Biology subjects

Colby, S.

Publications and source records attributed to Colby, S..

4 recordsLinked to original sources

bioRxiv2026

Emended description of the genus Henriciella: morphological and phylogenetic analysis identifies two subgenera with different reproductive strategies

Henriciella is the only genus with reported non-prosthecate members in the family Hyphomonadaceae, a large group of dimorphic prosthecate bacteria. Using type strains, we reinvestigated growth in media shown to support prosthecate morphotypes in marine caulobacters. We report that Marine Broth (MB) and Peptone Yeast Extract (PYE)-derived media supported prosthecate populations for all Henriciella species. Surprisingly, we observed that H. aquimarina, H. mobilis, and H. pelagia reproduce by tip budding whereas H. marina and H. liltoralis reproduce by asymmetric binary fission. These two observed reproductive strategies resemble those characterized for the model prosthecate bacteria Hyphomonas neptunium and Caulobacter crescentus. Phylogenomic analysis indicates that H. marina, H. litoralis, H. algicola, and H. barbarensis form a monophyletic subgenus that reproduces by asymmetric binary fission. We propose genus and species emendations that correct the record on this fascinating genus.

microbiology↗

bioRxiv2022

MerCat2: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from omics data

SummaryMerCat2 ("Mer - Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs k-mer counting of any length k, resulting in feature abundance counts tables, quality control reports, protein feature metrics, ecological diversity metrics, and graphical representation (i.e., PCA). MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons. Availability and implementationMerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: conda install MerCat2. ContactRichard Allen White III, UNC Charlotte, rwhit101@uncc.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Vanishing white matter disease expression of truncated EIF2B5 activates induced stress response

Vanishing White Matter disease (VWM) is a severe leukodystrophy of the central nervous system caused by mutations in subunits of the eukaryotic initiation factor 2B complex (eIF2B). Current models only partially recapitulate key disease features, and pathophysiology is poorly understood. Through development and validation of zebrafish (Danio rerio) models of VWM, we demonstrate that zebrafish eif2b mutants phenocopy VWM, including impaired somatic growth, early lethality, impaired myelination, loss of oligodendrocyte precursor cells, increased apoptosis in the CNS, and impaired motor swimming behavior. Expression of human EIF2B2 in the zebrafish eif2b2 mutant rescues lethality and CNS apoptosis, demonstrating conservation of function between zebrafish and human. In the mutants, intron 12 retention leads to expression of a truncated eif2b5 transcript. Expression of the truncated eif2b5 in wild-type larva impairs motor behavior and activates the ISR, suggesting that a feed-forward mechanism in VWM is a significant component of disease pathophysiology.

physiology↗

bioRxiv2019

Integration of Structured Biological Data Sources using Biological Expression Language

BackgroundThe integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology.\n\nResultsWe have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI.\n\nConclusionsThe philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.\n\nTweetA suite of independent Python packages for downloading, parsing, warehousing, and converting multi-modal and multi-scale biological databases to Biological Expression Language

bioinformatics↗