Biology subjects

Tawfiq, R.

Publications and source records attributed to Tawfiq, R..

5 recordsLinked to original sources

bioRxiv2026

In Other Words: Learning When to Trust Generated Protein Function Descriptions

Generative protein-to-text models write function descriptions in free-form English but give no indication of which to believe. Indeed, free text is hard to evaluate. We introduce a large language model judge of equivalence of predicted vs. ''true'' function descriptions, validated against blinded expert curation. We also present ProtTale, our sequence-to-text model, that learns from the judge's verdicts to estimate its own reliability, pJay. On unseen Swiss-Prot enzymes, ProtTale and three other leading methods tie on accuracy but are right about different proteins. Here, ProtTale's unique correct predictions outnumber all other methods' combined. Scored from sequence alone, pJay is a plug-in filter, raising all models' performance. On short proteins, it raises precision for every tested method nearly twofold. We argue that generative annotation becomes useful not by being right more often, but by knowing when it can be trusted---a standard that every generative model in biology should meet.

bioinformatics↗

bioRxiv2025

Beyond protein functions: evaluating completeness, coherence, and consistency of genome-scale function annotations

Protein function annotation has traditionally followed a reductionist approach, assigning functions to individual proteins acting in isolation. This paradigm treats each annotation as an independent fact, disconnected from the broader biological system. However, proteins operate within integrated cellular networks where their functions depend on genomic context and the presence of interacting partners. Here, we develop a genome-scale evaluation framework that assesses whether annotated protein functions could plausibly coexist within a living organism. We formalize three criteria grounded in systems biology principles: completeness (presence of essential functions), coherence (satisfaction of functional dependencies), and consistency (absence of mutually exclusive functions). Applying this framework to bacterial genomes, we evaluated manually curated annotations from six model organisms and computational predictions from six methods. While model organism annotations largely satisfied our constraints -- with violations primarily reflecting host- pathogen interactions -- all computational prediction methods systematically failed to produce biologically plausible genome-scale annotations. Methods achieved high accuracy for individual proteins yet produced incomplete metabolic pathways, incoherent protein complexes, and taxonomically impossible function combinations. These results reveal a fundamental disconnect between the reductionist annotation model and the systems-level requirements of biological organisms. Current computational methods amplify this disconnect as they are optimized for protein-level accuracy while ignoring genome-scale constraints. Our framework provides quantitative metrics for evaluating biological plausibility and establishes a foundation for developing system-aware annotation approaches. The shift from reductionist to systems-level perspectives will be essential for annotating the rapidly growing collection of sequenced genomes and metagenomes. Significance StatementProtein function prediction methods are evaluated by their accuracy on individual proteins, but proteins operate within integrated biological systems with strict functional requirements. We developed a framework that evaluates whether predicted protein functions could plausibly coexist in a living organism by checking for completeness of essential functions, coherence of functional dependencies, and consistency with biological constraints. While manually curated annotations largely satisfy these requirements, all computational prediction methods systematically fail to produce biologically viable genome-scale annotations. This reveals a fundamental disconnect between current evaluation paradigms and the systems-level requirements of biology, highlighting the need for prediction methods that consider genome-scale constraints rather than optimizing for individual protein accuracy.

bioinformatics↗

bioRxiv2024

Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia

The selection of a reference sequence in genome analysis is critical, as it serves as the foundation for all downstream analyses. Recently, the pangenome graph has been proposed as a data model that incorporates haplotypes from multiple individuals. Here we present JaSaPaGe, a pangenome graph reference for Saudi Arabian and Japanese populations, both of which have been significantly underrepresented in previous genomic studies. We constructed JaSaPaGe from high-quality phased diploid assemblies which were made utilizing PacBio high-fidelity long reads, Nanopore long reads, and Hi-C short reads of 9 Saudi and 10 Japanese individuals. Quality evaluation of the pangenome graph by variant calling showed that our pangenome outperformed earlier linear reference genomes (GRCh38 and T2T-CHM13) and showed comparable performance to the pangenome graph provided by the Human Pangenome Reference Consortium (HPRC), with more variants found in Japanese and Saudi samples using their population-specific pangenomes. This pangenome reference will serve as a valuable resource for both the research and clinical communities in Japan and Saudi Arabia.

genomics↗

bioRxiv2024

DeepGOMeta: Predicting functions for microbes}

Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction, as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is validated using novel evaluation strategies and applied to diverse microbial datasets. Data and code are available at https://github.com/bio-ontology-research-group/deepgometa

bioinformatics↗

bioRxiv2022

A personal, reference quality, fully annotated genome from a Saudi individual

We have used multiple sequencing approaches to sequence the genome of a volunteer from Saudi Arabia. We use the resulting data to generate a de novo assembly of the genome, and use different computational approaches to refine the assembly. As a consequence, we provide a contiguous assembly of the complete genome of an individual from Saudi Arabia for all chromosomes except chromosome Y, and label this assembly KSA001. We transferred genome annotations from reference genomes and predicted genome features using methods from Artificial Intelligence to fully annotate KSA001, and we make all primary sequencing data, the assembly, and the genome annotations freely available in public databases using the FAIR data principles.

bioinformatics↗