Channel-Independent Viterbi Algorithm (CIVA) for DNA Sequencing Xiaohua (Edward) Li Department of Electrical and Computer Engineering State University of New York at Binghamton
Outline • • • Introduction CIVA Use CIVA for base-calling Simulations Conclusions
Introduction: DNA sequencing • DNA sequencing (base-calling) • Procedure – template, PCR, electrophoresis, gel image, trace file – Base-caller
Introduction: Base-caller • Base-calling: detect DNA base sequence • Approaches – Manual reading, automated by heuristic knowledge – Image processing with signal models (ABI, Phred) – Deconvolution with communication (ISI) signal model, e. g. , MLSE, MAP
Proposed Method: CIVA • Our method: with ISI model, robust to signal irregularity • Difficulty comes from irregular trace signal – Amplitude and position jitter – Short signal, limited samples, yet time-varying • Solution: CIVA – joint symbol/position optimization – without channel estimation
CIVA: Basic Idea • List all possible symbol matrices S(n), • Find a probe for each possible S(n) • Use all probes to determine S(n) from X(n)
CIVA: Properties • CIVA: a trellis searching algorithm where metrics are calculated by probes • Properties – Near optimal for even ill-conditioned channels – No channel estimation, channel independent – High computational complexity • Applications – Direct application: system with simple signaling and short channel, e. g. , GSM, sensor networks, basecalling – Future: more application with complexity reduction
CIVA for Base-calling • Model trace signal with communication system • Channel effect introduces ISI
Symbol Matrix Structure
Probe Construction
Probe Construction Example
Trellis Metric Calculation
CIVA Trellis Search
Special Consideration for DNA Trace Signal • Amplitude jitter – solved inherently • Limited trace samples and time varying – fast convergence of CIVA • Timing jitter – looking for best timing for each sample
Simulations: Experiment 1 • A trace file with reference bases from Staden Package • Normalize trace, find approximate base interval, apply CIVA with M=P=1 (2 -tap channel. 25 trellis states, 125 transitional paths) • Results: less than 3% error compared with reference
Simulations: Experiment 1 • Two zoom-in sections – #1. with confident base detections – #2. with undetermined N
Simulations: Experiment 2 • A gel image from Prof. S. Gal with low quality • Scanning to trace signal
Simulations: Experiment 2 • Apply CIVA for base-calling • A zoom-in section
Conclusions • CIVA algorithm proposed for DNA sequence base-calling • Robust to signal irregularity with affordable computational complexity • Experiments show positive performance • More experiments are required for evaluation
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