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  • 1
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    Conserved and unique transcriptional features of pharyngeal arches in the skate (Leucoraja erinacea) and evolution of the jaw (2021)
    Oxford University Press
    Details
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hirschberger, C., Sleight, V. A., Criswell, K. E., Clark, S. J., & Gillis, J. A. Conserved and unique transcriptional features of pharyngeal arches in the skate (Leucoraja erinacea) and evolution of the jaw. Molecular Biology and Evolution, (2021): msab123, https://doi.org/10.1093/molbev/msab123
    Description: The origin of the jaw is a long-standing problem in vertebrate evolutionary biology. Classical hypotheses of serial homology propose that the upper and lower jaw evolved through modifications of dorsal and ventral gill arch skeletal elements, respectively. If the jaw and gill arches are derived members of a primitive branchial series, we predict that they would share common developmental patterning mechanisms. Using candidate and RNAseq/differential gene expression analyses, we find broad conservation of dorsoventral patterning mechanisms within the developing mandibular, hyoid and gill arches of a cartilaginous fish, the skate (Leucoraja erinacea). Shared features include expression of genes encoding members of the ventralising BMP and endothelin signalling pathways and their effectors, the joint markers nkx3.2 and gdf5 and pro-chondrogenic transcription factor barx1, and the dorsal territory marker pou3f3. Additionally, we find that mesenchymal expression of eya1/six1 is an ancestral feature of the mandibular arch of jawed vertebrates, while differences in notch signalling distinguish the mandibular and gill arches in skate. Comparative transcriptomic analyses of mandibular and gill arch tissues reveal additional genes differentially expressed along the dorsoventral axis of the pharyngeal arches, including scamp5 as a novel marker of the dorsal mandibular arch, as well as distinct transcriptional features of mandibular and gill arch muscle progenitors and developing gill buds. Taken together, our findings reveal conserved patterning mechanisms in the pharyngeal arches of jawed vertebrates, consistent with serial homology of their skeletal derivatives, as well as unique transcriptional features that may underpin distinct jaw and gill arch morphologies.
    Description: This work was supported by a Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship to CH, by a Wolfson College Junior Research Fellowship and MBL Whitman Early Career Fellowship to VAS, and by a Royal Society University Research Fellowship (UF130182 and URF\R\191007), Royal Society Research Grant (RG140377) and University of Cambridge Sir Isaac Newton Trust Grant (14.23z) to JAG.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Positive and negative forms of replicability in gene network analysis (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This has motivated a great deal of comparative evaluation and research into best practices. We explore the possibility that this may lead to overfitting in the field as a whole. Results: We construct a model of ‘research communities’ sampling from real gene network data and machine learning methods to characterize performance trends. Our analysis reveals an important principle limiting the value of replication, namely that targeting it directly causes ‘easy’ or uninformative replication to dominate analyses. We find that when sampling across network data and algorithms with similar variability, the relationship between replicability and accuracy is positive (Spearman’s correlation, r s ~0.33) but where no such constraint is imposed, the relationship becomes negative for a given gene function ( r s ~ –0.13). We predict factors driving replicability in some prior analyses of gene networks and show that they are unconnected with the correctness of the original result, instead reflecting replicable biases. Without these biases, the original results also vanish replicably. We show these effects can occur quite far upstream in network data and that there is a strong tendency within protein–protein interaction data for highly replicable interactions to be associated with poor quality control. Availability and implementation: Algorithms, network data and a guide to the code available at: https://github.com/wimverleyen/AggregateGeneFunctionPrediction . Contact: jgillis@cshl.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015)
    Oxford University Press
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    Publication Date: 2015-06-27
    Description: Motivation: RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. Results: We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were 〉20 samples with read depth 〉10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ~0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain ‘gold-standard’ co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology. Contact: jgillis@cshl.edu or sballouz@cshl.edu Supplementary information: Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 4
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    Assessing identity, redundancy and confounds in Gene Ontology annotations over time (2013)
    Oxford University Press
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    Publication Date: 2013-02-13
    Description: Motivation: The Gene Ontology (GO) is heavily used in systems biology, but the potential for redundancy, confounds with other data sources and problems with stability over time have been little explored. Results: We report that GO annotations are stable over short periods, with 3% of genes not being most semantically similar to themselves between monthly GO editions. However, we find that genes can alter their ‘functional identity’ over time, with 20% of genes not matching to themselves (by semantic similarity) after 2 years. We further find that annotation bias in GO, in which some genes are more characterized than others, has declined in yeast, but generally increased in humans. Finally, we discovered that many entries in protein interaction databases are owing to the same published reports that are used for GO annotations, with 66% of assessed GO groups exhibiting this confound. We provide a case study to illustrate how this information can be used in analyses of gene sets and networks. Availability: Data available at http://chibi.ubc.ca/assessGO . Contact: paul@chibi.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 5
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    FTO, obesity and the adolescent brain (2013)
    Oxford University Press
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    Publication Date: 2013-02-02
    Description: Genetic variations in fat mass- and obesity ( FTO )-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO . We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype–phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF ( P = 0.002) and lower brain volume ( P = 0.005)]. The same allele was also associated with higher lean body mass ( P = 0.03) and no difference in height ( P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 x 10 –6 . These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples ( n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 x 10 –9 . Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 6
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    Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data (2012)
    Oxford University Press
    Details
    Publication Date: 2012-08-25
    Description: : Gemma is a database, analysis software system and web site for genomics data re-use and meta-analysis. Currently, Gemma contains analyzed data from over 3300 expression profiling studies, yielding hundreds of millions of differential expression results and coexpression patterns (correlated expression) for retrieval and visualization. With optional registration users can save their own data and securely share it with other users. Web services and integration with third-party resources further increase the scope of the tools, which include a Cytoscape plugin. Availability: http://chibi.ubc.ca/Gemma , Apache 2.0 license. Contact: paul@chibi.ubc.ca
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 7
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    Measuring the wisdom of the crowds in network-based gene function inference (2015)
    Oxford University Press
    Details
    Publication Date: 2015-02-27
    Description: Motivation: Network-based gene function inference methods have proliferated in recent years, but measurable progress remains elusive. We wished to better explore performance trends by controlling data and algorithm implementation, with a particular focus on the performance of aggregate predictions. Results: Hypothesizing that popular methods would perform well without hand-tuning, we used well-characterized algorithms to produce verifiably ‘untweaked’ results. We find that most state-of-the-art machine learning methods obtain ‘gold standard’ performance as measured in critical assessments in defined tasks. Across a broad range of tests, we see close alignment in algorithm performances after controlling for the underlying data being used. We find that algorithm aggregation provides only modest benefits, with a 17% increase in area under the ROC (AUROC) above the mean AUROC. In contrast, data aggregation gains are enormous with an 88% improvement in mean AUROC. Altogether, we find substantial evidence to support the view that additional algorithm development has little to offer for gene function prediction. Availability and implementation: The supplementary information contains a description of the algorithms, the network data parsed from different biological data resources and a guide to the source code (available at: http://gillislab.cshl.edu/supplements/ ). Contact: jgillis@cshl.edu
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 8
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    A Theorem on Irregular Linearly Measurable Sets of Points (1935)
    Oxford University Press
    Details
    Publication Date: 1935-07-01
    Print ISSN: 0024-6107
    Electronic ISSN: 1469-7750
    Topics: Mathematics
    Published by Oxford University Press
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  • 9
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    Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015)
    Oxford University Press
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    Publication Date: 2015-02-28
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 10
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    Positive and negative forms of replicability in gene network analysis (2015)
    Oxford University Press
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    Publication Date: 2015-12-14
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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