Biology subjects

Halman, A.

Publications and source records attributed to Halman, A..

5 recordsLinked to original sources

bioRxiv2022

Toblerone: detecting exon deletion events in cancer using RNA-seq

Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions. Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.

bioinformatics↗

bioRxiv2021

REViewer: Haplotype-resolved visualization of read alignments in and around tandem repeats

BackgroundExpansions of short tandem repeats are the cause of many neurogenetic disorders including familial amyotrophic lateral sclerosis, Huntington disease, and many others. Multiple methods have been recently developed that can identify repeat expansions in whole genome or exome sequencing data. Despite the widely-recognized need for visual assessment of variant calls in clinical settings, current computational tools lack the ability to produce such visualizations for repeat expansions. Expanded repeats are difficult to visualize because they correspond to large insertions relative to the reference genome and involve many misaligning and ambiguously aligning reads. ResultsWe implemented REViewer, a computational method for visualization of sequencing data in genomic regions containing long repeat expansions. To generate a read pileup, REViewer reconstructs local haplotype sequences and distributes reads to these haplotypes in a way that is most consistent with the fragment lengths and evenness of read coverage. To create appropriate training materials for onboarding new users, we performed a concordance study involving 12 scientists involved in STR research. We used the results of this study to create a user guide that describes the basic principles of using REViewer as well as a guide to the typical features of read pileups that correspond to low confidence repeat genotype calls. Additionally, we demonstrated that REViewer can be used to annotate clinically-relevant repeat interruptions by comparing visual assessment results of 44 FMR1 repeat alleles with the results of triplet repeat primed PCR. For 38 of these alleles, the results of visual assessment were consistent with triplet repeat primed PCR. ConclusionsRead pileup plots generated by REViewer offer an intuitive way to visualize sequencing data in regions containing long repeat expansions. Laboratories can use REViewer to assess the quality of repeat genotype calls as well as to visually detect interruptions or other imperfections in the repeat sequence and the surrounding flanking regions.

bioinformatics↗

bioRxiv2021

STRipy: a graphical application for enhanced genotyping of pathogenic short tandem repeats in sequencing data

Short tandem repeats (STRs) are highly polymorphic with high mutation rates and expansions of STRs have been implicated as the causal variant in diseases. The application of genome sequencing in patients has recently allowed many new discoveries with over 50 disease causing loci known to date. There are several tools which allow genotyping of STRs from high-throughput sequencing (HTS) data. However, running these tools out of the box only allow around half of the known disease-causing loci to be genotyped, with lengths often limited to either read or fragment length which is less than the pathogenic cut-off for some diseases. While analysis tools can be customised to genotype extra loci, this requires proficiency in bioinformatics to set up, use, and analyse the resulting data, limiting their widespread usage by other researchers and clinicians. To address these issues, we have created a new software called STRipy that has an intuitive graphical interface and requires no specific skills for usage, thus significantly simplifying detection of STRs expansions from human HTS data. STRipy is able to target all known disease-causing STRs with genotyping performed with an established tool, ExpansionHunter, that is incorporated into the software. We have created additional functionality into STRipy to work with long alleles exceeding the fragment length. STRipy was validated using over 60 thousand simulated samples and was shown to work on whole genome sequencing of biological samples with pathogenic variants. Finally, we have used STRipy to acquire genotypes of pathogenic loci for thousands of samples from various populations which are provided to the user along with the data from the literature to assist with results interpretation. We believe the simplicity and breadth of STRipy will increase the testing of STR diseases in current datasets resulting in further diagnoses of rare diseases caused by STRs expansions.

bioinformatics↗

bioRxiv2020

Accuracy of short tandem repeats genotyping tools in whole exome sequencing data

BackgroundShort tandem repeats are important source of genetic variation, they are highly mutable and repeat expansions are associated dozens of human disorders, such as Huntingtons disease and spinocerebellar ataxias. Technical advantages in sequencing technology have made it possible to analyse these repeats at large scale, however, accurate genotyping is still a challenging task. We compared four different short tandem repeats genotyping tools on whole exome sequencing data to determine their genotyping performance and limits which will aid other researchers to choose a suitable tool and parameters for analysis. MethodsThe analysis was performed on the Simons Simplex Collection dataset where we used a novel method of evaluation with accuracy determined by the rate of homozygous calls on the X chromosome of male samples. In total we analysed 433 samples and around a million genotypes for evaluating tools on whole exome sequencing data. ResultsWe determined a relatively good performance of all tools when genotyping repeats of 3-6 bp in length which could be improved with coverage and quality score filtering. However, genotyping homopolymers was challenging for all tools and a high error rate was present across different thresholds of coverage and quality scores. Interestingly, dinucleotide repeats displayed a high error rate as well, which was found to be mainly caused by the AC/TG repeats. Overall, LobSTR was able to make the most calls and was also the fastest tool while RepeatSeq and HipSTR exhibited the lowest heterozygous error rate at low coverage. ConclusionsAll tools have different strengths and weaknesses and the choice may depend on the type of analysis. In this analysis we demonstrated the effect of using different filtering parameters and offered recommendations based on the trade-off between the best accuracy of genotyping and the highest number of calls.

bioinformatics↗

bioRxiv2017

STRetch: detecting and discovering pathogenic short tandem repeats expansions

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.\n\nHere we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.\n\nSTRetch is open source software, available from github.com/Oshlack/STRetch.

bioinformatics↗