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, base-calling 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