Many bioinformatics algorithms under the hood rely on statistical models trained on such data to predict – often from nucleotide or amino acid sequences – the structure of genes, the function, ...location, domain content and secondary structure of proteins, the interactions of proteins with other proteins and DNA, phenotypes, etc. In recent examples, researchers fit models to 244 000 variants of a gene in Escherichia coli to learn about the influence of sequence composition on translation (Cambray et al., ) and trained a deep learning network to predict protein expression in Saccharomyces cerevisiae from a set of 500 000 random 50‐nt 5′ UTR sequences (Cuperus et al., ). Ideally then, we would recreate such an environment for our machine learning algorithms in the laboratory, where we start with an initial ‘infant’ model of a certain regulatory system or protein function and let the computer decide what sequence designs to try out – a deep learning version of the ‘robot scientist’ (King et al., ). Combined with laboratory automation, many microbial experiments can (soon) be performed with minimal human intervention, ranging from strain construction and screening, such as operated by Amyris, Gingko, Transcriptic, etc., to full‐genome engineering or even the design of microbial ecologies.
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Nanopore technology provides a novel approach to DNA sequencing that yields long, label-free reads of constant quality. The first commercial implementation of this approach, the MinION, has shown ...promise in various sequencing applications. This review gives an up-to-date overview of the MinION's utility as a
sequencing device. It is argued that the MinION may allow for portable and affordable
sequencing of even complex genomes in the near future, despite the currently error-prone nature of its reads. Through continuous updates to the MinION hardware and the development of new assembly pipelines, both sequencing accuracy and assembly quality have already risen rapidly. However, this fast pace of development has also lead to a lack of overview of the expanding landscape of analysis tools, as performance evaluations are outdated quickly. As the MinION is approaching a state of maturity, its user community would benefit from a thorough comparative benchmarking effort of de novo assembly pipelines in the near future. An earlier version of this article can be found on bioRxiv.
Predicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on ...the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species.
Performance of the mouse model is competitive with that of the human CADD model and better than established methods like PhastCons conservation scores and SIFT. Like in the human case, performance varies for different genomic regions and is best for coding regions. We also show the benefits of generating a species-specific model over lifting variants to a different species or applying a generic model. With fewer genomic annotations, performance on the test set as well as on the three validation sets is still good.
It is feasible to construct species-specific CADD models even when annotations such as epigenetic markers are not available. The minimal requirement for these models is the availability of a set of genomes of closely related species that can be used to infer an ancestor genome and substitution rates for the data generation.
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Organ formation in animals and plants relies on precise control of cell state transitions to turn stem cell daughters into fully differentiated cells. In plants, cells cannot rearrange due to shared ...cell walls. Thus, differentiation progression and the accompanying cell expansion must be tightly coordinated across tissues. PLETHORA (PLT) transcription factor gradients are unique in their ability to guide the progression of cell differentiation at different positions in the growing Arabidopsis thaliana root, which contrasts with well-described transcription factor gradients in animals specifying distinct cell fates within an essentially static context. To understand the output of the PLT gradient, we studied the gene set transcriptionally controlled by PLTs. Our work reveals how the PLT gradient can regulate cell state by region-specific induction of cell proliferation genes and repression of differentiation. Moreover, PLT targets include major patterning genes and autoregulatory feedback components, enforcing their role as master regulators of organ development.
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Plants exhibit a vast array of sesquiterpenes, C15 hydrocarbons which often function as herbivore-repellents or pollinator-attractants. These in turn are produced by a diverse range of sesquiterpene ...synthases. A comprehensive analysis of these enzymes in terms of product specificity has been hampered by the lack of a centralized resource of sufficient functionally annotated sequence data. To address this, we have gathered 262 plant sesquiterpene synthase sequences with experimentally characterized products. The annotated enzyme sequences allowed for an analysis of terpene synthase motifs, leading to the extension of one motif and recognition of a variant of another. In addition, putative terpene synthase sequences were obtained from various resources and compared with the annotated sesquiterpene synthases. This analysis indicated regions of terpene synthase sequence space which so far are unexplored experimentally. Finally, we present a case describing mutational studies on residues altering product specificity, for which we analyzed conservation in our database. This demonstrates an application of our database in choosing likely-functional residues for mutagenesis studies aimed at understanding or changing sesquiterpene synthase product specificity.
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•Over 250 plant sesquiterpene synthases with experimentally characterized products have been gathered from literature.•Sequence similarity of sesquiterpene synthases reflects phylogeny more than product specificity.•Hundreds of sesquiterpenes derive from a small number of precursor carbocations.•Uncharacterized sequences are compared to characterized synthases in terms of product precursor.•Terpene synthase motifs are not as well-conserved or as strict as previously thought.•The dataset of synthases with known function allows for finding residue positions likely to be involved in product specificity.
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Abstract
Motivation
Knowledge of haplotypes, i.e. phased and ordered marker alleles on a chromosome, is essential to answer many questions in genetics and genomics. By generating short pieces of DNA ...sequence, high-throughput modern sequencing technologies make estimation of haplotypes possible for single individuals. In polyploids, however, haplotype estimation methods usually require deep coverage to achieve sufficient accuracy. This often renders sequencing-based approaches too costly to be applied to large populations needed in studies of Quantitative Trait Loci.
Results
We propose a novel haplotype estimation method for polyploids, TriPoly, that combines sequencing data with Mendelian inheritance rules to infer haplotypes in parent-offspring trios. Using realistic simulations of both short and long-read sequencing data for banana (Musa acuminata) and potato (Solanum tuberosum) trios, we show that TriPoly yields more accurate progeny haplotypes at low coverages compared to existing methods that work on single individuals. We also apply TriPoly to phase Single Nucleotide Polymorphisms on chromosome 5 for a family of tetraploid potato with 2 parents and 37 offspring sequenced with an RNA capture approach. We show that TriPoly haplotype estimates differ from those of the other methods mainly in regions with imperfect sequencing or mapping difficulties, as it does not rely solely on sequence reads and aims to avoid phasings that are not likely to have been passed from the parents to the offspring.
Availability and implementation
TriPoly has been implemented in Python 3.5.2 (also compatible with Python 2.7.3 and higher) and can be freely downloaded at https://github.com/EhsanMotazedi/TriPoly.
Supplementary information
Supplementary data are available at Bioinformatics online.
Abstract
Background
Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold ...information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).
Results
Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a “query mode” that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.
Conclusions
BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.
In stationary-phase Escherichia coli, Dps (DNA-binding protein from starved cells) is the most abundant protein component of the nucleoid. Dps compacts DNA into a dense complex and protects it from ...damage. Dps has also been proposed to act as a global regulator of transcription. Here, we directly examine the impact of Dps-induced compaction of DNA on the activity of RNA polymerase (RNAP). Strikingly, deleting the dps gene decompacted the nucleoid but did not significantly alter the transcriptome and only mildly altered the proteome during stationary phase. Complementary in vitro assays demonstrated that Dps blocks restriction endonucleases but not RNAP from binding DNA. Single-molecule assays demonstrated that Dps dynamically condenses DNA around elongating RNAP without impeding its progress. We conclude that Dps forms a dynamic structure that excludes some DNA-binding proteins yet allows RNAP free access to the buried genes, a behavior characteristic of phase-separated organelles.
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•In E. coli, the condensation of DNA by Dps is decoupled from transcription•Dps provides selective access of proteins to the encased DNA•RNA polymerase readily initiates transcription on Dps-protected promoters•Dps maintains a dynamic condensed structure during RNA chain elongation
Despite markedly condensing the bacterial chromosome, the nucleoid-structuring protein Dps selectively allows access by RNA polymerase and transcription factors at normal rates while excluding other factors such as restriction endonucleases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
A better understanding of genomic features influencing the location of meiotic crossovers (COs) in plant species is both of fundamental importance and of practical relevance for plant ...breeding. Using CO positions with sufficiently high resolution from four plant species Arabidopsis thaliana, Solanum lycopersicum (tomato), Zea mays (maize) and Oryza sativa (rice) we have trained machine‐learning models to predict the susceptibility to CO formation. Our results show that CO occurrence within various plant genomes can be predicted by DNA sequence and shape features. Several features related to genome content and to genomic accessibility were consistently either positively or negatively related to COs in all four species. Other features were found as predictive only in specific species. Gene annotation‐related features were especially predictive for maize, whereas in tomato and Arabidopsis propeller twist and helical twist (DNA shape features) and AT/TA dinucleotides were found to be the most important. In rice, high roll (another DNA shape feature) and low CA dinucleotide frequency in particular were found to be associated with CO occurrence. The accuracy of our models was sufficient for Arabidopsis and rice (area under receiver operating characteristic curve, AUROC > 0.5), and was high for tomato and maize (AUROC ≫ 0.5), demonstrating that DNA sequence and shape are predictive for meiotic COs throughout the plant kingdom.
Significance Statement
Understanding the genomic features influencing the location of crossovers is of fundamental importance for many areas of plant biology, but a consistent, simultaneous analysis of multiple plant species in order to compare the genomic determinants of crossovers is lacking. We apply machine learning to crossover datasets from four different plant species in order to develop predictive models for the occurrence of crossovers, and to learn about relevant and important features correlated with CO formation.
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Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but ...need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data.
To address this, we present a fast method for detecting homology groups across a large number of individuals and/or species. We adopted a k-mer based approach which considerably reduces the number of pairwise protein alignments without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method for detecting homology in large proteomes of bacteria, fungi, plants and Metazoa.
We clearly observed the trade-off between recall and precision in our homology inference. Favoring recall or precision strongly depends on the application. The clustering behavior of our program can be optimized for particular applications by altering a few key parameters. The program is available for public use at https://github.com/sheikhizadeh/pantools as an extension to our pan-genomic analysis tool, PanTools.
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