WGT: Tools and algorithms for recognizing, visualizing, and generating Wheeler graphs

A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check graphs that may have the Wheeler properties. Here, we present Wheelie, an algorithm that combines a renaming heuristic with a permutation solver (Wheelie-PR) or a Satisfiability Modulo Theory (SMT) solver (Wheelie-SMT) to check whether a given graph has the Wheeler properties, a problem that is NP-complete in general. Wheelie can check a variety of random and real-world graphs in far less time than any algorithm proposed to date. It can check a graph with 1,000s of nodes in seconds. We implement these algorithms together with complementary visualization tools in the WGT toolkit, available as open source software at https://github.com/Kuanhao-Chao/Wheeler_Graph_Toolkit.

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

BACKGROUND

Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.

RESULTS

pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.

CONCLUSIONS

We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Estimation of macro- and micro-genetic environmental sensitivity in unbalanced datasets

Genotype-by-environment interaction is caused by variation in genetic environmental sensitivity (GES), which can be subdivided into macro- and micro-GES. Macro-GES is genetic sensitivity to macro-environments (definable environments often shared by groups of animals), while micro-GES is genetic sensitivity to micro-environments (individual environments). A combined reaction norm and double hierarchical generalised linear model (RN-DHGLM) allows for simultaneous estimation of base genetic, macro- and micro-GES effects. The accuracy of variance components estimated using a RN-DHGLM has been explicitly studied for balanced data and recommendation of a data size with a minimum of 100 sires with at least 100 offspring each have been made. In the current study, the data size (numbers of sires and progeny) and structure requirements of the RN-DHGLM were investigated for two types of unbalanced datasets. Both datasets had a variable number of offspring per sire, but one dataset also had a variable number of offspring within macro-environments. The accuracy and bias of the estimated macro- and micro-GES effects and the estimated breeding values (EBVs) obtained using the RN-DHGLM depended on the data size. Reasonably accurate and unbiased estimates were obtained with data containing 500 sires with 20 offspring or 100 sires with 50 offspring, regardless of the data structure. Variable progeny group sizes, alone or in combination with an unequal number of offspring within macro-environments, had little impact on the dispersion of the EBVs or the bias and accuracy of variance component estimation, but resulted in lower accuracies of the EBVs. Compared to genetic correlations of zero, a genetic correlation of 0.5 between base genetic, macro- and micro-GES components resulted in a slight decrease in the percentage of replicates that converged out of 100 replicates, but had no effect on the dispersion and accuracy of variance component estimation or the dispersion of the EBVs. The results show that it is possible to apply the RN-DHGLM to unbalanced datasets to obtain estimates of variance due to macro- and micro-GES. Furthermore, the levels of accuracy and bias of variance estimates when analysing macro- and micro-GES simultaneously are determined by average family size, with limited impact from variability in family size and/or cohort size. This creates opportunities for the use of field data from populations with unbalanced data structures when estimating macro- and micro-GES.

A linear-time algorithm that avoids inverses and computes Jackknife (leave-one-out) products like convolutions or other operators in commutative semigroups

BACKGROUND

Data about herpesvirus microRNA motifs on human circular RNAs suggested the following statistical question. Consider independent random counts, not necessarily identically distributed. Conditioned on the sum, decide whether one of the counts is unusually large. Exact computation of the p-value leads to a specific algorithmic problem. Given n elements g 0 , g 1 , … , g n - 1 in a set G with the closure and associative properties and a commutative product without inverses, compute the jackknife (leave-one-out) products g ¯ j = g 0 g 1 ⋯ g j - 1 g j + 1 ⋯ g n - 1 ( 0 ≤ j < n ).

RESULTS

This article gives a linear-time Jackknife Product algorithm. Its upward phase constructs a standard segment tree for computing segment products like g i , j = g i g i + 1 ⋯ g j - 1 ; its novel downward phase mirrors the upward phase while exploiting the symmetry of g j and its complement g ¯ j . The algorithm requires storage for 2 n elements of G and only about 3 n products. In contrast, the standard segment tree algorithms require about n products for construction and log 2 n products for calculating each g ¯ j , i.e., about n log 2 n products in total; and a naïve quadratic algorithm using n - 2 element-by-element products to compute each g ¯ j requires n n - 2 products.

CONCLUSIONS

In the herpesvirus application, the Jackknife Product algorithm required 15 min; standard segment tree algorithms would have taken an estimated 3 h; and the quadratic algorithm, an estimated 1 month. The Jackknife Product algorithm has many possible uses in bioinformatics and statistics.

seq-seq-pan: building a computational pan-genome data structure on whole genome alignment

Background

The increasing application of next generation sequencing technologies has led to the availability of thousands of reference genomes, often providing multiple genomes for the same or closely related species. The current approach to represent a species or a population with a single reference sequence and a set of variations cannot represent their full diversity and introduces bias towards the chosen reference. There is a need for the representation of multiple sequences in a composite way that is compatible with existing data sources for annotation and suitable for established sequence analysis methods. At the same time, this representation needs to be easily accessible and extendable to account for the constant change of available genomes.

Results

We introduce seq-seq-pan, a framework that provides methods for adding or removing new genomes from a set of aligned genomes and uses these to construct a whole genome alignment. Throughout the sequential workflow the alignment is optimized for generating a representative linear presentation of the aligned set of genomes, that enables its usage for annotation and in downstream analyses.

Conclusions

By providing dynamic updates and optimized processing, our approach enables the usage of whole genome alignment in the field of pan-genomics. In addition, the sequential workflow can be used as a fast alternative to existing whole genome aligners for aligning closely related genomes.

seq-seq-pan is freely available at https://gitlab.com/rki_bioinformatics

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4401-3) contains supplementary material, which is available to authorized users.

The importance of data structure in statistical analysis of dendritic spine morphology

BACKGROUND

Dendritic spine morphology is heterogeneous and highly dynamic. To study the changing or aberrant morphology in test setups, often spines from several neurons from a few experimental units e.g. mice or primary neuronal cultures are measured. This strategy results in a multilevel data structure, which, when not properly addressed, has a high risk of producing false positive and false negative findings.

METHODS

We used mixed-effects models to deal with data with a multilevel data structure and compared this method to analyses at each level. We apply these statistical tests to a dataset of dendritic spine morphology parameters to illustrate advantages of multilevel mixed-effects model, and disadvantages of other models.

RESULTS

We present an application of mixed-effects models for analyzing dendritic spine morphology datasets while correcting for the data structure.

COMPARISON WITH EXISTING METHODS

We further show that analyses at spine level and aggregated levels do not adequately account for the data structure, and that they may lead to erroneous results.

CONCLUSION

We highlight the importance of data structure in dendritic spine morphology analyses and highly recommend the use of mixed-effects models or other appropriate statistical methods to deal with multilevel datasets. Mixed-effects models are easy to use and superior to commonly used methods by including the data structure and the addition of other explanatory variables, for example sex, and age, etc., as well as interactions between variables or between variables and level identifiers.

Impacts of structuring the electronic health record: a systematic review protocol and results of previous reviews

PURPOSE

This paper (1) presents the protocol of an on-going systematic literature review on the methods of structuring electronic health record (EHR) data and studying the impacts of implemented structures, thus laying basis for the analysis of the empirical articles (2) describes previous reviews published on the subject and retrieved during the search of bibliographic databases, and (3) presents a summary of the results of previous reviews.

METHODS

Cochrane instructions were exploited to outline the review protocol - phases and search elements. Test searches were conducted to refine the search. The abstracts and/or full texts of review papers captured by the search were read by two of the team members independently, with disagreements first negotiated between them and if necessary eventually resolved in the team meetings. Additional review articles were picked from the reference lists of the reviews included in our search results. The elements defined in the search strategy and analytic framework were converted to a data extraction tool, which was tested by extracting data from the reviews captured by the search. Descriptive analysis of the extracted data was conducted.

RESULTS

The 12-stage review protocol that we developed includes definition of the problem, the search strategy and search terms, testing the strategy, conducting the search, updating search from references found, removing duplicates, defining the inclusion and exclusion criteria, exclusion and inclusion of papers, definition of the analytic framework to extract data, extracting data and reporting results. Our searches in fifteen electronic bibliographic databases retrieved 27 reviews, of which 14 were included for full text analysis. Of these, 11 focused on medical and three on nursing record structures. The data structures included forms, ontologies, classifications and terminologies. Some evidence was found on data structure impact on information quality, process quality and efficiency, but not on patients or professionals.

CONCLUSIONS

The 12 step review protocol resulted in a variety of reviews of different ways to structure EHR data. None of them compared outcomes of different structuring methods; all had a narrower definition of the Intervention (a specific EHR structure) and Outcome (a specific impact category). Several reviews missed a clear connection between the data structures (interventions) and outcomes, indicating that the methods and applications for structuring patient data have rarely been viewed as independent variables. The review protocol should be defined in a manner that allows replication of the review. There are different ways of structuring patient data with varying impacts, which should be distinguished in further empirical studies, as well as reviews.