Prokaryotic Genome Sequences Research Articles

CPGminer: An Interactive Dashboard to Explore the Genomic Features and Taxonomy of Complete Prokaryotic Genomes.

Prokaryotes, the earliest forms of life on Earth, play crucial roles in global biogeochemical processes in virtually all ecosystems. The ever-increasing amount of prokaryotic genome sequencing data provides a wealth of information to examine fundamental and applied questions through systematic genome comparison. Genomic features, such as genome size and GC content, and taxonomy-centric genomic features of complete prokaryotic genomes (CPGs) are crucial for various fields of microbial research and education, yet they are often overlooked. Additionally, creating systematically curated datasets that align with research concerns is an essential yet challenging task for wet-lab researchers. In this study, we introduce CPGminer, a user-friendly tool that allows researchers to quickly and easily examine the genomic features and taxonomy of CPGs and curate genome datasets. We also provide several examples to demonstrate its practical utility in addressing descriptive questions.

Insertion sequence transposition inactivates CRISPR-Cas immunity

CRISPR-Cas immunity systems safeguard prokaryotic genomes by inhibiting the invasion of mobile genetic elements. Here, we screened prokaryotic genomic sequences and identified multiple natural transpositions of insertion sequences (ISs) into cas genes, thus inactivating CRISPR-Cas defenses. We then generated an IS-trapping system, using Escherichia coli strains with various ISs and an inducible cas nuclease, to monitor IS insertions into cas genes following the induction of double-strand DNA breakage as a physiological host stress. We identified multiple events mediated by different ISs, especially IS1 and IS10, displaying substantial relaxed target specificity. IS transposition into cas was maintained in the presence of DNA repair machinery, and transposition into other host defense systems was also detected. Our findings highlight the potential of ISs to counter CRISPR activity, thus increasing bacterial susceptibility to foreign DNA invasion.

Metagenomes and Metagenome-Assembled Genome Sequences from Nitrogen-Fixing Alder Nodules.

Bacterial nitrogen (N) fixation in alder nodules is a key process providing nitrogen to nutrient-limited arctic biomes. Here, 45 prokaryotic metagenome-assembled genome (MAG) sequences from root nodules of arctic alder are reported.

ProsmORF-pred: a machine learning-based method for the identification of small ORFs in prokaryotic genomes.

Small open reading frames (smORFs) encoding proteins less than 100amino acids (aa) are known to be important regulators of key cellular processes. However, their computational identification remains a challenge. Based on a comprehensive analysis of known prokaryotic small ORFs, we have developed the ProsmORF-pred resource which uses a machine learning (ML)-based method for prediction of smORFs in the prokaryotic genome sequences. ProsmORF-pred consists of two ML models, one for initiation site recognition in nucleic acid sequences upstream of putative start codons and the other uses translated amino acid sequences to decipher functional protein like sequences. The nucleotide sequence-based initiation site recognition model has been trained using longer ORFs (>100aa) in the same genome while the ML model for identification of protein like sequences has been trained using annotated smORFs from Escherichia coli. Comprehensive benchmarking of ProsmORF-pred reveals that its performance is comparable to other state-of-the-art approaches on the annotated smORF set derived from 32 prokaryotic genomes. Its performance is distinctly superior to other tools like PRODIGAL and RANSEPS for prediction of newly identified smORFs which have a length range of 10-30aa, where prediction of smORFs has been a major challenge. Apart from identification of smORFs in genomic sequences, ProsmORF-pred can also aid in functional annotation of the predicted smORFs based on sequence similarity and genomic neighbourhood similarity searches in ProsmORFDB, a well-curated database of known smORFs. ProsmORF-pred along with its backend database ProsmORFDB is available as a user-friendly web server (http://www.nii.ac.in/prosmorfpred.html).

Micro-synteny conservation analysis revealed the evolutionary history of bacterial biphenyl degradation pathway.

Phenolic compounds have been enlisted by the United States Environmental Protection Agency (USEPA) and the European Union (EU) as pollutants of priority concern. The biphenyl degradation pathway plays an essential role in prokaryote polychlorinated biphenyls degradation. Our understanding of prokaryotic pathways and their evolution has dramatically increased in recent years with the advancements in prokaryotic genome sequencing and analysis tools. In this work, we applied bioinformatics tools to study the evolution of the biphenyl degradation pathway focusing on the phylogeny and initiation of four representative species (Burkholderia xenovorans LB400, Polaromonas naphthalenivorans CJ2, Pseudomonas putida F1 and Rhodococcus jostii RHA1). These species contained partial or full concatenated genes from bph gene cluster (i.e. bphRbphA1A2A3A4BCKHJID). The aim was to establish this pathway's origin and development mode in the prokaryotic world. Genomic screening revealed that many bacterial species possess genes for the biphenyl degradation pathway. However, the micro-synteny conservation analysis indicated that massive gene recruitment events might have occurred during the evolution of the biphenyl degradation pathway. Combining with the phylogenetic positions, this work points to the evolutionary process of acquiring the biphenyl degradation pathway by different fragments through horizontal gene transfer in these bacterial groups. This study reports the first-ever evidence of the birth of this pathway in the represented species.

Approximate search for known gene clusters in new genomes using PQ-trees

Gene clusters are groups of genes that are co-locally conserved across various genomes, not necessarily in the same order. Their discovery and analysis is valuable in tasks such as gene annotation and prediction of gene interactions, and in the study of genome organization and evolution. The discovery of conserved gene clusters in a given set of genomes is a well studied problem, but with the rapid sequencing of prokaryotic genomes a new problem is inspired. Namely, given an already known gene cluster that was discovered and studied in one genomic dataset, to identify all the instances of the gene cluster in a given new genomic sequence. Thus, we define a new problem in comparative genomics, denoted PQ-Tree Search that takes as input a PQ-tree T representing the known gene orders of a gene cluster of interest, a gene-to-gene substitution scoring function h, integer arguments d_T and d_S, and a new sequence of genes S. The objective is to identify in S approximate new instances of the gene cluster; These instances could vary from the known gene orders by genome rearrangements that are constrained by T, by gene substitutions that are governed by h, and by gene deletions and insertions that are bounded from above by d_T and d_S, respectively. We prove that PQ-Tree Search is NP-hard and propose a parameterized algorithm that solves the optimization variant of PQ-Tree Search in O^*(2^{gamma }) time, where gamma is the maximum degree of a node in T and O^* is used to hide factors polynomial in the input size. The algorithm is implemented as a search tool, denoted PQFinder, and applied to search for instances of chromosomal gene clusters in plasmids, within a dataset of 1,487 prokaryotic genomes. We report on 29 chromosomal gene clusters that are rearranged in plasmids, where the rearrangements are guided by the corresponding PQ-trees. One of these results, coding for a heavy metal efflux pump, is further analysed to exemplify how PQFinder can be harnessed to reveal interesting new structural variants of known gene clusters.

Estimate of the sequenced proportion of the global prokaryotic genome

BackgroundSequencing prokaryotic genomes has revolutionized our understanding of the many roles played by microorganisms. However, the cell and taxon proportions of genome-sequenced bacteria or archaea on earth remain unknown. This study aimed to explore this basic question using large-scale alignment between the sequences released by the Earth Microbiome Project and 155,810 prokaryotic genomes from public databases.ResultsOur results showed that the median proportions of the genome-sequenced cells and taxa (at 100% identities in the 16S-V4 region) in different biomes reached 38.1% (16.4–86.3%) and 18.8% (9.1–52.6%), respectively. The sequenced proportions of the prokaryotic genomes in biomes were significantly negatively correlated with the alpha diversity indices, and the proportions sequenced in host-associated biomes were significantly higher than those in free-living biomes. Due to a set of cosmopolitan OTUs that are found in multiple samples and preferentially sequenced, only 2.1% of the global prokaryotic taxa are represented by sequenced genomes. Most of the biomes were occupied by a few predominant taxa with a high relative abundance and much higher genome-sequenced proportions than numerous rare taxa.ConclusionsThese results reveal the current situation of prokaryotic genome sequencing for earth biomes, provide a more reasonable and efficient exploration of prokaryotic genomes, and promote our understanding of microbial ecological functions.EHyGD1bcuvyriCzccWszavVideo

Bacterial and Archaeal Metagenome-Assembled Genome Sequences from Svalbard Permafrost.

Permafrost contains one of the least known soil microbiomes, where microbial populations reside in an ice-locked environment. Here, 56 prokaryotic metagenome-assembled genome (MAG) sequences from 13 phyla are reported. These MAGs will provide information on metabolic pathways that could mediate biogeochemical cycles in Svalbard permafrost.

Metagenomic Assembly and Prokaryotic Metagenome-Assembled Genome Sequences from the Northern Gulf of Mexico "Dead Zone".

Coastal regions experiencing declining dissolved oxygen are increasing in number and severity around the world. However, despite the importance of microbial metabolism in coastal hypoxia, few metagenomic surveys exist. Our data set from within the second largest human-caused hypoxic region provides opportunities to more deeply explore the microbiology of these systems.

Transcriptome Engineering with RNA-Targeting Type VI-D CRISPR Effectors

Class 2 CRISPR-Cas systems endow microbes with diverse mechanisms for adaptive immunity. Here, we analyzed prokaryotic genome and metagenome sequences to identify an uncharacterized family of RNA-guided, RNA-targeting CRISPR systems that we classify as type VI-D. Biochemical characterization and protein engineering of seven distinct orthologs generated a ribonuclease effector derived fromRuminococcus flavefaciens XPD3002 (CasRx) with robust activity in human cells. CasRx-mediated knockdown exhibits high efficiency and specificity relative to RNA interference across diverse endogenous transcripts. As one of the most compact single-effector Cas enzymes, CasRx can also be flexibly packaged into adeno-associated virus. We target virally encoded, catalytically inactive CasRx to cis elements of pre-mRNA to manipulate alternative splicing, alleviating dysregulated tau isoform ratios in a neuronal model of frontotemporal dementia. Our results present CasRx as a programmable RNA-binding module for efficient targeting of cellular RNA, enabling a general platform for transcriptome engineering and future therapeutic development.

Comparison of Methods of Detection of Exceptional Sequences in Prokaryotic Genomes.

Many proteins need recognition of specific DNA sequences for functioning. The number of recognition sites and their distribution along the DNA might be of biological importance. For example, the number of restriction sites is often reduced in prokaryotic and phage genomes to decrease the probability of DNA cleavage by restriction endonucleases. We call a sequence an exceptional one if its frequency in a genome significantly differs from one predicted by some mathematical model. An exceptional sequence could be either under- or over-represented, depending on its frequency in comparison with the predicted one. Exceptional sequences could be considered biologically meaningful, for example, as targets of DNA-binding proteins or as parts of abundant repetitive elements. Several methods to predict frequency of a short sequence in a genome, based on actual frequencies of certain its subsequences, are used. The most popular are methods based on Markov chain models. But any rigorous comparison of the methods has not previously been performed. We compared three methods for the prediction of short sequence frequencies: the maximum-order Markov chain model-based method, the method that uses geometric mean of extended Markovian estimates, and the method that utilizes frequencies of all subsequences including discontiguous ones. We applied them to restriction sites in complete genomes of 2500 prokaryotic species and demonstrated that the results depend greatly on the method used: lists of 5% of the most under-represented sites differed by up to 50%. The method designed by Burge and coauthors in 1992, which utilizes all subsequences of the sequence, showed a higher precision than the other two methods both on prokaryotic genomes and randomly generated sequences after computational imitation of selective pressure. We propose this method as the first choice for detection of exceptional sequences in prokaryotic genomes.

Global analysis of prokaryotic tRNA-derived cyclodipeptide biosynthesis

BackgroundAmong naturally occurring small molecules, tRNA-derived cyclodipeptides are a class that have attracted attention for their diverse and desirable biological activities. However, no tools are available to link cyclodipeptide synthases identified within prokaryotic genome sequences to their chemical products. Consequently, it is unclear how many genetically encoded cyclodipeptides represent novel products, and which producing organisms should be targeted for discovery.ResultsWe developed a pipeline for identification and classification of cyclodipeptide biosynthetic gene clusters and prediction of aminoacyl-tRNA substrates and complete chemical structures. We leveraged this tool to conduct a global analysis of tRNA-derived cyclodipeptide biosynthesis in 93,107 prokaryotic genomes, and compared predicted cyclodipeptides to known cyclodipeptide synthase products and all known chemically characterized cyclodipeptides. By integrating predicted chemical structures and gene cluster architectures, we created a unified map of known and unknown genetically encoded cyclodipeptides.ConclusionsOur analysis suggests that sizeable regions of the chemical space encoded within sequenced prokaryotic genomes remain unexplored. Our map of the landscape of genetically encoded cyclodipeptides provides candidates for targeted discovery of novel compounds. The integration of our pipeline into a user-friendly web application provides a resource for further discovery of cyclodipeptides in newly sequenced prokaryotic genomes.

DBBQs: dataBase of Bacterial Quality scores

BackgroundIt is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.ResultsProkaryotic genomic data from all sources were collected and combined to make a non-redundant set of bacterial genomes. The genome quality score for each was calculated by four different measurements: assembly quality, number of rRNA and tRNA genes, and the occurrence of conserved functional domains. The dataBase of Bacterial Quality scores (dBBQs) was designed to store and retrieve quality scores. It offers fast searching and download features which the result can be used for further analysis. In addition, the search results are shown in interactive JavaScript chart framework using DC.js. The analysis of quality scores across major public genome databases find that around 68% of the genomes are of acceptable quality for many uses.ConclusionsdBBQs (available at http://arc-gem.uams.edu/dbbqs) provides genome quality scores for all available prokaryotic genome sequences with a user-friendly Web-interface. These scores can be used as cut-offs to get a high-quality set of genomes for testing bioinformatics tools or improving the analysis. Moreover, all data of the four measurements that were combined to make the quality score for each genome, which can potentially be used for further analysis. dBBQs will be updated regularly and is freely use for non-commercial purpose.

Analysis of DNA and RNA Binding Properties of Borrelia burgdorferi Regulatory Proteins.

Bioinformatic approaches and a large volume of prokaryotic genome sequences have enabled rapid identification of regulatory proteins with features to bind DNA or RNA in a given prokaryote. However, biological relevance of these regulatory proteins requires methods to rapidly purify and determine their binding properties within the physiological context or life style of the organism. Here, we describe the experimental approaches to determine the nucleic acid binding properties of regulatory proteins of Borrelia burgdorferi using Borrelia host-adaptation Re.3gulator (BadR-a DNA binding protein) and Carbon storage regulators A of B. b urgdorferi (CsrABb-an RNA binding protein) as examples. Best laboratory practices associated with overexpression/purification of recombinant borrelial proteins, synthesis of target nucleic acid sequences, and electrophoretic mobility assays to assess the protein/nucleic acid interactions are described. The methods described are intended to facilitate empirical assessment of the binding affinity, co-factor requirements, quality of the interacting partners, and readily modifiable assay conditions to assess the binding properties to define known and unknown regulatory properties of nucleic acid binding proteins of B. burgdorferi.

ISEScan: automated identification of insertion sequence elements in prokaryotic genomes.

The insertion sequence (IS) elements are the smallest but most abundant autonomous transposable elements in prokaryotic genomes, which play a key role in prokaryotic genome organization and evolution. With the fast growing genomic data, it is becoming increasingly critical for biology researchers to be able to accurately and automatically annotate ISs in prokaryotic genome sequences. The available automatic IS annotation systems are either providing only incomplete IS annotation or relying on the availability of existing genome annotations. Here, we present a new IS elements annotation pipeline to address these issues. ISEScan is a highly sensitive software pipeline based on profile hidden Markov models constructed from manually curated IS elements. ISEScan performs better than existing IS annotation systems when tested on prokaryotic genomes with curated annotations of IS elements. Applying it to 2784 prokaryotic genomes, we report the global distribution of IS families across taxonomic clades in Archaea and Bacteria. ISEScan is implemented in Python and released as an open source software at https://github.com/xiezhq/ISEScan. hatang@indiana.edu. Supplementary data are available at Bioinformatics online.

ContEst16S: an algorithm that identifies contaminated prokaryotic genomes using 16S RNA gene sequences.

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

GFinisher: a new strategy to refine and finish bacterial genome assemblies.

Despite the development in DNA sequencing technology, improving the number and the length of reads, the process of reconstruction of complete genome sequences, the so called genome assembly, is still complex. Only 13% of the prokaryotic genome sequencing projects have been completed. Draft genome sequences deposited in public databases are fragmented in contigs and may lack the full gene complement. The aim of the present work is to identify assembly errors and improve the assembly process of bacterial genomes. The biological patterns observed in genomic sequences and the application of a priori information can allow the identification of misassembled regions, and the reorganization and improvement of the overall de novo genome assembly. GFinisher starts generating a Fuzzy GC skew graphs for each contig in an assembly and follows breaking down the contigs in critical points in order to reassemble and close them using jFGap. This has been successfully applied to dataset from 96 genome assemblies, decreasing the number of contigs by up to 86%. GFinisher can easily optimize assemblies of prokaryotic draft genomes and can be used to improve the assembly programs based on nucleotide sequence patterns in the genome. The software and source code are available at http://gfinisher.sourceforge.net/.

A novel gene identification algorithm with Bayesian classification

Abstract The rapid advances in the field of computational genomics and bioinformatics have motivated the development of innovative engineering methods for data acquisition, interpretation, and analysis. With the help of the later methods, many processes in molecular biology can be modeled and further analyzed. Identification and discovery of the coding regions in the genomic structure using computational algorithms is a clear example of such processes. This work proposes a novel application of well-known principles and concepts from communications theory and digital signal processing for the detection of protein coding regions in prokaryotic genomes. The proposed algorithm employs a polyphase complex mapping scheme to provide a numerical representation of the genomic sequences involved in the analysis. It then utilizes concepts in communications theory such as correlation, the maximal ratio combining (MRC) algorithm, and filtering to generate a signal whose peaks and troughs signify coding and noncoding regions, respectively. The proposed algorithm is applied to several prokaryotic genome sequences. Two Bayesian classifiers are designed to evaluate the performance of the proposed algorithm. The obtained simulation results show that the algorithm is able to efficiently and accurately identify protein coding regions with sensitivity and specificity values comparable to well-known gene detection methods in prokaryotes such as GLIMMER and GeneMark. This further proves the relevance of using communications theory concepts for genomic sequence analysis.

ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes.

The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs. The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations. ConsPred is implemented in Java, Perl and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/. thomas.rattei@univie.ac.atSupplementary information: Supplementary data are available at Bioinformatics online.