The Source of the Data Flood: Sequencing Technologies

Where does this huge amount of data come from? What are the costs of producing it? The answers to these questions lie in the impressive development of sequencing technologies, which have opened up many research opportunities and challenges, some of which are described in this issue. DNA sequencing is the process of “reading” a DNA fragment (referred to as a “read”) and determining the exact order of DNA bases (the four possible nucleotides, that are Adenine, Guanine, Cytosine, and Thymine) that compose a given DNA strand. Research in biology and medicine has been revolutionised and accelerated by the advances of DNA and even RNA sequencing biotechnologies

Towards High-Performance Haplo- type Assembly for Future Sequencing

The problem of Haplotype Assembly is an essential step in human genome analysis. Being the well known MEC model for its solution NP-hard, it is currently addressed by using algorithms that grow exponentially with the length of DNA fragments obtained by the sequencing process. Technological improvements will reduce fragmentation, increase fragment length and make such computational costs worst. WHATSHAP is a recently proposed novel approach which moves complexity from fragment length to fragment sovrapposition, improving the perspective of computational costs, but Haplotype Assembly still remains a demanding computational problem. Directions towards high-performance computing Haplotype Assembly for future sequencing, based on parallel WHATSHAP, are discussed in this paper

Lightweight Reference-Free Variation Detection using the Burrows-Wheeler Transform

Lightweight Reference-Free Variation Detection using the Burrows-Wheeler Transfor

Bases of motifs for generating repeated patterns with wild cards

Motif inference represents one of the most important areas of research in computational biology, and one of its oldest ones. Despite this, the problem remains very much open in the sense that no existing definition is fully satisfying, either in formal terms, or in relation to the biological questions that involve finding such motifs. Two main types of motifs have been considered in the literature: matrices (of letter frequency per position in the motif) and patterns. There is no conclusive evidence in favor of either, and recent work has attempted to integrate the two types into a single model. In this paper, we address the formal issue in relation to motifs as patterns. This is essential to get at a better understanding of motifs in general. In particular, we consider a promising idea that was recently proposed, which attempted to avoid the combinatorial explosion in the number of motifs by means of a generator set for the motifs. Instead of exhibiting a complete list of motifs satisfying some input constraints, what is produced is a basis of such motifs from which all the other ones can be generated. We study the computational cost of determining such a basis of repeated motifs with wild cards in a sequence. We give new upper and lower bounds on such a cost, introducing a notion of basis that is provably contained in (and, thus, smaller) than previously defined ones. Our basis can be computed in less time and space, and is still able to generate the same set of motifs. We also prove that the number of motifs in all bases defined so far grows exponentially with the quorum, that is, with the minimal number of times a motif must appear in a sequence, something unnoticed in previous work. We show that there is no hope to efficiently compute such bases unless the quorum is fixed

RIME: Repeat Identification

We present an algorithm for detecting long similar fragments occurring at least twice in a set of biological sequences. The problem becomes computationally challenging when the frequency of a repeat is allowed to increase and when a non-negligible number of insertions, deletions and substitutions are allowed. We introduce in this paper an algorithm, Rime1 1 Rime is also a reference to Coleridge's poem "The Rime of an Ancient Mariner" which contains many repetitions as a poetic device. (for Repeat Identification: long, Multiple, and with Edits) that performs this task, and manages instances whose size and combination of parameters cannot be handled by other currently existing methods. This is achieved by using a filter as a preprocessing step, and by then exploiting the information gathered by the filter in the following actual repeat inference step. To the best of our knowledge, Rime is the first algorithm that can accurately deal with very long repeats (up to a few thousands), occurring possibly several times, and with a rate of differences (substitutions and indels) allowed among copies of a same repeat of 10-15% or even more

Detecting Mutations by eBWT

In this paper we develop a theory describing how the extended Burrows-Wheeler Transform (EBWT) of a collection of DNA fragments tends to cluster together the copies of nucleotides sequenced from a genome G. Our theory accurately predicts how many copies of any nucleotide are expected inside each such cluster, and how an elegant and precise LCP array based procedure can locate these clusters in the EBWT. Our findings are very general and can be applied to a wide range of different problems. In this paper, we consider the case of alignment-free and reference-free SNPs discovery in multiple collections of reads. We note that, in accordance with our theoretical results, SNPs are clustered in the EBWT of the reads collection, and we develop a tool finding SNPs with a simple scan of the EBWT and LCP arrays. Preliminary results show that our method requires much less coverage than state-of-the-art tools while drastically improving precision and sensitivity

Mobilomics in Saccharomyces cerevisiae Strains

Background: Mobile Genetic Elements (MGEs) are selfish DNA integrated in the genomes. Their detection is mainly based on consensus-like searches by scanning the investigated genome against the sequence of an already identified MGE. Mobilomics aims at discovering all the MGEs in a genome and understanding their dynamic behavior: The data for this kind of investigation can be provided by comparative genomics of closely related organisms. The amount of data thus involved requires a strong computational effort, which should be alleviated.Results: Our approach proposes to exploit the high similarity among homologous chromosomes of different strains of the same species, following a progressive comparative genomics philosophy. We introduce a software tool based on our new fast algorithm, called regender, which is able to identify the conserved regions between chromosomes. Our case study is represented by a unique recently available dataset of 39 different strains of S.cerevisiae, which regender is able to compare in few minutes. By exploring the non-conserved regions, where MGEs are mainly retrotransposons called Tys, and marking the candidate Tys based on their length, we are able to locate a priori and automatically all the already known Tys and map all the putative Tys in all the strains. The remaining putative mobile elements (PMEs) emerging from this intra-specific comparison are sharp markers of inter-specific evolution: indeed, many events of non-conservation among different yeast strains correspond to PMEs. A clustering based on the presence/absence of the candidate Tys in the strains suggests an evolutionary interconnection that is very similar to classic phylogenetic trees based on SNPs analysis, even though it is computed without using phylogenetic information.Conclusions: The case study indicates that the proposed methodology brings two major advantages: (a) it does not require any template sequence for the wanted MGEs and (b) it can be applied to infer MGEs also for low coverage genomes with unresolved bases, where traditional approaches are largely ineffective

Output-Sensitive Pattern Extraction in Sequences

Genomic Analysis, Plagiarism Detection, Data Mining, Intrusion Detection, Spam Fighting and Time Series Analysis are just some examples of applications where extraction of recurring patterns in sequences of objects is one of the main computational challenges. Several notions of patterns exist, and many share the common idea of strictly specifying some parts of the pattern and to don\u27t care about the remaining parts. Since the number of patterns can be exponential in the length of the sequences, pattern extraction focuses on statistically relevant patterns, where any attempt to further refine or extend them causes a loss of significant information (where the number of occurrences changes). Output-sensitive algorithms have been proposed to enumerate and list these patterns, taking polynomial time O(n^c) per pattern for constant c > 1, which is impractical for massive sequences of very large length n. We address the problem of extracting maximal patterns with at most k don\u27t care symbols and at least q occurrences. Our contribution is to give the first algorithm that attains a stronger notion of output-sensitivity, borrowed from the analysis of data structures: the cost is proportional to the actual number of occurrences of each pattern, which is at most n and practically much smaller than n in real applications, thus avoiding the aforementioned cost of O(n^c) per pattern

Technology and Species independent Simulation of Sequencing data and Genomic Variants

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