Biology subjects

Barton, G. J.

Publications and source records attributed to Barton, G. J..

5 recordsLinked to original sources

bioRxiv2018

Proteus: an R package for downstream analysis of MaxQuant output

Proteus is a package for downstream analysis of MaxQuant evidence data in the R environment. It provides tools for peptide and protein aggregation, quality checks, data exploration and visualisation. Interactive analysis is implemented in the Shiny framework, where individual peptides or protein may be examined in the context of a volcano plot. Proteus performs differential expression analysis with the well-established tool limma, which offers robust treatment of missing data, frequently encountered in label-free mass-spectrometry experiments. We demonstrate on real and simulated data that limma results in improved sensitivity over random imputation combined with a t-test as implemented in the popular package Perseus. Embedding Proteus in R provides access to a wide selection of statistical and graphical tools for further analysis and reproducibility by scripting. Availability and implementation: The open-source R package, including example data and tutorials, is available to install from GitHub (https://github.com/bartongroup/proteus).

bioinformatics↗

bioRxiv2017

Identifying differential isoform abundance with RATs: a universal tool and a warning

MotivationThe biological importance of changes in gene and transcript expression is well recognised and is reflected by the wide variety of tools available to characterise these changes. Regulation via Differential Transcript Usage (DTU) is emerging as an important phenomenon. Several tools exist for the detection of DTU from read alignment or assembly data, but options for detection of DTU from alignment-free quantifications are limited.\n\nResultsWe present an R package named RATs - (Relative Abundance of Transcripts) - that identifies DTU transcriptome-wide directly from transcript abundance estimations. RATs is agnostic to quantification methods and exploits bootstrapped quantifications, if available, to inform the significance of detected DTU events. RATs contextualises the DTU results and shows good False Discovery performance (median FDR [≤]0.05) at all replication levels. We applied RATs to a human RNA-seq dataset associated with idiopathic pulmonary fibrosis with three DTU events validated by qRT-PCR. RATs found all three genes exhibited statistically significant changes in isoform proportions based on Ensembl v60 annotations, but the DTU for two were not reliably reproduced across bootstrapped quantifications. RATs also identified 500 novel DTU events that are enriched for eleven GO terms related to regulation of the response to stimulus, regulation of immune system processes, and symbiosis/parasitism. Repeating this analysis with the Ensembl v87 annotation showed the isoform abundance profiles of two of the three validated DTU genes changed radically. RATs identified 414 novel DTU events that are enriched for five GO terms, none of which are in common with those previously identified. Only 141 of the DTU evens are common between the two analyses, and only 8 are among the 248 reported by the original study. Furthermore, the original qRT-PCR probes no longer match uniquely to their original transcripts, calling into question the interpretation of these data. We suggest parallel full-length isoform sequencing, annotation pre-filtering and sequencing of the transcripts captured by qRT-PCR primers as possible ways to improve the validation of RNA-seq results in future experiments.\n\nAvailabilityThe package is available through Github at https://github.com/bartongroup/Rats.

bioinformatics↗

bioRxiv2017

Human Missense Variation is Constrained by Domain Structure and Highlights Functional and Pathogenic Residues

Human genome sequencing has generated population variant datasets containing millions of variants from hundreds of thousands of individuals1-3. The datasets show the genomic distribution of genetic variation to be influenced on genic and sub-genic scales by gene essentiality,1,4,5 protein domain architecture6 and the presence of genomic features such as splice donor/acceptor sites.2 However, the variant data are still too sparse to provide a comparative picture of genetic variation between individual protein residues in the proteome.1,6 Here, we overcome this sparsity for [~]25,000 human protein domains in 1,291 domain families by aggregating variants over equivalent positions (columns) in multiple sequence alignments of sequence-similar (paralagous) domains7,8. We then compare the resulting variation profiles from the human population to residue conservation across all species9 and find that the same tertiary structural and functional pressures that affect amino acid conservation during domain evolution constrain missense variant distributions. Thus, depletion of missense variants at a position implies that it is structurally or functionally important. We find such positions are enriched in known disease-associated variants (OR = 2.83, p {approx} 0) while positions that are both missense depleted and evolutionary conserved are further enriched in disease-associated variants (OR = 1.85, p = 3.3x10-17) compared to those that are only evolutionary conserved (OR = 1.29, p = 4.5x10-19). Unexpectedly, a subset of evolutionary Unconserved positions are Missense Depleted in human (UMD positions) and these are also enriched in pathogenic variants (OR = 1.74, p = 0.02). UMD positions are further differentiated from other unconserved residues in that they are enriched in ligand, DNA and protein binding interactions (OR = 1.59, p = 0.003), which suggests this stratification can identify functionally important positions. A different class of positions that are Conserved and Missense Enriched (CME) show an enrichment of ClinVar risk factor variants (OR = 2.27, p = 0.004). We illustrate these principles with the G-Protein Coupled Receptor (GPCR) family, Nuclear Receptor Ligand Binding Domain family and In Between Ring-Finger (IBR) domains and list a total of 343 UMD positions in 211 domain families. This study will have broad applications to: (a) providing focus for functional studies of specific proteins by mutagenesis; (b) refining pathogenicity prediction models; (c) highlighting which residue interactions to target when refining the specificity of small-molecule drugs.

genetics↗

bioRxiv2017

A study of the structural properties of sites modified by the O-linked 6-N-acetylglucosamine transferase

Protein O-GlcNAcylation (O-GlcNAc) is an essential post-translational modification (PTM) in higher eukaryotes. The O-linked {beta}-N-acetylglucosamine transferase (OGT), targets specific Serines and Threonines (S/T) in intracellular proteins. However, unlike phosphorylation, fewer than 25% of known O-GlcNAc sites match a clear sequence pattern. Accordingly, the three dimensional structures of O-GlcNAc sites were characterised to investigate the role of structure in molecular recognition. Of the 143/1,584 O-GlcNAc sites in 620 proteins were mapped to protein X-ray structures. The modified S/T were 1.7x more likely to be annotated in the REM465 field which defines missing residues in a protein structure, while 7 O-GlcNAc sites were solvent inaccessible and unlikely to be targeted by OGT. The 132/143 sites with complete backbone atoms clustered into 10 groups, but these were indistinguishable from clusters from unmodified S/T. This suggests there is no prevalent three-dimensional motif for OGT recognition. Predicted features from the 620 proteins were compared to unmodified S/T in O-GlcNAcylated proteins and globular proteins. The Jpred4 predicted secondary structure shows that modified S/T were more likely to be coils. 5/6 methods to predict intrinsic disorder indicated O-GlcNAcylated S/T to be significantly more disordered than unmodified S/T. Although the analysis did not find a pattern in the site three-dimensional structure, it revealed the residues around the modification site are likely to be disordered and suggests a potential role of secondary structure elements in OGT site recognition.

bioinformatics↗

bioRxiv2016

How well do RNA-Seq differential gene expression tools perform in higher eukaryotes?

RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, Differential Gene Expression (DGE) tools typically assume the form of the underlying distribution of gene expression. A recent highly replicated study revealed that RNA-seq gene expression measurements in yeast are best represented as being drawn from an underlying negative binomial distribution. In this paper, the statistical properties of gene expression in the higher eukaryote Arabidopsis thaliana are shown to be essentially identical to those from yeast despite the large increase in the size and complexity of the transcriptome: Gene expression measurements from this model plant species are consistent with being drawn from an underlying negative binomial or log-normal distribution and the false positive rate performance of nine widely used DGE tools is not strongly affected by the additional size and complexity of the A. thaliana transcriptome. For RNA-seq data, we therefore recommend the use of DGE tools that are based on the negative binomial distribution.

genomics↗