Prediction of mutation effects using a deep temporal convolutional network

2019 ◽  
Vol 36 (7) ◽  
pp. 2047-2052 ◽  
Author(s):  
Ha Young Kim ◽  
Dongsup Kim
Keyword(s):  
Latent Variable ◽  
Sequence Data ◽  
Generative Model ◽  
Supplementary Information ◽  
Biological Research ◽  
Sequence Alignments ◽  
Variable Model ◽  
Convolutional Network ◽  
Direct Optimization ◽  
Multiple Sequence

Abstract Motivation Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence. Results We show that this model is competitive with the VAE model when tested against a set of 42 high-throughput mutation scan experiments, with the mean improvement in Spearman rank correlation ∼0.023. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared with the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process. Availability and implementation Source code is available at https://github.com/ha01994/mutationTCN. Supplementary information Supplementary data are available at Bioinformatics online.

VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences

2019 ◽  
Author(s):  
Jun Wang ◽  
Pu-Feng Du ◽  
Xin-Yu Xue ◽  
Guang-Ping Li ◽  
Yuan-Ke Zhou ◽  
...  
Keyword(s):  
Sequence Data ◽  
Software Tool ◽  
Data Retrieval ◽  
Supplementary Information ◽  
Statistical Features ◽  
Biological Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Source Codes ◽  
Multiple Sequence Alignments

Abstract Summary Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments and statistical feature generation functions. Availability and implementation VisFeature is a desktop application that is implemented using JavaScript/Electron and R. The source codes of VisFeature are freely accessible from the GitHub repository (https://github.com/wangjun1996/VisFeature). The binary release, which includes an example dataset, can be freely downloaded from the same GitHub repository (https://github.com/wangjun1996/VisFeature/releases). Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals annonex2embl: automatic preparation of annotated DNA sequences for bulk submissions to ENA

2020 ◽  
Vol 36 (12) ◽  
pp. 3841-3848
Author(s):  
Michael Gruenstaeudl
Keyword(s):  
Dna Sequences ◽  
Large Scale ◽  
Sequence Data ◽  
Complete Sequence ◽  
Supplementary Information ◽  
Biological Research ◽  
Sequence Alignments ◽  
Easy Integration ◽  
Central Pillar ◽  
Python Package

Abstract Motivation The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. Results The author introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive. The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1500 fungal DNA sequences for database submission. Availability and implementation annonex2embl is freely available via the Python package index at http://pypi.python.org/pypi/annonex2embl. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protein contact prediction using metagenome sequence data and residual neural networks

2019 ◽  
Vol 36 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Qi Wu ◽  
Zhenling Peng ◽  
Ivan Anishchenko ◽  
Qian Cong ◽  
David Baker ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sequence Data ◽  
Prediction Method ◽  
Supplementary Information ◽  
Sequence Profile ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Benchmark Datasets ◽  
Metagenome Sequence

Abstract Motivation Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. Results Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10–13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks. Availability and implementation http://yanglab.nankai.edu.cn/mappred/. Supplementary information Supplementary data are available at Bioinformatics online.

QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction

2019 ◽  
Author(s):  
Fabian Sievers ◽  
Desmond G Higgins
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Reference Sequence ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Reference Sequences ◽  
Selection Of

Abstract Motivation Secondary structure prediction accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a multiple sequence alignment. SSPA correlates well with the sum-of-pairs score, if the results are averaged over many alignments but not on an alignment-by-alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest. Results We develop an improved strategy for selecting reference and non-reference sequences for a new benchmark, QuanTest2. In QuanTest2, SSPA and SP correlate better on an alignment-by-alignment basis than in QuanTest. Guide-trees for QuanTest2 are more balanced with respect to reference sequences than in QuanTest. QuanTest2 scores correlate well with other well-established benchmarks. Availability and implementation QuanTest2 is available at http://bioinf.ucd.ie/quantest2.tar, comprises of reference and non-reference sequence sets and a scoring script. Supplementary information Supplementary data are available at Bioinformatics online

scholarly journals Accurate inference of tree topologies from multiple sequence alignments using deep learning

2019 ◽  
Author(s):  
Anton Suvorov ◽  
Joshua Hochuli ◽  
Daniel R. Schrider
Keyword(s):  
Deep Learning ◽  
Parameter Space ◽  
Phylogenetic Trees ◽  
Strong Support ◽  
Biological Research ◽  
Learning Approaches ◽  
Sequence Alignments ◽  
Traditional Methods ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

AbstractReconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several “zones” of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. Here we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. While numerous practical challenges remain, these findings suggest that deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.

scholarly journals SECAPR - A bioinformatics pipeline for the rapid and user-friendly alignment of hybrid enrichment sequences, from raw reads to alignments

2018 ◽  
Author(s):  
Tobias Andermann ◽  
Angela Cano ◽  
Alexander Zizka ◽  
Christine Bacon ◽  
Alexandre Antonelli
Keyword(s):  
Evolutionary Biology ◽  
Sequence Data ◽  
Model Organisms ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequence Capture ◽  
Sequencing Platforms ◽  
User Friendly

Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing platforms such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (= hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Author(s):  
Thomas KF Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

ABSTRACTMultiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely-specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

2020 ◽  
Author(s):  
Saisai Sun ◽  
Wenkai Wang ◽  
Zhenling Peng ◽  
Jianyi Yang
Keyword(s):  
Neural Networks ◽  
Secondary Structure ◽  
Rna Structure ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Guide Rna ◽  
Multiple Sequence Alignments ◽  
Contact Distance ◽  
Distance Restraints

Abstract Motivation Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. Results We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. Availability and implementation The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Analysis of several key factors influencing deep learning-based inter-residue contact prediction

2019 ◽  
Author(s):  
Tianqi Wu ◽  
Jie Hou ◽  
Badri Adhikari ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Structural Information ◽  
Protein Structures ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Key Factors ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Ab Initio Approach

Abstract Motivation Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated. Results We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction. Availability and implementation https://github.com/multicom-toolbox/DNCON2/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Thomas K F Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

Abstract Multiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

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