Are head and neck versus abdominal paragangliomas driven by different single nucleotide events?

Paragangliomas (PGLs) are a heterogeneous group of tumours of the nonepithelial neuroendocrine type, with a significant percentage being genetically determined. They can develop from cells of the parasympathetic as well as the sympathetic nervous system. Tumours located in the head and neck usually have a parasympathetic origin, whereas those in the abdomen have a sympathetic origin.

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 whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best.

Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence data.

This study compared computational approaches to parallelization of an SNP calling workflow. The 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.

Long non-coding RNA variability in porcine skeletal muscle.

Recently, numerous studies including various tissues have been carried out on long non-coding RNAs (lncRNAs), but still, its variability has not yet been fully understood. In this study, we characterised the inter-individual variability of lncRNAs in pigs, in the context of number, length and expression. Transcriptomes collected from muscle tissue belonging to six Polish Landrace boars (PL1-PL6), including half-brothers (PL1-PL3), were investigated using bioinformatics (lncRNA identification and functional analysis) and statistical (lncRNA variability) methods.

Genetics, Genomics and Emerging Molecular Therapies of Pancreatic Cancer.

The number of cases of pancreatic cancers in 2019 in Poland was 3852 (approx. 2% of all cancers). The course of the disease is very fast, and the average survival time from the diagnosis is 6 months.

An effect of large-scale deletions and duplications on transcript expression.

Since copy number variants (CNVs) have been recognized as an important source of genetic and transcriptomic variation, we aimed to characterize the impact of CNVs located within coding, intergenic, upstream, and downstream gene regions on the expression of transcripts. Regions in which deletions occurred most often were introns, while duplications in coding regions. The transcript expression was lower for deleted coding (P = 0.

DNA sequence features underlying large-scale duplications and deletions in human.

Copy number variants (CNVs) may cover up to 12% of the whole genome and have substantial impact on phenotypes. We used 5867 duplications and 33,181 deletions available from the 1000 Genomes Project to characterise genomic regions vulnerable to CNV formation and to identify sequence features characteristic for those regions. The GC content for deletions was lower and for duplications was higher than for randomly selected regions.

Identification of functional features underlying heat stress response in Sprague-Dawley rats using mixed linear models.

Since global temperature is expected to rise by 2 °C in 2050 heat stress may become the most severe environmental factor. In the study, we illustrate the application of mixed linear models for the analysis of whole transcriptome expression in livers and adrenal tissues of Sprague-Dawley rats obtained by a heat stress experiment. By applying those models, we considered four sources of variation in transcript expression, comprising transcripts (1), genes (2), Gene Ontology terms (3), and Reactome pathways (4) and focussed on accounting for the similarity within each source, which was expressed as a covariance matrix.

Correction to: The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.

The original version on this paper contained an error. Figure 5 was published with the same image of Fig. 4.

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing-based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras.

Identification and annotation of breed-specific single nucleotide polymorphisms in Bos taurus genomes.

In Bos taurus the universality of the reference genome is biased towards genetic variation represented by only two related individuals representing the same Hereford breed. Therefore, results of genetic analyses based on this reference may not be reliable. The 1000 Bull Genomes resource allows for identification of breed-specific polymorphisms and for the construction of breed-specific reference genomes.

The assessment of inter-individual variation of whole-genome DNA sequence in 32 cows.

Despite the growing number of sequenced bovine genomes, the knowledge of the population-wide variation of sequences remains limited. In many studies, statistical methodology was not applied in order to relate findings in the sequenced samples to a population-wide level. Our goal was to assess the population-wide variation in DNA sequence based on whole-genome sequences of 32 Holstein-Friesian cows.