scholarly journals The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i219-i226 ◽  
Author(s):  
Moses Stamboulian ◽  
Rafael F Guerrero ◽  
Matthew W Hahn ◽  
Predrag Radivojac
Keyword(s):  
Prediction Accuracy ◽  
Homo Sapiens ◽  
Computational Prediction ◽  
Function Prediction ◽  
Supplementary Information ◽  
Orthologous Genes ◽  
Homologous Genes ◽  
Cast Doubt ◽  
Prediction Of Function ◽  
Function Transfer

Abstract Motivation The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The ‘ortholog conjecture’ proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Results We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy. Availability and implementation https://github.com/predragradivojac/oc. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction

2019 ◽  
Author(s):  
Moses Stamboulian ◽  
Rafael F. Guerrero ◽  
Matthew W. Hahn ◽  
Predrag Radivojac
Keyword(s):  
Prediction Accuracy ◽  
Function Versus ◽  
Homo Sapiens ◽  
Computational Prediction ◽  
Function Prediction ◽  
Orthologous Genes ◽  
Homologous Genes ◽  
Cast Doubt ◽  
Prediction Of Function ◽  
Function Transfer

AbstractThe computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The “ortholog conjecture” proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Here we use experimental annotations from over 40,000 proteins, drawn from over 80,000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of data that must be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Aiming to maximize the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy.

scholarly journals Gene function prediction in five model eukaryotes based on gene relative location through machine learning

2021 ◽  
Author(s):  
Flavio Pazos Obregón ◽  
Diego Silvera ◽  
Pablo Soto ◽  
Patricio Yankilevich ◽  
Gustavo Guerberoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Function ◽  
Homo Sapiens ◽  
Function Prediction ◽  
Gene Location ◽  
Supplementary Information ◽  
Relative Location ◽  
Gene Function Prediction ◽  
Ample Evidence ◽  
And Function

Motiviation: The function of most genes is unknown. The best results in gene function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene's function is not independent of its location, the few available examples of gene function prediction based on gene location relay on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Results: Here we predict thousands of gene functions in five eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models trained with features derived from the location of genes in the genomes to which they belong. To the best of our knowledge this is the first work in which gene function prediction is successfully achieved in eukaryotic genomes using predictive features derived exclusively from the relative location of the genes. Contact: [email protected] Supplementary information: http://gfpml.bnd.edu.uy

scholarly journals Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

2019 ◽  
Vol 14 (5) ◽  
pp. 432-445 ◽  
Author(s):  
Muniba Faiza ◽  
Khushnuma Tanveer ◽  
Saman Fatihi ◽  
Yonghua Wang ◽  
Khalid Raza
Keyword(s):  
Drosophila Melanogaster ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Computational Prediction ◽  
Transcriptional Level ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Prediction Tools ◽  
Mirna Targets

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

scholarly journals DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4586-4595 ◽  
Author(s):  
Peng Ni ◽  
Neng Huang ◽  
Zhi Zhang ◽  
De-Peng Wang ◽  
Fan Liang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Deep Learning ◽  
Bisulfite Sequencing ◽  
Homo Sapiens ◽  
Accurate Method ◽  
Supplementary Information ◽  
Nanopore Sequencing ◽  
Methylation State ◽  
State Prediction ◽  
Genome Level

Abstract Motivation The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. Results In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. Availability and implementation DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. Supplementary information Supplementary data are available at bioinformatics online.

scholarly journals Differentiating isoform functions with collaborative matrix factorization

2019 ◽  
Author(s):  
Keyao Wang ◽  
Jun Wang ◽  
Carlotta Domeniconi ◽  
Xiangliang Zhang ◽  
Guoxian Yu
Keyword(s):  
Matrix Factorization ◽  
Characteristic Curve ◽  
Function Prediction ◽  
Low Rank ◽  
Data Matrix ◽  
Supplementary Information ◽  
Genomic Databases ◽  
Gene Level ◽  
The Matrix ◽  
Level Function

Abstract Motivation Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. Results Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. As such, they have compromised performance. We propose a data integrative solution (called DisoFun) to Differentiate isoform Functions with collaborative matrix factorization. DisoFun assumes the functional annotations of genes are aggregated from those of key isoforms. It collaboratively factorizes the isoform data matrix and gene-term data matrix (storing Gene Ontology (GO) annotations of genes) into low-rank matrices to simultaneously explore the latent key isoforms, and achieve function prediction by aggregating predictions to their originating genes. In addition, it leverages the PPI network and GO structure to further coordinate the matrix factorization. Extensive experimental results show that DisoFun improves the AUROC (area under the receiver-operating characteristic curve) and AUPRC (area under the precision-recall curve) of existing solutions by at least 7.7% and 28.9%, respectively. We further investigate DisoFun on four exemplar genes (LMNA, ADAM15, BCL2L1, and CFLAR) with known functions at the isoform-level, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. Availability The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals XMAn v2—a database of Homo sapiens mutated peptides

2019 ◽  
Author(s):  
Marcela Aguilera Flores ◽  
Iulia M Lazar
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Homo Sapiens ◽  
Amino Acid Sequences ◽  
Mass Spectrometry Data ◽  
Supplementary Information ◽  
Tandem Mass ◽  
Mass Spectrometry Detection ◽  
Nonsense Mutations ◽  
Tandem Mass Spectrometry Data

Abstract Summary The ‘Unknown Mutation Analysis (XMAn)’ database is a compilation of Homo sapiens mutated peptides in FASTA format, that was constructed for facilitating the identification of protein sequence alterations by tandem mass spectrometry detection. The database comprises 2 539 031 non-redundant mutated entries from 17 599 proteins, of which 2 377 103 are missense and 161 928 are nonsense mutations. It can be used in conjunction with search engines that seek the identification of peptide amino acid sequences by matching experimental tandem mass spectrometry data to theoretical sequences from a database. Availability and implementation XMAn v2 can be accessed from github.com/lazarlab/XMAnv2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accurate and efficient gene function prediction using a multi-bacterial network

2020 ◽  
Author(s):  
Jeffrey N Law ◽  
Shiv D Kale ◽  
T M Murali
Keyword(s):  
Gene Function ◽  
Bacterial Species ◽  
Heterogeneous Data ◽  
Function Prediction ◽  
Label Propagation ◽  
Supplementary Information ◽  
Gene Function Prediction ◽  
Functional Annotations ◽  
A Genome ◽  
Multiple Species

Abstract Motivation Nearly 40% of the genes in sequenced genomes have no experimentally or computationally derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally based functional annotations and systematically transfer them to newly sequenced organisms on a genome-wide scale. However, the large sizes of such networks pose a challenge for the scalability of current methods. Results We develop a label propagation algorithm called FastSinkSource. By formally bounding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under 3 min for 200 bacterial species. Availability and implementation An implementation of our framework and all data used in this research are available at https://github.com/Murali-group/multi-species-GOA-prediction. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

2020 ◽  
Author(s):  
Amelia Villegas-Morcillo ◽  
Stavros Makrodimitris ◽  
Roeland C H J van Ham ◽  
Angel M Gomez ◽  
Victoria Sanchez ◽  
...  
Keyword(s):  
Protein Function ◽  
Prediction Models ◽  
Protein Function Prediction ◽  
3D Structure ◽  
Function Prediction ◽  
Feature Representation ◽  
Training Data ◽  
Supplementary Information ◽  
Molecular Function ◽  
Structure Information

Abstract Motivation Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. Results We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. Availability and implementation Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals EvolClust: automated inference of evolutionary conserved gene clusters in eukaryotes

2019 ◽  
Author(s):  
Marina Marcet-Houben ◽  
Toni Gabaldón
Keyword(s):  
Secondary Metabolism ◽  
Computational Prediction ◽  
Gene Clusters ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automated Inference ◽  
Or Gene ◽  
Conserved Gene ◽  
Genome Comparisons

Abstract Motivation The evolution and role of gene clusters in eukaryotes is poorly understood. Currently, most studies and computational prediction programs limit their focus to specific types of clusters, such as those involved in secondary metabolism. Results We present EvolClust, a python-based tool for the inference of evolutionary conserved gene clusters from genome comparisons, independently of the function or gene composition of the cluster. EvolClust predicts conserved gene clusters from pairwise genome comparisons and infers families of related clusters from multiple (all versus all) genome comparisons. Availability and implementation https://github.com/Gabaldonlab/EvolClust/. Supplementary information Supplementary data are available at Bioinformatics online.

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