LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences

Background The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. Results We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. Conclusions With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases

LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences

Background The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. Results We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. Conclusions With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases

Towards Automating Protein Structure Determination from NMR Data

Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods

The BioLighthouse: Reusable Software Design for Bioinformatics

Advances in next-generation sequencing have accelerated the field of microbiology by making accessible a wealth of information about microbiomes. Unfortunately, microbiome experiments are among the least reproducible in terms of bioinformatics. Software tools are often poorly documented, under-maintained, and commonly have arcane dependencies requiring significant time investment to configure them correctly. Microbiome studies are multidisciplinary efforts but communication and knowledge discrepancies make accessibility, reproducibility, and transparency of computational workflows difficult. The BioLighthouse uses Ansible roles, playbooks, and modules to automate configuration and execution of bioinformatics workflows. The roles and playbooks act as virtual laboratory notebooks by documenting the provenance of a bioinformatics workflow. The BioLighthouse was tested for platform dependence and data-scale dependence with a microbial profiling pipeline. The microbial profiling pipeline consisted of Cutadapt, FLASH2, and DADA2. The pipeline was tested on 3 canola root and soil microbiome datasets with differing orders of magnitude of data: 1 sample, 10 samples, and 100 samples. Each dataset was processed by The BioLighthouse with 10 unique parameter sets and outputs were compared across 8 computing environments for a total of 240 pipeline runs. Outputs after each step in the pipeline were tested for identity using the Linux diff command to ensure reproducible results. Testing of The BioLighthouse suggested no platform or data-scale dependence. To provide an easy way of maintaining environment reproducibility in user-space, Conda and the channel Bioconda were used for virtual environments and software dependencies for configuring bioinformatics tools. The BioLighthouse provides a framework for developers to make their tools accessible to the research community, for bioinformaticians to build bioinformatics workflows, and for the broader research community to consume these tools at a high level while knowing the tools will execute as intended

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).</p> <p>Results</p> <p>We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.</p> <p>Conclusions</p> <p>The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.</p

A human genome-wide library of local phylogeny predictions for whole-genome inference problems

<p>Abstract</p> <p>Background</p> <p>Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences. Detailed predictions of the full phylogenies are therefore of value in improving our ability to make further inferences about population history and sources of genetic variation. Making phylogenetic predictions on the scale needed for whole-genome analysis is, however, extremely computationally demanding.</p> <p>Results</p> <p>In order to facilitate phylogeny-based predictions on a genomic scale, we develop a library of maximum parsimony phylogenies within local regions spanning all autosomal human chromosomes based on Haplotype Map variation data. We demonstrate the utility of this library for population genetic inferences by examining a tree statistic we call 'imperfection,' which measures the reuse of variant sites within a phylogeny. This statistic is significantly predictive of recombination rate, shows additional regional and population-specific conservation, and allows us to identify outlier genes likely to have experienced unusual amounts of variation in recent human history.</p> <p>Conclusion</p> <p>Recent theoretical advances in algorithms for phylogenetic tree reconstruction have made it possible to perform large-scale inferences of local maximum parsimony phylogenies from single nucleotide polymorphism (SNP) data. As results from the imperfection statistic demonstrate, phylogeny predictions encode substantial information useful for detecting genomic features and population history. This data set should serve as a platform for many kinds of inferences one may wish to make about human population history and genetic variation.</p

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry