scholarly journals binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

2021 ◽  
Author(s):  
Oskar Hickl ◽  
Pedro Queirós ◽  
Paul Wilmes ◽  
Patrick May ◽  
Anna Heintz-Buschart
Keyword(s):  
Marker Gene ◽  
Simulated Data ◽  
Specific Marker ◽  
Data Sets ◽  
High Quality ◽  
Genomic Signatures ◽  
Gene Sets ◽  
Omic Data Integration ◽  
Simulated Data Sets ◽  
Lineage Specific Marker

The reconstruction of genomes is a critical step in genome-resolved metagenomics as well as for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms existing state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared to five widely used binning algorithms, binny recovers the most near-complete (>95% pure, >90% complete) and high-quality (>90% pure, >70% complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as from a real-world benchmark comprised of metagenomes from various environments. binny is implemented as Snakemake workflow and available from https://github.com/a-h-b/binny.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Benchmarking Statistical Multiple Sequence Alignment

2018 ◽  
Author(s):  
Michael Nute ◽  
Ehsan Saleh ◽  
Tandy Warnow
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structural Alignment ◽  
Estimation Method ◽  
Simulated Data ◽  
Protein Sequences ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

scholarly journals Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

2015 ◽  
Vol 11 (A29A) ◽  
pp. 205-207
Author(s):  
Philip C. Gregory
Keyword(s):  
Radial Velocity ◽  
State Of The Art ◽  
Simulated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Stellar Activity ◽  
Bayesian Fusion ◽  
Multiple State ◽  
Simulated Data Sets ◽  
Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets

2005 ◽  
Vol 37 (12) ◽  
pp. 1320-1322 ◽  
Author(s):  
Eleftheria Zeggini ◽  
William Rayner ◽  
Andrew P Morris ◽  
Andrew T Hattersley ◽  
Mark Walker ◽  
...  
Keyword(s):  
Sample Size ◽  
Large Scale ◽  
Simulated Data ◽  
Data Sets ◽  
Hapmap Sample ◽  
Tagging Snp ◽  
Simulated Data Sets

scholarly journals Evaluating Phylogenetic Informativeness as a Predictor of Phylogenetic Signal for Metazoan, Fungal, and Mammalian Phylogenomic Data Sets

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Francesc López-Giráldez ◽  
Andrew H. Moeller ◽  
Jeffrey P. Townsend
Keyword(s):  
Phylogenetic Signal ◽  
Simulated Data ◽  
Quantitative Measure ◽  
Data Sets ◽  
Phylogenetic Informativeness ◽  
Phylogenetic Resolution ◽  
Taxonomic Groups ◽  
Diverse Groups ◽  
Simulated Data Sets ◽  
Selection Of

Phylogenetic research is often stymied by selection of a marker that leads to poor phylogenetic resolution despite considerable cost and effort. Profiles of phylogenetic informativeness provide a quantitative measure for prioritizing gene sampling to resolve branching order in a particular epoch. To evaluate the utility of these profiles, we analyzed phylogenomic data sets from metazoans, fungi, and mammals, thus encompassing diverse time scales and taxonomic groups. We also evaluated the utility of profiles created based on simulated data sets. We found that genes selected via their informativeness dramatically outperformed haphazard sampling of markers. Furthermore, our analyses demonstrate that the original phylogenetic informativeness method can be extended to trees with more than four taxa. Thus, although the method currently predicts phylogenetic signal without specifically accounting for the misleading effects of stochastic noise, it is robust to the effects of homoplasy. The phylogenetic informativeness rankings obtained will allow other researchers to select advantageous genes for future studies within these clades, maximizing return on effort and investment. Genes identified might also yield efficient experimental designs for phylogenetic inference for many sister clades and outgroup taxa that are closely related to the diverse groups of organisms analyzed.

scholarly journals IMPROVED ESTIMATION OF COVARIANCE MATRIX IN HOTELLING’S T2 FOR MICROARRAY DATA

2016 ◽  
Vol 8 (2) ◽  
pp. 26 ◽  
Author(s):  
Suryaefiza Karjanto ◽  
Norazan Mohamed Ramli ◽  
Nor Azura Md Ghaninor Azura Md Ghani
Keyword(s):  
Covariance Matrix ◽  
Microarray Data ◽  
Simulated Data ◽  
Principal Component ◽  
Data Sets ◽  
Similar Data ◽  
Shrinkage Methods ◽  
Differential Gene ◽  
The Relationship ◽  
Simulated Data Sets

<p class="lead">The relationship between genes in gene set analysis in microarray data is analyzed using Hotelling’s <em>T</em><sup>2</sup> but the test cannot be applied when the number of samples is larger than the number of variables which is uncommon in the microarray. Thus, in this study, we proposed shrinkage approaches to estimating the covariance matrix in Hotelling’s <em>T<sup>2</sup></em> particularly to cater high dimensionality problem in microarray data. Three shrinkage covariance methods were proposed in this study and are referred as Shrink A, Shrink B and Shrink C. The analysis of the three proposed shrinkage methods was compared with the Regularized Covariance Matrix Approach and Kong’s Principal Component Analysis. The performances of the proposed methods were assessed using several cases of simulated data sets. In many cases, the Shrink A method performed the best, followed by the Shrink C and RCMAT methods. In contrast, both the Shrink B and KPCA methods showed relatively poor results. The study contributes to an establishment of modified multivariate approach to differential gene expression analysis and expected to be applied in other areas with similar data characteristics.</p>

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