Biology subjects

Keenan, S.

Publications and source records attributed to Keenan, S..

4 recordsLinked to original sources

bioRxiv2026

RNAseK is vital for epithelial proton pump activity in Drosophila melanogaster

The transporting epithelial tissues comprising the Drosophila melanogaster alimentary and renal systems are known to share a common set of enriched genes sometimes referred to as the "epitheliome", reflecting their shared transport functions. Core amongst these genes are the vha genes, which encode subunits of the large Vacuolar-type ATPase (V-ATPase) proton pump complex. However, many of the non-vha components of the epitheliome remain broadly uncharacterised. Here, we explore the role of RNAseK, a gene identified during unbiased epithelial screens in Drosophila whose function within insects is not yet known, though evidence from mammalian systems suggest a role in supporting proton pump activity. We demonstrate computationally that RNAseK is strongly conserved across evolutionary history, and that expression is regulated by the highly epithelial-specific dCLEAR motif. Seeking to understand why epithelial expression is so emphasised, we have assayed the effects of RNAseK knockdown in different epithelia throughout the fly. Across hindgut, midgut, and Malpighian tubules, we note profound defects in gross tissue morphology, transport activity, and fly survival. Mechanistically, our findings that RNAseK may co-localise with the V-ATPase complex, and that V-ATPase inhibition phenocopies RNAseK knockdown, suggest that RNAseK is a critical component of the proton transport axis across Drosophila tissues. Summary statementRNAseK is enriched throughout, and required in, Drosophila melanogaster epithelial tissues. Molecular evidence and evolutionary inferences suggest this is due to a role in the proton transport axis.

cell biology↗

bioRxiv2022

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: the case of bonobo calls

Despite the accumulation of data and studies, deciphering animal vocal communication remains highly challenging. While progress has been made with some species for which we now understand the information exchanged through vocal signals, researchers are still left struggling with sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets offer a valuable but distorted vision of communication systems. Adopting the best practices in their analysis is therefore essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces along with three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. We highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Finally, using a Uniform Manifold Approximation and Projection (UMAP), we show that classifiers generate parsimonious data descriptions which help to understand the clustering of the bonobo acoustic space. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) adopting Support Vector Machines as the baseline classification approach; iii) explicitly evaluating data leakage and possibly implementing a mitigation strategy; iv) visualizing the dataset with UMAPs applied to classifier predictions rather than to raw acoustic features.

animal behavior and cognition↗

bioRxiv2017

Genome Graphs

There is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

bioinformatics↗

bioRxiv2016

Ensembl Core Software Resources: storage and programmatic access for DNA sequence and genome annotation

The Ensembl software resources are a stable infrastructure to store, access and manipulate genome assemblies and their functional annotations. The Ensembl \"Core\" database and Application Programming Interface (API) was our first major piece of software infrastructure and remains at the centre of all of our genome resources. Since its initial design more than fifteen years ago, the number of publicly available genomic, transcriptomic and proteomic datasets has grown enormously, accelerated by continuous advances in DNA sequencing technology. Initially intended to provide annotation for the reference human genome, we have extended our framework to support the genomes of all species as well as richer assembly models. Cross-referenced links to other informatics resources facilitate searching our database with a variety of popular identifiers such as UniProt and RefSeq. Our comprehensive and robust framework storing a large diversity of genome annotations in one location serves as a platform for other groups to generate and maintain their own tailored annotation. Our databases and APIs are publicly available and all of our source code is released with a permissive Apache v2.0 licence at http://github.com/Ensembl.

bioinformatics↗