scholarly journals A fully-automated method discovers loss of mouse-lethal and human-monogenic disease genes in 58 mammals

2020 ◽  
Vol 48 (16) ◽  
pp. e91-e91
Author(s):  
Yatish Turakhia ◽  
Heidi I Chen ◽  
Amir Marcovitz ◽  
Gill Bejerano

Abstract Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (amino acid deletions and substitutions) and sister species support as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using human as reference, we discovered over 400 unique human ortholog erosion events across 58 mammals. This includes dozens of clade-specific losses of genes that result in early mouse lethality or are associated with severe human congenital diseases. Our discoveries yield intriguing potential for translational medical genetics and evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.

2019 ◽  
Author(s):  
Yatish Turakhia ◽  
Heidi I. Chen ◽  
Amir Marcovitz ◽  
Gill Bejerano

Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools and protein databases focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (deletion and non-synonymous substitution) as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence protein-coding gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using the hg38 human assembly as a reference, we discovered over 500 unique human genes affected by such high-confidence erosion events in different clades across 58 mammals. While most of these events likely have benign consequences, we also found dozens of clade-specific gene losses that result in early lethality in outgroup mammals or are associated with severe congenital diseases in humans. Our discoveries yield intriguing potential for translational medical genetics and for evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.


Author(s):  
M. E. J. Newman ◽  
R. G. Palmer

Developed after a meeting at the Santa Fe Institute on extinction modeling, this book comments critically on the various modeling approaches. In the last decade or so, scientists have started to examine a new approach to the patterns of evolution and extinction in the fossil record. This approach may be called "statistical paleontology," since it looks at large-scale patterns in the record and attempts to understand and model their average statistical features, rather than their detailed structure. Examples of the patterns these studies examine are the distribution of the sizes of mass extinction events over time, the distribution of species lifetimes, or the apparent increase in the number of species alive over the last half a billion years. In attempting to model these patterns, researchers have drawn on ideas not only from paleontology, but from evolutionary biology, ecology, physics, and applied mathematics, including fitness landscapes, competitive exclusion, interaction matrices, and self-organized criticality. A self-contained review of work in this field.


Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Apurva Badkas ◽  
Thanh-Phuong Nguyen ◽  
Laura Caberlotto ◽  
Jochen G. Schneider ◽  
Sébastien De Landtsheer ◽  
...  

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.


2015 ◽  
Vol 282 (1815) ◽  
pp. 20151421 ◽  
Author(s):  
Göran Arnqvist ◽  
Ahmed Sayadi ◽  
Elina Immonen ◽  
Cosima Hotzy ◽  
Daniel Rankin ◽  
...  

The ultimate cause of genome size (GS) evolution in eukaryotes remains a major and unresolved puzzle in evolutionary biology. Large-scale comparative studies have failed to find consistent correlations between GS and organismal properties, resulting in the ‘ C -value paradox’. Current hypotheses for the evolution of GS are based either on the balance between mutational events and drift or on natural selection acting upon standing genetic variation in GS. It is, however, currently very difficult to evaluate the role of selection because within-species studies that relate variation in life-history traits to variation in GS are very rare. Here, we report phylogenetic comparative analyses of GS evolution in seed beetles at two distinct taxonomic scales, which combines replicated estimation of GS with experimental assays of life-history traits and reproductive fitness. GS showed rapid and bidirectional evolution across species, but did not show correlated evolution with any of several indices of the relative importance of genetic drift. Within a single species, GS varied by 4–5% across populations and showed positive correlated evolution with independent estimates of male and female reproductive fitness. Collectively, the phylogenetic pattern of GS diversification across and within species in conjunction with the pattern of correlated evolution between GS and fitness provide novel support for the tenet that natural selection plays a key role in shaping GS evolution.


2013 ◽  
Vol 9 (5) ◽  
pp. e1003073 ◽  
Author(s):  
Wei-Hua Chen ◽  
Xing-Ming Zhao ◽  
Vera van Noort ◽  
Peer Bork

Author(s):  
Young Hyun Kim ◽  
Eun-Gyu Ha ◽  
Kug Jin Jeon ◽  
Chena Lee ◽  
Sang-Sun Han

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) dataset. Methods: In total, 2,760 DPRs from 746 subjects who had 2 to 17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test dataset included the latest DPR of each subject (746 images) and the other DPRs (2,014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, –3, and −5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)–applied images. Results: This model had rank-1,–3, and −5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 sec per epoch, and the prediction time for 746 test DPRs was short (3.2 sec/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.


2016 ◽  
Vol 113 (52) ◽  
pp. 15054-15059 ◽  
Author(s):  
Xiao Ji ◽  
Rachel L. Kember ◽  
Christopher D. Brown ◽  
Maja Bućan

Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication, and repetitive behavior. It is estimated that hundreds of genes contribute to ASD. We asked if genes with a strong effect on survival and fitness contribute to ASD risk. Human orthologs of genes with an essential role in pre- and postnatal development in the mouse [essential genes (EGs)] are enriched for disease genes and under strong purifying selection relative to human orthologs of mouse genes with a known nonlethal phenotype [nonessential genes (NEGs)]. This intolerance to deleterious mutations, commonly observed haploinsufficiency, and the importance of EGs in development suggest a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. With a comprehensive catalog of 3,915 mammalian EGs, we provide compelling evidence for a stronger contribution of EGs to ASD risk compared with NEGs. By examining the exonic de novo and inherited variants from 1,781 ASD quartet families, we show a significantly higher burden of damaging mutations in EGs in ASD probands compared with their non-ASD siblings. The analysis of EGs in the developing brain identified clusters of coexpressed EGs implicated in ASD. Finally, we suggest a high-priority list of 29 EGs with potential ASD risk as targets for future functional and behavioral studies. Overall, we show that large-scale studies of gene function in model organisms provide a powerful approach for prioritization of genes and pathogenic variants identified by sequencing studies of human disease.


2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Raj Bridgelall ◽  
Pan Lu ◽  
Denver D. Tolliver ◽  
Tai Xu

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.


2020 ◽  
Vol 48 (W1) ◽  
pp. W200-W207
Author(s):  
Simone Puccio ◽  
Giorgio Grillo ◽  
Arianna Consiglio ◽  
Maria Felicia Soluri ◽  
Daniele Sblattero ◽  
...  

Abstract High-Throughput Sequencing technologies are transforming many research fields, including the analysis of phage display libraries. The phage display technology coupled with deep sequencing was introduced more than a decade ago and holds the potential to circumvent the traditional laborious picking and testing of individual phage rescued clones. However, from a bioinformatics point of view, the analysis of this kind of data was always performed by adapting tools designed for other purposes, thus not considering the noise background typical of the ‘interactome sequencing’ approach and the heterogeneity of the data. InteractomeSeq is a web server allowing data analysis of protein domains (‘domainome’) or epitopes (‘epitome’) from either Eukaryotic or Prokaryotic genomic phage libraries generated and selected by following an Interactome sequencing approach. InteractomeSeq allows users to upload raw sequencing data and to obtain an accurate characterization of domainome/epitome profiles after setting the parameters required to tune the analysis. The release of this tool is relevant for the scientific and clinical community, because InteractomeSeq will fill an existing gap in the field of large-scale biomarkers profiling, reverse vaccinology, and structural/functional studies, thus contributing essential information for gene annotation or antigen identification. InteractomeSeq is freely available at https://InteractomeSeq.ba.itb.cnr.it/


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Julie A. Fitzpatrick ◽  
Nicolas Basty ◽  
Madeleine Cule ◽  
Yi Liu ◽  
Jimmy D. Bell ◽  
...  

AbstractPsoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurement and manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5000 participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (average dice score coefficient of 0.9046 ± 0.0058 for six-fold cross-validation). Iliopsoas muscle volumes were successfully measured in all 5000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts.


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