Protein function annotation using protein domain family resources

Das, Sayoni; Orengo, Christine A.

doi:10.1016/j.ymeth.2015.09.029

Cited by 37 publications

(29 citation statements)

References 69 publications

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“…The large difference between the selected number of genes might be due to the large difference in the number of samples available (and therefore used in analysis) for each species. Functional enrichment analysis of the variable genes revealed pathways related to the heart functions (supplementary table 4), confirming that at least part of the gene expression variability was reflecting biological differences. Hierarchical clustering of the mouse and rat dataset separately revealed clusters of probes and clusters of experiments (supplementary figure 5).…”

Section: Functional Annotation Transfer Across Rat and Mouse Using Hementioning

confidence: 64%

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Meta-analysis of liver and heart transcriptomic data for functional annotation transfer in mammalian orthologs

Reyes

Michoel

Joshi

et al. 2017

Preprint

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Functional annotation transfer across multi-gene family orthologs can lead to functional misannotations. We hypothesised that co-expression network will help predict functional orthologs amongst complex homologous gene families. To explore the use of transcriptomic data available in public domain to identify functionally equivalent ones from all predicted orthologs, we collected genome wide expression data in mouse and rat liver from over 1500 experiments with varied treatments. We used a hyper-graph clustering method to identify clusters of orthologous genes co-expressed in both mouse and rat. We validated these clusters by analysing expression profiles in each species separately, and demonstrating a high overlap. We then focused on genes in 18 homology groups with one-to-many or many-to-many relationships between two species, to discriminate between functionally equivalent and non-equivalent orthologs. Finally, we further applied our method by collecting heart transcriptomic data (over 1400 experiments) in rat and mouse to validate the method in an independent tissue.

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Section: Functional Annotation Transfer Across Rat and Mouse Using Hementioning

confidence: 64%

“…An experimental validation of each gene is impractical to this end as it demands high financial and time cost. It is estimated that only one percent of proteins have experimental functional annotations [4]. Bioinformatic approaches therefore provide an attractive alternative [5].…”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of liver and heart transcriptomic data for functional annotation transfer in mammalian orthologs

Reyes

Michoel

Joshi

et al. 2017

Preprint

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“…Current computational approaches for protein family classification include methods such as BLASTp and profile hidden Markov models (pHMMs) which compare sequences to a large database of pre-annotated sequences. However, inference using these alignment-based methods is computationally inefficient, as they require repeated comparison of sequences to an exponentially growing database of labeled family profiles and are limited by expensive, manually-tuned processing pipelines [27]. With the exponential growth of protein discovery, the development of more scalable approaches is required to overcome traditional bottlenecks [28].…”

Section: Task: Protein Family Classificationmentioning

confidence: 99%

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Nambiar

Liu

Hopkins

et al. 2020

Preprint

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The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.to identify functional characteristics is critical to understanding cellular functions as well as developing potential therapeutic applications [4]. Sequence-based methods to computationally infer protein characteristics have been critical for inferring protein function and other characteristics [5]. Thus, the development of computational methods to infer protein characteristics (which we generally describe as "protein prediction tasks") has become paramount in the field of bioinformatics and computational biology. Here, we develop a Transformer neural network to establish task-agnostic representations of protein sequences, and use the Transformer network to solve two protein prediction tasks. Background: Deep LearningDeep learning, a class of machine learning based on the use of artificial neural networks, has recently transformed the field of computational biology and medicine through its application towards long-standing problems such as image analysis, gene expression modeling, sequence variant calling, and putative drug discovery [6,7,8,9,10]. By leveraging deep learning, field specialists have been able to efficiently design and train models without the extensive feature engineering required by previous methods. In applying deep learning to sequence-based protein characterization tasks, we first consider the field of natural language processing (NLP), which aims to analyze human language through computational techniques [11]. Deep learning has recently proven to be a critical tool for NLP, achieving state-ofthe-art performance on benchmarks for named entity recognition, sentiment analysis, question answering, and text summarization, among others [12,13].Neural networks are functions that map one vector space to another. Thus, in order to use them for NLP tasks, we first need to represent words as real-valued vectors. Often referred to as word embeddings, these vector representations are typically "pre-trained" on an auxiliary task for which we have (or can automatically generate) a large amount of training data. The goal of this pre-training is to learn generically useful representations that enc...

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“…Proteins are often composed of discrete domains, and these can either be conceptualized as sub-sequences of independent protein sequences which share homology (and by extension evolutionary origin), 6 or alternatively, domains may be considered structurally, where they are subsections of the proteins which are compact, independently folding and observed to be shared between a variety of proteins. [7][8][9] An extension of this observation, that proteins can be decomposed into sets of domains, is the hypothesis that domains act as sub-functional units and when composed together, a protein's given combination of domains is what gives rise to the protein's overall specific function 10,11 In the following study, we show that protein domains can be embedded in a "semantically" meaningful vector space and that this embedding space reflects meaningful information about the functional roles (in terms of GO term assignments) of the individual protein domains.…”

Section: Introductionmentioning

confidence: 99%

Learning a functional grammar of protein domains using natural language word embedding techniques

Buchan

Jones

2019

Proteins

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In this paper, using Word2vec, a widely‐used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic “meaning” in the context of their functional contributions to the multi‐domain proteins in which they are found. Word2vec is a group of models which can be used to produce semantically meaningful embeddings of words or tokens in a fixed‐dimension vector space. In this work, we treat multi‐domain proteins as “sentences” where domain identifiers are tokens which may be considered as “words.” Using all InterPro (Finn et al. 2017) pfam domain assignments we observe that the embedding could be used to suggest putative GO assignments for Pfam (Finn et al. 2016) domains of unknown function.

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Protein function annotation using protein domain family resources

Cited by 37 publications

References 69 publications

Meta-analysis of liver and heart transcriptomic data for functional annotation transfer in mammalian orthologs

Meta-analysis of liver and heart transcriptomic data for functional annotation transfer in mammalian orthologs

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Learning a functional grammar of protein domains using natural language word embedding techniques

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