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Abstract Motivation Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): To maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs. Availability and Implementation The eCOMPASS executable, C ++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass. Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Summary Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. Availability and implementation TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Motivation Species delimitation, the process of deciding how to group a set of organisms into units called species, is one of the most challenging problems in computational evolutionary biology. While many methods exist for species delimitation, most based on the coalescent theory, few are scalable to very large datasets, and methods that scale tend to be not accurate. Species delimitation is closely related to species tree inference from discordant gene trees, a problem that has enjoyed rapid advances in recent years. Results In this article, we build on the accuracy and scalability of recent quartet-based methods for species tree estimation and propose a new method called SODA for species delimitation. SODA relies heavily on a recently developed method for testing zero branch length in species trees. In extensive simulations, we show that SODA can easily scale to very large datasets while maintaining high accuracy. Availability and implementation The code and data presented here are available on https://github.com/maryamrabiee/SODA. Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Summary Here, we present PhyloWGA, an open source R package for conducting phylogenetic analysis and investigation of whole genome data. Availabilityand implementation Available at Github (https://github.com/radamsRHA/PhyloWGA). Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Motivation Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. Availability and implementation The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Supplementary information Supplementary data are available at Bioinformatics online.