Biology subjects

McCarthy, J.

Publications and source records attributed to McCarthy, J..

22 records · Page 2Linked to original sources

bioRxiv2019

Incorporating prior information into signal-detection analyses across biologically informed gene-sets

Signal detection analyses are used to assess whether there is any evidence of signal within a large collection of hypotheses. For example, we may wish to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial information concerning the likely importance of each gene within each set. For example, deleterious variants within genes that show evidence of purifying selection are more likely to substantially affect the phenotype than genes that are not under purifying selection, at least for traits that are themselves subject to purifying selection. Here we improve such analyses by incorporating prior information into a higher-criticism-based signal detection analysis. We show that when this prior information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS), which implicates a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p-values for gene-sets containing known ALS genes.

genetics↗

bioRxiv2019

SEMA4C is a novel target to limit osteosarcoma growth, progression, and metastasis

Semaphorins, specifically type IV, are important regulators of axonal guidance and have been increasingly implicated in poor prognoses in a number of different solid cancers. In conjunction with their cognate PLXNB family receptors, type IV members have been increasingly shown to mediate oncogenic functions necessary for tumor development and malignant spread. In this study, we investigated the role of semaphorin 4C (SEMA4C) in osteosarcoma growth, progression, and metastasis. We investigated the expression and localization of SEMA4C in primary osteosarcoma patient tissues and its tumorigenic functions in these malignancies. We demonstrate that overexpression of SEMA4C promotes properties of cellular transformation, while RNAi knockdown of SEMA4C promotes adhesion and reduces cellular proliferation, colony formation, migration, wound healing, tumor growth, and lung metastasis. These phenotypic changes were accompanied by reductions in activated AKT signaling, G1 cell cycle delay, and decreases in expression of mesenchymal marker genes SNAI1, SNAI2, and TWIST1. Lastly, monoclonal antibody blockade of SEMA4C in vitro mirrored that of the genetic studies. Together, our results indicate a multi-dimensional oncogenic role for SEMA4C in metastatic osteosarcoma and more importantly that SEMA4C has actionable clinical potential.

cancer biology↗

bioRxiv2017

Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum functional genome and evolutionary origin of diatoms

Diatoms are one of the most successful and ecologically important groups of eukaryotic phytoplankton in the modern ocean. Deciphering their genomes is a key step towards better understanding of their biological innovations, evolutionary origins, and ecological underpinnings. Here, we have used 90 RNA-Seq datasets from different growth conditions combined with published expressed sequence tags and protein sequences from multiple taxa to explore the genome of the model diatom Phaeodactylum tricornutum, and introduce 1,489 novel genes. The new annotation additionally permitted the discovery for the first time of extensive alternative splicing (AS) in diatoms, including intron retention and exon skipping which increases the diversity of transcripts to regulate gene expression in response to nutrient limitations. In addition, we have used up-to-date reference sequence libraries to dissect the taxonomic origins of diatom genomes. We show that the P. tricornutum genome is replete in lineage-specific genes, with up to 47% of the gene models present only possessing orthologues in other stramenopile groups. Finally, we have performed a comprehensive de novo annotation of repetitive elements showing novel classes of TEs such as SINE, MITE, LINE and TRIM/LARD. This work provides a solid foundation for future studies of diatom gene function, evolution and ecology.

bioinformatics↗

bioRxiv2017

SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification

A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) based verification in Bos taurus cattle has been the ISAG 100 and 200 SNP panels. While these SNP sets have provided an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent for an animal at [≤]1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 Bos taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analysed different SNP densities to determine that at a minimum [≥]500 SNP are needed to consistently predict only one set of parents at a [≤]1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here a genomic sample QC pipeline to deal with the unique challenges of >1,000,000 genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise.

genetics↗