Biology subjects

Rymuza, J.

Publications and source records attributed to Rymuza, J..

5 recordsLinked to original sources

bioRxiv2024

Pivotal role of biallelic frequency analysis in identifying copy number alterations using genome-wide methods in tumors with a high level of aneuploidy

Chromosome number abnormalities is one of the hallmarks of cancer. DNA copy number alterations (CNA) are studied using various genome-wide methods. In our study we investigated CNA in human pituitary tumors using three platforms CytoSNP-850K microarrays, low-pass whole-genome sequencing (average x7 coverage, LPWGS), and Infinium Methylation EPIC array. Virtual karyotypes based on each dataset were generated using open-source software packages for each sample. Concordant CNA profiles were found for most of tumor. Surprisingly, substantial discrepancies between results from SNP arrays and LPWGS/EPIC arrays were identified in 20% of tumors, for which discrimination of true karyotype was required. B-allelic frequency data from SNP arrays was crucial to adjust normal ploidy level as ultimately verified with FISH. The discrepancy between virtual karyotypes was more pronounced the more CNAs were found. When CNAs covered more than half of genome the level of normal/diploid copy number was incorrectly set with methods, based solely on signal intensity/read-counts coverage. To conclude, CNA analysis with methods such as LPWGS and methylation arrays in highly aneuploid tumors are prone to a bias from improper normal ploidy level setting. These methods are commonly used therefore we aimed to aware the scientific community about this underestimated methodological problem.

genomics↗

bioRxiv2023

Methods for evaluating unsupervised vector representations of genomic regions

Representation learning models have become a mainstay of modern genomics. These models are trained to yield vector representations, or embeddings, of various biological entities, such as cells, genes, individuals, or genomic regions. Recent applications of unsupervised embedding approaches have been shown to learn relationships among genomic regions that define functional elements in a genome. Unsupervised representation learning of genomic regions is free of the supervision from curated metadata and can condense rich biological knowledge from publicly available data to region embeddings. However, there exists no method for evaluating the quality of these embeddings in the absence of metadata, making it difficult to assess the reliability of analyses based on the embeddings, and to tune model training to yield optimal results. To bridge this gap, we propose four evaluation metrics: the cluster tendency score (CTS), the reconstruction score (RCS), the genome distance scaling score (GDSS), and the neighborhood preserving score (NPS). The CTS and RCS statistically quantify how well region embeddings can be clustered and how well the embeddings preserve information in training data. The GDSS and NPS exploit the biological tendency of regions close in genomic space to have similar biological functions; they measure how much such information is captured by individual region embeddings in a set. We demonstrate the utility of these statistical and biological scores for evaluating unsupervised genomic region embeddings and provide guidelines for learning reliable embeddings. AvailabilityCode is available at https://github.com/databio/geniml

bioinformatics↗

bioRxiv2023

Methods for constructing and evaluating consensus genomic interval sets

The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose 3 novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose 3 novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution. Software is available at https://github.com/databio/geniml.

genomics↗

bioRxiv2023

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

MotivationData from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower-dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. ResultsWe implemented our approach in scEmbed, an unsupervised machine learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, pre-trained models on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. AvailabilityscEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

genomics↗

bioRxiv2022

Transcriptomic classification of pituitary neuroendocrine tumors causing acromegaly

Acromegaly results from growth hormone hypersecretion caused by somatotroph pituitary neuroendocrine tumor (PitNET). Our molecular profiling revealed that acromegaly-causing tumors form three distinct transcriptomic subgroups with different histological/clinical features. Transcriptomic subtypes of somatotroph tumors differ in the expression levels of numerous genes including those involved in hormone secretion and genes with known prognostic value. They can be distinguished by determining the expression of marker genes. Transcriptomic group 1 includes [~]20% of acromegaly patients with GNAS mutations-negative, mainly densely granulated tumors with NR5A1 (SF-1) and GIPR co-expression. Group 2 tumors are the most common (46%) and include mainly GNAS-mutated, densely granulated somatotroph and mixed PitNETs. They have significantly smaller size and express favorable prognosis-related genes. Group 3 includes predominantly sparsely granulated somatotroph PitNETs with low GNAS mutations frequency causing [~]35% of acromegaly cases. Ghrelin signaling is implied in their pathogenic mechanism, they have unfavorable gene expression profile, and invasive growth rate. Since a subgroup of somatotroph tumors have high NR5A1 expression, using SF-1 as classification marker specific to gonadotroph PitNETs could be reconsidered.

cancer biology↗