ClinQC

ClinQC performs quality control, filtering, trimming, and format standardization of Sanger and next-generation sequencing (Illumina, 454, Ion Torrent) data to produce Sanger-encoded high-quality reads for mutation identification in clinical genomics, including heterogeneous diseases such as cancer.

Key Features:

• Integration of Sanger and NGS data: Processes raw reads from Sanger sequencing and NGS platforms including Illumina, 454, and Ion Torrent.
• Format conversion and standardization: Converts input read files into FASTQ format to ensure compatibility across analyses.
• Adapter, primer, and contaminant removal: Removes adapters, PCR primers, and contaminating sequences to reduce technical artifacts.
• Low-quality read filtering: Filters out low-quality sequences to improve downstream sensitivity and specificity for mutation detection.
• Barcode demultiplexing and duplicate removal: Splits bar-coded samples and removes duplicate reads to support accurate sample-level analysis.
• Scalability and parallelization: Implemented in Python and leverages multiprocessing to process hundreds to thousands of samples in a single run.
• Comprehensive QC reporting: Generates detailed quality control reports documenting processing steps and outcomes.
• Output compatibility: Produces high-quality reads encoded with Sanger quality scores ready for downstream mutation screening.

Scientific Applications:

• Clinical variant detection: Facilitates identification and confirmation of disease-causing mutations for diagnostic and therapeutic applications.
• Cancer genomics: Improves mutation screening and data quality in studies of heterogeneous diseases such as cancer.
• Cross-platform data harmonization: Harmonizes Sanger and NGS datasets (Illumina, 454, Ion Torrent) for comparative analyses and pooled studies.
• Large-cohort sequencing studies: Enables processing of hundreds to thousands of samples for large-scale clinical research and mutation screening projects.

Methodology:

Conversion of input files to FASTQ; removal of adapters and PCR primers; filtering of low-quality reads and contaminants; splitting of bar-coded samples and removal of duplicates; output of standardized high-quality reads with Sanger-encoded quality scores and generation of QC reports; implemented in Python with multiprocessing.