systems (e.g., git), positions me as a well-rounded candidate for this role. I am enthusiastic about the chance to contribute to your ongoing and future projects, bringing my expertise in long-read sequencing data analysis, cancer biology, and immunology.

Personally, I am a fast learner, easy going, ready to do risky and difficult projects, dip in to new and unknown topics, easy integration with people around and get what I am looking for in a short time. My English is good enough and am very good at writing scientific manuscripts. I am also very much into managing a small group of post-graduate students and enjoy helping others around me in their research. I hope to apply my expertise to your ongoing or future projects. Moreover, I would be delighted to explore any other projects that your department may be working on. 

I received my Ph.D. in biometrical genetics from Sari Agricultural Sciences and Natural Resources University in Iran. My extensive experience includes a postdoctoral position at the Laboratory of Bioinformatics and Biostatistics (LBB) in 2019, where I contributed to projects in gene expression network analysis related to liver cancer, Alzheimer's disease, and multiple sclerosis. My work involved utilizing miRNAs, transcription factors, and co-expression network analysis to identify potential biomarkers and explore drug repurposing. I was involved in a project integrating various omics data types, including diverse types of NGS data (single-cell RNA-seq, single cell ATAC-seq, and bulk RNA-seq), metabolomics, proteomics, lipidomics, immunomics, and metagenomics (16S, 18S, and ITS regions), to develop a machine learning model for patient classification. My skills in large-scale NGS data analyses, R and Python programming, and experience with workflow management systems make my well-suited for conducting integrative data analysis for biological discovery. Moreover, My ability to collaborate within interdisciplinary teams aligns with the collaborative nature of the lab. My interests in computational algorithms, sequence alignments, Artificial intelligence, and biological discovery further enhance my suitability. I am enthusiastic about contributing my skills and knowledge to cutting-edge research, where I can leverage my expertise to uncover general principles of genome regulation, biomedicine and research through interdisciplinary approaches.

• Bioinformatics, network analysis, non-coding RNA discovery, drug repurposing, co-expression-based biomarker development in cancer, Python programming, machine learning, and delving deep into microbiome analysis.
• Engaged in a project focused on various cancers, eye diseases, Alzheimer's disease, and multiple sclerosis.
• Assisting other Ph.D. students in the lab to learn analysis protocols and write manuscripts. 
• Engaged in a project focused on inflammation, working with various omics data types, including genomics, metabolomics, microbiome and metagenome, immunomics (CyTOF), proteomics, single-cell RNA-seq, bulk RNA-seq, ChIP-seq, ATAC-seq, and integrating them to develop a machine learning model for patient classification.