Base Caller Summaries

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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


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


  • 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


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



  • 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


  • 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


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