Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Chair in Cancer Adaptation and Evolution



+44 (0)20 7594 2808l.magnani CV




137ICTEM buildingHammersmith Campus






BibTex format

author = {Coleman, I and Corleone, G and Arram, J and Ng, H-C and Magnani, L and Luk, W},
doi = {10.1186/s12859-020-3367-3},
journal = {BMC Bioinformatics},
title = {GeDi: applying su x arrays to increase the repertoire of detectable SNVs in tumour genomes},
url = {},
volume = {21},
year = {2020}

RIS format (EndNote, RefMan)

AB - BackgroundCurrent popular variant calling pipelines rely on the mapping coordinates of each input read to a reference genome in order to detect variants. Since reads deriving from variant loci that diverge in sequence substantially from the reference are often assigned incorrect mapping coordinates, variant calling pipelines that rely on mapping coordinates can exhibit reduced sensitivity.ResultsIn this work we present GeDi, a suffix array-based somatic single nucleotide variant (SNV) calling algorithm that does not rely on read mapping coordinates to detect SNVs and is therefore capable of reference-free and mapping-free SNV detection. GeDi executes with practical runtime and memory resource requirements, is capable of SNV detection at very low allele frequency (<1%), and detects SNVs with high sensitivity at complex variant loci, dramatically outperforming MuTect, a well-established pipeline.ConclusionBy designing novel suffix-array based SNV calling methods, we have developed a practical SNV calling software, GeDi, that can characterise SNVs at complex variant loci and at low allele frequency thus increasing the repertoire of detectable SNVs in tumour genomes. We expect GeDi to find use cases in targeted-deep sequencing analysis, and to serve as a replacement and improvement over previous suffix-array based SNV calling methods.
AU - Coleman,I
AU - Corleone,G
AU - Arram,J
AU - Ng,H-C
AU - Magnani,L
AU - Luk,W
DO - 10.1186/s12859-020-3367-3
PY - 2020///
SN - 1471-2105
TI - GeDi: applying su x arrays to increase the repertoire of detectable SNVs in tumour genomes
T2 - BMC Bioinformatics
UR -
UR -
VL - 21
ER -