GiG.M3Structure: Potential Higher-Order Quadruplex Sequence (PHOQS)...

In EfresBR/G4iMGrinder: G4iMGrinder: G4 Quadruplex, i-Motif and higher-order structures in DNA and RNA sequences search and analysis tool.

Potential Higher-Order Quadruplex Sequence (PHOQS) arrangement analyzer for Method 3A (M3A) candidates

Description

GiG.M3Structure examines a specified PHOQS (Potential Higher-Order Quadruplex Sequence) candidate identified by G4-iM Grinder's Method 3A (M3A). It searches for possible sub-unit conformations (using M2A results) that fit within the PHOQS region, attempting to identify the most probable arrangement of quadruplex sub-units. Three analytical approaches are available:

• HRA (Highest Ranking Assembly): sequentially seats the sub-units with the strongest scores, preferring known-to-form sequences.

• RAH (Ranking Assembly by Highest Score): explores all possible sub-unit seatings, returning the conformation with the highest overall mean score.

• RAnH (Ranking Assembly by Normalized Highest Score): explores all possible sub-unit seatings, returning the conformation with the highest “normalized” mean score (the mean score multiplied by the fraction of the PHOQS region actually occupied by PQS sub-units).

Usage

GiG.M3Structure(
  GiGList,
  M3ACandidate,
  MAXite
)

Arguments

GiGList	list. A G4iMGrinder result list (GiGList) containing M2A and M3A data frames. Typically produced by G4iMGrinder.
M3ACandidate	integer. The row number in GiGList\$PQSM3a corresponding to the PHOQS candidate of interest.
MAXite	integer. Number of iterations for the random seat-allocation algorithm. A higher number ensures more exhaustive exploration of potential sub-unit conformations, especially for large PHOQS or those with many candidate sub-units. For instance, 10,000 iterations may be sufficient for a ~100-nt region with ~30 potential sub-units.

Details

By combining the M2A (Method 2A) sub-units with a candidate from M3A (Method 3A), GiG.M3Structure locates all conformations that could explain how multiple quadruplex sub-units (PQS) might form a single higher-order structure. The iterative random approach attempts various ways of “seating” sub-units within the PHOQS, then records the best conformations under each scoring approach (HRA, RAH, and RAnH).

Ensure MAXite is large enough to sample all or most conformations in the PHOQS region; otherwise, some arrangements may be missed.

Value

A list describing the analysis of the selected PHOQS candidate:

M2	A data.frame of the potential sub-units (M2A results) located within the PHOQS region. An error is returned if no sub-units match the target region.
M3	A data.frame containing one row from GiGList\$PQSM3a, representing the selected PHOQS candidate.
Potential.Arrangements	A data.frame enumerating all discovered sub-unit conformations (arrangements) compatible with the PHOQS, found via random seat allocation repeated MAXite times.
Best.Arrangements	A list containing up to three entries—HRA, RAH, and RAnH—each storing the best conformation(s) discovered for the given PHOQS, depending on the selected scoring method.

Column Meanings

RES

Reference for sub-units (by row name) from GiGList\$PQSM2A used in the arrangement.

nPQS

Integer. Number of sub-units forming a particular conformation.

MeanScore

Numeric. Mean of the sub-unit scores for that conformation.

PQSLenghPercent

Numeric. Percentage of the PHOQS region occupied by the sub-units.

idenPQS

Reference for sub-units in the M2 data frame that form the conformation.

nMS

Numeric. The “normalized mean score” = MeanScore * (PQSLenghPercent/100). Higher values indicate a high average sub-unit score and a larger fraction of the PHOQS occupied by them.

Author(s)

Efres Belmonte-Reche

References

Belmonte-Reche, E. and Morales, J.C. (2019). G4-iM Grinder: when size and frequency matter. G-Quadruplex, i-Motif and higher order structure search and analysis tool. NAR Genomics and Bioinformatics, 2. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/nargab/lqz005")}.

https://academic.oup.com/nargab/article/2/1/lqz005/5576141

Examples

# Example usage:

# 1. Running G4iMGrinder on a DNA sequence
Rs <- G4iMGrinder(Name = "ExampleSeq", Sequence = "ACGT...")

# 2. Analyzing the first PHOQS in the M3A results (row 1 of Rs$PQSM3a),
#    performing 10,000 seat-allocation iterations
firstPHOQS <- GiG.M3Structure(
  GiGList = Rs,
  M3ACandidate = 1,
  MAXite = 10000
)

# Check the best arrangements found under HRA, RAH, and RAnH
print(firstPHOQS$Best.Arrangements)

EfresBR/G4iMGrinder documentation built on June 12, 2025, 3:52 a.m.