Steps
Step 1
Research Question Definition and Bioinformatics Analysis Plan Design
The core task of this step is to define molecular bioinformatics research questions and design the analysis plan. Choose research topics from genomics, transcriptomics, proteomics, metabolomics and other omics data.
• Define research direction: choose from genomic variant analysis (SNP, InDel, CNV, SV), gene expression analysis (differential expression, co-expression network), protein structure and function prediction, non-coding RNA analysis (miRNA, lncRNA, circRNA), single-cell sequencing, metagenomics
• Data acquisition: determine data sources—public databases (GEO, TCGA, ENCODE, NCBI SRA, UniProt, PDB) or own sequencing data; clarify data types (sequencing, microarray, proteomics) and formats (FASTQ, BAM, VCF, FASTA)
• Analysis pipeline design: design complete bioinformatics analysis pipeline; determine analysis software and tools (BWA, GATK, STAR, DESeq2, Cytoscape, BLAST, Pymol, R/Bioconductor, Python/Biopython); develop QC plan and analysis timeline
Deliverable: Bioinformatics analysis proposal (research questions, data sources, analysis pipeline, tool list, QC plan) | Quality standard: Clear research question, scientific and reasonable analysis pipeline, appropriate tool selection, detailed and feasible plan
Step 2
Data Acquisition and Preprocessing
The core task of this step is to acquire raw omics data and perform data preprocessing and quality control. Raw sequencing data usually contains low-quality bases, adapter contamination, duplicate sequences.
• Data download and organization: download raw data from NCBI SRA, GEO, TCGA (using SRA Toolkit, Aspera); organize sample information (groups, phenotypes, clinical data); establish project directory structure (raw data, intermediate results, final results, scripts, docs)
• Raw data quality assessment: use FastQC, MultiQC for sequencing data QC; check base quality distribution, GC content, adapter residues, duplicate sequences, k-mer abnormalities; generate QC report
• Data preprocessing and cleaning: use Trimmomatic, Cutadapt, fastp for data cleaning—remove adapter sequences, trim low-quality bases (Q<20), filter too-short sequences, remove rRNA contamination, remove PCR duplicates (Picard); perform QC again to confirm cleaning effect
Deliverable: Preprocessed dataset (cleaned data, QC reports, data organization table, analysis directory structure) | Quality standard: Complete and accurate data, strict QC, good cleaning effect, standardized data organization
Step 3
Core Bioinformatics Analysis
The core task of this step is to perform main bioinformatics analysis and obtain key results. Depending on research type, analysis includes sequence alignment, variant detection, gene expression quantification, differential analysis, enrichment analysis.
• Sequence alignment and quantification: genomic analysis uses BWA, Bowtie2 for reads alignment to reference genome; transcriptome analysis uses STAR, HISAT2 for RNA-seq alignment, featureCounts, RSEM or Salmon for gene expression quantification; use Samtools, BEDTools for BAM file processing
• Differential analysis and variant detection: gene expression differential analysis uses DESeq2, edgeR, limma; screen DEGs (thresholds like |log2FC|≥1, padj<0.05); genomic variant analysis uses GATK HaplotypeCaller, Samtools mpileup for SNP and InDel detection; use ANNOVAR, VEP for variant annotation
• Functional enrichment analysis: use DAVID, Metascape, clusterProfiler for GO enrichment analysis (BP, CC, MF) and KEGG/Reactome pathway enrichment analysis; draw enrichment bubble plots, bar charts, heatmaps
Deliverable: Core analysis results (DEG list, variant annotation table, enrichment analysis results, visualization charts) | Quality standard: Standard analysis pipeline, accurate and reliable results, clear and professional visualization
Step 4
Advanced Analysis and Systems Biology Integration
The core task of this step is to perform advanced bioinformatics analysis and integrate multi-omics data from a systems biology perspective. Advanced analysis includes network analysis, machine learning, multi-omics integration, single-cell analysis.
• Network analysis: use WGCNA to build gene co-expression network, identify modules and hub genes; use STRING, Cytoscape to build PPI network; perform network topology analysis (degree, betweenness, closeness); identify key nodes
• Multi-omics integration and machine learning: use mixOmics, MOFA for multi-omics data integration (genome, transcriptome, proteome, metabolome); use machine learning methods (random forest, SVM, LASSO, deep learning) to build classification or prognostic models; perform feature selection and model evaluation
• Advanced analysis: perform specialized analysis based on research direction—single-cell sequencing analysis (Seurat, Scanpy: dimensionality reduction, clustering, cell annotation, pseudotime analysis, cell-cell communication); protein structure prediction and analysis (AlphaFold, SWISS-MODEL, PyMOL visualization, molecular docking with AutoDock); metagenomics analysis (taxonomic annotation, functional analysis, diversity analysis)
Deliverable: Advanced analysis results (network analysis graphs, machine learning models, multi-omics integration results, specialized analysis outputs) | Quality standard: Advanced analysis methods, in-depth and insightful results, good model performance, reasonable interpretation
Step 5
Result Visualization and Report Writing
The core task of this step is to generate high-quality visualizations of bioinformatics analysis results and write a complete analysis report. Bioinformatics results are complex and diverse; good visualization clearly presents key findings.
• Data visualization: use R (ggplot2, pheatmap, ComplexHeatmap, ggplot2 extensions), Python (Matplotlib, Seaborn, Plotly) to produce high-quality statistical graphs—volcano plots, heatmaps, scatter plots, boxplots, enrichment plots, survival curves, network diagrams, Circos plots
• Write analysis report: follow bioinformatics analysis report standards, including project background, analysis objectives, data sources, analysis methods and pipeline, results presentation, biological interpretation and discussion, conclusions, appendix (code, parameters, file list); minimum 4000 words
• Reproducibility assurance: organize all analysis scripts (Shell, R, Python); use Snakemake or Nextflow to build reproducible analysis pipeline; document software versions and parameters; provide README documentation; ensure reproducible analysis results
Deliverable: Bioinformatics analysis report (PDF format with all results and high-quality charts), analysis scripts, reproducible analysis pipeline | Quality standard: Complete report structure, beautiful and professional charts, in-depth analysis, reproducible results