BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. RESULTS: Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks . CONCLUSIONS: Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.
The genome-wide association study (GWAS) is a popular genomic approach that identifies genomic regions associated with a phenotype and, thus, aims to discover causative mutations (CM) in the genes underlying the phenotype. However, GWAS discoveries are limited by many factors and typically identify associated genomic regions without the further ability to compare the viability of candidate genes and actual CMs. Therefore, the current methodology is limited to CM identification. In our recent work, we presented a novel approach to an empowered "GWAS to Genes" strategy that we named Synthetic phenotype to causative mutation (SP2CM). We established this strategy to identify CMs in soybean genes and developed a web-based tool for accuracy calculation (AccuTool) for a reference panel of soybean accessions. Here, we describe our further development of the tool that extends its utilization for other species and named it AccuCalc. We enhanced the tool for the analysis of datasets with a low-frequency distribution of a rare phenotype by automated formatting of a synthetic phenotype and added another accuracy-based GWAS evaluation criterion to the accuracy calculation. We designed AccuCalc as a Python package for GWAS data analysis for any user-defined species-independent variant calling format (vcf) or HapMap format (hmp) as input data. AccuCalc saves analysis outputs in user-friendly tab-delimited formats and also offers visualization of the GWAS results as Manhattan plots accentuated by accuracy. Under the hood of Python, AccuCalc is publicly available and, thus, can be used conveniently for the SP2CM strategy utilization for every species.
- MeSH
- celogenomová asociační studie * metody MeSH
- fenotyp MeSH
- genom MeSH
- genomika * metody MeSH
- mutace MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
PURPOSE: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for sequence-level variant classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource Familial Hypercholesterolemia (FH) Variant Curation Expert Panel was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. In this study, we provide consensus recommendations for the most common FH-associated gene, LDLR, where >2300 unique FH-associated variants have been identified. METHODS: The multidisciplinary FH Variant Curation Expert Panel met in person and through frequent emails and conference calls to develop LDLR-specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate, and commentary, consensus among experts was reached. RESULTS: The consensus LDLR variant modifications to existing ACMG/AMP guidelines include (1) alteration of population frequency thresholds, (2) delineation of loss-of-function variant types, (3) functional study criteria specifications, (4) cosegregation criteria specifications, and (5) specific use and thresholds for in silico prediction tools, among others. CONCLUSION: Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of patients with FH.
- MeSH
- genetická variace genetika MeSH
- genetické testování metody MeSH
- genom lidský * genetika MeSH
- genomika metody MeSH
- hyperlipoproteinemie typ II * genetika MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- MeSH
- genomika * ekonomika metody trendy MeSH
- vznik druhů (genetika) MeSH
- Publikační typ
- novinové články MeSH
Identification of genomic variability in population plays an important role in the clinical diagnostics of human genetic diseases. Thanks to rapid technological development in the field of massive parallel sequencing technologies, also known as next-generation sequencing (NGS), complex genomic analyses are now easier and cheaper than ever before, which consequently leads to more effective utilization of these techniques in clinical practice. However, interpretation of data from NGS is still challenging due to several issues caused by natural variability of DNA sequences in human populations. Therefore, development and realization of projects focused on description of genetic variability of local population (often called "national or digital genome") with a NGS technique is one of the best approaches to address this problem. The next step of the process is to share such data via publicly available databases. Such databases are important for the interpretation of variants with unknown significance or (likely) pathogenic variants in rare diseases or cancer or generally for identification of pathological variants in a patient's genome. In this paper, we have compiled an overview of published results of local genome sequencing projects from United Kingdom and Europe together with future plans and perspectives for newly announced ones.
- MeSH
- genomika metody MeSH
- lidé MeSH
- nádory * genetika MeSH
- sekvenování celého genomu MeSH
- vysoce účinné nukleotidové sekvenování * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Geografické názvy
- Spojené království MeSH
Alzheimer's disease (AD) is a disabling neurodegenerative disorder that leads to long-term functional and cognitive impairment and greatly reduces life expectancy. Early genetic studies focused on tracking variations in genome-wide DNA sequences discovered several polymorphisms and novel susceptibility genes associated with AD. However, despite the numerous risk factors already identified, there is still no fully satisfactory explanation for the mechanisms underlying the onset of the disease. Also, as with other complex human diseases, the causes of low heritability are unclear. Epigenetic mechanisms, in which changes in gene expression do not depend on changes in genotype, have attracted considerable attention in recent years and are key to understanding the processes that influence age-related changes and various neurological diseases. With the recent use of massive sequencing techniques, methods for studying epigenome variations in AD have also evolved tremendously, allowing the discovery of differentially expressed disease traits under different conditions and experimental settings. This is important for understanding disease development and for unlocking new potential AD therapies. In this work, we outline the genomic and epigenomic components involved in the initiation and development of AD and identify potentially effective therapeutic targets for disease control.
- MeSH
- Alzheimerova nemoc genetika patologie MeSH
- epigeneze genetická * MeSH
- genetická predispozice k nemoci * MeSH
- genomika metody MeSH
- lidé MeSH
- metylace DNA * MeSH
- regulace genové exprese * MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
R-loops are common non-B nucleic acid structures formed by a three-stranded nucleic acid composed of an RNA-DNA hybrid and a displaced single-stranded DNA (ssDNA) loop. Because the aberrant R-loop formation leads to increased mutagenesis, hyper-recombination, rearrangements, and transcription-replication collisions, it is regarded as important in human diseases. Therefore, its prevalence and distribution in genomes are studied intensively. However, in silico tools for R-loop prediction are limited, and therefore, we have developed the R-loop tracker tool, which was implemented as a part of the DNA Analyser web server. This new tool is focused upon (1) prediction of R-loops in genomic DNA without length and sequence limitations; (2) integration of R-loop tracker results with other tools for nucleic acids analyses, including Genome Browser; (3) internal cross-evaluation of in silico results with experimental data, where available; (4) easy export and correlation analyses with other genome features and markers; and (5) enhanced visualization outputs. Our new R-loop tracker tool is freely accessible on the web pages of DNA Analyser tools, and its implementation on the web-based server allows effective analyses not only for DNA segments but also for full chromosomes and genomes.
- MeSH
- algoritmy * MeSH
- DNA chemie genetika MeSH
- genomika metody MeSH
- internet statistika a číselné údaje MeSH
- lidé MeSH
- nestabilita genomu * MeSH
- R-smyčka * MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Závěrečná zpráva o řešení grantu Agentury pro zdravotnický výzkum MZ ČR
Nestr.
Mass of multiple myeloma aberrant cells represents at the time of diagnosis a mixture of multiple independent clones that vary in size with time. Population of aberrant cells of minimal residual disease (MRD) represents only a subset of clones from their complex pool, which survived the treatment. The reduction of the number of multiple myeloma clones is associated with decreased genetic variability. Decrease in intratumor heterogeneity after frontline therapy allows implementation of individualized therapy targeting residual clone. Genome wide sequencing combined with gene expression profiling of residual clone will enable to identify key set of mutations and/or affected pathways that can be eliminated by some of the novel targeted therapeutic agents.
Masa aberantních buněk mnohočetného myelomu je v době diagnózy složena ze směsi několika nezávislých klonů s proměnlivou velikostí v čase. Populace aberantních buněk minimální reziduální nemoci (MRN) představuje pouze podmnožinu klonů z původního množství, které přežily léčbu. Zmenšení počtu klonů mnohočetného myelomu je spojeno se snížením celkové genetické variability. Snížení heterogenity uvnitř nádoru po aplikaci primární terapie dovoluje zařazení individualizované léčby zacílené na zbytkový klon. Genomové sekvenování spolu s profilováním genové exprese ve zbytkovém klonu umožní identifikovat sadu klíčových mutací a/nebo poškozených metabolických drah, které mohou být odstraněny některou z nových cílených terapeutických látek.
- MeSH
- buněčné klony MeSH
- cílená molekulární terapie metody MeSH
- genom lidský MeSH
- genomika metody MeSH
- individualizovaná medicína metody MeSH
- lidé MeSH
- mnohočetný myelom farmakoterapie genetika MeSH
- mutace MeSH
- průtoková cytometrie metody MeSH
- reziduální nádor farmakoterapie genetika MeSH
- sekvenční analýza DNA metody MeSH
- Check Tag
- lidé MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- onkologie
- genetika, lékařská genetika
- NLK Publikační typ
- závěrečné zprávy o řešení grantu AZV MZ ČR
Závěrečná zpráva o řešení grantu Agentury pro zdravotnický výzkum MZ ČR
Nestr.
Drug resistance, acquired as a result of clonal evolution and selection, is a limiting factor of the efficacy of chemotherapy. Genomic alterations in solid cancers can be characterized by deep sequencing of cell-free tumor DNA (ctDNA) released into plasma from cancer cells, representing a non-invasive approach to monitor patients reaction to therapy. In this project, we will track genomic evolution of cancer in relation to the specific therapy by a whole exome sequencing of cfDNA isolated from advanced colon cancer patients. The main aim is to identify mutations in all DNA repair genes associated with chemoresistance. We will compare obtained outcomes in pre- and post-treatment plasma samples in good and non-responders to chemotherapy. The presence of mutations will by independently validated on a larger set of patients. Identified mutations associated with treatment response will be comprehensively tested for their functionality and impact on migration, apoptosis, and proliferation. The significance of the project lays on improvement of the therapy efficacy in colon cancer patien...
Rezistence na leky, vznikající v důsledku evoluce a selekce klonu nádorových buněk, je limitujícím faktorem pro účinnost protinádorové terapie. Z cirkulující nádorové ctDNA vyskytující se v plasmě lze metodou sekvenace zjišťovat genomické změny u solidních nádorů. Takovýto přístup umožňuje neinvazivní vyšetření pacienta. V předkládaném projektu budeme pomoci exomové sekvenace ctDNA sledovat genomickou evoluci buněk nádorů tračníka ve vztahu k léčbě. Hlavním záměrem je identifikovat mutace spjaté s rezistencí vůči chemoterapeutikům v genech DNA opravy. Porovnáme výsledky získané z párových vzorků plasmy odebraných před a po léčbě u pacientu se špatnou nebo dobrou odpovědí na chemoterapii. Nalezené mutace budou následně ověřeny na větším souboru pacientu. Mutace asociované s odpovědi na léčbu budou dále komplexně zkoumány z hlediska jejich vztahu a účinku na migraci, apoptózu a proliferaci nádorových buněk. Nejvýznamnějším přínosem projektu je možnost zlepšení účinnosti terapie u pacientů s nádory tračníka.
- MeSH
- chemorezistence genetika MeSH
- genomika metody MeSH
- lidé MeSH
- mutace MeSH
- nádorové biomarkery MeSH
- nádory tračníku diagnóza farmakoterapie genetika MeSH
- oprava DNA genetika účinky léků MeSH
- sekvenování exomu metody MeSH
- Check Tag
- lidé MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- onkologie
- genetika, lékařská genetika
- gastroenterologie
- NLK Publikační typ
- závěrečné zprávy o řešení grantu AZV MZ ČR
