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Zielezinski A, Girgis HZ, Bernard G, Leimeister CA, Tang K, Dencker T, Lau AK, Röhling S, Choi JJ, Waterman MS, Comin M, Kim SH, Vinga S, Almeida JS, Chan CX, James BT, Sun F, Morgenstern B, Karlowski WM. Benchmarking of alignment-free sequence comparison methods. Genome Biol 2019; 20:144. [PMID: 31345254 PMCID: PMC6659240 DOI: 10.1186/s13059-019-1755-7] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 07/03/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. RESULTS Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. CONCLUSION The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland
| | - Hani Z Girgis
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | | | - Chris-Andre Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Kujin Tang
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
| | - Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Anna Katharina Lau
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Sophie Röhling
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Jae Jin Choi
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Michael S Waterman
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Sung-Hou Kim
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NIH/NCI), Bethesda, USA
| | - Cheong Xin Chan
- Institute for Molecular Bioscience, and School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Benjamin T James
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | - Fengzhu Sun
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland.
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Pizzi C, Ornamenti M, Spangaro S, Rombo SE, Parida L. Efficient Algorithms for Sequence Analysis with Entropic Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:117-128. [PMID: 28113780 DOI: 10.1109/tcbb.2016.2620143] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA). We performed an extensive experimental campaign showing that our algorithms, beside being faster, make it possible the analysis of longer sequences, even for high degrees of resolution, than state of the art algorithms.
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Comin M, Antonello M. On the comparison of regulatory sequences with multiple resolution Entropic Profiles. BMC Bioinformatics 2016; 17:130. [PMID: 26987840 PMCID: PMC4797186 DOI: 10.1186/s12859-016-0980-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 03/06/2016] [Indexed: 11/28/2022] Open
Abstract
Background Enhancers are stretches of DNA (100–1000 bp) that play a major role in development gene expression, evolution and disease. 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). Although the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult and it cannot be carried out with traditional alignment-based techniques. Results The use of fast similarity measures, like alignment-free measures, to detect related regulatory sequences is crucial to understand functional correlation between two enhancers. In this paper we study the use of alignment-free measures for the classification of CRMs. However, alignment-free measures are generally tied to a fixed resolution k. Here we propose an alignment-free statistic, called \documentclass[12pt]{minimal}
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\begin{document}$EP^{*}_{2}$\end{document}EP2∗, that is based on multiple resolution patterns derived from the Entropic Profiles (EPs). The Entropic Profile is a function of the genomic location that captures the importance of that region with respect to the whole genome. As a byproduct we provide a formula to compute the exact variance of variable length word counts, a result that can be of general interest also in other applications. Conclusions We evaluate several alignment-free statistics on simulated data and real mouse ChIP-seq sequences. The new statistic, \documentclass[12pt]{minimal}
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\begin{document}$EP^{*}_{2}$\end{document}EP2∗, is highly successful in discriminating functionally related enhancers and, in almost all experiments, it outperforms fixed-resolution methods. We implemented the new alignment-free measures, as well as traditional ones, in a software called EP-sim that is freely available: http://www.dei.unipd.it/~ciompin/main/EP-sim.html.
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Affiliation(s)
- Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Morris Antonello
- Department of Information Engineering, University of Padova, Padova, Italy
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Vinga S. Information theory applications for biological sequence analysis. Brief Bioinform 2014; 15:376-89. [PMID: 24058049 PMCID: PMC7109941 DOI: 10.1093/bib/bbt068] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/17/2013] [Indexed: 01/13/2023] Open
Abstract
Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.
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Affiliation(s)
- Susana Vinga
- IDMEC, Instituto Superior Técnico - Universidade de Lisboa (IST-UL), Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Tel.: +351-218419504; Fax: +351-218498097;
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An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design. BMC Res Notes 2011; 4:168. [PMID: 21619643 PMCID: PMC3117723 DOI: 10.1186/1756-0500-4-168] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2010] [Accepted: 05/27/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques. FINDINGS The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php CONCLUSIONS In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.
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