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A degenerate or indeterminate string on an alphabet Σ is a sequence of non-empty subsets of Σ. Given a degenerate string t of length n and its Burrows–Wheeler transform we present a new method for searching for a degenerate pattern of length m in t running in O ( m n ) time on a constant size alphabet Σ. Furthermore, it is a hybrid pattern matching technique that works on both regular and degenerate strings. A degenerate string is said to be conservative if its number of non-solid letters is upper-bounded by a fixed positive constant q; in this case we show that the search time complexity is O ( q m 2 ) for counting the number of occurrences and O ( q m 2 + occ ) for reporting the found occurrences where occ is the number of occurrences of the pattern in t. Experimental results show that our method performs well in practice.

Since the advent of next-generation sequencing in the early 2000s, the volume of bioinformatics software tools and databases has exploded and continues to grow rapidly. Documenting this evolution on a global and time-dependent scale is a challenging task, limited by the scarcity of comprehensive tool repositories. We collected data from over ~23,000 references classified in the OMICtools database, spanning the last 26 years of bioinformatics to present a data-supported snapshot of bioinformatics software tool evolution and the current status, to shed light on future directions and opportunities in this field. The present review explores new aspects of computational biology, including country partnerships, trends in technologies and area of development, research and development (R&D) investments and coding languages. This is the most comprehensive systematic overview of the field to date and provides the community with insights and knowledge on the direction of the development and evolution of bioinformatics software tools, highlighting the increasing complexity of analysis., 9 pages

International audience

A degenerate or indeterminate string on an alphabet $\Sigma$ is a sequence of non-empty subsets of $\Sigma$. Given a degenerate string $t$ of length $n$, we present a new method based on the Burrows--Wheeler transform for searching for a degenerate pattern of length $m$ in $t$ running in $O(mn)$ time on a constant size alphabet $\Sigma$. Furthermore, it is a hybrid pattern-matching technique that works on both regular and degenerate strings. A degenerate string is said to be conservative if its number of non-solid letters is upper-bounded by a fixed positive constant $q$; in this case we show that the search complexity time is $O(qm^2)$. Experimental results show that our method performs well in practice., Comment: 7 pages, 1 figure

International audience; The Range Minimum Query problem consists in answering efficiently to a simple question: " what is the minimal element appearing between two specified indices of a given array? ". In this paper we present a novel approach that offers a satisfying trade-off between time and space. Moreover we show how the structure can be easily maintained whenever an insertion, modification or deletion modifies the array.

An absent word of a word y of length n is a word that does not occur in y. It is a minimal absent word if all its proper factors occur in y. Minimal absent words have been computed in genomes of organisms from all domains of life; their computation also provides a fast alternative for measuring approximation in sequence comparison. There exists an \(\mathcal {O}(n)\)-time and \(\mathcal {O}(n)\)-space algorithm for computing all minimal absent words on a fixed-sized alphabet based on the construction of suffix array (Barton et al., 2014). An implementation of this algorithm was also provided by the authors and is currently the fastest available. In this article, we present a new \(\mathcal {O}(n)\)-time and \(\mathcal {O}(n)\)-space algorithm for computing all minimal absent words; it has the desirable property that, given the indexing data structure at hand, the computation of minimal absent words can be executed in parallel. Experimental results show that a multiprocessing implementation of this algorithm can accelerate the overall computation by more than a factor of two compared to state-of-the-art approaches. By excluding the indexing data structure construction time, we show that the implementation achieves near-optimal speed-ups.

In this paper, we study word regularities and in particular extensions of the notion of the word period: quasiperiodicity, covers and seeds. We present overviews of algorithms for computing the quasiperiodicity, the covers and the seeds of a given word. We also present an overview of an algorithm that finds maximal word factors with the above regularities. Finally, we show how Fine and Wilf's Theorem fails if we try to extend it directly to quasiperiodicity, as well as a new property on concatenation of periodic words.

Medical imaging plays an important role in radiotherapy. Dose painting consists in the application of a nonuniform dose prescription on a tumoral region, and is based on an efficient segmentation of biological target volumes (BTV). It is derived from PET images, that highlight tumoral regions of enhanced glucose metabolism (FDG), cell proliferation (FLT) and hypoxia (FMiso). In this paper, a framework based on Belief Function Theory is proposed for BTV segmentation and for creating 3D parametric images for dose painting. We propose to take advantage of neighboring voxels for BTV segmentation, and also multi-tracer PET images using information fusion to create parametric images. The performances of BTV segmentation was evaluated on an anthropomorphic phantom and compared with two other methods. Quantitative results show the good performances of our method. It has been applied to data of five patients suffering from lung cancer. Parametric images show promising results by highlighting areas where a high frequency or dose escalation could be planned.

Part 8: First Workshop on Algorithms for Data and Text Mining in Bioinformatics (WADTMB 2012); International audience; In the post-genomic era there has been an explosion in the amount of genomic data available and the primary research problems have moved from being able to produce interesting biological data to being able to efficiently process and store this information. In this paper we present efficient data structures and algorithms for the High Similarity Sequencing Problem. In the High Similarity Sequencing Problem we are given the sequences S0, S1, …, Sk where Sj = $e_{j_1} I_{\sigma_1}e_{j_2} I_{\sigma_2} e_{j_3} I_{\sigma_3}, \dots,e_{j_\ell} I_{\sigma_\ell}$ and must perform pattern matching on the set of sequences. In this paper we present time and memory efficient datastructures by exploiting their extensive similarity, our solution leads to a query time of $O(m + vk \log \ell + \frac{m occ_v v}{w} + \frac{PSC(p)m}{w})$ with a memory usage of O(N logN + vk logvk).

International audience; Recently new algorithms appeared for updating the Burrows-Wheeler transform or the suffix array, when the text they index is modified. These algorithms proceed by reordering entries and the number of such reordered entries may be as high as the length of the text. However, in practice, these algorithms are faster for updating the Burrows-Wheeler transform or the suffix array than the fastest reconstruction algorithms. In this article we focus on the number of elements to be reordered for real-life texts. We show that this number is related to LCP values and that, on average, L_ave entries are reordered, where L_ave denotes the average LCP value, defined as the average length of the longest common prefix between two consecutive sorted suffixes. Since we know little about the LCP distribution for real-life texts, we conduct experiments on a corpus that consists of DNA sequences and natural language texts. The results show that apart from texts containing large repetitions, the average LCP value is close to the one expected on a random text.

In imaging, physical phenomena and acquisition system often induce an alteration of the information. It results in the presence of noise and partial volume effect corresponding respectively to uncertainties and imprecisions. To cope with these different imperfections, we propose a method based on information fusion using Belief function theory. First, it takes advantage of neighborhood information and combination rules on mono-modal images in order to reduce uncertainties due to noise while considering imprecisions due to partial volume effect on disjunctions. Imprecisions are then reduced using information coming from multi-modal images. Results obtained on simulated images using various signal to noise ratio and medical images show its ability to segment multi-modal images having both noise and partial volume effect.

Novel high-throughput (Deep) sequencing technology methods have redefined the way genome sequencing is performed. They are able to produce tens of millions of short sequences (reads) in a single experiment and with a much lower cost than previous sequencing methods. In this paper, we present a new algorithm for addressing the problem of efficiently mapping millions of short reads to a reference genome. In particular, we define and solve the Massive Approximate Pattern Matching problem for mapping short sequences to a reference genome.

Novel high-throughput (Deep) sequencing technology methods have redefined the way genome sequencing is performed. They are able to produce tens of millions of short sequences (reads) in a single experiment and with a much lower cost than previous sequencing methods. In this paper, we present a new algorithm for addressing the problem of efficiently mapping millions of short reads to a reference genome. In particular, we define and solve the Massive Approximate Pattern Matching problem for mapping short sequences to a reference genome.

The suffix tree data structure has been intensively described, studied and used in the eighties and nineties, its linear-time construction counterbalancing his space-consuming requirements. An equivalent data structure, the suffix array, has been described by Manber and Myers in 1990. This space-economical structure has been neglected during more than a decade, its construction being too slow. Since 2003, several linear-time suffix array construction algorithms have been proposed, and this structure has slowly replaced the suffix tree in many string processing problems. All these constructions are building the suffix array from the text, and any edit operation on the text leads to the construction of a brand new suffix array. In this article, we are presenting an algorithm that modifies the suffix array and the Longest Common Prefix (LCP) array when the text is edited (insertion, substitution or deletion of a letter or a factor). This algorithm is based on a recent four-stage algorithm developed for dynamic Burrows–Wheeler Transforms (BWT). For minimizing the space complexity, we are sampling the Suffix Array, a technique used in BWT-based compressed indexes. We furthermore explain how this technique can be adapted for maintaining a sample of the Extended Suffix Array, containing a sample of the Suffix Array, a sample of the Inverse Suffix Array and the whole LCP array. Our practical experiments show that it operates very well in practice, being quicker than the fastest suffix array construction algorithm.

Novel high throughput sequencing technology methods have redefined the way genome sequencing is performed. They are able to produce tens of millions of short sequences (reads) in a single experiment and with a much lower cost than previous sequencing methods. Due to this massive amount of data generated by the above systems, efficient algorithms for mapping short sequences to a reference genome are in great demand. In this paper, we present a practical algorithm for addressing the problem of efficiently mapping uniquely occuring short reads to a reference genome. This requires the classification of these short reads into unique and duplicate matches. In particular, we define and solve the Massive Exact Unique Pattern Matching problem in genomes.

High throughput, (or next generation) sequencing technologies have opened new and exciting opportunities in the use of DNA sequences. The new emerging technologies mark the beginning of a new era of high throughput short read sequencing: they have the potential to assemble a bacterial genome during a single experiment and at a moderate cost. In this paper, we address the problem of efficiently mapping millions of degenerate and weighted sequences to a reference genome with respect to whether they occur exactly once in the genome or not, and by taking probability scores into consideration. In particular, we define and solve the Massive Exact and Approximate Unique Pattern Matching problem for degenerate and weighted sequences derived from high throughput sequencing technologies.

We report a whole-genome shotgun assembly (called WGSA) of the human genome generated at Celera in 2001. The Celera-generated shotgun data set consisted of 27 million sequencing reads organized in pairs by virtue of end-sequencing 2-kbp, 10-kbp, and 50-kbp inserts from shotgun clone libraries. The quality-trimmed reads covered the genome 5.3 times, and the inserts from which pairs of reads were obtained covered the genome 39 times. With the nearly complete human DNA sequence [National Center for Biotechnology Information (NCBI) Build 34] now available, it is possible to directly assess the quality, accuracy, and completeness of WGSA and of the first reconstructions of the human genome reported in two landmark papers in February 2001 [Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) Science 291, 1304–1351; International Human Genome Sequencing Consortium (2001) Nature 409, 860–921]. The analysis of WGSA shows 97% order and orientation agreement with NCBI Build 34, where most of the 3% of sequence out of order is due to scaffold placement problems as opposed to assembly errors within the scaffolds themselves. In addition, WGSA fills some of the remaining gaps in NCBI Build 34. The early genome sequences all covered about the same amount of the genome, but they did so in different ways. The Celera results provide more order and orientation, and the consortium sequence provides better coverage of exact and nearly exact repeats.

We recently introduced evolutive tandem repeats with jump (using Hamming distance) (Proc. MFCS’02: the 27th Internat. Symp. Mathematical Foundations of Computer Science, Warszawa, Otwock, Poland, August 2002, Lecture Notes in Computer Science, Vol. 2420, Springer, Berlin, pp. 292–304) which consist in a series of almost contiguous copies having the following property: the Hamming distance between two consecutive copies is always smaller than a given parameter e. In this article, we present a significative improvement that speeds up the detection of evolutive tandem repeats. It is based on the progressive computation of distances between candidate copies participating to the evolutive tandem repeat. It leads to a new algorithm, still quadratic in the worst case, but much more efficient on average, authorizing larger sequences to be processed.

International audience; Here we introduce two new notions of approximate matching with application in computer assisted music analysis. We present algorithms for each notion of approximation: for approximate string matching and for computing approximate squares.

In this paper, we present a solution to the extreme similarity sequencing problem. The extreme similarity sequencing problem consists of finding occurrences of a pattern p in a set S(0), S(1), …, S(k), of sequences of equal length, where S(i), for all 1≤i≤k, differs from S(0) by a constant number of errors - around 10 in practice. We present an asymptotically fast O(n + occ logocc) time algorithm, as well as a practical O(nk/w) time algorithm for solving this problem, where n is the length of a sequence, occ is the number of candidate occurrences reported by our technique, w is the size of the machine word, and the total number of errors is bounded by k - the number of sequences.

Novel high-throughput (Deep) sequencing technologies have redefined the way genome sequencing is performed. They are able to produce millions of short sequences in a single experiment and with a much lower cost than previous methods. In this paper, we address the problem of efficiently mapping and classifying millions of short sequences to a reference genome, based on whether they occur exactly once in the genome or not, and by taking into consideration probability scores. In particular, we design algorithms for Massive Exact and Approximate Pattern Matching of short degenerate and weighted sequences, derived from Deep sequencing technologies, to a reference genome.

International audience

Medical imaging plays an important role in radiotherapy. Dose painting consists in the application of a nonuniform dose prescription on a tumoral region, and is based on an efficient segmentation of Biological Target Volumes (BTV). It is derived from PET images, that highlight tumoral regions of enhanced glucose metabolism (FDG), cell proliferation (FLT) and hypoxia (FMiso). In this paper, a framework based on Belief Function Theory is proposed for BTV segmentation and for creating 3D parametric images for dose painting. We propose to take advantage of neighboring voxels for BTV segmentation, and also multi-tracer PET images using information fusion to create parametric images. The performances of BTV segmentation was evaluated on an anthropomorphic phantom and compared with two other methods. Quantitative results show the good performances of our method. It has been applied to data of five patients suffering from lung cancer. Parametric images show promising results by highlighting areas where a high frequency or dose escalation could be planned.