1,720,997 research outputs found
eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants
Get PDFWhile advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2
ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest
Get PDFNext-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data.</div
COMPUTATIONAL STUDY OF TRANSCRIPTIONAL LANDSCAPES FROM RNA-SEQ DATA
Get PDFSince the inception of genetic science in the days of Gregor Johann Mendel, the major focus of genetics has been to identify functional units, genes, passed through generations and to determine how variation affects the development of an organism. While genome projects allowed us to collect complete and accurate DNA sequences of individual organisms, novel functional elements are being routinely discovered. When RNA sequencing technologies appeared in the late 2000s, they opened a new view over all transcriptional activity of cells at varying levels of resolution. However, the imperfections of both technical and biological processes, the growing amounts of data as well as the overall complexity of eukaryotic genomes underscore the need for novel analytical approaches to discover new and improve understanding of known genes and isoforms.
This work begins with an overview of the status of human gene annotation and presents a comprehensive discussion of existing and new methods for creating complete and accurate gene catalogs, via comparative genomics and RNA sequencing. Through careful analysis of large RNA-seq datasets, we annotate effects of transcriptional artifacts and inaccuracies in gene expression on downstream analysis. Moreover, we identify defining properties of validated transcription that distinguish it from the effervescent noise. To address the challenges presented by noisy transcription and improve gene expression analysis, we introduce TieBrush, a comprehensive suite of tools for efficient processing of multi-sample sequencing datasets. This suite allows for the creation of condensed representations of data, facilitating the identification of shared transcriptional motifs and enhancing downstream analysis. Furthermore, to enhance our understanding of alternative splicing and distinguish functional isoforms from noise at protein-coding loci, we develop ORFanage. This highly efficient and accurate system assigns open reading frames (ORFs) to gene transcripts, thereby improving gene annotations. Finally, we employ all presented techniques to design a complete sample-to-annotation protocol for annotating genes and transcripts. We apply this protocol to create CHESS 3 - an improved human genome annotation, identifying multiple novel tissue specific isoforms while increasing consistency and reliability of known transcript models.
In addition to advancements in gene annotation, this thesis briefly explores the critical role of large representative datasets of viral genomes in acquiring novel insights into diseases. More specifically, we discuss how pangenomic analysis of HIV-1 facilitated novel insights into viral persistence and how the challenges of mass sequencing of SARS-CoV-2 genomes required a novel approach for identification of first emerging recombinant lineages
NOVEL METHODS IMPROVE GENOME ANNOTATION
Get PDFIn the era of high-throughput sequencing, comprehensive genome annotation has become critical for understanding the functional complexities of life. Here we explore the development and application of computational methods for genome annotation, focusing on human transcriptome analysis, prokaryotic gene prediction, and circular RNA annotation. The primary contributions of this work span three distinct areas, each addressing unique challenges in the field. First, we develop a structure-guided isoform identification approach that utilizes three-dimensional protein structure predictions to identify functional human gene isoforms. Our method evaluates over 230,000 isoforms of human protein-coding genes assembled from thousands of RNA sequencing experiments across various human tissues. We identify hundreds of isoforms with more confidently predicted structure and potentially superior function compared to canonical isoforms, thus demonstrating the potential of protein structure prediction as a powerful tool for genome annotation and transcriptome analysis. Second, we present a universal protein model for prokaryotic gene prediction, Balrog, which employs a temporal convolutional network to analyze amino acid sequences from a diverse set of microbial genomes. Balrog eliminates the need for genome-specific training and matches or outperforms existing state-of-the-art gene finding tools. Lastly, we introduce alignment-free methods for annotating circular RNA in humans, leveraging a simple k-mer-based data structure
COMPUTATIONAL STUDY OF TRANSCRIPTIONAL LANDSCAPES FROM RNA-SEQ DATA
Get PDFSince the inception of genetic science in the days of Gregor Johann Mendel, the major focus of genetics has been to identify functional units, genes, passed through generations and to determine how variation affects the development of an organism. While genome projects allowed us to collect complete and accurate DNA sequences of individual organisms, novel functional elements are being routinely discovered. When RNA sequencing technologies appeared in the late 2000s, they opened a new view over all transcriptional activity of cells at varying levels of resolution. However, the imperfections of both technical and biological processes, the growing amounts of data as well as the overall complexity of eukaryotic genomes underscore the need for novel analytical approaches to discover new and improve understanding of known genes and isoforms.
This work begins with an overview of the status of human gene annotation and presents a comprehensive discussion of existing and new methods for creating complete and accurate gene catalogs, via comparative genomics and RNA sequencing. Through careful analysis of large RNA-seq datasets, we annotate effects of transcriptional artifacts and inaccuracies in gene expression on downstream analysis. Moreover, we identify defining properties of validated transcription that distinguish it from the effervescent noise. To address the challenges presented by noisy transcription and improve gene expression analysis, we introduce TieBrush, a comprehensive suite of tools for efficient processing of multi-sample sequencing datasets. This suite allows for the creation of condensed representations of data, facilitating the identification of shared transcriptional motifs and enhancing downstream analysis. Furthermore, to enhance our understanding of alternative splicing and distinguish functional isoforms from noise at protein-coding loci, we develop ORFanage. This highly efficient and accurate system assigns open reading frames (ORFs) to gene transcripts, thereby improving gene annotations. Finally, we employ all presented techniques to design a complete sample-to-annotation protocol for annotating genes and transcripts. We apply this protocol to create CHESS 3 - an improved human genome annotation, identifying multiple novel tissue specific isoforms while increasing consistency and reliability of known transcript models.
In addition to advancements in gene annotation, this thesis briefly explores the critical role of large representative datasets of viral genomes in acquiring novel insights into diseases. More specifically, we discuss how pangenomic analysis of HIV-1 facilitated novel insights into viral persistence and how the challenges of mass sequencing of SARS-CoV-2 genomes required a novel approach for identification of first emerging recombinant lineages
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
NOVEL METHODS IMPROVE GENOME ANNOTATION
Get PDFIn the era of high-throughput sequencing, comprehensive genome annotation has become critical for understanding the functional complexities of life. Here we explore the development and application of computational methods for genome annotation, focusing on human transcriptome analysis, prokaryotic gene prediction, and circular RNA annotation. The primary contributions of this work span three distinct areas, each addressing unique challenges in the field. First, we develop a structure-guided isoform identification approach that utilizes three-dimensional protein structure predictions to identify functional human gene isoforms. Our method evaluates over 230,000 isoforms of human protein-coding genes assembled from thousands of RNA sequencing experiments across various human tissues. We identify hundreds of isoforms with more confidently predicted structure and potentially superior function compared to canonical isoforms, thus demonstrating the potential of protein structure prediction as a powerful tool for genome annotation and transcriptome analysis. Second, we present a universal protein model for prokaryotic gene prediction, Balrog, which employs a temporal convolutional network to analyze amino acid sequences from a diverse set of microbial genomes. Balrog eliminates the need for genome-specific training and matches or outperforms existing state-of-the-art gene finding tools. Lastly, we introduce alignment-free methods for annotating circular RNA in humans, leveraging a simple k-mer-based data structure
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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