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1.
Nat Genet ; 2021 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-33603233

RESUMO

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

2.
Nat Protoc ; 16(2): 1276-1296, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33462443

RESUMO

RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.

3.
Nat Commun ; 12(1): 529, 2021 01 22.
Artigo em Inglês | MEDLINE | ID: mdl-33483494

RESUMO

Aberrant splicing is a major cause of rare diseases.  However, its prediction from genome sequence alone remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we develop FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1 causing mucolipidosis. FRASER automatically controls for latent confounders, which are widespread and affect sensitivity substantially. Moreover, FRASER is based on a count distribution and multiple testing correction, thus reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor loss of sensitivity. Applying FRASER to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.


Assuntos
Algoritmos , Processamento Alternativo , Biologia Computacional/métodos , RNA-Seq/métodos , Análise de Sequência de RNA/métodos , Internet , Íntrons/genética , Software
4.
J Clin Invest ; 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-33001864

RESUMO

BACKGROUND: Transcriptome sequencing (RNA-seq) improves diagnostic rates in individuals with suspected Mendelian conditions to varying degrees, primarily by directing the prioritization of candidate DNA variants identified on exome or genome sequencing (ES/GS). Here we implemented an RNA-seq guided method to diagnose individuals across a wide range of ages and clinical phenotypes. METHODS: One hundred fifteen undiagnosed adult and pediatric patients with diverse phenotypes and 67 family members (182 total individuals) underwent RNA-seq from whole blood and fibroblasts at the Baylor College of Medicine (BCM) Undiagnosed Diseases Network (UDN) clinical site from 2014-2020. We implemented a workflow to detect outliers in gene expression and splicing for cases that remained undiagnosed despite standard genomic and transcriptomic analysis. RESULTS: The transcriptome-directed approach resulted in a diagnostic rate of 12% across the entire cohort, or 17% after excluding cases solved on ES/GS alone. Newly diagnosed conditions included Koolen-de Vries syndrome (KANSL1), Renpenning syndrome (PQBP1), TBCK-associated encephalopathy, NSD2- and CLTC-related intellectual disability, and others, all with negative conventional genomic testing, including ES and chromosomal microarray (CMA). Fibroblasts exhibited higher and more consistent expression of clinically relevant genes than whole blood. In solved cases with RNA-seq from both tissues, the causative defect was missed in blood in half the cases but none from fibroblasts. CONCLUSION: For our cohort of undiagnosed individuals with suspected Mendelian conditions, transcriptome-directed genomic analysis facilitated diagnoses, primarily through the identification of variants missed on ES and CMA.

5.
Dev Cell ; 51(5): 632-644.e6, 2019 12 02.
Artigo em Inglês | MEDLINE | ID: mdl-31630981

RESUMO

Gene transcription in eukaryotes is regulated through dynamic interactions of a variety of different proteins with DNA in the context of chromatin. Here, we used mass spectrometry for absolute quantification of the nuclear proteome and methyl marks on selected lysine residues in histone H3 during two stages of Drosophila embryogenesis. These analyses provide comprehensive information about the absolute copy number of several thousand proteins and reveal unexpected relationships between the abundance of histone-modifying and -binding proteins and the chromatin landscape that they generate and interact with. For some histone modifications, the levels in Drosophila embryos are substantially different from those previously reported in tissue culture cells. Genome-wide profiling of H3K27 methylation during developmental progression and in animals with reduced PRC2 levels illustrates how mass spectrometry can be used for quantitatively describing and comparing chromatin states. Together, these data provide a foundation toward a quantitative understanding of gene regulation in Drosophila.


Assuntos
Montagem e Desmontagem da Cromatina , Embrião não Mamífero/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Código das Histonas , Animais , Cromatina/genética , Cromatina/metabolismo , Proteínas de Drosophila/genética , Proteínas de Drosophila/metabolismo , Drosophila melanogaster , Histona-Lisina N-Metiltransferase/genética , Histona-Lisina N-Metiltransferase/metabolismo , Histonas/genética , Histonas/metabolismo , Proteoma/genética , Proteoma/metabolismo
6.
Hum Mutat ; 40(9): 1215-1224, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31301154

RESUMO

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.


Assuntos
Processamento Alternativo , Biologia Computacional/métodos , Mutação , Proteínas/genética , Animais , Congressos como Assunto , Aptidão Genética , Humanos , Modelos Genéticos , Homologia de Sequência do Ácido Nucleico
7.
Hum Mutat ; 40(9): 1243-1251, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31070280

RESUMO

Pathogenic genetic variants often primarily affect splicing. However, it remains difficult to quantitatively predict whether and how genetic variants affect splicing. In 2018, the fifth edition of the Critical Assessment of Genome Interpretation proposed two splicing prediction challenges based on experimental perturbation assays: Vex-seq, assessing exon skipping, and MaPSy, assessing splicing efficiency. We developed a modular modeling framework, MMSplice, the performance of which was among the best on both challenges. Here we provide insights into the modeling assumptions of MMSplice and its individual modules. We furthermore illustrate how MMSplice can be applied in practice for individual genome interpretation, using the MMSplice VEP plugin and the Kipoi variant interpretation plugin, which are directly applicable to VCF files.


Assuntos
Biologia Computacional/métodos , Variação Genética , Processamento de RNA , Congressos como Assunto , Éxons , Predisposição Genética para Doença , Humanos , Íntrons , Modelos Genéticos , Software
9.
Nat Rev Genet ; 20(7): 389-403, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30971806

RESUMO

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.


Assuntos
Aprendizado Profundo , Genômica/métodos , Modelos Genéticos , Redes Neurais de Computação , Sequência de Bases , Simulação por Computador , Humanos , Aprendizado de Máquina Supervisionado , Aprendizado de Máquina não Supervisionado
10.
Elife ; 82019 04 26.
Artigo em Inglês | MEDLINE | ID: mdl-31025937

RESUMO

RNA splicing is an essential part of eukaryotic gene expression. Although the mechanism of splicing has been extensively studied in vitro, in vivo kinetics for the two-step splicing reaction remain poorly understood. Here, we combine transient transcriptome sequencing (TT-seq) and mathematical modeling to quantify RNA metabolic rates at donor and acceptor splice sites across the human genome. Splicing occurs in the range of minutes and is limited by the speed of RNA polymerase elongation. Splicing kinetics strongly depends on the position and nature of nucleotides flanking splice sites, and on structural interactions between unspliced RNA and small nuclear RNAs in spliceosomal intermediates. Finally, we introduce the 'yield' of splicing as the efficiency of converting unspliced to spliced RNA and show that it is highest for mRNAs and independent of splicing kinetics. These results lead to quantitative models describing how splicing rates and yield are encoded in the human genome.


Assuntos
Genoma Humano , Processamento de RNA , Perfilação da Expressão Gênica , Humanos , Células K562 , Cinética , Modelos Teóricos , Análise de Sequência de RNA , Spliceossomos/metabolismo
11.
Genome Biol ; 20(1): 48, 2019 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-30823901

RESUMO

Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict effects of variants on exon skipping, splice site choice, splicing efficiency, and pathogenicity, with matched or higher performance than state-of-the-art. Our models, available in the repository Kipoi, apply to variants including indels directly from VCF files.


Assuntos
Processamento Alternativo , Variação Genética , Modelos Genéticos , Redes Neurais de Computação , Doenças Genéticas Inatas
12.
Mol Syst Biol ; 15(2): e8503, 2019 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-30777892

RESUMO

Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.


Assuntos
Genoma Humano/genética , Proteoma/genética , Distribuição Tecidual/genética , Transcriptoma/genética , Regulação da Expressão Gênica/genética , Humanos , Espectrometria de Massas/métodos , Proteômica/métodos , RNA Mensageiro/genética , Análise de Sequência de RNA/métodos
13.
Mol Syst Biol ; 15(2): e8513, 2019 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-30777893

RESUMO

Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects are still lacking. Here, we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We estimated by regression the contribution of known sequence determinants of protein synthesis and degradation in addition to 45 mRNA and 3 protein sequence motifs that we found by association testing. While PTR ratios span more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided functional support for two novel UTR motifs, and an immobilized mRNA affinity competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our integrative model led to a new metric of codon optimality that captures the effects of codon frequency on protein synthesis and degradation. Altogether, this study shows that a large fraction of PTR ratio variation in human tissues can be predicted from sequence, and it identifies many new candidate post-transcriptional regulatory elements.


Assuntos
Proteínas/genética , Proteoma/genética , Distribuição Tecidual/genética , Transcriptoma/genética , Regulação da Expressão Gênica/genética , Genoma Humano/genética , Humanos , Espectrometria de Massas/métodos , Proteômica/métodos , RNA Mensageiro/genética , Análise de Sequência de RNA/métodos
14.
Am J Hum Genet ; 103(6): 907-917, 2018 12 06.
Artigo em Inglês | MEDLINE | ID: mdl-30503520

RESUMO

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (Outlier in RNA-Seq Finder), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read-count expectations according to the gene covariation resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best recall of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of controlling for covariation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a dataset, for identifying outlier samples with too many aberrantly expressed genes, and for detecting aberrant gene expression on the basis of false-discovery-rate-adjusted p values. Overall, OUTRIDER provides an end-to-end solution for identifying aberrantly expressed genes and is suitable for use by rare-disease diagnostic platforms.


Assuntos
Expressão Gênica/genética , Variação Genética/genética , RNA/metabolismo , Análise de Sequência de RNA/métodos , Algoritmos , Perfilação da Expressão Gênica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos
15.
Sci Rep ; 8(1): 16719, 2018 11 13.
Artigo em Inglês | MEDLINE | ID: mdl-30425284

RESUMO

In hyper-IgE syndromes (HIES), a group of primary immunodeficiencies clinically overlapping with atopic dermatitis, early diagnosis is crucial to initiate appropriate therapy and prevent irreversible complications. Identification of underlying gene defects such as in DOCK8 and STAT3 and corresponding molecular testing has improved diagnosis. Yet, in a child and her newborn sibling with HIES phenotype molecular diagnosis was misleading. Extensive analyses driven by the clinical phenotype identified an intronic homozygous DOCK8 variant c.4626 + 76 A > G creating a novel splice site as disease-causing. While the affected newborn carrying the homozygous variant had no expression of DOCK8 protein, in the index patient molecular diagnosis was compromised due to expression of altered and wildtype DOCK8 transcripts and DOCK8 protein as well as defective STAT3 signaling. Sanger sequencing of lymphocyte subsets revealed that somatic alterations and reversions revoked the predominance of the novel over the canonical splice site in the index patient explaining DOCK8 protein expression, whereas defective STAT3 responses in the index patient were explained by a T cell phenotype skewed towards central and effector memory T cells. Hence, somatic alterations and skewed immune cell phenotypes due to selective pressure may compromise molecular diagnosis and need to be considered with unexpected clinical and molecular findings.


Assuntos
Fatores de Troca do Nucleotídeo Guanina/genética , Íntrons/genética , Síndrome de Job/genética , Mutação , Sítios de Splice de RNA/genética , Sequência de Bases , Pré-Escolar , Biologia Computacional , Feminino , Regulação da Expressão Gênica/genética , Humanos , Lactente , Síndrome de Job/patologia , Técnicas de Diagnóstico Molecular , Gravidez , Fator de Transcrição STAT3/metabolismo , Transdução de Sinais/genética
16.
PLoS One ; 13(7): e0199938, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29995917

RESUMO

The accurate quantification of cellular and mitochondrial bioenergetic activity is of great interest in medicine and biology. Mitochondrial stress tests performed with Seahorse Bioscience XF Analyzers allow the estimation of different bioenergetic measures by monitoring the oxygen consumption rates (OCR) of living cells in multi-well plates. However, studies of the statistical best practices for determining aggregated OCR measurements and comparisons have been lacking. Therefore, to understand how OCR behaves across different biological samples, wells, and plates, we performed mitochondrial stress tests in 126 96-well plates involving 203 fibroblast cell lines. We show that the noise of OCR is multiplicative, that outlier data points can concern individual measurements or all measurements of a well, and that the inter-plate variation is greater than the intra-plate variation. Based on these insights, we developed a novel statistical method, OCR-Stats, that: i) robustly estimates OCR levels modeling multiplicative noise and automatically identifying outlier data points and outlier wells; and ii) performs statistical testing between samples, taking into account the different magnitudes of the between- and within-plate variations. This led to a significant reduction of the coefficient of variation across plates of basal respiration by 45% and of maximal respiration by 29%. Moreover, using positive and negative controls, we show that our statistical test outperforms the existing methods, which suffer from an excess of either false positives (within-plate methods), or false negatives (between-plate methods). Altogether, this study provides statistical good practices to support experimentalists in designing, analyzing, testing, and reporting the results of mitochondrial stress tests using this high throughput platform.


Assuntos
Mitocôndrias/metabolismo , Análise Serial de Tecidos/métodos , Linhagem Celular , Respiração Celular , Metabolismo Energético , Fibroblastos/citologia , Modelos Estatísticos , Consumo de Oxigênio
17.
BMC Bioinformatics ; 19(1): 247, 2018 06 27.
Artigo em Inglês | MEDLINE | ID: mdl-29945559

RESUMO

BACKGROUND: GenoGAM (Genome-wide generalized additive models) is a powerful statistical modeling tool for the analysis of ChIP-Seq data with flexible factorial design experiments. However large runtime and memory requirements of its current implementation prohibit its application to gigabase-scale genomes such as mammalian genomes. RESULTS: Here we present GenoGAM 2.0, a scalable and efficient implementation that is 2 to 3 orders of magnitude faster than the previous version. This is achieved by exploiting the sparsity of the model using the SuperLU direct solver for parameter fitting, and sparse Cholesky factorization together with the sparse inverse subset algorithm for computing standard errors. Furthermore the HDF5 library is employed to store data efficiently on hard drive, reducing memory footprint while keeping I/O low. Whole-genome fits for human ChIP-seq datasets (ca. 300 million parameters) could be obtained in less than 9 hours on a standard 60-core server. GenoGAM 2.0 is implemented as an open source R package and currently available on GitHub. A Bioconductor release of the new version is in preparation. CONCLUSIONS: We have vastly improved the performance of the GenoGAM framework, opening up its application to all types of organisms. Moreover, our algorithmic improvements for fitting large GAMs could be of interest to the statistical community beyond the genomics field.


Assuntos
Genômica/métodos , Humanos
18.
Elife ; 72018 01 18.
Artigo em Inglês | MEDLINE | ID: mdl-29345616

RESUMO

Loss of the sense of smell is among the first signs of natural aging and neurodegenerative diseases such as Alzheimer's and Parkinson's. Cellular and molecular mechanisms promoting this smell loss are not understood. Here, we show that Drosophila melanogaster also loses olfaction before vision with age. Within the olfactory circuit, cholinergic projection neurons show a reduced odor response accompanied by a defect in axonal integrity and reduction in synaptic marker proteins. Using behavioral functional screening, we pinpoint that expression of the mitochondrial reactive oxygen scavenger SOD2 in cholinergic projection neurons is necessary and sufficient to prevent smell degeneration in aging flies. Together, our data suggest that oxidative stress induced axonal degeneration in a single class of neurons drives the functional decline of an entire neural network and the behavior it controls. Given the important role of the cholinergic system in neurodegeneration, the fly olfactory system could be a useful model for the identification of drug targets.


Assuntos
Envelhecimento/patologia , Neurônios Colinérgicos/patologia , Estresse Oxidativo , Animais , Drosophila melanogaster , Modelos Animais , Degeneração Neural/patologia , Bulbo Olfatório/patologia , Superóxido Dismutase/metabolismo
19.
Bioinformatics ; 34(8): 1261-1269, 2018 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-29155928

RESUMO

Motivation: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results: Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox. Availability and implementation: Spline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017. Contact: avsec@in.tum.de or gagneur@in.tum.de. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Genômica/métodos , Modelos Genéticos , Redes Neurais de Computação , Sequências Reguladoras de Ácido Nucleico , DNA , Células Hep G2 , Humanos , Células K562 , Aprendizado de Máquina , Ligação Proteica , Proteínas/metabolismo , RNA , Análise de Sequência de DNA/métodos , Análise de Sequência de RNA/métodos , Software
20.
RNA ; 23(11): 1648-1659, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28802259

RESUMO

The stability of mRNA is one of the major determinants of gene expression. Although a wealth of sequence elements regulating mRNA stability has been described, their quantitative contributions to half-life are unknown. Here, we built a quantitative model for Saccharomyces cerevisiae based on functional mRNA sequence features that explains 59% of the half-life variation between genes and predicts half-life at a median relative error of 30%. The model revealed a new destabilizing 3' UTR motif, ATATTC, which we functionally validated. Codon usage proves to be the major determinant of mRNA stability. Nonetheless, single-nucleotide variations have the largest effect when occurring on 3' UTR motifs or upstream AUGs. Analyzing mRNA half-life data of 34 knockout strains showed that the effect of codon usage not only requires functional decapping and deadenylation, but also the 5'-to-3' exonuclease Xrn1, the nonsense-mediated decay genes, but not no-go decay. Altogether, this study quantitatively delineates the contributions of mRNA sequence features on stability in yeast, reveals their functional dependencies on degradation pathways, and allows accurate prediction of half-life from mRNA sequence.


Assuntos
Estabilidade de RNA/genética , RNA Fúngico/genética , RNA Fúngico/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Regiões 3' não Traduzidas/genética , Sequência de Bases , Códon/genética , Códon/metabolismo , Técnicas de Inativação de Genes , Genes Fúngicos , Meia-Vida , Modelos Biológicos , Degradação do RNAm Mediada por Códon sem Sentido/genética , Iniciação Traducional da Cadeia Peptídica , Elementos Reguladores de Transcrição , Schizosaccharomyces/genética , Schizosaccharomyces/metabolismo
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