Biology subjects

Shepherd, L.

Publications and source records attributed to Shepherd, L..

4 recordsLinked to original sources

bioRxiv2023

Ancient mitogenomes reveal evidence for the late Miocene dispersal of mergansers to the Southern Hemisphere

Mergansers are riverine and coastal piscivorous ducks that are widespread throughout North America and Eurasia but uncommon in the Southern Hemisphere. One species occurs in South America and at least two extinct species from New Zealand. It has been proposed that Southern Hemisphere mergansers were founded by independent dispersal events from the Northern Hemisphere. However, some morphological and behavioural evidence suggests that Southern Hemisphere mergansers may be closely related to one another. They share several characteristics that differ from Northern Hemisphere mergansers (e.g., non-migratory vs. migratory, sexual monochromatism vs. sexual dichromatism, long vs. short pair bonds). We sequenced complete mitogenomes from the Brazilian merganser and an extinct merganser from New Zealand--the Auckland Island merganser. Our results show the Brazilian and Auckland Island mergansers form a monophyletic clade with the common merganser, and that a sister relationship between Southern Hemisphere mergansers cannot be rejected. We cannot exclude the possibility that the Brazilian and Auckland Island mergansers descend from a single dispersal event from the Northern Hemisphere at least seven million years ago. Nuclear (palaeo)genomic data may help to resolve the relationship between living and extinct mergansers, including merganser fossils from New Zealand that have not been subjected to palaeogenetic analysis.

genetics↗

bioRxiv2019

Tximeta: reference sequence checksums for provenance identification in RNA-seq

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

bioinformatics↗

bioRxiv2019

BiocPkgTools: Toolkit for Mining the Bioconductor Package Ecosystem

MotivationThe Bioconductor project, a large collection of open source software for the comprehension of large-scale biological data, continues to grow with new packages added each week, motivating the development of software tools focused on exposing package metadata to developers and users. The resulting BiocPkgTools package facilitates access to extensive metadata in computable form covering the Bioconductor package ecosystem, facilitating downstream applications such as custom reporting, data and text mining of Bioconductor package text descriptions, graph analytics over package dependencies, and custom search approaches.\n\nResultsThe BiocPkgTools package has been incorporated into the Bioconductor project, installs using standard procedures, and runs on any system supporting R. It provides functions to load detailed package metadata, longitudinal package download statistics, package dependencies, and Bioconductor build reports, all in \"tidy data\" form. BiocPkgTools can convert from tidy data structures to graph structures, enabling graphbased analytics and visualization. An end-user-friendly graphical package explorer aids in task-centric package discovery. Full documentation and example use cases are included.\n\nAvailabilityThe BiocPkgTools software and complete documentation are available from Bioconductor (https://bioconductor.org/packages/BiocPkgTools).

bioinformatics↗

bioRxiv2018

HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor

Phase 1 of the NIH Human Microbiome Project (HMP) investigated 18 body subsites of 239 healthy American adults, to produce the first comprehensive reference for the composition and variation of the \"healthy\" human microbiome. Publicly-available data sets from amplicon sequencing of two 16S rRNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

bioinformatics↗