Biology subjects

Holmes, I.

Publications and source records attributed to Holmes, I..

14 recordsLinked to original sources

bioRxiv2026

Apollo 3: Multi-Species Genome Curation

We present Apollo 3, a new manual genome annotation tool that integrates with the JBrowse 2 genome browser. Its functionality is inspired by existing manual genome annotation tools such as Apollo, Artemis, and Otter, but uses an updated and more scalable architecture and technology stack. It allows the simultaneous editing of multiple genomes, including the visualization of synteny to inform those annotations. Apollo 3 can be used as a standalone annotation editor, or it can be installed on a server and used collaboratively. We describe the application's design, features, and use cases.

genomics↗

bioRxiv2026

Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution

Most statistical phylogenetics analyses use relatively simple continuous-time finite-state Markov models of point substitution to describe molecular evolution, often keeping sequence length fixed, ignoring insertions and deletions (indels) entirely, and making little (if any) allowance for variations in selection pressure due to interactions between amino acids. The simplistic assumptions of these models limit the realism of phylogenetics. We extend the TKF92 model - the canonical hierarchical model combining an outer birth-death process for indels with an inner finite-state Markov chain for substitutions - by introducing additional nesting and latent states, allowing for structural heterogeneity. We compare these TKF92 extensions (which are derived as exact solutions of instantaneous processes, and in which evolutionary time naturally appears as a matrix exponential coefficient) to two classes of neural seq2seq model that are not derived in such a way, but instead take evolutionary time as an input feature: the first class of model being constrained to enforce a TKF92-like structure, and the second lacking any such constraint. We evaluate the per-character perplexities of all models on splits of the PFam database of aligned protein domains. A nested TKF-based model with only 32,000 parameters is highly competitive with neural networks containing tens of millions of parameters, outperforming all but two of the neural architectures tested. Our results indicate that approaches grounded in molecular evolutionary theory may be more parameter-efficient and provide a better fit to real alignments than unconstrained alternatives, supporting the incorporation of CTMC-based model structure within future neural phylogenetic approaches.

bioinformatics↗

bioRxiv2022

JBrowse 2: A modular genome browser with views of synteny and structural variation

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. JBrowse 2 retains the core features of the open-source JavaScript genome browser JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. The software readily allows users to share sessions, open multiple genomes or views, and navigate quickly between these views. It can be embedded in a web page, used as a standalone desktop application, or run from Jupyter notebooks or R sessions. Using a plugin framework, developers can create new data adapters, track types, and visualizations. These improvements are enabled by a ground-up redesign of the JBrowse architecture using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

bioinformatics↗

bioRxiv2022

CellScape: Protein structure visualization with vector graphics cartoons

MotivationIllustrative renderings of proteins are useful aids for scientific communication and education. Nevertheless, few software packages exist to automate the generation of these visualizations. ResultsWe introduce CellScape, a tool designed to generate 2D molecular cartoons from atomic coordinates and combine them into larger cellular scenes. These illustrations can outline protein regions in different levels of detail. Unlike most molecular visualization tools which use raster image formats, these illustrations are represented as vector graphics, making them easily editable and composable with other graphics. Availability and ImplementationCellScape is implemented in Python 3 and freely available at https://github.com/jordisr/cellscape. It can be run as a command-line tool or interactively in a Jupyter notebook. Contactjordisr@berkeley.edu

bioinformatics↗

bioRxiv2022

The salivary, metal-binding peptide histatin-5 buffers extracellular copper availability

Antimicrobial peptides (AMPs) are key components of diverse host innate immune systems. The family of human salivary AMPs known as histatins bind Zn and Cu. Fluctuations in Zn and Cu availability play significant roles in the host innate immune response (so-called "nutritional immunity"). Thus, we hypothesised that histatins contribute to nutritional immunity by influencing host Zn and/or Cu availability. We posited that histatins limit Zn availability (promote bacterial Zn starvation) and/or raise Cu availability (promote bacterial Cu poisoning). To test this hypothesis, we examined the interactions between histatin-5 (Hst5) and Group A Streptococcus (GAS), which colonises the human oropharynx. Our results showed that Hst5 does not strongly influence Zn availability. Hst5 did not induce expression of Zn-responsive genes in GAS, nor did it suppress growth of mutant strains that are impaired in Zn transport. Biochemical examination of purified peptides confirmed that Hst5 binds Zn only weakly. By contrast, Hst5 bound Cu tightly and it strongly influenced Cu availability. However, Hst5 did not promote Cu toxicity. Instead, Hst5 suppressed expression of Cu-inducible genes, stopped intracellular accumulation of Cu, and rescued growth of a {Delta}copA mutant strain that is impaired in Cu efflux. We thus proposed a new role for salivary histatins as major Cu buffers in saliva that contribute to microbial homeostasis in the oral cavity and oropharynx by reducing the potential negative effects of Cu exposure (e.g. from food) to microbes. Our results raise broad questions regarding the physiological roles of diverse metal-binding AMPs and the management of host metal availability during host-microbe interactions.

microbiology↗

bioRxiv2021

Evidence of stock connectivity, hybridization and misidentification in white anglerfish support the need for adopting a genetics-informed fisheries management approach

Understanding population connectivity within a species as well as potential interactions with its close relatives is crucial to define management units and to derive efficient management actions. However, although genetics can reveal mismatches between biological and management units and other relevant but hidden information such as species misidentification or hybridization, the uptake of genetic methods by the fisheries management process is far from having been consolidated. Here, we have assessed the power of genetics to better understand the population connectivity of white angelfish (Lophius piscatorius) and its interaction with its sister species, the black anglerfish (L. budegassa). Our analyses, based on thousands of genome-wide single nucleotide polymorphisms, show three findings that are crucial for white anglerfish management. We found i) that white anglerfish is likely composed of a single panmictic population throughout the Northeast Atlantic, challenging the three-stock based management, ii) that a fraction of specimens classified as white anglerfish using morphological characteristics are genetically identified as black anglerfish (L. budegassa) and iii) that the two Lophius species naturally hybridize leading to a population of hybrids of up to 20% in certain areas. Our results set the basics for a genetics-informed white anglerfish assessment framework that accounts for stock connectivity, revises and establishes new diagnostic characters for Lophius species identification and evaluates the effect of hybrids in the current and future assessments of the white anglerfish. Furthermore, our study contributes to provide additional evidence of the potentially negative consequences of ignoring genetic data for assessing fisheries resources.

evolutionary biology↗

bioRxiv2021

CNVpytor: a tool for CNV/CNA detection and analysis from read depth and allele imbalance in whole genome sequencing

Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth (RD). Herein, we present an extension of CNVnator developed in Python -- CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency (BAF) likelihood information from single nucleotide polymorphism and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number neutral losses of heterozygosity. CNVpytor is significantly faster than CNVnator--particularly for parsing alignment files (2 to 20 times faster)--and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria and annotated. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

bioinformatics↗

bioRxiv2020

Approximation of Indel Evolution by Differential Calculus of Finite State Automata

We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by Pair Hidden Markov Models (Pair HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating Pair HMM. The TKF model emerges as an exact solution to these equations for the special case of single-residue indels. For the general case, the equations can be solved by numerical integration. Using simulated data we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by Expectation-Maximization. Our code and data are available at https://github.com/ihh/trajectory-likelihood.

bioinformatics↗

bioRxiv2020

Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing

Nanopore technology allows for direct sequencing of individual DNA duplexes. However, its higher error rate compared to other sequencing methods has limited its application in situations where deep coverage is unavailable, such as detection of rare variants or characterization of highly polymorphic samples. In principle, 2X coverage is available even for single duplexes, using Oxford Nanopore Technologies 1D2 protocol or related methods which sequence both strands of the duplex consecutively. Using both strands should improve accuracy; however, most neural network basecaller architectures are designed to operate on single strands. We have developed a general approach for improving accuracy of 1D2 and related protocols by finding the consensus of two neural network basecallers, by combining a constrained profile-profile alignment with a heuristic variant of beam search. When run on a basecalling neural network we trained, our consensus algorithm improves median basecall accuracy from 86.2% (for single-read decoding) to 92.1% (for pair decoding). Our software can readily be adapted to work with the output of other basecallers, such as the recently released Bonito basecaller. Although Bonito operates only on individual strands and was not designed to leverage the 1D2 protocol, our method lifts its median accuracy from 93.3% to 97.7%, more than halving the median error rate. This surpasses the maximum accuracy achievable with Guppy, an alternate basecaller which was designed to include pair decoding of 1D2 reads. Our software PoreOver, including both our neural network basecaller and our consensus pair decoder (which can be separably applied to improve other basecallers), is implemented in Python 3 and C++11 and is freely available at https://github.com/jordisr/poreover.

bioinformatics↗

bioRxiv2020

Machine Boss: Rapid Prototyping of Bioinformatic Automata

MotivationMany C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. ResultsWe present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line "recipes" for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage, and nanopore sequence analysis. Availability and ImplementationMachine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/. ContactIan Holmes, ihh@berkeley.edu

bioinformatics↗

bioRxiv2019

JBrowse Connect: A server API to connect JBrowse instances and users

We describe JBrowse Connect, an optional expansion to JBrowse that allows live messaging via WebSockets, notifications for new annotation tracks, heavy-duty analyses initiated by the user from within the browser, and other dynamic features. We present JBlast, an example application of JBrowse Connect that allows users to specify and execute BLAST searches, managed by a Galaxy instance, as well as tracking job progress and viewing results, all in the context of the browser.

bioinformatics↗

bioRxiv2019

Apollo: Democratizing genome annotation

Genome annotation is the process of identifying the location and function of a genomes encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.\n\nIn this paper we first outline some of Apollos newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. O_LISource: https://github.com/GMOD/Apollo\nC_LIO_LILicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.md\nC_LIO_LIDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apollo\nC_LIO_LIRequirements: JDK 1.8, Node v6.0+\nC_LIO_LIUser guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/\nC_LIO_LIMailing list: apollo@lists.lbl.gov\nC_LI

bioinformatics↗

bioRxiv2018

Estimation of Rearrangement Break Rates Across the Genome

Genomic rearrangements provide an important source of novel functions by recombining genes and motifs throughout and between genomes. However, understanding how rearrangement functions to shape genomes is hard because reconstructing rearrangements is a combinatoric problem which often has many solutions. In lieu of reconstructing the history of rearrangements, we answer the question of where rearrangements are occurring in the genome by remaining agnostic to the types of rearrangement and solving the simpler problem of estimating the rate at which double-strand breaks occur at every site in a genome. We phrase this problem in graph theoretic terms and find that it is a special case of the minimum cover problem for an interval graph. We employ and modify existing algorithms for efficiently solving this problem. We implement this method as a Python program, named BRAG, and use it to estimate the break rates in the genome of the model Ascomycete mold, Neurospora crassa. We find evidence that rearrangements are more common in the subtelomeric regions of the chromosomes, which facilitates the evolution of novel genes.

genomics↗

bioRxiv2017

WTFgenes: What's The Function of these genes? Static sites for model-based gene set analysis

A common technique for interpreting experimentally-identified lists of genes is to look for enrichment of genes associated to particular ontology terms. The most common technique uses the hypergeometric distribution; more recently, a model-based approach was proposed. These approaches must typically be run using downloaded software, or on a server. We develop a collapsed likelihood for model-based gene set analysis and present WTFgenes, an implementation of both hypergeometric and model-based approaches, that can be published as a static site with computation run in JavaScript on the user's web browser client. Apart from hosting files, zero server resources are required: the site can (for example) be served directly from Amazon S3 or GitHub Pages. A C++11 implementation yielding identical results runs roughly twice as fast as the JavaScript version. WTFgenes is available from https://github.com/evoldoers/wtfgenes under the BSD3 license. A demonstration for the Gene Ontology is usable at https://evoldoers.github.io/wtfgo. Contact: Ian Holmes ihholmes+wtfgenes@gmail.com.

bioinformatics↗