BackgroundA major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.ResultsWe conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.ConclusionsThe top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1037-6) contains supplementary material, which is available to authorized users.
Motivation: Fusion genes created by genomic rearrangements can be potent drivers of tumorigenesis.
Motivation: Gene blocks are genes co-located on the chromosome. In many cases, gene blocks are conserved between bacterial species, sometimes as operons, when genes are co-transcribed. The conservation is rarely absolute: gene loss, gain, duplication, block splitting and block fusion are frequently observed. An open question in bacterial molecular evolution is that of the formation and breakup of gene blocks, for which several models have been proposed. These models, however, are not generally applicable to all types of gene blocks, and consequently cannot be used to broadly compare and study gene block evolution. To address this problem, we introduce an event-based method for tracking gene block evolution in bacteria.Results: We show here that the evolution of gene blocks in proteobacteria can be described by a small set of events. Those include the insertion of genes into, or the splitting of genes out of a gene block, gene loss, and gene duplication. We show how the event-based method of gene block evolution allows us to determine the evolutionary rateand may be used to trace the ancestral states of their formation. We conclude that the event-based method can be used to help us understand the formation of these important bacterial genomic structures.Availability and implementation: The software is available under GPLv3 license on http://github.com/reamdc1/gene_block_evolution.git. Supplementary online material: http://iddo-friedberg.net/operon-evolutionContact: i.friedberg@miamioh.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Background In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of ‘brain to behaviour’ pathogenic mechanisms, remains largely unknown. Methods We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases). Results We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10–11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10–11, oligodendrocyte, p < 1.31 × 10–09). Conclusion Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.
The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10−57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1–100 kb size range (p-value < 9.080 × 10−67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10−9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.
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