Towards Distance-Based Phylogenetic Inference in Average-Case Linear-Time

Computing genetic evolution distances among a set of taxa dominates the running time of many phylogenetic inference methods. Most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method

NGS4Cloud: Cloud-based NGS Data Processing

Motivation and challenges: Next-Generation Sequencing (NGS) technologies are greatly increasing the amount of genomic computer data, revolutionizing the biosciences field and leading to the development of more complex NGS Data Analysis techniques [2]. These techniques, known as pipelines or workflows, consist of running and refining a series of intertwined computational analysis and visualization tasks on large amounts of data. These pipelines involve the use of multiple software tools and data resources in a staged fashion, with the output of one tool being passed as input to the next one. To simplify the design and execution of biomedical workflows by end users, especially those that use multiple software tools and data resources, a number of scientific workflow systems have been developed over the past decade. Examples include Galaxy [1] and Swift [3]. However, most of these scientific workflow systems cannot be easily deployed and most of the times are only available to users with access to specialized IT support. There are two main issues to address in the design of an execution environment to these pipelines. First, due to the complexity of configuring and parametrizing pipelines, the use of NGS Data Analysis techniques is not an easy task for a user without IT knowledge. Second, knowing input data can be as much as terabytes and petabytes, pipelines execution require, in general, a great amount of computational resources.info:eu-repo/semantics/publishedVersio

Order-Preserving Pattern Matching Indeterminate Strings

Given an indeterminate string pattern p and an indeterminate string text t, the problem of order-preserving pattern matching with character uncertainties (muOPPM) is to find all substrings of t that satisfy one of the possible orderings defined by p. When the text and pattern are determinate strings, we are in the presence of the well-studied exact order-preserving pattern matching (OPPM) problem with diverse applications on time series analysis. Despite its relevance, the exact OPPM problem suffers from two major drawbacks: 1) the inability to deal with indetermination in the text, thus preventing the analysis of noisy time series; and 2) the inability to deal with indetermination in the pattern, thus imposing the strict satisfaction of the orders among all pattern positions. In this paper, we provide the first polynomial algorithms to answer the muOPPM problem when: 1) indetermination is observed on the pattern or text; and 2) indetermination is observed on both the pattern and the text and given by uncertainties between pairs of characters. First, given two strings with the same length m and O(r) uncertain characters per string position, we show that the muOPPM problem can be solved in O(mr lg r) time when one string is indeterminate and r in N^+ and in O(m^2) time when both strings are indeterminate and r=2. Second, given an indeterminate text string of length n, we show that muOPPM can be efficiently solved in polynomial time and linear space