Software Develop




1. Business Overview

As life science research continues to deepen, bioinformatics—as a core interdisciplinary field—is moving from basic research toward broad industrial applications. From genomics and proteomics to synthetic biology and personalized medicine, the scale and complexity of biological data keep growing, posing unprecedented challenges to efficient, stable, and scalable software systems.

Our bioinformatics consulting platform focuses on professional‑level custom development and technical services for biological software, using C++ as the core language and integrating modern computing architectures with algorithm design concepts. We are committed to providing researchers, biotech companies, and medical institutions with high‑performance, deployable, and maintainable software solutions.

We believe that only by fully understanding the complex needs of the biological domain and rigorously implementing them with engineering rigor can we build truly valuable and deployable bioinformatics tools.

2. Core Business Capabilities

1. High‑Performance Algorithm Implementation

We excel at reconstructing and optimizing complex bioinformatics algorithms in C++. Compared with scripting languages like Python and R, our implementations deliver over 10× speed improvements in big‑data scenarios, especially for:

  • Genomic Alignment and Assembly: Rapid implementation of FM‑index, BWT, and other compressed indexing algorithms; scalable to whole‑genome (WGS) level.
  • Variant Detection and Annotation: High‑performance VCF parsing and SNP/INDEL calling logic; supports batch parallel processing.
  • Multi‑Threaded Graph Algorithms: De Bruijn graph construction and compression for assembly, plus subgraph mining for regulatory‑network analysis.

2. Industrial‑Grade Software Architecture

Unlike academic scripts, we adopt a modular + interface‑driven system architecture that offers:

  • High Pluggability: Flexible composition of modules (e.g., alignment, annotation, visualization);
  • Cross‑Platform Deployment: Native support for Linux, Windows, and macOS; embeddable in HPC clusters or cloud platforms;
  • Comprehensive API Exposure: C++ SDK, REST API, Python bindings, and more;
  • High Unit‑Test Coverage: Ensures maintainability and long‑term sustainability.

3. Custom Development Services

In addition to off‑the‑shelf tools, we offer highly tailored development services, including:

  • Research‑Project Tooling: Build custom analysis pipelines and visualization front‑ends for research groups;
  • Enterprise Product Incubation: Help biotech companies engineer in‑house algorithms into customer‑ready products;
  • Data‑Security and Compliance: Adhere to Chinese and international data‑security regulations (e.g., GDPR, data‑export controls);
  • On‑Premise Deployment: Deploy within clients’ data centers to ensure data never leaves the network.

3. Modular Business Composition

1. Data Pre‑processing Module

  • FastQ quality control (adapter trimming, low‑quality read filtering)
  • BAM/CRAM/VCF parsing and index building
  • Multi‑format conversions (FASTA, GFF, VCF, SAM, BED)

2. Analysis Engine Module

  • Alignment engine (lightweight BWA‑like, Minimap2‑like implementations)
  • Variant‑calling engine (GATK‑style workflow reconstruction)
  • Expression quantification and differential analysis (DESeq2 logic re‑implementation)
  • Gene‑function annotation and pathway enrichment

3. Data Visualization Module

  • Supports CLI output of SVG/PNG/PDF graphics or interactive web front‑ends:
    • Expression heatmaps, PCA/UMAP plots
    • Variant‑frequency spectra, mutation stack plots
    • Molecular network diagrams, GO/KEGG circular enrichment charts

4. Front‑end/Back‑end Integration Module

  • React/Vue frameworks for interactive front‑ends
  • Backend support via Flask, FastAPI, or embedded C++ microservices
  • Built‑in authentication and permission controls (OAuth2/Token)

4. Typical Application Cases

Case 1: University Research Group WGS Analysis Platform

Client Requirements:

  • Private whole‑genome analysis platform
  • Batch uploads and queued computing
  • Interactive annotation results interface

Our Solution:

  • C++ implementation of the full FastQ→VCF pipeline
  • Python bindings for Jupyter integration
  • React front‑end with embedded GO‑annotation visualization
  • On‑premise deployment to an HPC cluster

Results:

  • Analysis throughput increased by over 5×
  • Support for continuous processing of 10 + TB datasets
  • Average feedback time reduced from 24 h to under 3 h

Case 2: Synthetic‑Biology Company Strain‑Design Tool

Client Requirements:

  • Automated pathway reconstruction and optimization
  • Bulk sequence editing via internal database calls

Our Solution:

  • Pathway search and optimization logic via graph algorithms in C++
  • REST API for external integration
  • Embedded SVG‑based DNA‑sequence editor

Results:

  • Fully automated pipeline from sequence input to optimal build suggestions
  • Significantly reduced experimental cost and iteration cycles

5. Technical Advantages & Differentiators

Aspect Our Advantages Common Issues with Traditional Solutions
Performance C++ high‑performance implementation, multi‑thread & SIMD support Python/R single‑threaded, poor concurrency
Architecture Industrial‑grade modular design, adaptable to diverse scenarios Academic codebases are fragmented and hard to maintain
Security & Compliance On‑premise deployments ensure data confidentiality Open‑source scripts often lack guarantees
Customization Deep involvement in project logic and algorithm design Generic tools often misalign with specific needs
Support Long‑term maintenance and version upgrades Academic tools frequently lack ongoing support

6. Collaboration Process

  1. Requirements Analysis: deeply understand client background, data types, and analysis goals;
  2. Technology Selection & Solution Design: define optimal language, architecture, and algorithm plan;
  3. Development & Implementation: agile iterations delivering core features;
  4. Testing & Acceptance: functional, performance, and compliance testing;
  5. Deployment & Launch: on‑premises servers, HPC, or cloud platforms;
  6. Long‑Term Maintenance: annual maintenance agreements covering bug fixes and feature enhancements.

7. Conclusion

We understand that bioinformatics tools are not mere code concatenations but a deep integration of algorithms, engineering, requirements, and business. Through an engineering‑first approach, we aim to deliver reliable computational tools for life sciences, empowering research breakthroughs and technology deployment.

We welcome research institutions, biotech companies, and university teams to partner with us in building future‑ready bioinformatics products.

For custom services, technical consultations, or partnership inquiries, please contact us.

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