Biology subjects

Mielczarek, M.

Publications and source records attributed to Mielczarek, M..

4 recordsLinked to original sources

bioRxiv2024

Exploring the impact of sequence context on errors in SNP genotype calling with Whole Genome Sequencing data using AI-based autoencoder approach

A critical step in the analysis of WGS data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect SNP and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in twenty Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The data set was divided into the correct set of SNPs (666,333 SNPs) and the incorrect set of SNPs (4,557 SNPs). The training data set consisted of only the correct SNPs, while the test data set contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% ({+/-}0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC trimer was due to mislabeling a call for C. Incorrect T instead A call was associated with the presence of T in the neighboring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

bioinformatics↗

bioRxiv2024

Exploring the distribution of single nucleotide polymorphisms across human exons and introns

Among all types of mutations, single nucleotide polymorphisms are the most common type of genomic variation. In our study, we explore the counts of single nucleotide polymorphisms in particular exons and introns of the human genome based on the data set of 1,222 individuals of Polish origin that comprises 41,836,187 polymorphisms. In particular, chromosomes 1 and 22 were considered as representatives of two markedly different DNA molecules, since HSA01 represents the longest and HSA22 is one of the shortest chromosomes. The results demonstrate that outer (first, last) exons as well as the first introns harbour significantly more SNPs than other genic regions. The observed differences in counts reflect the distinct functional roles of those genomic units.

bioinformatics↗

bioRxiv2024

Nextflow vs. plain Bash: Different Approaches to the Parallelisation of SNP Calling from the Whole Genome Sequence Data

This study compared computational approaches to parallelisation of an SNP calling workflow. Data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling. Three approaches to parallelisation were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow process, and (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15% to 27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the numbers of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.

bioinformatics↗

bioRxiv2023

An explainable deep learning classifier of bovine mastitis based on whole genome sequence data - circumventing the p>>>n problem

The most serious drawback underlying the biological annotation of Whole Genome Sequence data is the p>>n problem, meaning that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). Therefore, the major aim of the study was to propose a way to circumvent the problem by combining a LASSO logistic regression model with Deep Learning (DL). That was illustrated by a practical biological problem of classification of cows into mastitis-susceptible or mastitis-resistant, based on genotypes of Single Nucleotide Polymorphisms (SNPs) identified in their WGS. Among several DL architectures proposed via optimisation of DL hyperparameters using the Optuna software, imposed on different SNP sub-sets defined by LASSO logistic regressions with different penalty values, the architecture with 204,642 SNPs was selected as the best one. This architecture was composed of 2 layers with respectively 7 and 46 units per layer as well as respective drop-out rates of 0.210 and 0.358. The classification of the test data set resulted in the AUC=0.750, accuracy=0.650, sensitivity=0.600, and specificity=0.700 was selected as the best model and thus proceeded to genomic and functional annotations. Significant SNPs were selected based on the SHapley Additive exPlanation values transformed to Z-scores to assess the underlying type I-error. These SNPs were annotated to genes. As a final result, a single GO term related to the biological process and thirteen GO terms related to the molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Author SummaryOur objective is to distinguish between cows that are susceptible and resistant to bovine mastitis by analysing their genomic data. However, we face a significant challenge due to the large number of single nucleotide polymorphisms (SNPs) and limited sample size. To address this challenge, we utilize two methods: feature selection algorithms and deep learning. We experiment with various ways of implementing these techniques and evaluate their performance on a validation set. Our findings reveal that the optimal approach can accurately predict a cows susceptibility or resistance status around 65% of the time. Additionally, we employ a technique to identify the most crucial SNPs and their biological functions. Our results indicate that some of these SNPs are related to immune response or protein synthesis pathways, implying that they may affect the cows health and productivity.

bioinformatics↗