scholarly journals MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome

2019 ◽  
Vol 35 (21) ◽  
pp. 4474-4477 ◽  
Author(s):  
Anjun Ma ◽  
Minxuan Sun ◽  
Adam McDermaid ◽  
Bingqiang Liu ◽  
Qin Ma
Keyword(s):  
Supplementary Information ◽  
Computational Pipeline ◽  
Data Types ◽  
Human Gut ◽  
Dna And Rna ◽  
Gene Expression Matrix ◽  
Environmental Interactions ◽  
Gene Level ◽  
Functional Profiling ◽  
Expression Matrix

Abstract Motivation Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Results Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. Availability and implementation The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters

2020 ◽  
Vol 36 (20) ◽  
pp. 5054-5060
Author(s):  
Xiangyu Liu ◽  
Di Li ◽  
Juntao Liu ◽  
Zhengchang Su ◽  
Guojun Li
Keyword(s):  
Gene Expression ◽  
Biological Data ◽  
Supplementary Information ◽  
Gene Expression Matrix ◽  
Real Gene ◽  
Powerful Approach ◽  
Number Of Genes ◽  
Functional Patterns ◽  
Robustness To Noise ◽  
Expression Matrix

Abstract Motivation Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Results We designed RecBic to identify various trend-preserving biclusters, particularly, those with narrow shapes, i.e. clusters where the number of genes is larger than the number of conditions/samples. Given a gene expression matrix, RecBic starts with a column seed, and grows it into a full-sized bicluster by simply repetitively comparing real numbers. When tested on simulated datasets in which the elements of implanted trend-preserving biclusters and those of the background matrix have the same distribution, RecBic was able to identify the implanted biclusters in a nearly perfect manner, outperforming all the compared salient tools in terms of accuracy and robustness to noise and overlaps between the clusters. Moreover, RecBic also showed superiority in identifying functionally related genes in real gene expression datasets. Availability and implementation Code, sample input data and usage instructions are available at the following websites. Code: https://github.com/holyzews/RecBic/tree/master/RecBic/. Data: http://doi.org/10.5281/zenodo.3842717. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome

2019 ◽  
Vol 35 (24) ◽  
pp. 5397-5397 ◽  
Author(s):  
Anjun Ma ◽  
Minxuan Sun ◽  
Adam McDermaid ◽  
Bingqiang Liu ◽  
Qin Ma
Keyword(s):  
Computational Pipeline ◽  
Gene Level ◽  
Functional Profiling

scholarly journals Improved indel detection in DNA and RNA via realignment with ABRA2

2019 ◽  
Vol 35 (17) ◽  
pp. 2966-2973 ◽  
Author(s):  
Lisle E Mose ◽  
Charles M Perou ◽  
Joel S Parker
Keyword(s):  
Variant Calling ◽  
Substantial Improvement ◽  
Supplementary Information ◽  
Common Source ◽  
Data Types ◽  
Indel Detection ◽  
Dna And Rna ◽  
Wide Range ◽  
Whole Genomes ◽  
Variant Detection

Abstract Motivation Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection. Results We present here ABRA2, a redesign of the original ABRA implementation that offers support for realignment of both RNA and DNA short reads. The process results in improved accuracy and scalability including support for human whole genomes. Results demonstrate substantial improvement in indel detection for a variety of data types, including those that were not previously supported by ABRA. Further, ABRA2 results in broad improvements to variant calling accuracy across a wide range of post-processing workflows including whole genomes, targeted exomes and transcriptome sequencing. Availability and implementation ABRA2 is implemented in a combination of Java and C/C++ and is freely available to all from: https://github.com/mozack/abra2. Supplementary information Supplementary data are available at Bioinformatics online.

The gene expression matrix: Towards the extraction of genetic network architectures

1997 ◽  
Vol 30 (3) ◽  
pp. 1815-1824 ◽  
Author(s):  
Roland Somogyi ◽  
Stefanie Fuhrman ◽  
Manor Askenazi ◽  
Andy Wuensche
Keyword(s):  
Gene Expression ◽  
Genetic Network ◽  
Network Architectures ◽  
Gene Expression Matrix ◽  
Expression Matrix

scholarly journals Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

2020 ◽  
Vol 36 (10) ◽  
pp. 3263-3265 ◽  
Author(s):  
Lucas Czech ◽  
Pierre Barbera ◽  
Alexandros Stamatakis
Keyword(s):  
Phylogenetic Trees ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Computationally Efficient ◽  
Data Types ◽  
Low Level ◽  
Phylogenetic Placement ◽  
Command Line Tool ◽  
High Level

Abstract Summary We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven. Availability and implementation Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa. 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 Unsupervised discovery of phenotype-specific multi-omics networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4336-4343 ◽  
Author(s):  
W Jenny Shi ◽  
Yonghua Zhuang ◽  
Pamela H Russell ◽  
Brian D Hobbs ◽  
Margaret M Parker ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Wide Spectrum ◽  
Supplementary Information ◽  
Chronic Obstructive ◽  
Phenotypic Traits ◽  
Omics Data ◽  
Data Types ◽  
Obstructive Pulmonary Disease ◽  
Quantitative Phenotype ◽  
Treatment Plans

Abstract Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA–mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA–mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. Availability and implementation The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

2019 ◽  
Vol 35 (17) ◽  
pp. 3055-3062 ◽  
Author(s):  
Amrit Singh ◽  
Casey P Shannon ◽  
Benoît Gautier ◽  
Florian Rohart ◽  
Michaël Vacher ◽  
...  
Keyword(s):  
Data Integration ◽  
Biomarker Discovery ◽  
Predictive Performance ◽  
Supplementary Information ◽  
Integrative Approach ◽  
Data Types ◽  
Common Information ◽  
Molecular Features ◽  
Study Designs ◽  
Integration Analysis

Abstract Motivation In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. Results Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. Availability and implementation DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters’ choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Functional geometry of protein interactomes

2019 ◽  
Vol 35 (19) ◽  
pp. 3727-3734 ◽  
Author(s):  
Noël Malod-Dognin ◽  
Nataša Pržulj
Keyword(s):  
Functional Organization ◽  
Protein Complexes ◽  
Simplicial Complexes ◽  
Higher Order ◽  
Biological Information ◽  
Supplementary Information ◽  
Data Types ◽  
Ppi Networks ◽  
Functional Geometry ◽  
Better Than

Abstract Motivation Protein–protein interactions (PPIs) are usually modeled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactomes, including protein complexes. Results To model the multi-scale organization of these complex biological systems, we utilize simplicial complexes from computational geometry. The question is how to mine these new representations of protein interactomes to reveal additional biological information. To address this, we define simplets, a generalization of graphlets to simplicial complexes. By using simplets, we define a sensitive measure of similarity between simplicial complex representations that allows for clustering them according to their data types better than clustering them by using other state-of-the-art measures, e.g. spectral distance, or facet distribution distance. We model human and baker’s yeast protein interactomes as simplicial complexes that capture PPIs and protein complexes as simplices. On these models, we show that our newly introduced simplet-based methods cluster proteins by function better than the clustering methods that use the standard PPI networks, uncovering the new underlying functional organization of the cell. We demonstrate the existence of the functional geometry in the protein interactome data and the superiority of our simplet-based methods to effectively mine for new biological information hidden in the complexity of the higher-order organization of protein interactomes. Availability and implementation Codes and datasets are freely available at http://www0.cs.ucl.ac.uk/staff/natasa/Simplets/. Supplementary information Supplementary data are available at Bioinformatics online.

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