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Biology subjects

Gambin, T.

Publications and source records attributed to Gambin, T..

6 recordsLinked to original sources
bioRxiv2026

nf-rare-var-assoc: a comprehensive genetic association pipeline with rich QC

Genetic association analyses of exome sequencing data pose distinct methodological challenges in small cohorts, which are common in rare disease research. Data filtering prior to association testing requires particular care, especially when analyzing datasets generated under different study designs. Most of current genetic association tools need to be integrated into complex data processing pipelines, whose implementation requires technical expertise and is prone to error. To alleviate mentioned issues we built a comprehensive, high performance Nextflow workflow for genetic association testing, which includes all commonly performed data filtering and preparation steps, controlled by a rich set of modifiable parameters. We compared our software against existing tools incorporating quality control and aggregated association testing steps and found our solution to offer the most complete suite of data preparation, association testing and reporting modules, making small-cohort association analyses accessible to teams with limited technical expertise.

bioinformatics↗
bioRxiv2025

polars-bio - fast, scalable and out-of-core operations on large genomic interval datasets

MotivationGenomic studies very often rely on computational intensive analyses of relationships between features, which are typically represented as intervals along one-dimensional coordinate system (such as positions on a chromosome). In this context, Python programming language is extensively used for manipulation and analysis of data stored in a tabular-form of rows and columns called dataframe. Pandas is the most-widely used Python dataframe package and is criticized for its inefficiencies and scalability problems that its novel alternative - Polars - tries to address with a vectorized backend written in Rust programming language. Resultspolars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic intervals datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. Single-thread benchmarking results confirm that for operations such as count overlaps is 38x and coverage is 15x faster than the state-of-the-art bioframe library. Our implementation of overlap operation consumes 90x less memory in streaming mode. Multi-thread benchmarks show good scalability and up to 282x speedup for count overlaps operation when executed with 8 CPU cores. To the best of our knowledge, polars-bio is the fastest single-node library for genomic interval dataframes in Python. Availability and implementationpolars-bio is an open source Python package distributed under the Apache License available for all main platforms, such as Linux, macOS and Windows in the PyPI registry. The web page is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio

bioinformatics↗
bioRxiv2025

Meta-analysis of RNA-seq gene fusion detection tools: performance and variability across benchmarks

In this study, we conducted a comprehensive meta-analysis of ten publicly available benchmark tests evaluating the performance of gene fusion detection tools using RNA-seq data. Our analysis focused on key performance metrics, including sensitivity, precision and F1 scores. We evaluated how the tools perform in different data sets. We examined the impact of data set characteristics such as sample type (real or simulated) and read length, as well as additional sample and sequencing parameters on the results. In addition to evaluating performance, we analyzed the organization and design of the benchmark tests, highlighting good practices such as clear descriptions of the datasets, detailed instrument parameters and transparent methodologies. However, we also identified common pitfalls, including insufficient reproducibility information, limited diversity of datasets, and the lack of widely accepted gold standard datasets. These limitations make it difficult to consistently evaluate tools and compare across benchmarks. By synthesizing these findings, we make recommendations for future benchmark projects, emphasizing the need for standardization, increased transparency and the development of robust truth sets. This study aims to help the community create more reliable and reproducible benchmark tests, ultimately accelerating the development and evaluation of gene fusion detection tools for clinical and research applications.

bioinformatics↗
bioRxiv2019

Integrated Sequencing & Array Comparative Genomic Hybridization in Familial Parkinson’s Disease

BackgroundParkinsons disease (PD) is a genetically heterogeneous condition; both single nucleotide variants (SNVs) and copy number variants (CNVs) are important genetic risk factors. We examined the utility of combining exome sequencing and genome-wide array-based comparative genomic hybridization (aCGH) for identification of PD genetic risk factors. MethodsWe performed exome sequencing on 110 subjects with PD and a positive family history; 99 subjects were also evaluated using genome-wide aCGH. We interrogated exome sequencing and array comparative genomic hybridization data for pathogenic SNVs and CNVs at Mendelian PD gene loci. SNVs were confirmed via Sanger sequencing. CNVs were confirmed with custom-designed high-density aCGH, droplet digital PCR, and breakpoint sequencing. ResultsUsing exome sequencing, we discovered individuals with known pathogenic single nucleotide variants in GBA (p.E365K, p.T408M, p.N409S, p.L483P) and LRRK2 (p.R1441G and p.G2019S). Two subjects were each double heterozygotes for variants in GBA and LRRK2. Based on aCGH, we additionally discovered cases with an SNCA duplication and heterozygous intragenic GBA deletion. Five additional subjects harbored both SNVs (p.N52fs, p.T240M, p.P437L, p.W453*) and likely disrupting CNVs at the PARK2 locus, consistent with compound heterozygosity. In nearly all cases, breakpoint sequencing revealed microhomology, a mutational signature consistent with CNV formation due to DNA replication errors. ConclusionsIntegrated exome sequencing and aCGH yielded a genetic diagnosis in 19.3% of our familial PD cohort. Our analyses highlight potential mechanisms for SNCA and PARK2 CNV formation, uncover multilocus pathogenic variation, and identify novel SNVs and CNVs for further investigation as potential PD risk alleles.

genetics↗
bioRxiv2018

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

BackgroundDepth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next generation sequencing pipelines, including the analyses of RNA-seq data, detection of copy number variants, or quality control procedures.\n\nResultsBuilding upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching more than 100x speedup over the state-of-the-art tools. Performance and scalability of our solution allows for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface.\n\nConclusionsSeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.

bioinformatics↗
bioRxiv2018

Clustering-based optimization method of reference set selection for improved CNV callers performance

BackgroundThere are over 25 tools dedicated for the detection of Copy Number Variants (CNVs) using Whole Exome Sequencing (WES) data based on read depth analysis.\n\nThe tools reported consist of several steps, including: (i) calculation of read depth for each sequencing target, (ii) normalization, (iii) segmentation and (iv) actual CNV calling. The essential aspect of the entire process is the normalization stage, in which systematic errors and biases are removed and the reference sample set is used to increase the signal-to-noise ratio.\n\nAlthough some CNV calling tools use dedicated algorithms to obtain the optimal reference sample set, most of the advanced CNV callers do not include this feature.\n\nTo our knowledge, this work is the first attempt to assess the impact of reference sample set selection on CNV detection performance.\n\nMethodsWe used WES data from the 1000 Genomes project to evaluate the impact of various methods of reference sample set selection on CNV calling performance of three chosen state-of-the-art tools: CODEX, CNVkit and exomeCopy. Two naive solutions (all samples as reference set and random selection) as well as two clustering methods (k-means and k nearest neighbours with a variable number of clusters or group sizes) have been evaluated to discover the best performing sample selection method.\n\nResults and ConclusionsThe performed experiments have shown that the appropriate selection of the reference sample set may greatly improve the CNV detection rate. In particular, we found that smart reduction of reference sample size may significantly increase the algorithms precision while having negligible negative effect on sensitivity. We observed that a complete CNV calling process with the k-means algorithm as the selection method has significantly better time complexity than kNN-based solution.

bioinformatics↗