Technical Training




1. Industry Background and Development Opportunities

With the rapid adoption and advancement of various “omics” technologies—such as high-throughput sequencing (NGS), single-cell sequencing, spatial omics, and mass-spectrometry proteomics—modern life sciences research has entered a data-driven era. Massive volumes of genomic, transcriptomic, epigenomic, proteomic, and metabolomic data are continually generated, posing unprecedented challenges for data processing, analysis, integration, and visualization. Research institutions, pharmaceutical companies, agricultural genetic improvement programs, and public health monitoring all urgently need interdisciplinary talents who understand biological principles and also master computing and data science.

However, most bioinformatics training on the market today remains at the “tool-usage” level, lacking in-depth exploration of underlying algorithmic principles and paying little attention to engineering or productizing research methods. This fails to meet the rigid demand from research teams and industry for efficient, reproducible, and sustainable development. Moreover, the heavy reliance on “pre-recorded videos + quizzes” in online training formats makes it difficult for learners to receive timely expert Q&A and hands-on guidance, significantly diminishing learning outcomes and slowing the growth of practical skills.

Against this backdrop, we propose a training philosophy of “Research-Driven · Engineering-Enabled · Practice-First,” delivered through “Live Online + In-Person Workshops + Corporate Training” to offer systematic, engineering-oriented, project-based, and customized bioinformatics training services for research institutes, pharmaceutical and agricultural enterprises, universities, and graduate students. Our goal is to empower participants to independently undertake complex research projects or industrial-scale data analysis and tool development as soon as they complete the program.

2. Training Positioning and Core Values

  1. Research-Driven
    • Frontier Case Studies: Course examples are drawn from the latest open-access publications and our own projects (e.g., spatial transcriptomics tumor microenvironment analysis, single-cell multi-omics integration), ensuring learners stay at the cutting edge.
    • Hands-On with Real Datasets: Each module includes mixed samples from public databases (TCGA, GEO, ENA) and proprietary enterprise data, covering applications in human, plant, animal, and microbial contexts.
  2. Engineering-Enabled
    • In-Depth Algorithm Analysis: We dissect key algorithms such as FM-index, Burrows–Wheeler transform (BWT), Hidden Markov Models (HMM), and clustering algorithms (Louvain, Leiden) to help learners grasp the underlying mechanics.
    • High-Performance Tool Reengineering: Participants are guided to reengineer core modules in C++/Rust, applying parallel programming (OpenMP, MPI), SIMD acceleration, and memory-pool management to transform academic prototypes into production-grade tools.
  3. Practice-First
    • Project-Based Learning Path: Course content is tightly integrated with real research or industry projects; learners “learn by doing” and submit deployable analysis reports and code repositories as deliverables.
    • Assignment + Mentoring Loop: Practical assignments conclude each chapter, reviewed online by teaching assistants and instructors to correct misunderstandings and optimize workflows, ensuring true mastery.
  4. Multi-Delivery
    • Live Online Classes: Small cohorts (10–15 participants) with real-time Q&A and instructor feedback, plus full recordings.
    • In-Person Workshops: Three to seven days of immersive bootcamps with enterprise-grade servers and HPC clusters to simulate real research or production environments.
    • Corporate Training: On-site or remote delivery tailored to teams of 5–50, integrating proprietary data and compliance requirements.

3. Technology Platform and Learning Environment

  1. Cloud Lab Platform
    • Kubernetes-orchestrated JupyterHub and RStudio Server clusters provide ready-to-use online environments.
    • Preinstalled bioinformatics tools (FastQC, Trimmomatic, HISAT2, STAR, Cell Ranger, Seurat, Nextflow, Snakemake, etc.) with support for custom environment extensions.
  2. Local and Private Deployment
    • Deploy Docker/Singularity containers on local machines or private cloud to ensure data security and compliance.
    • Automated scripts and Ansible/Helm deployment playbooks help teams quickly set up dedicated bioinformatics platforms.
  3. High-Performance Computing Support
    • Integrations with SLURM/PBS and Kubernetes Batch Jobs distribute large-scale tasks across GPU and CPU clusters for maximum efficiency.
    • Parallel I/O acceleration (Lustre, GlusterFS, NFS) optimizes large-file read/write performance.
  4. Version Control and Collaboration
    • Built-in GitLab/GitHub Enterprise instances for code hosting, issue tracking, and CI/CD automation.
    • GitOps-based pipelines enable efficient iteration and reproducibility of workflows and code.

4. Detailed Curriculum Structure

4.1 Foundational Skills Reinforcement

  • Linux and Shell
    • User and permission management; filesystem principles.
    • Common commands (grep, awk, sed, xargs) and batch processing.
    • Shell scripting best practices, functions, and modularization.
  • Programming Environments
    • Python: data structures, OOP, package management (conda, virtualenv), bioinformatics libraries (Biopython, pandas, numpy).
    • R: data frames and matrices, tidyverse workflows, ggplot2 advanced plotting, Rcpp and package development.
  • Databases and APIs
    • NCBI Entrez, Ensembl REST API, UCSC TrackHub, PharmGKB.
    • Hands-on: batch gene annotation, building local annotation databases, managing large-scale annotations with SQLite/PostgreSQL.

4.2 High-Throughput Sequencing Data Analysis

  • Preprocessing and QC
    • Automated FastQC/MultiQC report generation.
    • Trimmomatic and Cutadapt parameter optimization and scripting.
  • Alignment and Quantification
    • DNA alignment: BWA-MEM, Bowtie2, Minimap2 principles and applications.
    • RNA alignment: HISAT2 vs. STAR selection criteria.
    • Transcript quantification: featureCounts, HTSeq, Salmon, Kallisto comparisons and use cases.
  • Variant Calling and Annotation
    • GATK best practices workflow including BaseRecalibrator and HaplotypeCaller.
    • FreeBayes, Strelka2, DeepVariant tool comparisons.
    • Annotation pipelines with ANNOVAR and SnpEff, integrating multiple databases.
  • Differential Expression and Enrichment
    • DESeq2, edgeR, limma-voom methodology comparisons.
    • GO/KEGG enrichment, GSEA, ReactomePA visualization and interpretation.

4.3 Multi-Omics and Special Applications

  • Single-Cell RNA-seq
    • Cell Ranger and kallisto|bus parameter tuning.
    • Practical use of Seurat v4 and Scanpy: filtering, normalization, dimensionality reduction, clustering, differential analysis, cell annotation.
    • Trajectory and pseudotime analysis with Monocle3 and Slingshot.
    • Spatial omics data processing and visualization for platforms such as Visium and Xenium.
  • Epigenomics
    • ATAC-seq workflows with FastQTL, MACS2, and deepTools SignalTrack.
    • Methylation sequencing: BS-seeker2, Bismark alignment and DMR calling.
    • ChIP-seq analysis: Bowtie2, MACS2 peak calling, DiffBind differential analysis.
  • Metagenomics and Microbiome
    • QIIME2: OTU vs. ASV concepts and plugin workflows.
    • Taxonomic profiling with Kraken2 and MetaPhlAn3.
    • α/β diversity, LEfSe, and PICRUSt2 functional prediction.

4.4 Algorithm Development and Engineering

  • Core Algorithm Analysis
    • FM-index, Burrows–Wheeler Transform (BWT), Shortest Common Superstring (SCS) algorithms.
    • k-mer acceleration for alignment, indexing, filtering, and compression techniques.
  • Parallel and Distributed Computing
    • Practical OpenMP and MPI programming.
    • Spark-based distributed processing for RNA-seq and metagenomic data.
  • Tool Engineering
    • C++/Rust bindings for Python/R using PyO3 and rpy2.
    • Performance benchmarking and profiling with gprof, Valgrind, and perf.
    • Packaging and distribution via Conda, Bioconda, CRAN, and Crate.io.
  • Workflow Automation
    • Snakemake and Nextflow end-to-end pipeline templates.
    • Reproducibility and traceability with workflow visualization and automated reports (MultiQC, reportdown).

4.5 Customized Project Practicum

  • For real research or industry projects, we offer the following advanced customization:
    1. Needs assessment: in-depth interviews to identify research or industry pain points and goals.
    2. Process design and prototyping: from data acquisition, preprocessing, and analysis to visualization report, delivering an MVP.
    3. Performance optimization and scaling: tuning and resource scheduling for large-scale sample sets (10,000+ cases).
    4. Results interpretation and reporting: bilingual (Chinese/English) analysis reports, figures, and presentation materials.
    5. Secondary development and maintenance: integration of workflows into enterprise systems, with long-term support and upgrades.

5. Value-Added Services and Ecosystem Support

  • Private Deployment and Data Security
    • Best-practice deployment of on-prem GitLab/GitHub Enterprise, Kubernetes, and HPC clusters.
    • Compliance support: GDPR, HIPAA, FDA/CFDA data management and audit processes.
  • Academic Seminars and Networking
    • Quarterly online technical salons with renowned domestic and international experts sharing the latest papers and practices.
    • In-person advanced workshops to foster deep industry–academia collaboration.
  • Tool and Plugin Development
    • Custom Snakemake/Nextflow plugins based on our proprietary codebase.
    • Rapid prototyping services with R Shiny and Dash for interactive web reports.
  • Training Materials and Documentation
    • Comprehensive lecture slides, code examples, user manuals, and technical whitepapers.
    • Dedicated client knowledge base, continuously updated with new tools, algorithms, and best practices.
  • Technical Support and Community Management
    • 24×7 ticketing system and email response for initial troubleshooting.
    • Professional community platform with real-time instructor Q&A.

6. Frequently Asked Questions (FAQ)

  1. What prerequisites are required?
    Basic Linux proficiency and introductory knowledge of at least one programming language (Python or R) are recommended; those with zero background may start with the foundational skills module.
  2. How is learning effectiveness ensured?
    A three-fold loop of small-group live classes, practical assignments, and instructor mentoring; immersive real-time interaction during in-person workshops; and TA support for code review and Q&A.
  3. Can I use our company’s internal data?
    Yes—corporate training can be fully tailored to use your proprietary datasets within a private environment.
  4. Will source code and documentation be provided?
    Absolutely—100% of teaching materials, example scripts, workflow templates, and whitepapers are included to support ongoing learning and deployment.
  5. How are course fees calculated?
    Fees are based on training duration, delivery format, customization level, and number of participants; options include standard packages and advanced custom packages.

7. Enrollment Process and Business Support

  1. Initial Consultation: Complete the online request form or call us to discuss team size, project background, and training objectives.
  2. Customized Proposal: Our consulting team will deliver a detailed training plan and quote based on your needs.
  3. Contract Signing: Finalize business negotiations, sign the contract, and arrange the deposit.
  4. Resource Preparation: Coordinate hardware/software environments, training schedules, and instructor availability.
  5. Training Delivery: Conduct live online classes, in-person workshops, and mentoring as planned.
  6. Evaluation & Delivery: Collect participant feedback and provide final analysis reports and technical documentation.
  7. Follow-Up Support: Three months of free technical consultation and community Q&A to ensure successful implementation.

8. Contact Information

Official Website: www.yycbiolabs.com

Business Email: 0755@yycbiolabs.com

Phone/WeChat: +86-0755-23199041

Office Address: Shekou, Nanshan District, Shenzhen, China

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