Biology subjects

Bitton, D. A.

Publications and source records attributed to Bitton, D. A..

13 recordsLinked to original sources

bioRxiv2025

Ovo, an Open-Source Ecosystem for De Novo Protein Design

The protein design field is rapidly advancing, with frequent emergence of new models and pipelines for designing de novo proteins with tailored properties and functions not found in nature. However, the current tool landscape is fragmented, tools are hard to install and deploy, and require significant computational expertise to integrate into end-to-end, scalable pipelines. A particular challenge is managing many sequences, structures, and metrics for downstream testing and retrospective analysis of input parameters. To address this need, we introduce Ovo, an open-source de novo protein design ecosystem that consolidates models, workflows, data management, and interactive visualization into a scalable, infrastructure-agnostic platform. Ovo features Nextflow-based workflow orchestration, a storage layer, and both command-line and graphical interfaces that democratize scaffold design, binder design and diversification, and validation workflows. Ovos novel ProteinQC module computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. Ovo plugins let the community add new workflows and user interfaces to accelerate adoption of emerging methods and facilitate community-driven benchmarking. Ovo lowers engineering barriers and demystifies the design process, allowing experts and non-technical users to design proteins at scale. With community-driven development, Ovo can accelerate de novo protein design and advance discovery in therapeutics and biotechnology.

bioinformatics↗

bioRxiv2025

InCytokine, an open-source software, reveals a TREM2 variant specific cytokine signature

Cytokine and chemokine profiling is central to understanding inflammatory processes and the mechanisms driving diverse diseases. We introduce InCytokine, an open-source tool for semiquantitative analysis of cytokine and chemokine data generated by protein array technologies. InCytokine features robust and modular image-processing workflows, including automated spot detection, template alignment, normalization, quality-control measures and quantitative intensity summarization to deliver consistent and reliable readouts from profiling assays. We evaluated InCytokine by profiling wild-type microglia, TREM2 knockout, and Alzheimers-associated TREM2 R47H variant cells in response of lipopolysaccharide and sulfatide exposure. Differential expression analysis revealed unique sulfatide-specific and genotype-specific cytokine signatures in TREM2 variants. We also report an intriguing modulation of DPP4 and a divergent expression pattern of ENA-78 in TREM2 variants in response to lipopolysaccharide and sulfatide treatment. Such distinct expression signatures raise the possibility that TREM2 variants may play a role in modulating inflammatory signaling relevant to cardio-metabolic and Alzheimers disease. These signatures were corroborated using transcriptional profiling of the same microglia cells, revealing also a good concordance between protein array and RNA sequencing technologies. Taken together, InCytokine is an interactive, user-friendly web application for rapid, reproducible, and scalable analysis of protein array data, proven to generate meaningful insights for drug and biomarker discovery campaigns in pharmaceutical settings.

bioinformatics↗

bioRxiv2024

AnNoBrainer, an Automated Annotation of Mouse Brain Images using Deep Learning

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that less rigorous, less time-consuming methods of histopathological assessment are employed by non-pathologists, especially for early discovery and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ([~]50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts histopathological assessment standards (>85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

neuroscience↗

bioRxiv2022

TraceTrack, an Open-Source Software for Batch Processing, Alignment and Visualization of Sanger Sequencing Chromatograms

BackgroundDespite the advent of Next Generation Sequencing technology and its widespread applications, Sanger sequencing remains instrumental for molecular biology subcloning work in biological and medical research and indispensable for drug discovery campaigns. Although Sanger sequencing technology has been long established, existing software for processing and visualization of trace file chromatograms are limited in terms of functionality, scalability, and availability for commercial use. ResultsTo fill this gap, we developed TraceTrack, an open-source web application tool for batch alignment, analysis and visualization of Sanger trace files. TraceTrack offers high throughput matching of trace files to reference sequences, rapid identification of mutations and an intuitive chromatogram analysis. Comparative analysis between TraceTrack and existing software tools highlights the advantages of TraceTrack with regards to batch processing, visualization and export functionalities. TraceTrack is available at https://github.com/Merck/TraceTrack and also at https://tracetrack.dichlab.org as a web application. ConclusionTraceTrack is a web application for batch processing and visualization of Sanger trace file chromatograms that meets the increasing demand of industrial sequence validation workflows in pharmaceutical settings.

bioinformatics↗

bioRxiv2022

mRNAid, an Open-Source Platform for Therapeutic mRNA Design and Optimization Strategies

Recent COVID-19 vaccines unleashed the potential of mRNA-based therapeutics. mRNA optimization is indispensable for reducing immunogenicity, ensuring stability, and maximizing protein output. We present mRNAid, an experimentally validated software for mRNA optimization and visualization that generates mRNA sequences with comparable if not superior characteristics to commercially optimized sequences. To encompass all aspects of mRNA design, we also interrogated the impact of uridine content, nucleoside analogs and UTRs on expression and immunogenicity.

bioinformatics↗

bioRxiv2021

PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, there are currently no adequate molecular design capabilities that inform medicinal chemistry decisions on peptide programs. More specifically, SAR (Structure-Activity Relationship) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multi-sequence alignment of non-natural amino acids and enhanced HELM (Hierarchical Editing Language for Macromolecules) visualization. Via stepwise SAR analysis of a ChEMBL peptide dataset, we demonstrate PepSeAs power to accelerate decision making in lead optimization campaigns in pharmaceutical settings. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

bioinformatics↗

bioRxiv2021

BioCompute Objects to communicate a viral detection pipeline with potential for use in a regulatory environment

The volume of nucleic acid sequence data has exploded in recent years, and with it, the challenge of finding and transforming relevant data into meaningful information. Processing the abundance of data can require a dynamic ecosystem of customized tools. As analysis pipelines become more complex, there is an increased difficulty in communicating analysis details in a way that is understandable yet of sufficient detail to make informed decisions about results or repeat the analysis. This may be of particular interest to institutions and private companies that need to communicate complex computations in a regulatory environment. To meet this need for standard reporting, the open source BioCompute framework was developed as a standardized mechanism for communicating the details of an analysis in a concise and organized way, and other tools and interfaces were subsequently developed according to the standard. The goal of BioCompute is to streamline the process of communicating computational analyses. Reports that conform to the BioCompute standard are called BioCompute Objects (BCOs). Here, a comprehensive suite of BCOs is presented, representing interconnected elements of a computation that is modeled after those that might be found in a regulatory submission, but which can be shared publicly. Because BCOs are human and machine readable, they can be displayed in customized ways to further improve their utility, and an example of a collapsible format is shown. The work presented here serves as a real world implementation that imitates actual submissions, providing concrete examples. As an example, a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines, is developed and rigorously tested to establish a rate of false positive detection, and is described in a BCO report. That pipeline relies on a specially curated database for alignment, and a set of synthetic reads for testing, both of which are also descriptively packaged in their own BCOs. All of the sufficiently complex processes associated with this analysis are therefore represented as BCOs that can be cross-referenced, demonstrating the modularity of BCOs, their ability to organize tremendous complexity, and their use in a lifelike regulatory environment.

bioinformatics↗

bioRxiv2021

BioPhi: A platform for antibody design, humanization and humanness evaluation based on natural antibody repertoires and deep learning

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains the primary route for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS database using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. Together, BioPhi offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. BioPhi is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.

bioinformatics↗

bioRxiv2020

Mutation Maker, An Open Source Oligo Design Software for Mutagenesis and De Novo Gene Synthesis Experiments

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multi-site random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in-silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

bioinformatics↗

bioRxiv2018

A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward for in-silico BGC identification.

bioinformatics↗

bioRxiv2018

Fitness Landscape of the Fission Yeast Genome

BackgroundNon-protein-coding regions of eukaryotic genomes remain poorly understood. Diversity studies, comparative genomics and biochemical outputs of genomic sites can be indicators of functional elements, but none produce fine-scale genome-wide descriptions of all functional elements.\n\nResultsTowards the generation of a comprehensive description of functional elements in the haploid Schizosaccharomyces pombe genome, we generated transposon mutagenesis libraries to a density of one insertion per 13 nucleotides of the genome. We applied a five-state hidden Markov model (HMM) to characterise insertion-depleted regions at nucleotide-level resolution. HMM-defined functional constraint was consistent with genetic diversity, comparative genomics, gene-expression data and genome annotation.\n\nConclusionsWe infer that transposon insertions lead to fitness consequences in 90% of the genome, including 80% of the non-protein-coding regions, reflecting the presence of numerous non-coding elements in this compact genome that have functional roles. Display of this data in genome browsers provides fine-scale views of structure-function relationships within specific genes.

genomics↗

bioRxiv2017

Cdk9, Spt5 and histone H2B mono-ubiquitylation cooperate to ensure antisense suppression by the Clr6-CII/Rpd3S HDAC complex

Cyclin-dependent kinase 9 (Cdk9) and histone H2B monoubiquitylation (H2Bub1) are both implicated in elongation by RNA polymerase II (RNAPII). In fission yeast, Cdk9 and H2Bub1 regulate each other through a feedback loop involving phosphorylation of the elongation factor Spt5. Conversely, genetic interactions suggest opposing functions of H2Bub1 and Cdk9 through an Spt5-independent pathway. To understand these interactions, we performed RNA-seq analysis after H2Bub1 loss, Cdk9 inhibition, or both. Either Cdk9 inhibition or H2Bub1 loss increased levels of antisense transcription initiating within coding regions of distinct subsets of genes; ablation of both pathways led to antisense derepression affecting over half the genome. Cdk9 and H2Bub1 cooperate to suppress antisense transcription by promoting function of the Clr6-CII histone deacetylase (HDAC) complex. H2Bub1 plays a second role, in opposition to Clr6-CII, to promote sense transcription in subtelomeric regions. Therefore, functional genomics revealed both collaborative and antagonistic functions of H2Bub1 and Cdk9.

molecular biology↗

bioRxiv2017

Long non-coding RNA repertoire and regulation by nuclear exosome, cytoplasmic exonuclease and RNAi in fission yeast

Transcriptomes feature pervasive, but poorly defined long non-coding RNAs (lncRNAs). We identify 5775 novel lncRNAs in Schizosaccharomyces pombe, nearly 4-times the previously annotated lncRNAs. Most lncRNAs become derepressed under genetic and physiological perturbations, especially during late meiosis. These lncRNAs are targeted by three RNA-processing pathways: the nuclear exosome, cytoplasmic exonuclease and RNAi, with substantial coordination and redundancy among pathways. We classify lncRNAs into cryptic unstable transcripts (CUTs), Xrn1-sensitive unstable transcripts (XUTs), and Dicer-sensitive unstable transcripts (DUTs). XUTs and DUTs are enriched for antisense lncRNAs, while CUTs are often bidirectional and actively translated. The cytoplasmic exonuclease and RNAi repress thousands of meiotically induced RNAs. Antisense lncRNA and sense mRNA expression often negatively correlate in the physiological, but not the genetic conditions. Intergenic and bidirectional lncRNAs emerge from nucleosome-depleted regions, upstream of positioned nucleosomes. This broad survey of the S. pombe lncRNA repertoire and characteristics provides a rich resource for functional analyses.

genomics↗