Peptide occurences at different lengths, found in UniProt Feb 2018 release (UniProtKB/Swiss-Prot 2018_02 - February 28, 2018). An output of the Leverhulme funded project: Understanding the biological ramifications of a 'forbidden' tetramer peptide nullomer. The dataset was generated using version 1.0.0 of the NullPeptides software (Git Hub repo: https://github.com/stevenshave/NullomerPeptides)

This is all the data associated with the paper 'Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast': a description of the cell segmentation software developed in the Swain Laboratory for segmenting microscopy images of yeast (Saccharomyces cerevisiae) cells in the ALCATRAS microfluidic device. Included are the raw images, ground truth segmentation and the automated segmentations of all the software tested. The format is the data structure used with the published software (available at https://github.com/pswain/segmentation-software). The data set includes images both from our own lab (https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx550/4103414/Morphologically-Constrained-and-Data-Informed-Cell) and from that of Professor Nan Hao (in Press at PNAS, 10.1073/pnas.1703379114). Please cite both.

This dataset is simulated metagenomic data created by Rebecca (Becky) Smith, PhD student at the Roslin Institute in Mick Watson's group. This data is described in detail in Smith et al. 2022, but briefly these reads were simulated using InSilicoSeq (https://doi.org/10.1093/bioinformatics/bty630) with the hiseq exponential model, and 150bp. The genomes used to create this data are from the Hungate Collection (paper at https://www.nature.com/articles/nbt.4110 and sequences at https://genome.jgi.doe.gov/portal/HungateCollection/HungateCollection.info.html ).

BioJS is an open source library of web components for visualisation of biological data in the web. A presentation given at the BiVi conference, Edinburgh 17-12-2014.

Supplementary data for Latysheva N, Junker VL, Palmer WJ, Codd GA and Barker D, 'The evolution of nitrogen fixation in cyanobacteria' (2012, Bioinformatics 28:603-606, doi:10.1093/bioinformatics/bts008). This supplementary data is the corrected version of March 2012, which supersedes the version on the Web site of the journal Bioinformatics. For further details, see 'README.pdf' within the zipfile.

Bone is now regarded to be a key regulator of a number of metabolic processes, in addition to the regulation of mineral metabolism. However, our understanding of complex bone metabolic interactions at a systems level remains rudimentary. In vitro molecular biology and bioinformatics approaches have frequently been used to understand the mechanistic changes underlying disease at the cell level, however, these approaches lack the capability to interrogate dynamic multi-bone metabolic interactions in vivo. Here we present a novel and integrative approach to understand complex bone metabolic interactions in vivo using total-body positron emission tomography (PET) network analysis of murine 18F-FDG scans, as a biomarker of glucose metabolism in bones. In this report we show that different bones within the skeleton have a unique glucose metabolism and form a complex metabolic network, which could not be identified using single tissue simplistic PET standard uptake values analysis. The application of our approach could reveal new physiological and pathological tissue interactions beyond skeletal metabolism, due to PET radiotracers diversity and the advent of clinical total-body PET systems. Karla J. Suchacki, Carlos J. Alcaide-Corral, Samah Nimale, Mark G. Macaskill, Roland H. Stimson, Colin Farquharson, Tom C. Freeman and Adriana A. S. Tavares

The three-dimensional organisation of chromosomes can be probed using methods such as Capture-C. However it is unclear how such population level data relates to the organisation within a single cell, and the mechanisms leading to the observed interactions are still largely obscure. We present a polymer modelling scheme based on the assumption that chromosome architecture is maintained by protein bridges which form chromatin loops. To test the model we perform FISH experiments and also compare with Capture-C data. Starting merely from the locations of protein binding sites, our model accurately predicts the experimentally observed chromatin interactions, revealing a population of 3D conformations.

A comprehensive biochemical knowledge-base on human metabolism, this community-driven, consensus metabolic reconstruction integrates metabolic information from five different resources: * Recon 1, a global human metabolic reconstruction (Duarte et al, PNAS, 104(6), 1777-1782, 2007) * EHMN, Edinburgh Human Metabolic Network (Hao et al., BMC Bioinformatics 11, 393, 2010) * HepatoNet1, a liver metabolic reconstruction (Gille et al., Molecular Systems Biology 6, 411, 2010), * Ac/FAO module, an acylcarnitine/fatty acid oxidation module (Sahoo et al., Molecular bioSystems 8, 2545-2558, 2012), * a human small intestinal enterocytes reconstruction (Sahoo and Thiele, submitted). Additionally, more than 370 transport and exchange reactions were added, based on a literature review. Recon 2 is fully semantically annotated (Le Nov��re, N. et al. Nat Biotechnol 23, 1509-1515, 2005) with references to persistent and publicly available chemical and gene databases, unambiguously identifying its components and increasing its applicability for third-party users. Here you can explore the content of the reconstruction by searching/browsing metabolites and reactions. Recon 2 predictive model is available in the Systems Biology Markup Language format.