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Automated SNP Detection System for Genomic Data Analysis
  1. case
  2. Automated SNP Detection System for Genomic Data Analysis

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Automated SNP Detection System for Genomic Data Analysis

altoroslabs.com
Medical
Biotechnology
Information technology

Challenges in Genomic Data Processing

Research laboratories face significant delays and high costs in processing terabytes of genomic data for single-nucleotide polymorphism (SNP) detection. Existing manual workflows struggle with processing 150GB datasets within 24-hour deadlines, while cloud infrastructure costs escalate with increasing data volumes.

About the Client

Provider of life sciences research services including DNA sequencing, bioanalytical testing, and mass spectrometry for pharmaceutical and academic institutions

Key Project Goals

  • Develop automated SNP detection pipeline for FASTA/FASTQ formats
  • Achieve 24-hour processing SLA for 150GB genomic datasets
  • Optimize infrastructure costs through hybrid cloud/on-premise deployment
  • Create unified web interface for genome analysis tools

System Functionality Requirements

  • Web-based interface integrating Bowtie, SAMtools, and SOAPsnp
  • Automated workflow for DNA read alignment and consensus sequence assembly
  • Cloud resource optimization engine for cost/performance balancing
  • Data processing monitoring dashboard with SLA tracking
  • Secure HDFS-based storage integration

Technology Stack Requirements

Apache Hadoop
Amazon Web Services (AWS)
Amazon EMR
Java
Perl
MapReduce
Linux

System Integration Needs

  • Existing laboratory information management systems (LIMS)
  • Institutional AWS cloud accounts
  • Local HDFS storage clusters
  • Genomic reference database APIs

Non-Functional Requirements

  • Horizontal scalability to handle petabyte-scale datasets
  • 24/7 system availability with <1% downtime
  • HIPAA-compliant data security controls
  • Processing performance metrics dashboard
  • Cross-browser compatibility (Chrome, Firefox, Safari)

Expected Business Impact

Implementation of this genomic analysis platform will reduce SNP detection processing time from weeks to hours while cutting infrastructure costs by 40-60%. The hybrid deployment model will enable seamless scaling to meet growing demand for genomic research services, supporting 23+ monthly processing cycles of 150GB datasets with guaranteed SLA compliance.

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