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1. Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

   https://doi.org/10.1093/bioadv/vbac055  

   Balaban, Metin ; Bristy, Nishat Anjum ; Faisal, Ahnaf ; Bayzid, Md Shamsuzzoha ; Mirarab, Siavash ; Lengauer, ed., Thomas ( August 2022 , Bioinformatics Advances)

   Summary: While alignment has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods can simplify the analysis, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for emerging forms of data, such as genome skims, which do not permit assembly. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is their reliance on simplified models of sequence evolution such as Jukes–Cantor. If we can estimate frequencies of base substitutions in an alignment-free setting, we can compute pairwise distances under more complex models. However, since the strand of DNA sequences is unknown for many forms of genome-wide data, which arguably present the best use case for alignment-free methods, the most complex models that one can use are the so-called no strand-bias models. We show how to calculate distances under a four-parameter no strand-bias model called TK4 without relying on alignments or assemblies. The main idea is to replace letters in the input sequences and recompute Jaccard indices between k-mer sets. However, on larger genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that alignment-free distances can be highly accurate when genomes evolve under the assumed models and study the accuracy on assembled and unassembled biological data.

   Our software is available open source at https://github.com/nishatbristy007/NSB.

   Supplementary data are available at Bioinformatics Advances online.

    

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2. Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

   https://doi.org/10.1093/bioinformatics/btac423  

   Cui, Elvis Han ; Song, Dongyuan ; Wong, Weng Kee ; Li, Jingyi Jessica ; Mathelier, ed., Anthony ( June 2022 , Bioinformatics)

   Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.

   Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene’s expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.

   The Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM.

   Supplementary data are available at Bioinformatics online.

    

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3. FunImageJ: a Lisp framework for scientific image processing

   https://doi.org/10.1093/bioinformatics/btx710  

   Harrington, Kyle I. S. ; Rueden, Curtis T. ; Eliceiri, Kevin W. ; Murphy, ed., Robert ( November 2017 , Bioinformatics)

   FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis.

   Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at http://imagej.net/FunImageJ.

   Supplementary data are available at Bioinformatics online.

    

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4. An evolutionary model motivated by physicochemical properties of amino acids reveals variation among proteins

   https://doi.org/10.1093/bioinformatics/bty261  

   Braun, Edward L. ( June 2018 , Bioinformatics)

   The relative rates of amino acid interchanges over evolutionary time are likely to vary among proteins. Variation in those rates has the potential to reveal information about constraints on proteins. However, the most straightforward model that could be used to estimate relative rates of amino acid substitution is parameter-rich and it is therefore impractical to use for this purpose.

   A six-parameter model of amino acid substitution that incorporates information about the physicochemical properties of amino acids was developed. It showed that amino acid side chain volume, polarity and aromaticity have major impacts on protein evolution. It also revealed variation among proteins in the relative importance of those properties. The same general approach can be used to improve the fit of empirical models such as the commonly used PAM and LG models.

   Perl code and test data are available from https://github.com/ebraun68/sixparam.

   Supplementary data are available at Bioinformatics online.

    

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5. FastRFS: fast and accurate Robinson-Foulds Supertrees using constrained exact optimization

   https://doi.org/10.1093/bioinformatics/btw600  

   Vachaspati, Pranjal ; Warnow, Tandy ; Chauve, ed., Cedric ( September 2016 , Bioinformatics)

   The estimation of phylogenetic trees is a major part of many biological dataset analyses, but maximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate-sized datasets. Supertree methods, which are used to construct trees from trees computed on subsets, are critically important tools for enabling the statistical estimation of phylogenies for large and potentially heterogeneous datasets. Supertree estimation is itself NP-hard, and no current supertree method has sufficient accuracy and scalability to provide good accuracy on the large datasets that supertree methods were designed for, containing thousands of species and many subset trees.

   We present FastRFS, a new method based on a dynamic programming method we have developed to find an exact solution to the Robinson-Foulds Supertree problem within a constrained search space. FastRFS has excellent accuracy in terms of criterion scores and topological accuracy of the resultant trees, substantially improving on competing methods on a large collection of biological and simulated data. In addition, FastRFS is extremely fast, finishing in minutes on even very large datasets, and in under an hour on a biological dataset with 2228 species.

   FastRFS is available on github at https://github.com/pranjalv123/FastRFS

   Supplementary data are available at Bioinformatics online.

    

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