Syllabus
Unit Topics
I Unit-1: Research: The search for knowledge
2 Acquiring knowledge on Research, Qualities of a Research
3 Hypothesis, induction and deduction in research
4 Scientific Methods, Research Methodology and Research Methods,
5 Research Process, Academic Research,
6 Philosophy of Research: Philosophy and Science, Epistemology, Empiricism
7 Rationalism Experimental Research: Cause-Effect Relationships
8 Hypothesis in Experiments,
9 Validation of Experiments, Experimental Design.
Scientific Methods, Research
Methodology
Scientific Methods
• The scientific method is a systematic approach to investigating
the world around us, involving observation, questioning,
forming hypotheses, testing predictions, acquire new
knowledge and drawing conclusions.
Key Elements
• scientists observe something interesting or unusual in the
world.
• Based on the observation, a question is formulated to
investigate the phenomenon.
• A testable explanation or prediction of hypothesis is
proposed to answer the question.
• The hypothesis is tested through controlled experiments or
observations.
• The collected data is analyzed to determine if the
hypothesis is supported.
• Based on the analysis, a conclusion is drawn about the
hypothesis and its implications.
• The findings are shared with others in the scientific
community through publications, presentations, etc.
Importance of the Scientific
Method
• The scientific method encourages scientists to look at the
world objectively and to avoid bias in their investigations.
• By following a systematic approach, scientists can ensure that
their findings can be replicated by others.
• The scientific method allows for the gradual accumulation of
knowledge and the development of new theories.
• By using the scientific method, we can better understand the
natural world and the phenomena we observe.
Research Methodology
Research methodology
• Research methodology refers to the systematic approach and
procedures used in a research study to ensure validity,
reliability, and rigor in the data collection and analysis
process.
• Research methodology in bioinformatics involves using
computational tools and techniques to analyze biological
data, often from high-throughput experiments like genomics
and proteomics.
• This field combines computer science, statistics, and biology
to identify patterns, understand biological processes, and
develop new technologies.
Data Collection and Storage:
• Utilizing databases and technologies to collect, store, and
organize biological data.
Data Analysis: Employing algorithms and software to analyze
large datasets, including sequence alignment, gene finding,
and protein structure prediction.
Method Development: Creating new algorithms and tools for
analyzing specific biological questions.
Interdisciplinary Approach: Drawing on various disciplines like
biology, chemistry, and computer science to address complex
problems.
Problem Definition
• Identify a specific biological question or hypothesis.
• Examples:
• Finding genes associated with a disease.
• Predicting protein structure or function.
• Studying evolutionary relationships using genome sequences.
Literature Review
• Review previous studies, databases, and tools.
• Understand current limitations and gaps.
• Helps in defining objectives and choosing the right tools.
Data Collection
• Types of data: Genomic sequences, transcriptomes, proteomes,
expression profiles, etc.
• Sources:
• Public databases: NCBI, ENSEMBL, UniProt, GEO, TCGA, etc.
• Experimental data from collaborators or in-house research.
Data Preprocessing
• Cleaning and quality control:
• Removing low-quality reads (in NGS data).
• Normalizing gene expression data.
• Formatting and integrating different data types.
• Tools: FASTQC, Trimmomatic, R/Bioconductor packages, etc.
Data Analysis & Computational Techniques
• Depending on the objective, methods may include:
Statistical Analysis
• Hypothesis testing
• Correlation and regression
• PCA, clustering (e.g., k-means, hierarchical clustering)
Algorithm Development
• Designing or improving computational methods (e.g., for sequence alignment,
gene prediction)
Machine Learning/AI
• Classification and prediction (e.g., cancer subtype prediction)
• Tools: scikit-learn, TensorFlow etc.
Sequence Analysis
• BLAST, HMMER, Clustal Omega, MAFFT
• Multiple sequence alignments, motif finding
Genomic/Transcriptomic Analysis
• Genome assembly, annotation, variant calling
• RNA-seq analysis: alignment (e.g., STAR, HISAT2),
quantification (e.g., HTSeq, featureCounts)
Visualization
• Data is visualized to interpret and communicate results.
• Tools: R (ggplot2, heatmaps), Python (matplotlib, seaborn),
Cytoscape, UCSC Genome Browser.
Interpretation of Results
• Biological relevance is assessed.
• Functional enrichment (e.g., GO, KEGG analysis)
• Pathway mapping and disease association studies.
Validation
• In silico validation using cross-validation or benchmark datasets.
• Experimental validation (e.g., wet-lab experiments like qPCR,
Western blot).
Documentation & Reporting
• Writing papers, reports, and maintaining reproducible research:
• Sharing code (GitHub) and data (NCBI SRA, GEO, etc.)
