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We analyzed the dynamics of carbon balance components: gross primary production (GPP) and total ecosystem respiration (TER), of a boreal Scots pine forest in Southern Finland. The main focus is on investigations of environmental drivers of GPP and TER and how they affect the inter-annual variation in the carbon balance in autumn (September–December). We used standard climate data and CO2 exchange measurements collected by the eddy covariance (EC) technique over 11 years. EC data revealed that increasing autumn temperature significantly enhances TER: the temperature sensitivity was 9.5 gC m−2 °C−1 for the period September–October (early autumn when high radiation levels still occur) and 3.8 gC m−2 °C−1 for November–December (late autumn with suppressed radiation level). The cumulative GPP was practically independent of the temperature in early autumn. In late autumn, air temperature could explain part of the variation in GPP but the temperature sensitivity was very weak, less than 1 gC m−2 °C−1. Two models, a stand photosynthesis model (COCA) and a global vegetation model (ORCHIDEE), were used for estimating stand GPP and its sensitivity to the temperature. The ORCHIDEE model was tested against the observations of GPP derived from EC data. The stand photosynthesis model COCA predicted that under a predescribed 3–6 °C temperature increase, the temperature sensitivity of 4–5 gC m−2 °C−1 in GPP may appear in early autumn. The analysis by the ORCHIDEE model revealed the model sensitivity to the temporal treatment of meteorological forcing. The model predictions were similar to observed ones when the site level 1/2-hourly time step was applied, but the results calculated by using daily meteorological forcing, interpolated to 1/2-hourly time step, were biased. This is due to the nonlinear relationship between the processes and the environmental factors.

Motivation: Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing., Results: We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average., Availability and Implementation: Software implementation is available from https://github.com/jttoivon/moder2., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents monomeric motifs as standard position-specific probability matrices (PPMs), and dimeric motifs as pairs of monomeric PPMs, with associated orientation and spacing preferences. For dimers the model represents deviations from pure modular model of two independent monomers, thus making co-operative binding effects explicit. MODER can analyze in reasonable time tens of Mbps of training data. We validated the tool on HT-SELEX and ChIP-seq data. Our findings include some TFs whose expected model has palindromic symmetry but the observed model is directional.

Motivation: New long read sequencing technologies, like PacBio SMRT and Oxford NanoPore, can produce sequencing reads up to 50 000 bp long but with an error rate of at least 15%. Reducing the error rate is necessary for subsequent utilization of the reads in, e.g. de novo genome assembly. The error correction problem has been tackled either by aligning the long reads against each other or by a hybrid approach that uses the more accurate short reads produced by second generation sequencing technologies to correct the long reads., Results: We present an error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k -mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher., Availability and Implementation: LoRMA is freely available at http://www.cs.helsinki.fi/u/lmsalmel/LoRMA/ ., Contact: leena.salmela@cs.helsinki.fi., (© The Author 2016. Published by Oxford University Press.)

Motivation: While the position weight matrix (PWM) is the most popular model for sequence motifs, there is growing evidence of the usefulness of more advanced models such as first-order Markov representations, and such models are also becoming available in well-known motif databases. There has been lots of research of how to learn these models from training data but the problem of predicting putative sites of the learned motifs by matching the model against new sequences has been given less attention. Moreover, motif site analysis is often concerned about how different variants in the sequence affect the sites. So far, though, the corresponding efficient software tools for motif matching have been lacking., Results: We develop fast motif matching algorithms for the aforementioned tasks. First, we formalize a framework based on high-order position weight matrices for generic representation of motif models with dinucleotide or general q -mer dependencies, and adapt fast PWM matching algorithms to the high-order PWM framework. Second, we show how to incorporate different types of sequence variants , such as SNPs and indels, and their combined effects into efficient PWM matching workflows. Benchmark results show that our algorithms perform well in practice on genome-sized sequence sets and are for multiple motif search much faster than the basic sliding window algorithm., Availability and Implementation: Implementations are available as a part of the MOODS software package under the GNU General Public License v3.0 and the Biopython license ( http://www.cs.helsinki.fi/group/pssmfind )., Contact: janne.h.korhonen@gmail.com., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Previous studies have reported that chromosome synteny in Lepidoptera has been well conserved, yet the number of haploid chromosomes varies widely from 5 to 223. Here we report the genome (393 Mb) of the Glanville fritillary butterfly (Melitaea cinxia; Nymphalidae), a widely recognized model species in metapopulation biology and eco-evolutionary research, which has the putative ancestral karyotype of n=31. Using a phylogenetic analyses of Nymphalidae and of other Lepidoptera, combined with orthologue-level comparisons of chromosomes, we conclude that the ancestral lepidopteran karyotype has been n=31 for at least 140 My. We show that fusion chromosomes have retained the ancestral chromosome segments and very few rearrangements have occurred across the fusion sites. The same, shortest ancestral chromosomes have independently participated in fusion events in species with smaller karyotypes. The short chromosomes have higher rearrangement rate than long ones. These characteristics highlight distinctive features of the evolutionary dynamics of butterflies and moths.

The problem of finding the locations in DNA sequences that match a given motif describing the binding specificities of a transcription factor (TF) has many applications in computational biology. This problem has been extensively studied when the position weight matrix (PWM) model is used to represent motifs. We investigate it under the feature motif model, a generalization of the PWM model that does not assume independence between positions in the pattern while being compatible with the original PWM. We present a new method for finding the binding sites of a transcription factor in a DNA sequence when the feature motif model is used to describe transcription factor binding specificities. The experimental results on random and real data show that the search algorithm is fast in practice.

Although the proteins that read the gene regulatory code, transcription factors (TFs), have been largely identified, it is not well known which sequences TFs can recognize. We have analyzed the sequence-specific binding of human TFs using high-throughput SELEX and ChIP sequencing. A total of 830 binding profiles were obtained, describing 239 distinctly different binding specificities. The models represent the majority of human TFs, approximately doubling the coverage compared to existing systematic studies. Our results reveal additional specificity determinants for a large number of factors for which a partial specificity was known, including a commonly observed A- or T-rich stretch that flanks the core motifs. Global analysis of the data revealed that homodimer orientation and spacing preferences, and base-stacking interactions, have a larger role in TF-DNA binding than previously appreciated. We further describe a binding model incorporating these features that is required to understand binding of TFs to DNA., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Motivation: Assembling genomes from short read data has become increasingly popular, but the problem remains computationally challenging especially for larger genomes. We study the scaffolding phase of sequence assembly where preassembled contigs are ordered based on mate pair data., Results: We present MIP Scaffolder that divides the scaffolding problem into smaller subproblems and solves these with mixed integer programming. The scaffolding problem can be represented as a graph and the biconnected components of this graph can be solved independently. We present a technique for restricting the size of these subproblems so that they can be solved accurately with mixed integer programming. We compare MIP Scaffolder to two state of the art methods, SOPRA and SSPACE. MIP Scaffolder is fast and produces better or as good scaffolds as its competitors on large genomes., Availability: The source code of MIP Scaffolder is freely available at http://www.cs.helsinki.fi/u/lmsalmel/mip-scaffolder/., Contact: leena.salmela@cs.helsinki.fi.

Background: The discovery of surprisingly frequent patterns is of paramount interest in bioinformatics and computational biology. Among the patterns considered, those consisting of pairs of solid words that co-occur within a prescribed maximum distance -or gapped factors- emerge in a variety of contexts of DNA and protein sequence analysis. A few algorithms and tools have been developed in connection with specific formulations of the problem, however, none can handle comprehensively each of the multiple ways in which the distance between the two terms in a pair may be defined., Results: This paper presents efficient algorithms and tools for the extraction of all pairs of words up to an arbitrarily large length that co-occur surprisingly often in close proximity within a sequence. Whereas the number of such pairs in a sequence of n characters can be Θ(n4), it is shown that an exhaustive discovery process can be carried out in O(n2) or O(n3), depending on the way distance is measured. This is made possible by a prudent combination of properties of pattern maximality and monotonicity of scores, which lead to reduce the number of word pairs to be weighed explicitly, while still producing also the scores attained by any of the pairs not explicitly considered. We applied our approach to the discovery of spaced dyads in DNA sequences., Conclusions: Experiments on biological datasets prove that the method is effective and much faster than exhaustive enumeration of candidate patterns. Software is available freely by academic users via the web interface at http://bcb.dei.unipd.it:8080/dyweb.

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and protein contexts. In this paper, we present fast algorithms for the problem of finding significant matches of such matrices. Our algorithms are of the online type, and they generalize classical multipattern matching, filtering, and superalphabet techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as well as against some other online and index-based algorithms proposed in the literature. Compared to the brute-force O(mn) approach, our solutions can be faster by a factor that is proportional to the matrix length m. Our multiple-matrix filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches (p = 0.0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.

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.

The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.

In the wake of numerous sequenced genomes becoming available, computational methods for the reconstruction of metabolic networks have received considerable attention. Here, we review recent methods and software tools useful along the reconstruction workflow, from sequence annotation and network assembly to model verification and testing against experimental data. Reconstruction methods can be divided into three categories, depending on the magnitude of network context which is taken into account in the process of assembling the metabolic model: First, each enzyme may be predicted independently by annotation transfer or machine learning methods. Second, the presence of a metabolic pathway may be detected from genome and experimental evidence, often utilizing a reference pathway database. Third, the method may attempt to directly reconstruct a consistent metabolic network without relying on predefined reference pathways. Regardless of the chosen context, all methods strive to reconstruct genome-scale metabolic reconstructions. Currently a gap exists between software platforms dedicated to genome annotation and computational tools for automatically repairing network inconsistencies and validating against measurement data. We argue that to accelerate the reconstruction efforts, computational tools need to be developed that bridge the phases of the reconstruction workflow. In particular, the goal of finding consistent metabolic models suitable for computational analysis should be taken into account already in the beginning phases of reconstruction.

Unlabelled: MOODS (MOtif Occurrence Detection Suite) is a software package for matching position weight matrices against DNA sequences. MOODS implements state-of-the-art online matching algorithms, achieving considerably faster scanning speed than with a simple brute-force search. MOODS is written in C++, with bindings for the popular BioPerl and Biopython toolkits. It can easily be adapted for different purposes and integrated into existing workflows. It can also be used as a C++ library., Availability: The package with documentation and examples of usage is available at http://www.cs.helsinki.fi/group/pssmfind. The source code is also available under the terms of a GNU General Public License (GPL).

Homozygosity for the G allele of rs6983267 at 8q24 increases colorectal cancer (CRC) risk approximately 1.5 fold. We report here that the risk allele G shows copy number increase during CRC development. Our computer algorithm, Enhancer Element Locator (EEL), identified an enhancer element that contains rs6983267. The element drove expression of a reporter gene in a pattern that is consistent with regulation by the key CRC pathway Wnt. rs6983267 affects a binding site for the Wnt-regulated transcription factor TCF4, with the risk allele G showing stronger binding in vitro and in vivo. Genome-wide ChIP assay revealed the element as the strongest TCF4 binding site within 1 Mb of MYC. An unambiguous correlation between rs6983267 genotype and MYC expression was not detected, and additional work is required to scrutinize all possible targets of the enhancer. Our work provides evidence that the common CRC predisposition associated with 8q24 arises from enhanced responsiveness to Wnt signaling.

With genome analysis expanding from the study of genes to the study of gene regulation, 'regulatory genomics' utilizes sequence information, evolution and functional genomics measurements to unravel how regulatory information is encoded in the genome.

ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate (13)C metabolic flux analysis. The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in (13)C metabolic flux analysis, where the mappings of carbon atoms from substrates into products in the model are required. ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool. The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Background: Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from 13C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the 13C isotopomer data are typically needed., Results: We present a novel analytic framework for estimating metabolic flux ratios in the cell from 13C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, 13C isotopomer measurement techniques, substrates and substrate labelling patterns. By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations--as well as predict when the flux ratios are unobtainable by linear means--also for substrates not related to glucose., Conclusion: The core of 13C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Over recent years, five European PhD programmes have organized a series of 'Bioinformatics Research and Education Workshops'. These workshops address the needs of first-year PhD students and have been designed to combine a maximum of educational impact and scientific stimulation with a minimum of financial and administrative effort. We describe the BREW experience and argue that this type of event constitutes an attractive component of PhD education in computational biology and beyond.

Melanoma is notorious for its high tendency to metastasize and its refractoriness to treatment thereafter. Metastasis is believed to occur mostly through the lymphatic system, and the status of sentinel lymph nodes is currently recognized as the best prognostic indicator. Unfortunately, the lymphatic metastatic process is still poorly understood and the occurrence of sentinel node metastases (micrometastases) may be underestimated. We performed genome-wide gene expression analyses of melanoma lymph node micrometastases and macrometastases, and of primary melanomas and benign naevi, to characterize the early metastatic cells molecularly and to disclose the best diagnostic markers and rational targets for therapy. Significance analysis of microarrays identified 22 over- and five under-expressed genes with > or = four-fold changes in the micrometastases. Of these genes, MLANA, TYR, MIA, ERBB3, PRAME, and SPP1 were tested as potential markers by RT-PCR and immunohistochemistry. In a prospective study of 160 patients, our graded MLANA and TYR RT-PCR analyses disclosed clinically significant metastases, as assessed by disease recurrence, better than histological and immunohistochemical examinations. These results strongly suggest the clinical implementation of quantifiable RT-PCR assays to confirm and complement the pathological examination of sentinel node metastases. Furthermore, SPP1 and PRAME proved valuable as melanoma-specific markers capable of differentiating melanoma cells from benign naevi in the sentinel lymph nodes. Importantly, these two genes may also prove to be ideal targets for drug development and therapy. Most molecular traits of the micrometastases were already present in the primary tumours, suggesting that micrometastasis to sentinel lymph nodes is a fairly non-selective process.

Malignant melanomas are characterized by their high propensity to invade and metastasize, but the molecular mechanisms of these traits have remained elusive. Our DNA microarray analyses of benign nevi and melanoma tissue specimens revealed that the genes encoding extracellular matrix proteins tenascin-C (TN-C), fibronectin (FN), and procollagen-I (PCOL-I) are highly upregulated in invasive and metastatic melanomas. The expression and distribution of these proteins were further studied by immunohistochemistry in benign nevi, radially and vertically growing melanomas, sentinel node micrometastases, and macrometastases. TN-C was increased in all invasive tumours and metastases, especially at invasion fronts, but not in benign nevi or non-invasive melanomas. Significantly, the intensity of TN-C staining correlated with metastasis to sentinel lymph nodes, better than tumour thickness (Breslow). Moreover, TN-C, FN, and PCOL-I appeared to co-localize in the tumours and form tubular meshworks and channels ensheathing the melanoma cells. Our data suggest that melanoma invasion is associated with the formation of special channel-like structures, providing a new concept, structured tumour cell spreading. Altogether, these data provide potential new prognostic markers and therapeutic targets/strategies for preventing melanoma dissemination.

Motivation: Flux estimation using isotopomer information of metabolites is currently the most reliable method to obtain quantitative estimates of the activity of metabolic pathways. However, the development of isotopomer measurement techniques for intermediate metabolites is a demanding task. Careful planning of isotopomer measurements is thus needed to maximize the available flux information while minimizing the experimental effort., Results: In this paper we study the question of finding the smallest subset of metabolites to measure that ensure the same level of isotopomer information as the measurement of every metabolite in the metabolic network. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give methods for obtaining exact and fast approximate solutions, and evaluate empirically the efficacy of the proposed methods by analyzing a metabolic network that models the central carbon metabolism of Saccharomyces cerevisiae.

Understanding the regulation of human gene expression requires knowledge of the "second genetic code," which consists of the binding specificities of transcription factors (TFs) and the combinatorial code by which TF binding sites are assembled to form tissue-specific enhancer elements. Using a novel high-throughput method, we determined the DNA binding specificities of GLIs 1-3, Tcf4, and c-Ets1, which mediate transcriptional responses to the Hedgehog (Hh), Wnt, and Ras/MAPK signaling pathways. To identify mammalian enhancer elements regulated by these pathways on a genomic scale, we developed a computational tool, enhancer element locator (EEL). We show that EEL can be used to identify Hh and Wnt target genes and to predict activated TFs based on changes in gene expression. Predictions validated in transgenic mouse embryos revealed the presence of multiple tissue-specific enhancers in mouse c-Myc and N-Myc genes, which has implications for organ-specific growth control and tumor-type specificity of oncogenes.

This protocol describes the use of Enhancer Element Locator (EEL), a computer program that was designed to locate distal enhancer elements in long mammalian sequences. EEL will predict the location and structure of conserved enhancers after being provided with two orthologous DNA sequences and binding specificity matrices for the transcription factors (TFs) that are expected to contribute to the function of the enhancers to be identified. The freely available EEL software can analyze two 1-Mb sequences with 100 TF motifs in about 15 min on a modern Windows, Linux or Mac computer. The output provides several hypotheses about enhancer location and structure for further evaluation by an expert on enhancer function.

Motivation: cDNA amplified fragment length polymorphism (cDNA-AFLP) is one of the few genome-wide level expression profiling methods capable of finding genes that have not yet been cloned or even predicted from sequence but have interesting expression patterns under the studied conditions. In cDNA-AFLP, a complex cDNA mixture is divided into small subsets using restriction enzymes and selective PCR. A large cDNA-AFLP experiment can require a substantial amount of resources, such as hundreds of PCR amplifications and gel electrophoresis runs, followed by manual cutting of a large number of bands from the gels. Our aim was to test whether this workload can be reduced by rational design of the experiment., Results: We used the available genomic sequence information to optimize cDNA-AFLP experiments beforehand so that as many transcripts as possible could be profiled with a given amount of resources. Optimization of the selection of both restriction enzymes and selective primers for cDNA-AFLP experiments has not been performed previously. The in silico tests performed suggest that substantial amounts of resources can be saved by the optimization of cDNA-AFLP experiments.

We propose a novel method to identify functionally related genes based on comparisons of neighborhoods in gene networks. This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets. The character of the predicted gene relationships depends on the underlying networks;they concern biological processes rather than the molecular function. We used the method to analyze gene networks derived from genome-wide chromatin immunoprecipitation experiments, a large-scale gene deletion study, and from the genomic positions of consensus binding sites for transcription factors of the yeast Saccharomyces cerevisiae. We identified 816 functional relationships between 159 genes and show that these relationships correspond to protein-protein interactions, co-occurrence in the same protein complexes, and/or co-occurrence in abstracts of scientific articles. Our results suggest functions for seven previously uncharacterized yeast genes: KIN3 and YMR269W may be involved in biological processes related to cell growth and/or maintenance, whereas IES6, YEL008W, YEL033W, YHL029C, YMR010W, and YMR031W-A are likely to have metabolic functions.

We describe a new probabilistic method for finding haplotype blocks that is based on the use of the minimum description length (MDL) principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly and colleagues. The results expose some problems that exist in the current methods for the evaluation of the significance of predicted block boundaries. Our method, MDL block finder, can be used to compare block borders in different sample sets, and we demonstrate this by applying the MDL-based method to define the block structure in chromosomes from population isolates.

We describe a new method for finding haplotype blocks based on the use of the minimum description length principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks, and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly et al. The results are in relatively good agreement with the published results, but also show clear differences in the predicted block boundaries and their strengths. We also give results on the block structure in population isolates.

Motivation: Measuring transcriptional expression levels (transcriptional profiling) has become one of the most important methods in functional genomics. Still, new measuring methods are needed to obtain more reliable, quantitative data about transcription on a genomic scale. In this paper we concentrate on certain computational optimization problems arising in the design of one such novel method. From a computational point of view the key feature of the new method is that the hybridized probes are distinguished from each other based on their different size. Therefore the probes have to be assigned into pools such that the probes in the same pool have unique sizes different enough from each other. Identification of expressed RNA is given by probe pool and probe size while quantification is given by the label of the probe, e.g. fluorescence intensity., Results: We show how to computationally find the probes and assign them into pools for a whole genome such that (i) each gene has a specific probe suitable for amplification and hybridization, and (ii) the expression level measurement can be done in a minimal number of pools separable by electrophoresis in order to minimize the total experiment cost of the measurement. Our main result is a polynomial-time approximation algorithm for assigning the probes into pools. We demonstrate the feasibility of the procedure by selecting probes for the yeast genome and assigning them into less than 100 pools. The probe sequences and their assignment into pools are available for academic research on request from the authors.

Motivation: Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts)., Results: We have developed a data analysis method for comparing mRNA measurements of gene disruption experiments with information about gene promoters. The method was applied to a well-known dataset to uncover correlations between known transcription factor binding site motifs in the upstream regions of all S. cerevisiae genes and the gene expression changes in various gene disruption experiments. The possible explanations of the correlations were categorized and analyzed using e.g. expression cascades. Several correlations turned out to be consistent with existing biological knowledge while some new ones suggest themselves for further study., Availability: The resulting tables are available at http://www.cs.helsinki.fi/u/kpalin/CorrDisrupt/.

A method is presented that reliably detects spherical viruses from a wide variety of noisy low-contrast electron micrographs. Such detection is one of the first image analysis steps in the computer-aided reconstruction of three-dimensional density distribution models of viruses. Particle detection is based on the comparison of intensity in a circular area and in the surrounding ring followed by a number of tests to validate the potential particles. The only required input from the user in addition to the micrograph is an approximate radius of the particle. The method has been implemented as program ETHAN that has been tested for several different data sets. ETHAN has also successfully been used to detect DNA-less virus particles for an actual reconstruction., (Copyright 2000 Academic Press.)

We have developed a set of methods and tools for automatic discovery of putative regulatory signals in genome sequences. The analysis pipeline consists of gene expression data clustering, sequence pattern discovery from upstream sequences of genes, a control experiment for pattern significance threshold limit detection, selection of interesting patterns, grouping of these patterns, representing the pattern groups in a concise form and evaluating the discovered putative signals against existing databases of regulatory signals. The pattern discovery is computationally the most expensive and crucial step. Our tool performs a rapid exhaustive search for a priori unknown statistically significant sequence patterns of unrestricted length. The statistical significance is determined for a set of sequences in each cluster with respect to a set of background sequences allowing the detection of subtle regulatory signals specific for each cluster. The potentially large number of significant patterns is reduced to a small number of groups by clustering them by mutual similarity. Automatically derived consensus patterns of these groups represent the results in a comprehensive way for a human investigator. We have performed a systematic analysis for the yeast Saccharomyces cerevisiae. We created a large number of independent clusterings of expression data simultaneously assessing the "goodness" of each cluster. For each of the over 52,000 clusters acquired in this way we discovered significant patterns in the upstream sequences of respective genes. We selected nearly 1,500 significant patterns by formal criteria and matched them against the experimentally mapped transcription factor binding sites in the SCPD database. We clustered the 1,500 patterns to 62 groups for which we derived automatically alignments and consensus patterns. Of these 62 groups 48 had patterns that have matching sites in SCPD database.

We performed a systematic analysis of gene upstream regions in the yeast genome for occurrences of regular expression-type patterns with the goal of identifying potential regulatory elements. To achieve this goal, we have developed a new sequence pattern discovery algorithm that searches exhaustively for a priori unknown regular expression-type patterns that are over-represented in a given set of sequences. We applied the algorithm in two cases, (1) discovery of patterns in the complete set of >6000 sequences taken upstream of the putative yeast genes and (2) discovery of patterns in the regions upstream of the genes with similar expression profiles. In the first case, we looked for patterns that occur more frequently in the gene upstream regions than in the genome overall. In the second case, first we clustered the upstream regions of all the genes by similarity of their expression profiles on the basis of publicly available gene expression data and then looked for sequence patterns that are over-represented in each cluster. In both cases we considered each pattern that occurred at least in some minimum number of sequences, and rated them on the basis of their over-representation. Among the highest rating patterns, most have matches to substrings in known yeast transcription factor-binding sites. Moreover, several of them are known to be relevant to the expression of the genes from the respective clusters. Experiments on simulated data show that the majority of the discovered patterns are not expected to occur by chance.

A computer program for the mobile phase optimization of high-performance liquid chromatography (HPLC) is described. The desirability function technique combined to the prisma mixture design was employed to enhance the quality of HPLC separations. The use of statistical models to predict the behaviour of retention times (tR) and band broadening at the different eluent compositions obtained by prisma was examined for dansyl amides and coumarins. The study showed that the dependence between the eluent composition and tR values of dansyl amides and coumarins can be expressed using quadratic regression models with a high degree of accuracy. Band broadening given by means of the band width at half-height (wh) was described by a linear regression model. Both models were used in calculating and predicting the resolution (Rs) in various solvent combinations. The desirability function converted the calculated (Rs) value into the desirability value (D), and the overall optimum was then defined by means of the overall desirability. The optimal eluent mixtures for the separation of compounds were easily read from the contour plot inside the horizontal plane of the prisma model. A good separation was achieved using the optimized solvent combination. Depending on the aims of the optimal separation, the program allows either optimization of critical pairs or achieving the overall optimum giving a reasonable separation for as many compounds as possible.

We have examined methods and developed a general software tool for finding and analyzing combinations of transcription factor binding sites that occur relatively often in gene upstream regions (putative promoter regions) in the yeast genome. Such frequently occurring combinations may be essential parts of possible promoter classes. The regions upstream to all genes were first isolated from the yeast genome database MIPS using the information in the annotation files of the database. The ones that do not overlap with coding regions were chosen for further studies. Next, all occurrences of the yeast transcription factor binding sites, as given in the IMD database, were located in the genome and in the selected regions in particular. Finally, by using a general purpose data mining software in combination with our own software, which parametrizes the search, we can find the combinations of binding sites that occur in the upstream regions more frequently than would be expected on the basis of the frequency of individual sites. The procedure also finds so-called association rules present in such combinations. The developed tool is available for use through the WWW.

We consider the problem of automatic discovery of patterns and the corresponding subfamilies in a set of biosequences. The sequences are unaligned and may contain noise of unknown level. The patterns are of the type used in PROSITE database. In our approach we discover patterns and the respective subfamilies simultaneously. We develop a theoretically substantiated significance measure for a set of such patterns and an algorithm approximating the best pattern set and the subfamilies. The approach is based on the minimum description length (MDL) principle. We report a computing experiment correctly finding subfamilies in the family of chromo domains and revealing new strong patterns.

Some algorithms are described for the search of regions in a nucleic acid sequence that, when translated into amino acids, are homologous to a given amino acid pattern. All algorithms are modifications of the dynamic programming method for sequence comparison such that the translation of codons is taken into account. One of the algorithms has been implemented as a FORTRAN 77 program. The program operates on files that follow the format of the EMBL Nucleotide Sequence Data Library.

A program package, called SEQAID, to support DNA sequencing is presented. The program automatically assembles long DNA sequences from short fragments with minimal user interaction. Various tools for controlling the assembling process are also available. The main novel features of the system are that SEQAID implements several new well-behaved algorithms based on a mathematical model of the problem. It also utilizes available information on restriction fragments to detect illegitimate overlaps and to find relationships between separately assembled sequence blocks. Experiences with the system are reported including an extremely pathological real sequence which offers an interesting benchmark for this kind of programs.