Chemical graph generators

Chemical graph generators are software packages to generate computer representations of chemical structures adhering to certain boundary conditions. Their development is a research topic of cheminformatics. Chemical graph generators are used in areas such as virtual library generation in drug design, in molecular design with specified properties, called inverse QSAR/QSPR, as well as in organic synthesis design, retrosynthesis or in systems for computer-assisted structure elucidation (CASE). CASE systems again have regained interest for the structure elucidation of unknowns in computational metabolomics, a current area of computational biology.     

Confidence intervals by constrained optimization—An algorithm and software package for practical identifiability analysis in systems biology

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models’ predictability: how accurately can the models’ parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters’ confidence intervals. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling.We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article.The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).     

Evolving Digital Ecological Networks

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved).

This is a “Topic Page” article for PLOS Computational Biology.

     

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.     

DNA G-quadruplexes for native mass spectrometry in potassium: a database of validated structures in electrospray-compatible conditions

G-quadruplex DNA structures have become attractive drug targets, and native mass spectrometry can provide detailed characterization of drug binding stoichiometry and affinity, potentially at high throughput. However, the G-quadruplex DNA polymorphism poses problems for interpreting ligand screening assays. In order to establish standardized MS-based screening assays, we studied 28 sequences with documented NMR structures in (usually ∼100 mM) potassium, and report here their circular dichroism (CD), melting temperature (T_m), NMR spectra and electrospray mass spectra in 1 mM KCl/100 mM trimethylammonium acetate. Based on these results, we make a short-list of sequences that adopt the same structure in the MS assay as reported by NMR, and provide recommendations on using them for MS-based assays. We also built an R-based open-source application to build and consult a database, wherein further sequences can be incorporated in the future. The application handles automatically most of the data processing, and allows generating custom figures and reports. The database is included in the g4dbr package (https://github.com/EricLarG4/g4dbr) and can be explored online (https://ericlarg4.github.io/G4_database.html).     

Seeker: alignment-free identification of bacteriophage genomes by deep learning

Recent advances in metagenomic sequencing have enabled discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. Most of the existing detection methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct, highly divergent bacteriophage families. Here we present Seeker, a deep-learning tool for alignment-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and differentiation of phage sequences from bacterial ones, even when those phages exhibit little sequence similarity to established phage families. We comprehensively validate Seeker''s ability to identify previously unidentified phages, and employ this method to detect unknown phages, some of which are highly divergent from the known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly Python package (github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.     

The Jaw of the Worm: GTPase-activating Protein EAT-17 Regulates Grinder Formation in Caenorhabditis elegans

Constitutive transport of cellular materials is essential for cell survival. Although multiple small GTPase Rab proteins are required for the process, few regulators of Rabs are known. Here we report that EAT-17, a novel GTPase-activating protein (GAP), regulates RAB-6.2 function in grinder formation in Caenorhabditis elegans. We identified EAT-17 as a novel RabGAP that interacts with RAB-6.2, a protein that presumably regulates vesicle trafficking between Golgi, the endoplasmic reticulum, and plasma membrane to form a functional grinder. EAT-17 has a canonical GAP domain that is critical for its function. RNA interference against 25 confirmed and/or predicted RABs in C. elegans shows that RNAi against rab-6.2 produces a phenotype identical to eat-17. A directed yeast two-hybrid screen using EAT-17 as bait and each of the 25 RAB proteins as prey identifies RAB-6.2 as the interacting partner of EAT-17, confirming that RAB-6.2 is a specific substrate of EAT-17. Additionally, deletion mutants of rab-6.2 show grinder defects identical to those of eat-17 loss-of-function mutants, and both RAB-6.2 and EAT-17 are expressed in the terminal bulb of the pharynx where the grinder is located. Collectively, these results suggest that EAT-17 is a specific GTPase-activating protein for RAB-6.2. Based on the conserved function of Rab6 in vesicular transport, we propose that EAT-17 regulates the turnover rate of RAB-6.2 activity in cargo trafficking for grinder formation.     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.

     

Caenorhabditis elegans PIG-1/MELK Acts in a Conserved PAR-4/LKB1 Polarity Pathway to Promote Asymmetric Neuroblast Divisions

Asymmetric cell divisions produce daughter cells with distinct sizes and fates, a process important for generating cell diversity during development. Many Caenorhabditis elegans neuroblasts, including the posterior daughter of the Q cell (Q.p), divide to produce a larger neuron or neuronal precursor and a smaller cell that dies. These size and fate asymmetries require the gene pig-1, which encodes a protein orthologous to vertebrate MELK and belongs to the AMPK-related family of kinases. Members of this family can be phosphorylated and activated by the tumor suppressor kinase LKB1, a conserved polarity regulator of epithelial cells and neurons. In this study, we present evidence that the C. elegans orthologs of LKB1 (PAR-4) and its partners STRAD (STRD-1) and MO25 (MOP-25.2) regulate the asymmetry of the Q.p neuroblast division. We show that PAR-4 and STRD-1 act in the Q lineage and function genetically in the same pathway as PIG-1. A conserved threonine residue (T169) in the PIG-1 activation loop is essential for PIG-1 activity, consistent with the model that PAR-4 (or another PAR-4-regulated kinase) phosphorylates and activates PIG-1. We also demonstrate that PIG-1 localizes to centrosomes during cell divisions of the Q lineage, but this localization does not depend on T169 or PAR-4. We propose that a PAR-4-STRD-1 complex stimulates PIG-1 kinase activity to promote asymmetric neuroblast divisions and the generation of daughter cells with distinct fates. Changes in cell fate may underlie many of the abnormal behaviors exhibited by cells after loss of PAR-4 or LKB1.     

The Role of Dbf4-Dependent Protein Kinase in DNA Polymerase ζ-Dependent Mutagenesis in Saccharomyces cerevisiae

The yeast Dbf4-dependent kinase (DDK) (composed of Dbf4 and Cdc7 subunits) is an essential, conserved Ser/Thr protein kinase that regulates multiple processes in the cell, including DNA replication, recombination and induced mutagenesis. Only DDK substrates important for replication and recombination have been identified. Consequently, the mechanism by which DDK regulates mutagenesis is unknown. The yeast mcm5-bob1 mutation that bypasses DDK’s essential role in DNA replication was used here to examine whether loss of DDK affects spontaneous as well as induced mutagenesis. Using the sensitive lys2ΔA746 frameshift reversion assay, we show DDK is required to generate “complex” spontaneous mutations, which are a hallmark of the Polζ translesion synthesis DNA polymerase. DDK co-immunoprecipitated with the Rev7 regulatory, but not with the Rev3 polymerase subunit of Polζ. Conversely, Rev7 bound mainly to the Cdc7 kinase subunit and not to Dbf4. The Rev7 subunit of Polζ may be regulated by DDK phosphorylation as immunoprecipitates of yeast Cdc7 and also recombinant Xenopus DDK phosphorylated GST-Rev7 in vitro. In addition to promoting Polζ-dependent mutagenesis, DDK was also important for generating Polζ-independent large deletions that revert the lys2ΔA746 allele. The decrease in large deletions observed in the absence of DDK likely results from an increase in the rate of replication fork restart after an encounter with spontaneous DNA damage. Finally, nonepistatic, additive/synergistic UV sensitivity was observed in cdc7Δ pol32Δ and cdc7Δ pol30-K127R,K164R double mutants, suggesting that DDK may regulate Rev7 protein during postreplication “gap filling” rather than during “polymerase switching” by ubiquitinated and sumoylated modified Pol30 (PCNA) and Pol32.     

Real-time resolution of short-read assembly graph using ONT long reads

A streaming assembly pipeline utilising real-time Oxford Nanopore Technology (ONT) sequencing data is important for saving sequencing resources and reducing time-to-result. A previous approach implemented in npScarf provided an efficient streaming algorithm for hybrid assembly but was relatively prone to mis-assemblies compared to other graph-based methods. Here we present npGraph, a streaming hybrid assembly tool using the assembly graph instead of the separated pre-assembly contigs. It is able to produce more complete genome assembly by resolving the path finding problem on the assembly graph using long reads as the traversing guide. Application to synthetic and real data from bacterial isolate genomes show improved accuracy while still maintaining a low computational cost. npGraph also provides a graphical user interface (GUI) which provides a real-time visualisation of the progress of assembly. The tool and source code is available at https://github.com/hsnguyen/assembly.     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

The Drosophila wings apart Gene Anchors a Novel,Evolutionarily Conserved Pathway of Neuromuscular Development

wings apart (wap) is a recessive, semilethal gene located on the X chromosome in Drosophila melanogaster, which is required for normal wing-vein patterning. We show that the wap mutation also results in loss of the adult jump muscle. We use complementation mapping and gene-specific RNA interference to localize the wap locus to the proximal X chromosome. We identify the annotated gene CG14614 as the gene affected by the wap mutation, since one wap allele contains a non-sense mutation in CG14614, and a genomic fragment containing only CG14614 rescues the jump-muscle phenotypes of two wap mutant alleles. The wap gene lies centromere-proximal to touch-insensitive larva B and centromere-distal to CG14619, which is tentatively assigned as the gene affected in introverted mutants. In mutant wap animals, founder cell precursors for the jump muscle are specified early in development, but are later lost. Through tissue-specific knockdowns, we demonstrate that wap function is required in both the musculature and the nervous system for normal jump-muscle formation. wap/CG14614 is homologous to vertebrate wdr68, DDB1 and CUL4 associated factor 7, which also are expressed in neuromuscular tissues. Thus, our findings provide insight into mechanisms of neuromuscular development in higher animals and facilitate the understanding of neuromuscular diseases that may result from mis-expression of muscle-specific or neuron-specific genes.     

Complete genome sequence of the moderate thermophile Anaerobaculum mobile type strain (NGAT)

Anaerobaculum mobile Menes and Muxí 2002 is one of three described species of the genus Anaerobaculum, family Synergistaceae, phylum Synergistetes. This anaerobic and motile bacterium ferments a range of carbohydrates and mono- and dicarboxylic acids with acetate, hydrogen and CO₂ as end products. A. mobile NGA^T is the first member of the genus Anaerobaculum and the sixth member of the phylum Synergistetes with a completely sequenced genome. Here we describe the features of this bacterium, together with the complete genome sequence, and annotation. The 2,160,700 bp long single replicon genome with its 2,053 protein-coding and 56 RNA genes is part of the Genomic Encyclopedia of Bacteria and Archaea project.