GRAFIMO: Variant and haplotype aware motif scanning on pangenome graphs.
Manuel TognonVincenzo BonniciErik P GarrisonRosalba GiugnoRosalba GiugnoPublished in: PLoS computational biology (2021)
Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO.
Keyphrases
- electron microscopy
- transcription factor
- high resolution
- circulating tumor
- dna binding
- cell free
- single molecule
- copy number
- binding protein
- poor prognosis
- computed tomography
- body mass index
- physical activity
- genome wide
- human health
- quality improvement
- nucleic acid
- risk assessment
- gene expression
- mass spectrometry
- body weight
- weight loss
- dna methylation
- magnetic resonance
- convolutional neural network
- genetic diversity
- artificial intelligence