Esko Ukkonen

Bio: Esko Ukkonen is an academic researcher from University of Helsinki. The author has contributed to research in topics: String searching algorithm & String (computer science). The author has an hindex of 45, co-authored 200 publications receiving 11846 citations. Previous affiliations of Esko Ukkonen include Max Planck Society & Helsinki Institute for Information Technology.

On-line construction of suffix trees

Abstract: An on-line algorithm is presented for constructing the suffix tree for a given string in time linear in the length of the string. The new algorithm has the desirable property of processing the string symbol by symbol from left to right. It always has the suffix tree for the scanned part of the string ready. The method is developed as a linear-time version of a very simple algorithm for (quadratic size) suffixtries. Regardless of its quadratic worst case this latter algorithm can be a good practical method when the string is not too long. Another variation of this method is shown to give, in a natural way, the well-known algorithms for constructing suffix automata (DAWGs).

Algorithms for approximate string matching

Abstract: The edit distance between strings a 1 … a m and b 1 … b n is the minimum cost s of a sequence of editing steps (insertions, deletions, changes) that convert one string into the other. A well-known tabulating method computes s as well as the corresponding editing sequence in time and in space O ( mn ) (in space O (min( m, n )) if the editing sequence is not required). Starting from this method, we develop an improved algorithm that works in time and in space O ( s · min( m, n )). Another improvement with time O ( s · min( m, n )) and space O ( s · min( s, m, n )) is given for the special case where all editing steps have the same cost independently of the characters involved. If the editing sequence that gives cost s is not required, our algorithms can be implemented in space O (min( s, m, n )). Since s = O (max( m, n )), the new methods are always asymptotically as good as the original tabulating method. As a by-product, algorithms are obtained that, given a threshold value t , test in time O ( t · min( m, n )) and in space O (min( t, m, n )) whether s ⩽ t . Finally, different generalized edit distances are analyzed and conditions are given under which our algorithms can be used in conjunction with extended edit operation sets, including, for example, transposition of adjacent characters.

Approximate string-matching with q -grams and maximal matches

Abstract: We study approximate string matching in connection with two string distance functions that are computable in linear time. The first function is based on the so-called $q$-grams. An algorithm is given for the associated string matching problem that finds the locally best approximate occurences of pattern $P$, $|P|=m$, in text $T$, $|T|=n$, in time $O(n\log (m-q))$. The occurences with distance $\leq k$ can be found in time $O(n\log k)$. The other distance function is based on finding maximal common substrings and allows a form of approximate string matching in time $O(n)$. Both distances give a lower bound for the edit distance (in the unit cost model), which leads to fast hybrid algorithms for the edit distance based string matching.

Genome-Wide Analysis of ETS-Family DNA-Binding In Vitro and In Vivo

Abstract: Members of the large ETS family of transcription factors (TFs) have highly similar DNA-binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA-binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA-binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high-throughput microwell-based TF DNA-binding specificity assay, and protein-binding microarrays (PBMs). Both approaches reveal that the ETS-binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino-acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

DnaSP v5

Abstract: Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser. Availability: Freely available to academic users from: http://www.ub.edu/dnasp Contact: [email protected]

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Robust enumeration of cell subsets from tissue expression profiles

Abstract: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)

CAP3: A DNA Sequence Assembly Program

Abstract: The shotgun sequencing strategy has been used widely in genome sequencing projects. A major phase in this strategy is to assemble short reads into long sequences. A number of DNA sequence assembly programs have been developed (Staden 1980; Peltola et al. 1984; Huang 1992; Smith et al. 1993; Gleizes and Henaut 1994; Lawrence et al. 1994; Kececioglu and Myers 1995; Sutton et al. 1995; Green 1996). The FAKII program provides a library of routines for each phase of the assembly process (Larson et al. 1996). The GAP4 program has a number of useful interactive features (Bonfield et al. 1995). The PHRAP program clips 5′ and 3′ low-quality regions of reads and uses base quality values in evaluation of overlaps and generation of contig sequences (Green 1996). TIGR Assembler has been used in a number of megabase microbial genome projects (Sutton et al. 1995). Continued development and improvement of sequence assembly programs are required to meet the challenges of the human, mouse, and maize genome projects. We have developed the third generation of the CAP sequence assembly program (Huang 1992). The CAP3 program includes a number of improvements and new features. A capability to clip 5′ and 3′ low-quality regions of reads is included in the CAP3 program. Base quality values produced by PHRED (Ewing et al. 1998) are used in computation of overlaps between reads, construction of multiple sequence alignments of reads, and generation of consensus sequences. Efficient algorithms are employed to identify and compute overlaps between reads. Forward–reverse constraints are used to correct assembly errors and link contigs. Results of CAP3 on four BAC data sets are presented. The performance of CAP3 was compared with that of PHRAP on a number of BAC data sets. PHRAP often produces longer contigs than CAP3 whereas CAP3 often produces fewer errors in consensus sequences than PHRAP. It is easier to construct scaffolds with CAP3 than with PHRAP on low-pass data with forward–reverse constraints. An unusual feature of CAP3 is the use of forward–reverse constraints in the construction of contigs. A forward–reverse constraint is often produced by sequencing of both ends of a subclone. A forward–reverse constraint specifies that the two reads should be on the opposite strands of the DNA molecule within a specified range of distance. By sequencing both ends of each subclone, a large number of forward–reverse constraints are produced for a cosmid or BAC data set. A difficulty with use of forward–reverse constraints in assembly is that some of the forward–reverse constraints are incorrect because of errors in lane tracking and cloning. Our strategy for dealing with this difficulty is based on the observation that a majority of the constraints are correct and wrong constraints usually occur randomly. Thus, a few unsatisfied constraints in a contig may not be sufficient to indicate an assembly error in the contig. However, if a sufficient number of constraints are all inconsistent with a join in a contig and all support an alternative join, it is likely that the current join is an error, and the alternative join should be made.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.