AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

Abstract Motivation A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. Results Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88–95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. Availability and implementation AssessORF is available as an R package via the Bioconductor package repository. Supplementary information Supplementary data are available at Bioinformatics online.

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ngsReports: a Bioconductor package for managing FastQC reports and other NGS related log files

Bioinformatics ◽

10.1093/bioinformatics/btz937 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2587-2588 ◽

Cited By ~ 10

Author(s):

Christopher M Ward ◽

Thu-Hien To ◽

Stephen M Pederson

Keyword(s):

Quality Control ◽

R Package ◽

Supplementary Information ◽

Bioconductor Package ◽

Supplementary Data ◽

Large Sample ◽

Log Files ◽

Shiny App ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Abstract Motivation High throughput next generation sequencing (NGS) has become exceedingly cheap, facilitating studies to be undertaken containing large sample numbers. Quality control (QC) is an essential stage during analytic pipelines and the outputs of popular bioinformatics tools such as FastQC and Picard can provide information on individual samples. Although these tools provide considerable power when carrying out QC, large sample numbers can make inspection of all samples and identification of systemic bias a challenge. Results We present ngsReports, an R package designed for the management and visualization of NGS reports from within an R environment. The available methods allow direct import into R of FastQC reports along with outputs from other tools. Visualization can be carried out across many samples using default, highly customizable plots with options to perform hierarchical clustering to quickly identify outlier libraries. Moreover, these can be displayed in an interactive shiny app or HTML report for ease of analysis. Availability and implementation The ngsReports package is available on Bioconductor and the GUI shiny app is available at https://github.com/UofABioinformaticsHub/shinyNgsreports. Supplementary information Supplementary data are available at Bioinformatics online.

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iMIRAGE: an R package to impute microRNA expression using protein-coding genes

Bioinformatics ◽

10.1093/bioinformatics/btz939 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2608-2610

Author(s):

Aritro Nath ◽

Jeremy Chang ◽

R Stephanie Huang

Keyword(s):

Gene Expression ◽

Small Rnas ◽

Transcriptional Regulators ◽

R Package ◽

Machine Learning Algorithms ◽

Supplementary Information ◽

Expression Data ◽

Protein Coding ◽

Altered Protein ◽

Independent Test

Abstract Summary MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. Availability and implementation The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Simultaneous gene finding in multiple genomes

10.7287/peerj.preprints.1296v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Stefanie König ◽

Lars Romoth ◽

Lizzy Gerischer ◽

Mario Stanke

Keyword(s):

Genome Alignment ◽

Whole Genome ◽

Gene Finding ◽

Dual Decomposition ◽

Closely Related Species ◽

Protein Coding ◽

Decomposition Approach ◽

New Approach ◽

Gene Structures ◽

Gains And Losses

As whole genome sequencing is taking on ever-increasing dimensions, the new challenge is the accurate and consistent annotation of entire clades of genomes. We address this problem with a new approach to comparative gene finding that takes a multiple genome alignment of closely related species and simultaneously predicts the location and structure of protein-coding genes in all input genomes, thereby exploiting negative selection and sequence conservation. The model prefers potential gene structures in the different genomes that are in agreement with each other, or – if not – where the exon gains and losses are plausible given the species tree. We formulate the multi-species gene finding problem as a binary labeling problem on a graph. The resulting optimization problem is NP hard, but can be efficiently approximated using a subgradient-based dual decomposition approach. The proposed method was tested on a whole-genome alignment of 12 Drosophila species and its accuracy evaluated on D. melanogaster. The method is being implemented as an extension to the gene finder AUGUSTUS.

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ensembldb: an R package to create and use Ensembl-based annotation resources

Bioinformatics ◽

10.1093/bioinformatics/btz031 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3151-3153 ◽

Cited By ~ 13

Author(s):

Johannes Rainer ◽

Laurent Gatto ◽

Christian X Weichenberger

Keyword(s):

Coordinate System ◽

Positional Information ◽

R Package ◽

Research Data ◽

Supplementary Information ◽

Reproducible Research ◽

Bioconductor Package ◽

Supplementary Data ◽

Reference Coordinate System ◽

Associated Data

Abstract Summary Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software. Availability and implementation ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb). Supplementary information Supplementary data are available at Bioinformatics online.

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Designing a general method for predicting the regulatory relationships between long noncoding RNAs and protein-coding genes based on multi-omics characteristics

Bioinformatics ◽

10.1093/bioinformatics/btz886 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2025-2032

Author(s):

Yuwei Zhang ◽

Tianfei Yi ◽

Huihui Ji ◽

Guofang Zhao ◽

Yang Xi ◽

...

Keyword(s):

Noncoding Rna ◽

Cross Validation ◽

Machine Learning Algorithms ◽

Supplementary Information ◽

Protein Coding ◽

Protein Coding Genes ◽

Negative Results ◽

Independent Test ◽

Multiple Characteristics ◽

Test Sets

Abstract Motivation Long noncoding RNA (lncRNA) has been verified to interact with other biomolecules especially protein-coding genes (PCGs), thus playing essential regulatory roles in life activities and disease development. However, the inner mechanisms of most lncRNA–PCG relationships are still unclear. Our study investigated the characteristics of true lncRNA–PCG relationships and constructed a novel predictor with machine learning algorithms. Results We obtained the 307 true lncRNA-PCG pairs from database and found that there are significant differences in multiple characteristics between true and random lncRNA–PCG sets. Besides, 3-fold cross-validation and prediction results on independent test sets show the great AUC values of LR, SVM and RF, among which RF has the best performance with average AUC 0.818 for cross-validation, 0.823 and 0.853 for two independent test sets, respectively. In case study, some candidate lncRNA–PCG relationships in colorectal cancer were found and HOTAIR–COMP interaction was specially exemplified. The proportion of the reported pairs in the predicted positive results was significantly higher than that in negative results (P < 0.05). Supplementary information Supplementary data are available at Bioinformatics online.

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Balrog: A universal protein model for prokaryotic gene prediction

10.1101/2020.09.06.285304 ◽

2020 ◽

Author(s):

Markus J. Sommer ◽

Steven L. Salzberg

Keyword(s):

High Throughput Sequencing ◽

Prokaryotic Genome ◽

Amino Acid Sequences ◽

Specific Gene ◽

Universal Model ◽

Gene Finding ◽

Convolutional Network ◽

Protein Coding ◽

Microbial Genomes ◽

Protein Coding Genes

AbstractLow-cost, high-throughput sequencing has led to an enormous increase in the number of sequenced microbial genomes, with well over 100,000 genomes in public archives today. Automatic genome annotation tools are integral to understanding these organisms, yet older gene finding methods must be retrained on each new genome. We have developed a universal model of prokaryotic genes by fitting a temporal convolutional network to amino-acid sequences from a large, diverse set of microbial genomes. We incorporated the new model into a gene finding system, Balrog (Bacterial Annotation by Learned Representation Of Genes), which does not require genome-specific training and which matches or outperforms other state-of-the-art gene finding tools. Balrog is freely available under the MIT license at https://github.com/salzberg-lab/Balrog.Author summaryAnnotating the protein-coding genes in a newly sequenced prokaryotic genome is a critical part of describing their biological function. Relative to eukaryotic genomes, prokaryotic genomes are small and structurally simple, with 90% of their DNA typically devoted to protein-coding genes. Current computational gene finding tools are therefore able to achieve close to 99% sensitivity to known genes using species-specific gene models.Though highly sensitive at finding known genes, all current prokaryotic gene finders also predict large numbers of additional genes, which are labelled as “hypothetical protein” in GenBank and other annotation databases. Many hypothetical gene predictions likely represent true protein-coding sequence, but it is not known how many of them represent false positives. Additionally, all current gene finding tools must be trained specifically for each genome as a preliminary step in order to achieve high sensitivity. This requirement limits their ability to detect genes in fragmented sequences commonly seen in metagenomic samples.We took a data-driven approach to prokaryotic gene finding, relying on the large and diverse collection of already-sequenced genomes. By training a single, universal model of bacterial genes on protein sequences from many different species, we were able to match the sensitivity of current gene finders while reducing the overall number of gene predictions. Our model does not need to be refit on any new genome. Balrog (Bacterial Annotation by Learned Representation of Genes) represents a fundamentally different yet effective method for prokaryotic gene finding.

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NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

10.1101/2021.08.02.453487 ◽

2021 ◽

Author(s):

Federico Agostinis ◽

Chiara Romualdi ◽

Gabriele Sales ◽

Davide Risso

Keyword(s):

Dimensionality Reduction ◽

Single Cell ◽

R Package ◽

Batch Effect ◽

Supplementary Information ◽

Bioconductor Package ◽

Rna Seq ◽

Sequencing Data ◽

Bioconductor Project ◽

Single Cell Rna Sequencing

Summary: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information: Supplementary data are available at Bioinformatics online.

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Accurate annotation of protein coding sequences with IDTAXA

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab080 ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Nicholas P Cooley ◽

Erik S Wright

Keyword(s):

Antibiotic Resistance ◽

R Package ◽

Protein Coding ◽

Common Error ◽

New Approach ◽

Coding Sequences ◽

Microbial Genomes ◽

Prokaryotic Genomes ◽

Multi Level ◽

Novel Associations

Abstract The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA’s utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

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metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data

Bioinformatics ◽

10.1093/bioinformatics/btz136 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3870-3872 ◽

Cited By ~ 1

Author(s):

Nathan D Olson ◽

Nidhi Shah ◽

Jayaram Kancherla ◽

Justin Wagner ◽

Joseph N Paulson ◽

...

Keyword(s):

16S Rrna ◽

Marker Gene ◽

R Package ◽

Supplementary Information ◽

Bioconductor Package ◽

Rrna Sequence ◽

16S Rrna Sequence ◽

Reference Databases ◽

Supplementary Material ◽

Database Comparison

Abstract Summary We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R. Availability and implementation https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html. Supplementary information Supplementary material is available at Bioinformatics online.

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Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

Bioinformatics ◽

10.1093/bioinformatics/btz023 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3028-3037 ◽

Cited By ~ 8

Author(s):

Eyal Simonovsky ◽

Ronen Schuster ◽

Esti Yeger-Lotem

Keyword(s):

Drug Target ◽

Large Scale ◽

Target Genes ◽

Supplementary Information ◽

Protein Coding ◽

Expression Levels ◽

Expression Variability ◽

Protein Coding Genes ◽

Approved Drugs ◽

Variable Genes

Abstract Motivation The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon. Results To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development. Availability and implementation LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV). Supplementary information Supplementary data are available at Bioinformatics online.

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