A Parallel Non-Alignment Based Approach to Efficient Sequence Comparison using Longest Common Subsequences

Biological sequence comparison programs have revolutionized the practice of biochemistry, and molecular and evolutionary biology. Pairwise comparison of genomic sequences is a popular method of choice for analyzing genetic sequence data. However the quality of results from most sequence comparison methods are significantly affected by small perturbations in the data and furthermore, there is a dearth of computational tools to compare sequences beyond a certain length. In this paper, we describe a parallel algorithm for comparing genetic sequences using an alignment free-method based on computing the Longest Common Subsequence (LCS) between genetic sequences. We validate the quality of our results by comparing the phylogenetic tress obtained from ClustalW and LCS. We also show through complexity analysis of the isoefficiency and by empirical measurement of the running time that our algorithm is very scalable

An optimized TOPS+ comparison method for enhanced TOPS models

This article has been made available through the Brunel Open Access Publishing Fund.Background Although methods based on highly abstract descriptions of protein structures, such as VAST and TOPS, can perform very fast protein structure comparison, the results can lack a high degree of biological significance. Previously we have discussed the basic mechanisms of our novel method for structure comparison based on our TOPS+ model (Topological descriptions of Protein Structures Enhanced with Ligand Information). In this paper we show how these results can be significantly improved using parameter optimization, and we call the resulting optimised TOPS+ method as advanced TOPS+ comparison method i.e. advTOPS+. Results We have developed a TOPS+ string model as an improvement to the TOPS [1-3] graph model by considering loops as secondary structure elements (SSEs) in addition to helices and strands, representing ligands as first class objects, and describing interactions between SSEs, and SSEs and ligands, by incoming and outgoing arcs, annotating SSEs with the interaction direction and type. Benchmarking results of an all-against-all pairwise comparison using a large dataset of 2,620 non-redundant structures from the PDB40 dataset [4] demonstrate the biological significance, in terms of SCOP classification at the superfamily level, of our TOPS+ comparison method. Conclusions Our advanced TOPS+ comparison shows better performance on the PDB40 dataset [4] compared to our basic TOPS+ method, giving 90 percent accuracy for SCOP alpha+beta; a 6 percent increase in accuracy compared to the TOPS and basic TOPS+ methods. It also outperforms the TOPS, basic TOPS+ and SSAP comparison methods on the Chew-Kedem dataset [5], achieving 98 percent accuracy. Software Availability: The TOPS+ comparison server is available at http://balabio.dcs.gla.ac.uk/mallika/WebTOPS/.This article is available through the Brunel Open Access Publishing Fun

Data compression for sequencing data

Post-Sanger sequencing methods produce tons of data, and there is a general agreement that the challenge to store and process them must be addressed with data compression. In this review we first answer the question “why compression” in a quantitative manner. Then we also answer the questions “what” and “how”, by sketching the fundamental compression ideas, describing the main sequencing data types and formats, and comparing the specialized compression algorithms and tools. Finally, we go back to the question “why compression” and give other, perhaps surprising answers, demonstrating the pervasiveness of data compression techniques in computational biology

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

Bit-parallel and SIMD alignment algorithms for biological sequence analysis

High-throughput next-generation sequencing techniques have hugely decreased the cost and increased the speed of sequencing, resulting in an explosion of sequencing data. This motivates the development of high-efficiency sequence alignment algorithms. In this thesis, I present multiple bit-parallel and Single Instruction Multiple Data (SIMD) algorithms that greatly accelerate the processing of biological sequences. The first chapter describes the BitPAl bit-parallel algorithms for global alignment with general integer scoring, which assigns integer weights for match, mismatch, and insertion/deletion. The bit-parallel approach represents individual cells in an alignment scoring matrix as bits in computer words and emulates the calculation of scores by a series of logic operations. Bit-parallelism has previously been applied to other pattern matching problems, producing fast algorithms. In timed tests, we show that BitPAl runs 7 - 25 times faster than a standard iterative algorithm. The second part involves two approaches to alignment with substitution scoring, which assigns a potentially different substitution weight to every pair of alphabet characters, better representing the relative rates of different mutations. The first approach extends the existing BitPAl method. The second approach is a new SIMD algorithm that uses partial sums of adjacent score differences. I present a simple partial sum method as well as one that uses parallel scan for additional acceleration. Results demonstrate that these algorithms are significantly faster than existing SIMD dynamic programming algorithms. Finally, I describe two extensions to the partial sums algorithm. The first adds support for affine gap penalty scoring. Affine gap scoring represents the biological likelihood that it is more likely for gaps to be continuous than to be distributed throughout a region by introducing a gap opening penalty and a gap extension penalty. The second extension is an algorithm that uses the partial sums method to calculate the tandem alignment of a pattern against a text sequence using a single pattern copy. Next generation sequencing data provides a wealth of information to researchers. Extracting that information in a timely manner increases the utility and practicality of sequence analysis algorithms. This thesis presents a family of algorithms which provide alignment scores in less time than previous algorithms

Compiling a domain specific language for dynamic programming

Steffen P. Compiling a domain specific language for dynamic programming. Bielefeld (Germany): Bielefeld University; 2006

Mining Cell Transition Data

Cell transition data is obtained from a cellular phone that switches its current serving cell tower. The data consists of a sequence of transition events, which are pairs of cell identifiers and transition times. The focus of this thesis is applying data mining methods to such data, developing new algorithms, and extracting knowledge that will be a solid foundation on which to build location-aware applications. In addition to a thorough exploration of the features of the data, the tools and methods developed in this thesis provide solutions to three distinct research problems. First, we develop clustering algorithms that produce a reliable mapping between cell transitions and physical locations observed by users of mobile devices. The main clustering algorithm operates in online fashion, and we consider also a number of offline clustering methods for comparison. Second, we define the concept of significant locations, known as bases, and give an online algorithm for determining them. Finally, we consider the task of predicting the movement of the user, based on historical data. We develop a prediction algorithm that considers paths of movement in their entirety, instead of just the most recent movement history. All of the presented methods are evaluated with a significant body of real cell transition data, collected from about one hundred different individuals. The algorithms developed in this thesis are designed to be implemented on a mobile device, and require no extra hardware sensors or network infrastructure. By not relying on external services and keeping the user information as much as possible on the user s own personal device, we avoid privacy issues and let the users control the disclosure of their location information.Matkapuhelinverkoissa tukiasemat palvelevat tietyllä alueella olevia puhelimia. Tukiaseman radiosignaalin kattamaa aluetta kutsutaan soluksi. Vaihtaessaan käyttämäänsä tukiasemaa puhelin tuottaa solusiirtymädataa, joka koostuu sarjasta solutunnisteita ja siirtymisaikoja. Vaikka data ei sisälläkään paikkojen koordinaatteja, siitä voidaan silti oppia tunnistamaan puhelimen käyttäjälle tärkeitä paikkoja sekä ennustamaan hänen liikkumistaan paikkojen välillä. Väitöskirjatyössä sovelletaan tiedonlouhinnan menetelmiä solusiirtymädataan ja tavoitteena on tuottaa työkaluja paikkatietoisten sovellusten kehittämiseen. Saatuja tuloksia testataan aineistolla, joka on kerätty noin sadalta matkapuhelimen käyttäjältä. Datassa on erityispiirteitä, jotka tekevät sen käsittelystä haastavaa. Yksi keskeinen ongelma on se, että solun vaihtuminen ei aina johdu käyttäjän liikkumisesta, vaan puhelin voi vaihtaa käyttämäänsä tukiasemaa myös muista syistä. Ja kääntäen: yksittäisen solun sisällä on mahdollista liikkua jonkin matkaa ennen solun vaihtumista. Solujen ja fyysisten paikkojen vastaavuuksien löytämiseksi työssä esitellään klusterointimenetelmä, joka yhdistää samaan paikkaan todennäköisesti liittyviä soluja suuremmiksi kokonaisuuksiksi. Kyseessä on ns. online-algoritmi, joka analysoi solusiirtymävirtaa ja päivittää klusterien joukkoa reaaliaikaisesti. Työn tuloksena syntyneet algoritmit voidaan toteuttaa käytännössä kaikilla mobiililaitealustoilla, eikä niiden käyttö vaadi toimenpiteitä puhelinoperaattorilta tai -valmistajalta. Koska menetelmät vaativat vain vähän puhelimen laskenta- ja tallennuskapasiteettia, käyttäjän paikkatietoja ei tarvitse lähettää puhelimesta eteenpäin jatkokäsittelyä varten. Tämä turvaa puhelimen käyttäjän yksityisyyttä, sillä käyttäjä voi päättää itse, kenelle ja kuinka paljon tietoja luovutetaan