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In shubham1637/DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms

@mainpage %DIAlign: Alignment of MS/MS chromatograms @tableofcontents

Data Independent Acqusition (DIA) is a widely used method in mass-spectrometry based proteomics. DIAlign is a software for the alignment of MS/MS chromatograms across runs. We have DIAlign namespace that includes functions for building similarity matrix from raw MS2 chromatograms. On the similarity matrix, affine-alignment is performed using dynamic programming. Aligned indices, alignment path through the matrix and intermediate cumulative scoring matrices are included in the alignment object.

@section section0 MS/MS Chromatogram group A chromatogram is a time-series signal with time and intensity. We define a group as a collection of chromatograms having the same time values. The algorithm aligns a chromatogram group from run A to the same of run B. Both groups must contain the same number of chromatograms.

@section section1 Similarity Matrix

The first step is to build a similatry score matrix between both chromatogram groups. Function @link DIAlign::SimilarityMatrix::getSimilarityMatrix getSimilarityMatrix @endlink returns the score matrix. Following similarity score methods are implemented: dotProductMasked, dotProduct, cosineAngle, cosine2Angle, euclideanDist, covariance, correlation.

@subsection subsection1 Gap Penalty

Gap penalty is calculated from the distribution of similarity scores. @link DIAlign::getGapPenalty getGapPenalty @endlink function calculates the penalty score as a certain quantile value from the similarity score distribution.

@subsection subsection2 Constraint

The hybrid alignment requires constraing the similarity matrix to avoid a large drift. To achieve this a constraining function is calculated externally and scores outside the certain distance from this function is penalized. @link DIAlign::ConstrainMatrix ConstrainMatrix @endlink has functions to implement it. In the given example below, scoring matrix is represented as a grid between (A1, B1) and (A2, B2). X represents unconstrained scores, whereas, whitespace region is constrained.

@verbatim B1 B2 A1 +---------------+ |XX | | XXX | | XXX | | XXX | | XXX | | XXX | | XXX | | XXX | A2 +---------------+ @endverbatim

@section section2 Affine alignment We use dynamic programming for finding highest-scoring path through the score matrix. To avoid execursions in horizontal or vertical direction, affine-alignment is used with low gap-opening penalty and high gap-extension penalties.

Given a similarity score matrix s of size n x m, or affine-alignment we calculate three matrices M, A and B each of size (n+1) x (m+1). Matrix M stores the highest score for the alignment of Ai to Bj. Matrix A stores the best score for the alignment of Ai to Bj given Ai is aligned to a gap. * Matrix B stores the highest score for the alignment of Bj to Ai, given Bj is aligned to a gap.

Example: We have following similarity matrix. Gap opening penalty = 22, gap extension penalty = 7 s | 1 | 2 | 3 | 4 | 5 --|--|--|--|--|--- 1 | 10 | -2 | -2 | -2 | -2 2 | -2 | 10 | -2 | -2 | -2 3 | -2 | -2 | -2 | 10 | -2

@subsection subsecce Initialization The corresponding M, A and B matrices are initialized with some set of rules for overlap and non-overlap alignment. See initialization for @ref Init_section1 and @ref Init_section2 for our example.

@subsection subsection3 Best score calculation

Highest score for each cell is calculated using the following equations: \f$ M_{i,j} = max \left{\begin{matrix} M_{i-1,j-1} + s_{i,j} \ A_{i-1,j-1} + s_{i,j} \ B_{i-1,j-1} + s_{i,j} \end{matrix}\right} \f$

\f$ A_{i,j} = max \left{\begin{matrix} M_{i-1,j} - gapOpen \ A_{i-1,j} - gapExtension \ B_{i-1,j} - gapOpen \end{matrix}\right} \f$

\f$ B_{i,j} = max \left{\begin{matrix} M_{i,j-1} - gapOpen \ A_{i,j-1} - gapOpen \ B_{i,j-1} - gapExtension \end{matrix}\right} \f$

It is clear that these matrices restricts the direction. Matrix M only allows diagonal path (upperleft to downright), matrix A allows up to down and matrix B permits left to right path only. The example matrices are presented for @ref Matrices_section1 and @ref Matrices_section2 .

@subsection subsection4 Traceback

After calculating best cumulative score, we perform traceback and get the alignment path.

Alignment paths for Overlap alignment:

Alignment paths for non-Overlap alignment:

@see * Gupta S, Ahadi S, Zhou W, Röst H. "DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics." Mol Cell Proteomics. 2019 Apr;18(4):806-817. doi: https://doi.org/10.1074/mcp.TIR118.001132 Epub 2019 Jan 31. * CNPN 2018 Poster doi: https://doi.org/10.6084/m9.figshare.6200837.v1 * HUPO 2018 Poster doi: https://doi.org/10.6084/m9.figshare.7121696.v2 * https://www.coursera.org/lecture/bioinformatics-pku/alignment-with-affine-gap-penalty-and-calculation-of-time-complexity-of-the-0ya7X * https://www.cs.cmu.edu/~ckingsf/bioinfo-lectures/gaps.pdf * Biological Sequence Analysis (Chapter 2) by Durbin, Eddy, Krogh, and Mitchison.

shubham1637/DIAlignR documentation built on March 29, 2023, 8:45 p.m.