Ritu Kundu

Popping Superbubbles and Discovering Clumps: Recent Developments in Biological Sequence Analysis

The information that can be inferred or predicted from knowing the genomic sequence of an organism is astonishing. String algorithms are critical to this process. This paper provides an overview of two particular problems that arise during computational molecular biology research, and recent algorithmic developments in solving them.

Accurate and Efficient Methods to Improve Multiple Circular Sequence Alignment

Multiple sequence alignment is a core computational task in bioinformatics and has been extensively studied over the past decades. This computation requires an implicit assumption on the input data: the left- and right-most position for each sequence is relevant. However, this is not the case for circular structures; for instance, MtDNA. Efforts have been made to address this issue but it is far from being solved. We have very recently introduced a fast algorithm for approximate circular string matching Barton et al., Algo Mol Biol, 2014. Here, we first show how to extend this algorithm for approximate circular dictionary matching; and, then, apply this solution with agglomerative hierarchical clustering to find a sufficiently good rotation for each sequence. Furthermore, we propose an alternative method that is suitable for more divergent sequences. We implemented these methods in BEAR, a programme for improving multiple circular sequence alignment. Experimental results, using real and synthetic data, show the high accuracy and efficiency of these new methods in terms of the inferred likelihood-based phylogenies.

Linear-time superbubble identification algorithm for genome assembly

DNA sequencing is the process of determining the exact order of the nucleotide bases of an individuals genome in order to catalogue sequence variation and understand its biological implications. Whole-genome sequencing techniques produce masses of data in the form of short sequences known as reads. Assembling these reads into a whole genome constitutes a major algorithmic challenge. Most assembly algorithms utilise de Bruijn graphs constructed from reads for this purpose. A critical step of these algorithms is to detect typical motif structures in the graph caused by sequencing errors and genome repeats, and filter them out; one such complex subgraph class is a so-called superbubble. In this paper, we propose an O ( n + m ) -time algorithm to detect all superbubbles in a directed acyclic graph with n vertices and m (directed) edges, improving the best-known O ( m log ? m ) -time algorithm by Sung et al.

Near-Optimal Computation of Runs over General Alphabet via Non-Crossing LCE Queries

Longest common extension queries (LCE queries) and runs are ubiquitous in algorithmic stringology. Linear-time algorithms computing runs and preprocessing for constant-time LCE queries have been known for over a decade. However, these algorithms assume a linearly-sortable integer alphabet. A recent breakthrough paper by Bannai et al. (SODA 2015) showed a link between the two notions: all the runs in a string can be computed via a linear number of LCE queries. The first to consider these problems over a general ordered alphabet was Kosolobov (Inf. Process. Lett., 2016), who presented an \(\mathcal {O}(n (\log n)^{2/3})\)-time algorithm for answering \(\mathcal {O}(n)\) LCE queries. This result was improved by Gawrychowski et al. (CPM 2016) to \(\mathcal {O}(n \log \log n)\) time. In this work we note a special non-crossing property of LCE queries asked in the runs computation. We show that any n such non-crossing queries can be answered on-line in \(\mathcal {O}(n \alpha (n))\) time, where \(\alpha (n)\) is the inverse Ackermann function, which yields an \(\mathcal {O}(n \alpha (n))\)-time algorithm for computing runs.

Linear algorithm for conservative degenerate pattern matching

A degenerate symbol x ? over an alphabet Σ is a non-empty subset of Σ, and a sequence of such symbols is a degenerate string. A degenerate string is said to be conservative if its number of non-solid symbols is upper-bounded by a fixed positive constant k. We consider here the matching problem of conservative degenerate strings and present the first linear-time algorithm that can find, for given degenerate strings P ? and T ? of total length n containing k non-solid symbols in total, the occurrences of P ? in T ? in O(nk) time.

Efficient Pattern Matching in Elastic-Degenerate Texts

Motivated by applications in bioinformatics, in what follows, we extend the notion of gapped strings to elastic-degenerate strings. An elastic-degenerate string can been seen as an ordered collection of solid (standard) strings interleaved by elastic-degenerate symbols; each such symbol corresponds to a set of two or more variable-length solid strings. In this article, we present an algorithm for solving the pattern matching problem with a solid pattern and an elastic-degenerate text running in \(\mathcal {O}(N+\alpha \gamma mn)\) time; where m is the length of the pattern; n and N are the length and total size of the elastic-degenerate text, respectively; \(\alpha \) and \(\gamma \) are parameters, respectively representing the maximum number of strings in any elastic-degenerate symbol of the text and the maximum number of elastic-degenerate symbols spanned by any occurrence of the pattern in the text. The space used by the proposed algorithm is \(\mathcal {O}(N)\).

A candidate hardware and software reference setup for kinesthetic codec standardization

Recently, the IEEE P1918.1 standardization activity has been initiated for defining a framework for the Tactile Internet. Within this activity, IEEE P1918.1.1 is a task group for the standardization of Haptic Codecs for the Tactile Internet. Primary goal of the task group is to define/develop codecs for both closed-loop (kinesthetic information exchange) and open-loop (tactile information exchange) communications. In this paper, we propose a reference hardware and software setup for the evaluation of kinesthetic codecs. The setup defines a typical teleoperation scenario in a virtual environment for the realization of closed-loop kinesthetic interactions. For the installation and testing of the setup, we provide detailed guidelines in the paper. The paper also provides sample data traces for both static and dynamic kinesthetic interactions. These data traces may be used for preliminary testing of kinesthetic codecs. The paper also provides links for the download of both the reference setup and the data traces.

Efficient Computation of Clustered-Clumps in Degenerate Strings

Given a finite set of patterns, a clustered-clump is a maximal overlapping set of occurrences of such patterns. Several solutions have been presented for identifying clustered-clumps based on statistical, probabilistic, and most recently, formal language theory techniques. Here, motivated by applications in molecular biology and computer vision, we present efficient algorithms, using String Algorithm techniques, to identify clustered-clumps in a given text. The proposed algorithms compute in \(\mathcal {O}(n+m)\) time the occurrences of all clustered-clumps for a given set of degenerate patterns \(\tilde{\mathcal {P}}\) and/or degenerate text \(\tilde{T}\) of total lengths m and n, respectively; such that the total number of non-solid symbols in \(\tilde{\mathcal {P}}\) and \(\tilde{T}\) is bounded by a fixed positive integer d.

Mining Acute Stroke Patients’ Data Using Supervised Machine Learning

Analysis of data for identifying patterns and building models has been used as a strong tool in different domains, including medical domains. In this paper, we analyse the registry of brain stroke patients collected over fifteen years in south London hospitals, known as South London Stroke Register. Our attempt is to identify the similar patterns between patients’ background and living conditions, their cognitive ability, the treatments they received, and the speed of their cognitive recovery; based on which most effective treatment can be predicted for new admitted patients. We designed a novel strategy which takes into account two different approaches. First is to predict, for each of the potential intervention treatments, whether that particular treatment would lead to recovery of a new patient or not. Second is to suggest a treatment (treatments) for the patient based on those that were given to the patients who have recovered and are most similar to the new patient. We built different classifiers using various state of the art machine learning algorithms. These algorithms were evaluated and compared based on three performance metrics, defined in this paper. Given that time is very crucial for stroke patients, main motivation of this research work is identifying the most effective treatment immediately for a new patient, and potentially increase the probability of their cognitive recovery.