Biology subjects

Whitford, W.

Publications and source records attributed to Whitford, W..

5 recordsLinked to original sources

bioRxiv2026

Protocol for designing and interpreting minigene assays to validate candidate splice altering variants

Variants affecting RNA splicing are a major contributor to human disease, yet the consequences of variants outside of the canonical splice motifs are often difficult to determine. Here, we present a protocol for minigene-based evaluation of candidate splice-altering variants. The methodology described includes locus-specific insert design, commercial gene fragment synthesis, and long-read sequencing. The combined approach enables rapid assay development and nucleotide level resolution of the effect on splice isoforms in vitro, providing a scalable framework for functional validation of predicted cryptic splice variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/723105v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1b6b44eorg.highwire.dtl.DTLVardef@1f795c4org.highwire.dtl.DTLVardef@189aa22org.highwire.dtl.DTLVardef@100a833_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

Optimised multiplex amplicon sequencing for mutation identification using the MinION nanopore sequencer

ObjectiveRapid, cost-effective identification of genetic variants in small candiate genomic regions remains a challenge, particularly for less well equipped or lower throughput laboratories. Application of Oxford Nanopore Technologies MinION sequencer has the potential to fulfil this requirement. We have developed a multiplexing assay which pools PCR amplicons for MinION sequencing to enable sequencing of multiple templates from multiple individuals which could be applied to gene-targeted diagnostics. MethodsA combined strategy of barcoding and sample pooling was developed for simultaneous multiplex MinION sequencing of 100 PCR amplicons, spanning 30 loci in DNA isolated from 82 neurodevelopmental cases and family members. The target regions were chosen for further interegation because a potentially disease-causative variants had been identified in affected individuals by Illumina exome sequencing. The pooled MinION sequences were deconvoluted by aligning to custom references using the guppy aligner software. ResultsOur multiplexing approach produced interpretable and expected sequence from 29 of the 30 targeted genetic loci. The sequence variant which was not correctly resolved in the MinION sequence was adjacent to a five nucleotide homopolymer. It is already known that homopolymers present a resolution problem with the MinION approach. Interstingly despite equimolar quantities of PCR amplicon pooled for sequencing, significant variation in the depth of coverage (139x - 21,499x; mean = 9,050, std err = 538.21) was observed. We observed independent relationships between depth of coverage and target length, and depth of coverage and GC content. These relationships demonstrate biases of the MinION sequencer for longer templates and those with lower GC content. ConclusionWe demonstrate an efficient approach for variant discovery or confirmation from short DNA templates using the MinION sequencing device. With less than 140x depth of coverage required for accurate genotyping, the methodology described here allows for rapid highly multiplexed targeted sequencing of large numbers of samples in a minimally equipped laboratory.

bioinformatics↗

bioRxiv2018

Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data

BackgroundWhole genome sequencing (WGS) has increased in popularity and decreased in cost over the past decade, rendering this approach as a viable and sensitive method for variant detection. In addition to its utility for single nucleotide variant detection, WGS data has the potential to detect Copy Number Variants (CNV) to fine resolution. Many CNV detection software packages have been developed exploiting four main types of data: read pair, split read, read depth, and assembly based methods. The aim of this study was to evaluate the efficiency of each of these main approaches in detecting deletions.\n\nMethodsWGS data and high confidence deletion calls for the individual NA12878 from the Genome in a Bottle consortium were the benchmark dataset. The performance of Breakdancer, CNVnator, Delly, FermiKit, and Pindel was assessed by comparing the accuracy and sensitivity of each software package in detecting deletions exceeding 1kb.\n\nResultsThere was considerable variability in the outputs of the different WGS CNV detection programs. The best performance was seen from Breakdancer and Delly, with 92.6% and 96.7% sensitivity, respectively and 34.5% and 68.5% false discovery rate (FDR), respectively. In comparison, Pindel, CNVnator, and FermiKit were less effective with sensitivities of 69.1%, 66.0%, and 15.8%, respectively and FDR of 91.3%, 69.0%, and 31.7%, respectively. Concordance across software packages was poor, with only 27 of the total 612 benchmark deletions identified by all five methodologies.\n\nConclusionsThe WGS based CNV detection tools evaluated show disparate performance in identifying deletions [≥]1kb, particularly those utilising different input data characteristics. Software that exploits read pair based data had the highest sensitivity, namely Breakdancer and Delly. Breakdancer also had the second lowest false discovery rate. Therefore, in this analysis read pair methods (Breakdancer in particular) were the best performing approaches for the identification of deletions [≥]1kb, balancing accuracy and sensitivity. There is potential for improvement in the detection algorithms, particularly for reducing FDR. This analysis has validated the utility of WGS based CNV detection software to reliably identify deletions, and these findings will be of use when choosing appropriate software for deletion detection, in both research and diagnostic medicine.

genomics↗

bioRxiv2018

Identifying, understanding, and correcting technical biases on the sex chromosomes in next-generation sequencing data

Mammalian X and Y chromosomes share a common evolutionary origin and retain regions of high sequence similarity. This sequence homology can cause the mismapping of short sequencing reads derived from the sex chromosomes and affect variant calling and other downstream analyses. Understanding and correcting this problem is critical for medical genomics and population genomic inference. Here, we characterize how sequence homology can affect analyses on the sex chromosomes and present XYalign, a new tool that: (1) facilitates the inference of sex chromosome complement from next-generation sequencing data; (2) corrects erroneous read mapping on the sex chromosomes; and (3) tabulates and visualizes important metrics for quality control such as mapping quality, sequencing depth, and allele balance. We show how these metrics can be used to identify XX and XY individuals across diverse sequencing experiments, including low and high coverage whole genome sequencing, and exome sequencing. We also show that XYalign corrects mismapped reads on the sex chromosomes, resulting in more accurate variant calling. Finally, we discuss how the flexibility of the XYalign framework can be leveraged for other use cases including the identification of aneuploidy on the autosomes. XYalign is available open source under the GNU General Public License (version 3).

bioinformatics↗

bioRxiv2018

RBV: Read balance validator, a tool for prioritising copy number variations in germline conditions

BackgroundThe popularisation and decreased cost of genome resequencing has resulted in an increased use in molecular diagnostics. While there are a number of established and high quality bioinfomatic tools for identifying small genetic variants including single nucleotide variants and indels, currently there is no established standard for the detection of copy number variants (CNVs) from sequence data. The requirement for CNV detection from high throughput sequencing has resulted in the development of a large number of software packages. These tools typically utilise the sequence data characteristics: read depth, split reads, read pairs, and assembly-based techniques. However the additional source of information from read balance, defined as relative proportion of reads of each allele at each position, has been underutilised in the existing applications.\n\nResultsWe present Read Balance Validator (RBV), a bioinformatic tool which uses read balance for prioritisation and validation of putative CNVs. The software simultaneously interrogates nominated regions for the presence of deletions or multiplications, and can differentiate larger CNVs from diploid regions. Additionally, the utility of RBV to test for inheritance of CNVs is demonstrated in this report.\n\nConclusionsRBV is a CNV validation and prioritisation bioinformatic tool for both genome and exome sequencing available as a python package from https://github.com/whitneywhitford/RBV

bioinformatics↗