Base Caller Summaries

Standard Illumina Base Caller (Bustard)

Sequencing-by-Synthesis (SBS)

• DNA sample obtained, containing many copies of same sequences and randomly fragmented
• Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
• DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
• Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
• Fluorophores and terminators removed and process repeated for L cycles

Image Analysis

• Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
• Signal for each cluster characterized as time series data of fluorescence intensities and noise

Base Calling

• Converts fluorescence signals into actual sequence data with quality scores
• Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
• Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
• Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
• Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
  • Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

General Noise Factors

• Phasing
  • Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
• Fading
  • Decay in fluorescent signal intensity with each cycle
  • Likely attributable to material loss during sequencing
• Crosstalk
  • C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
  • Likely caused by overlap in dye emission frequencies
• T Accumulation
  • The fluorophores used for thymine are not always removed properly after each iteration
  • Intensity of T signal increases across sequencing run

Alta-Cyclic

Training Stage

• Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
• Half of training set used for cross-validation

Base Calling Stage

• Reports all sequences from run with optimized parameters

Differences from Standard Illumina Base Caller

• Calling parameters optimized empirically and tested to enhance accuracy of each run
• Calculates phasing parameters based on parametric model
• Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

Probabilistic Base Calling

• Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
  • Extended IUPAC alphabet to code ambiguous bases
  • Information criterion to control length of trustable reads
• Reduced systematic bias by addressing:
  • Crosstalk
  • Dephasing
  • Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
• Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
• Does not consider fine-tuning image analysis

BayesCall

• Model-based approach to base calling
• Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
• Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

Swift

Performs both image analysis and base calling

Image Analysis

• Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
• Image correlation – alignment of images to reference cycle
• Object identification and intensity extraction

Base Calling

• Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
• Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
• After correction, base with maximum intensity chosen as called base

Ibis

Method

• Estimate sequencing chemistry model as a parameter directly from data using statistical learning
• Training set from Bustard output using raw cluster intensities
• Used a base caller with SVM classifiers for each cycle that have intensity values of the current cycle as well as the previous and following cycles (if they exist)
• Data set created by aligning raw reads with mismatches for a fraction of the tiles to a reference sequence
  • Half of this set used as a training set and the other half as a test set used to check results of training
• Estimate parameters for calculating a quality score given class assignment and distances to the classification/decision boundary from SVM

Comparison

• Unlike AltaCyclic, includes base-specific phasing parameters so can correct raw intensities for T accumulation
• Does not call an 'N' character for poor quality bases
• Process unique as causes of sequencing error not modeled separately
  • Consider causes together by using neighboring signals in statistical learning procedure

References

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. Nature Methods 5:679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. Genome Research 19:1884-1895

Kircher, M., Stenzel, U., Kelso, J. (2009) Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol. 10(8):Article R83

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. BMC Bioinformatics 9:Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. Bioinformatics 25:2194-2199