Matteo Comin

Alignment-free phylogeny of whole genomes using underlying subwords

BackgroundWith the progress of modern sequencing technologies a large number of complete genomes are now available. Traditionally the comparison of two related genomes is carried out by sequence alignment. There are cases where these techniques cannot be applied, for example if two genomes do not share the same set of genes, or if they are not alignable to each other due to low sequence similarity, rearrangements and inversions, or more specifically to their lengths when the organisms belong to different species. For these cases the comparison of complete genomes can be carried out only with ad hoc methods that are usually called alignment-free methods.MethodsIn this paper we propose a distance function based on subword compositions called Underlying Approach (UA). We prove that the matching statistics, a popular concept in the field of string algorithms able to capture the statistics of common words between two sequences, can be derived from a small set of “independent” subwords, namely the irredundant common subwords. We define a distance-like measure based on these subwords, such that each region of genomes contributes only once, thus avoiding to count shared subwords a multiple number of times. In a nutshell, this filter discards subwords occurring in regions covered by other more significant subwords.ResultsThe Underlying Approach (UA) builds a scoring function based on this set of patterns, called underlying. We prove that this set is by construction linear in the size of input, without overlaps, and can be efficiently constructed. Results show the validity of our method in the reconstruction of phylogenetic trees, where the Underlying Approach outperforms the current state of the art methods. Moreover, we show that the accuracy of UA is achieved with a very small number of subwords, which in some cases carry meaningful biological information.Availabilityhttp://www.dei.unipd.it/∼ciompin/main/underlying.html

PROuST: a comparison method of three-dimensional structures of proteins using indexing techniques.

We present a new method for protein structure comparison that combines indexing and dynamic programming (DP). The method is based on simple geometric features of triplets of secondary structures of proteins. These features provide indexes to a hash table that allows fast retrieval of similarity information for a query protein. After the query protein is matched with all proteins in the hash table producing a list of putative similarities, the dynamic programming algorithm is used to align the query protein with each protein of this list. Since the pairwise comparison with DP is applied only to a small subset of proteins and, furthermore, DP re-uses information that is already computed and stored in the hash table, the approach is very fast even when searching the entire PDB. We have done extensive experimentation showing that our approach achieves results of quality comparable to that of other existing approaches but is generally faster.

The irredundant class method for remote homology detection of protein sequences.

The automatic classification of protein sequences into families is of great help for the functional prediction and annotation of new proteins. In this article, we present a method called Irredundant Class that address the remote homology detection problem. The best performing methods that solve this problem are string kernels, that compute a similarity function between pairs of proteins based on their subsequence composition. We provide evidence that almost all string kernels are based on patterns that are not independent, and therefore the associated similarity scores are obtained using a set of redundant features, overestimating the similarity between the proteins. To specifically address this issue, we introduce the class of irredundant common patterns. Loosely speaking, the set of irredundant common patterns is the smallest class of independent patterns that can describe all common patterns in a pair of sequences. We present a classification method based on the statistics of these patterns, named Irredundant Class. Results on benchmark data show that the Irredundant Class outperforms most of the string kernels previously proposed, and it achieves results as good as the current state-of-the-art method Local Alignment, but using the same pairwise information only once.

Motifs in Ziv-Lempel-Welch Clef

We present variants of classical data compression paradigms by Ziv, Lempel, and Welch in which the phrases used in compression are selected among suitably chosen motifs, defined here as strings of intermittently solid and wild characters that recur more or less frequently in the source textstring. This notion emerged primarily in the analysis of biological sequences and molecules. Whereas the number of motifs in a sequence or family may be exponential in the size of the input, a linear-sized basis of irredundant motifs may be defined such that any other motif can be obtained by the union of a suitable subset from the basis. Previous study has exposed the advantages of using irredundant motifs in lossy as well as lossless offline compression. In the present paper, we examine adaptations and extensions of classical incremental ZL and ZLW paradigms. First, hybrid schemata are proposed along these lines, in which motifs may be discovered and selected off-line, while the parse and encoding is still conducted on-line. The performances thus obtained improve on the one hand over previous off-line implementations of motif-based compression, and on the other, over the traditionally best implementations of ZLW. On the basis of this, both lossy and lossless motif-based schemata are introduced and tested that follow more closely the ZL and ZLW paradigms.

Detection of subtle variations as consensus motifs

We address the problem of detecting consensus motifs, that occur with subtle variations, across multiple sequences. These are usually functional domains in DNA sequences such as transcriptional binding factors or other regulatory sites. The problem in its generality has been considered difficult and various benchmark data serve as the litmus test for different computational methods. We present a method centered around unsupervised combinatorial pattern discovery. The parameters are chosen using a careful statistical analysis of consensus motifs. This method works well on the benchmark data and is general enough to be extended to a scenario where the variation in the consensus motif includes indels (along with mutations). We also present some results on detection of transcription binding factors in human DNA sequences.

Fast computation of entropic profiles for the detection of conservation in genomes

The information theory has been used for quite some time in the area of computational biology. In this paper we discuss and improve the function Entropic Profile, introduced by Vinga and Almeida in [23]. The Entropic Profiler is a function of the genomic location that captures the importance of that region with respect to the whole genome. We provide a linear time linear space algorithm called Fast Entropic Profile, as opposed to the original quadratic implementation. Moreover we propose an alternative normalization that can be also efficiently implemented. We show that Fast EP is suitable for large genomes and for the discovery of motifs with unbounded length.

Clustering of reads with alignment-free measures and quality values

BackgroundThe data volume generated by Next-Generation Sequencing (NGS) technologies is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads.Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %).ResultsIn this scenario it will be fundamental to exploit quality value information within the alignment-free framework. To the best of our knowledge this is the first study that incorporates quality value information and k-mers counts, in the context of alignment-free measures, for the comparison of reads data. Based on this principles, in this paper we present a family of alignment-free measures called Dq-type. A set of experiments on simulated and real reads data confirms that the new measures are superior to other classical alignment-free statistics, especially when erroneous reads are considered. Also results on de novo assembly and metagenomic reads classification show that the introduction of quality values improves over standard alignment-free measures. These statistics are implemented in a software called QCluster (http://www.dei.unipd.it/~ciompin/main/qcluster.html).

MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures

MOTIVATION Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Taxonomic analysis of microbial communities, a process referred to as binning, is one of the most challenging tasks when analyzing metagenomic reads data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species and the limitations due to short read lengths and sequencing errors. RESULTS MetaProb is a novel assembly-assisted tool for unsupervised metagenomic binning. The novelty of MetaProb derives from solving a few important problems: how to divide reads into groups of independent reads, so that k-mer frequencies are not overestimated; how to convert k-mer counts into probabilistic sequence signatures, that will correct for variable distribution of k-mers, and for unbalanced groups of reads, in order to produce better estimates of the underlying genome statistic; how to estimate the number of species in a dataset. We show that MetaProb is more accurate and efficient than other state-of-the-art tools in binning both short reads datasets (F-measure 0.87) and long reads datasets (F-measure 0.97) for various abundance ratios. Also, the estimation of the number of species is more accurate than MetaCluster. On a real human stool dataset MetaProb identifies the most predominant species, in line with previous human gut studies. AVAILABILITY AND IMPLEMENTATION https://bitbucket.org/samu661/metaprob CONTACTS cinzia.pizzi@dei.unipd.it or comin@dei.unipd.it SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Beyond fixed-resolution alignment-free measures for mammalian enhancers sequence comparison

The cell-type diversity is to a large degree driven by transcription regulation, i.e., enhancers. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of cis-regulatory modules (CRMs). Even if the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult. A similarity measure to detect related regulatory sequences is crucial to understand functional correlation between two enhancers. This will allow large-scale analyses, clustering and genome-wide classifications. In this paper we present Under2, a parameter-free alignment-free statistic based on variable-length words. As opposed to traditional alignment-free methods, which are based on fixed-length patterns or, in other words, tied to a fixed resolution, our statistic is built upon variable-length words, and thus multiple resolutions are allowed. This will capture the great variability of lengths of CRMs. We evaluate several alignment-free statistics on simulated data and real ChIP-seq sequences. The new statistic is highly successful in discriminating functionally related enhancers and, in almost all experiments, it outperforms fixed-resolution methods. Finally, experiments on mouse enhancers show that Under2 can separate enhancers active in different tissues. Availability: http://www.dei.unipd.it/~ciompin/main/UnderIICRMS.html.

Fast entropic profiler: an information theoretic approach for the discovery of patterns in genomes

Information theory has been used for quite some time in the area of computational biology. In this paper we present a pattern discovery method, named Fast Entropic Profiler, that is based on a local entropy function that captures the importance of a region with respect to the whole genome. The local entropy function has been introduced by Vinga and Almeida in , here we discuss and improve the original formulation. We provide a linear time and linear space algorithm called Fast Entropic Profiler ( FastEP), as opposed to the original quadratic implementation. Moreover we propose an alternative normalization that can be also efficiently implemented. We show that FastEP is suitable for large genomes and for the discovery of patterns with unbounded length. FastEP is available at http://www.dei.unipd.it/~ciompin/main/FastEP.html.