ProtiGeno: a prokaryotic short gene finder using protein language models

• URL: http://arxiv.org/abs/2307.10343v1
• Date: Wed, 19 Jul 2023 16:46:42 GMT
• Title: ProtiGeno: a prokaryotic short gene finder using protein language models
• Authors: Tony Tu, Gautham Krishna, Amirali Aghazadeh
• Abstract summary: Current gene finders are highly sensitive in finding long genes, but their sensitivity decreases noticeably in finding shorter genes. We develop a deep learning-based method called ProtiGeno, specifically targeting short prokaryotic genes. In systematic large-scale experiments on 4,288 prokaryotic genomes, we demonstrate that ProtiGeno predicts short coding and noncoding genes with higher accuracy and recall than the current state-of-the-art gene finders.
• Score: 1.2354076490479513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology. Although the current gene finders are highly sensitive in finding long genes, their sensitivity decreases noticeably in finding shorter genes (<180 nts). The culprit is insufficient annotated gene data to identify distinguishing features in short open reading frames (ORFs). We develop a deep learning-based method called ProtiGeno, specifically targeting short prokaryotic genes using a protein language model trained on millions of evolved proteins. In systematic large-scale experiments on 4,288 prokaryotic genomes, we demonstrate that ProtiGeno predicts short coding and noncoding genes with higher accuracy and recall than the current state-of-the-art gene finders. We discuss the predictive features of ProtiGeno and possible limitations by visualizing the three-dimensional structure of the predicted short genes. Data, codes, and models are available at https://github.com/tonytu16/protigeno.

Related papers

• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling [60.91599380893732]
  VQDNA is a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning. By leveraging vector-quantized codebooks as learnable vocabulary, VQDNA can adaptively tokenize genomes into pattern-aware embeddings.
  arXiv  Detail & Related papers  (2024-05-13T20:15:03Z)
• Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity [3.972930262155919]
  We propose a framework taking advantage of existing large models for gene vectorization to predict habitat specificity from entire microbial genome sequences. We train and validate our approach on a large dataset of high quality microbiome genomes from different habitats.
  arXiv  Detail & Related papers  (2024-05-09T09:34:51Z)
• Efficient and Scalable Fine-Tune of Language Models for Genome Understanding [49.606093223945734]
  We present textscLingo: textscLanguage prefix ftextscIne-tuning for textscGentextscOmes. Unlike DNA foundation models, textscLingo strategically leverages natural language foundation models' contextual cues. textscLingo further accommodates numerous downstream fine-tune tasks by an adaptive rank sampling method.
  arXiv  Detail & Related papers  (2024-02-12T21:40:45Z)
• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution [76.97231739317259]
  We present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets using a model with orders of magnitude less parameters and pretraining data.
  arXiv  Detail & Related papers  (2023-06-27T20:46:34Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model [3.0643865202019698]
  We propose a new solution named SemanticCAP to identify accessible regions of the genome. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of gene sequences. Compared with other systems under public benchmarks, our model proved to have better performance.
  arXiv  Detail & Related papers  (2022-04-05T11:47:58Z)
• Feature extraction using Spectral Clustering for Gene Function Prediction [0.4492444446637856]
  This paper presents a novel in silico approach for to the annotation problem that combines cluster analysis and hierarchical multi-label classification. The proposed approach is applied to a case study on Zea mays, one of the most dominant and productive crops in the world.
  arXiv  Detail & Related papers  (2022-03-25T10:17:36Z)
• SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression [8.326669256957352]
  We present SimpleChrome, a deep learning model that learns the histone modification representations of genes. The features learned from the model allow us to better understand the latent effects of cross-gene interactions and direct gene regulation on the target gene expression.
  arXiv  Detail & Related papers  (2020-12-15T23:30:36Z)
• Mining Functionally Related Genes with Semi-Supervised Learning [0.0]
  We introduce a rich set of features and use them in conjunction with semisupervised learning approaches. The framework of learning with positive and unlabeled examples (LPU) is shown to be especially appropriate for mining functionally related genes.
  arXiv  Detail & Related papers  (2020-11-05T20:34:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.