scholarly journals Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype

2019 ◽  
Author(s):  
Matej Mihelčić ◽  
Tomislav Šmuc ◽  
Fran Supek
Keyword(s):  
Gene Function ◽  
Structural Variation ◽  
Predictive Power ◽  
State Of The Art ◽  
Gene Clustering ◽  
High Confidence ◽  
Gene Functions ◽  
Predict Gene Function ◽  
Guilt By Association ◽  
Clustering Patterns

AbstractGenes with similar roles in the cell are known to cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions – corresponding to semantically distant Gene Ontology terms – are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26-46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that the effect of structural variation on gene function distribution across chromosomes may be used to predict phenotype of individuals from their genome sequence.

scholarly journals Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Matej Mihelčić ◽  
Tomislav Šmuc ◽  
Fran Supek
Keyword(s):  
Gene Function ◽  
Structural Variation ◽  
Predictive Power ◽  
State Of The Art ◽  
Gene Clustering ◽  
High Confidence ◽  
Gene Functions ◽  
Predict Gene Function ◽  
Guilt By Association ◽  
Clustering Patterns

AbstractGenes with similar roles in the cell cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions – corresponding to semantically distant Gene Ontology terms – are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26–46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that copy number-neutral structural variation that shapes gene function distribution across chromosomes can predict phenotype of individuals from their genome sequence.

scholarly journals Enabling multiscale variation analysis with genome graphs

2021 ◽  
Author(s):  
Brice Letcher ◽  
Martin Hunt ◽  
Zamin Iqbal
Keyword(s):  
Genetic Variation ◽  
Directed Acyclic Graph ◽  
Structural Variation ◽  
Reference Genome ◽  
Multiple Scales ◽  
State Of The Art ◽  
Variant Calling ◽  
Variation Analysis ◽  
New Algorithms ◽  
Genome Graph

AbstractBackgroundStandard approaches to characterising genetic variation revolve around mapping reads to a reference genome and describing variants in terms of differences from the reference; this is based on the assumption that these differences will be small and provides a simple coordinate system. However this fails, and the coordinates break down, when there are diverged haplotypes at a locus (e.g. one haplotype contains a multi-kilobase deletion, a second contains a few SNPs, and a third is highly diverged with hundreds of SNPs). To handle these, we need to model genetic variation that occurs at different length-scales (SNPs to large structural variants) and that occurs on alternate backgrounds. We refer to these together as multiscale variation.ResultsWe model the genome as a directed acyclic graph consisting of successive hierarchical subgraphs (“sites”) that naturally incorporate multiscale variation, and introduce an algorithm for genotyping, implemented in the software gramtools. This enables variant calling on different sequence backgrounds. In addition to producing regular VCF files, we introduce a JSON file format based on VCF, which records variant site relationships and alternate sequence backgrounds.We show two applications. First, we benchmark gramtools against existing state-of-the-art methods in joint-genotyping 17 M. tuberculosis samples at long deletions and the overlapping small variants that segregate in a cohort of 1,017 genomes. Second, in 706 African and SE Asian P. falciparum genomes, we analyse a dimorphic surface antigen gene which possesses variation on two diverged backgrounds which appeared to not recombine. This generates the first map of variation on both backgrounds, revealing patterns of recombination that were previously unknown.ConclusionsWe need new approaches to be able to jointly analyse SNP and structural variation in cohorts, and even more to handle variants on different genetic backgrounds. We have demonstrated that by modelling with a directed, acyclic and locally hierarchical genome graph, we can apply new algorithms to accurately genotype dense variation at multiple scales. We also propose a generalisation of VCF for accessing multiscale variation in genome graphs, which we hope will be of wide utility.

scholarly journals Multimodal Speaker Diarization Using a Pre-Trained Audio-Visual Synchronization Model

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5163 ◽  
Author(s):  
Ahmad ◽  
Zubair ◽  
Alquhayz ◽  
Ditta
Keyword(s):  
High Probability ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Talk Shows ◽  
Speaker Diarization ◽  
High Confidence ◽  
Effective Technique ◽  
Complex State ◽  
Synchronization Model ◽  
Short Video

Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detection technique and are then fed to the pre-trained model. This model is a two streamed network which matches audio frames with their respective visual input segments. On the basis of high confidence video segments inferred by the model, the respective audio frames are used to train Gaussian mixture model (GMM)-based clusters. This method helps in generating speaker specific clusters with high probability. We tested our approach on a popular subset of AMI meeting corpus consisting of 5.4 h of recordings for audio and 5.8 h of different set of multimodal recordings. A significant improvement is noticed with the proposed method in term of DER when compared to conventional and fully supervised audio based speaker diarization. The results of the proposed technique are very close to the complex state-of-the art multimodal diarization which shows significance of such simple yet effective technique.

scholarly journals Recent advances in gene function prediction using context-specific coexpression networks in plants

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 153 ◽  
Author(s):  
Chirag Gupta ◽  
Andy Pereira
Keyword(s):  
Gene Function ◽  
Complex Traits ◽  
Large Fraction ◽  
Genetic Correlations ◽  
Short Review ◽  
Cellular Functions ◽  
Gene Functions ◽  
Plant Genes ◽  
Context Specific ◽  
Coexpression Networks

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

scholarly journals Automatic consistency assurance for literature-based gene ontology annotation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiyu Chen ◽  
Nicholas Geard ◽  
Justin Zobel ◽  
Karin Verspoor
Keyword(s):  
Gene Ontology ◽  
High Precision ◽  
State Of The Art ◽  
Biological Database ◽  
Research Papers ◽  
Go Annotation ◽  
Human In The Loop ◽  
Gene Functions ◽  
Different Types ◽  
Biological Literature

Abstract Background Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. Results In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Conclusions Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.

MUTATIONS WITH DOMINANT EFFECTS ON THE BEHAVIOR AND MORPHOLOGY OF THE NEMATODE CAENORHABDITIS ELEGANS

Genetics ◽  
1986 ◽  
Vol 113 (4) ◽  
pp. 821-852
Author(s):  
Eun-Chung Park ◽  
H Robert Horvitz
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Sample ◽  
Gene Function ◽  
The Novel ◽  
Wild Type ◽  
Nematode Caenorhabditis Elegans ◽  
Type Function ◽  
C Elegans ◽  
Wild Type Phenotype ◽  
Gene Functions

ABSTRACT We have analyzed 31 mutations that have dominant effects on the behavior or morphology of the nematode Caenorhabditis elegans. These mutations appear to define 15 genes. We have studied ten of these genes in some detail and have been led to two notable conclusions. First, loss of gene function for four of these ten genes results in a wild-type phenotype; if these genes represent a random sample from the genome, then we would estimate that null mutations in about half of the genes in C. elegans would result in a nonmutant phenotype. Second, the dominant effects of mutations in nine of these ten genes are caused by novel gene functions, and in all nine cases the novel function is antagonized by the wild-type function.

scholarly journals Transient Gene Expression is an Effective Experimental Tool for the Research into the Fine Mechanisms of Plant Gene Function: Advantages, Limitations, and Solutions

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1187
Author(s):  
Alexander A. Tyurin ◽  
Alexandra V. Suhorukova ◽  
Ksenia V. Kabardaeva ◽  
Irina V. Goldenkova-Pavlova
Keyword(s):  
Gene Expression ◽  
Transient Expression ◽  
Gene Function ◽  
Transient Gene Expression ◽  
Physiological Role ◽  
Gene Products ◽  
Plant Gene ◽  
Gene Functions ◽  
Insight Into

A large data array on plant gene expression accumulated thanks to comparative omic studies directs the efforts of researchers to the specific or fine effects of the target gene functions and, as a consequence, elaboration of relatively simple and concurrently effective approaches allowing for the insight into the physiological role of gene products. Numerous studies have convincingly demonstrated the efficacy of transient expression strategy for characterization of the plant gene functions. The review goals are (i) to consider the advantages and limitations of different plant systems and methods of transient expression used to find out the role of gene products; (ii) to summarize the current data on the use of the transient expression approaches for the insight into fine mechanisms underlying the gene function; and (iii) to outline the accomplishments in efficient transient expression of plant genes. In general, the review discusses the main and critical steps in each of the methods of transient gene expression in plants; areas of their application; main results obtained using plant objects; their contribution to our knowledge about the fine mechanisms of the plant gene functions underlying plant growth and development; and clarification of the mechanisms regulating complex metabolic pathways.

scholarly journals Partial Label Learning with Self-Guided Retraining

2019 ◽  
Vol 33 ◽  
pp. 3542-3549 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Confidence ◽  
Infinity Norm ◽  
Real World Datasets ◽  
Partial Label Learning ◽  
Optimization Efficiency

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

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