SNA – a toolbox for the stoichiometric analysis of metabolic networks

Background  

Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem.     

Unsupervised statistical clustering of environmental shotgun sequences

Background  

The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Proof-of-principle investigation of an algorithmic model of adenosine-mediated angiogenesis

Background  

We investigated an algorithmic approach to modelling angiogenesis controlled by vascular endothelial growth factor (VEGF), the anti-angiogenic soluble VEGF receptor 1 (sVEGFR-1) and adenosine (Ado). We explored its feasibility to test angiogenesis-relevant hypotheses. We illustrated its potential to investigate the role of Ado as an angiogenesis modulator by enhancing VEGF activity and antagonizing sVEGFR-1.     

Automatic layout and visualization of biclusters

Background  

Biclustering has emerged as a powerful algorithmic tool for analyzing measurements of gene expression. A number of different methods have emerged for computing biclusters in gene expression data. Many of these algorithms may output a very large number of biclusters with varying degrees of overlap. There are no systematic methods that create a two-dimensional layout of the computed biclusters and display overlaps between them.     

GIGA: a simple,efficient algorithm for gene tree inference in the genomic age

Background  

Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost.     

Effective ambiguity checking in biosequence analysis

Background  

Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity. As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking.     

Bayesian clustering and feature selection for cancer tissue samples

Background  

The versatility of DNA copy number amplifications for profiling and categorization of various tissue samples has been widely acknowledged in the biomedical literature. For instance, this type of measurement techniques provides possibilities for exploring sets of cancerous tissues to identify novel subtypes. The previously utilized statistical approaches to various kinds of analyses include traditional algorithmic techniques for clustering and dimension reduction, such as independent and principal component analyses, hierarchical clustering, as well as model-based clustering using maximum likelihood estimation for latent class models.     

Computing paths and cycles in biological interaction graphs

Background  

Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.     

DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment

Background  

DIALIGN-T is a reimplementation of the multiple-alignment program DIALIGN. Due to several algorithmic improvements, it produces significantly better alignments on locally and globally related sequence sets than previous versions of DIALIGN. However, like the original implementation of the program, DIALIGN-T uses a a straight-forward greedy approach to assemble multiple alignments from local pairwise sequence similarities. Such greedy approaches may be vulnerable to spurious random similarities and can therefore lead to suboptimal results. In this paper, we present DIALIGN-TX, a substantial improvement of DIALIGN-T that combines our previous greedy algorithm with a progressive alignment approach.     

Application of Wavelet Packet Transform to detect genetic polymorphisms by the analysis of inter-Alu PCR patterns

Background  

The analysis of Inter-Alu PCR patterns obtained from human genomic DNA samples is a promising technique for a simultaneous analysis of many genomic loci flanked by Alu repetitive sequences in order to detect the presence of genetic polymorphisms. Inter-Alu PCR products may be separated and analyzed by capillary electrophoresis using an automatic sequencer that generates a complex pattern of peaks. We propose an algorithmic method based on the Haar-Walsh Wavelet Packet Transformation (WPT) for an efficient detection of fingerprint-type patterns generated by PCR-based methodologies. We have tested our algorithmic approach on inter-Alu patterns obtained from the genomic DNA of three couples of monozygotic twins, expecting that the inter-Alu patterns of each twins couple will show differences due to unavoidable experimental variability. On the contrary the differences among samples of different twins are supposed to originate from genetic variability. Our goal is to automatically detect regions in the inter-Alu pattern likely associated to the presence of genetic polymorphisms.     

dConsensus: a tool for displaying domain assignments by multiple structure-based algorithms and for construction of a consensus assignment

Background  

Partitioning of a protein into structural components, known as domains, is an important initial step in protein classification and for functional and evolutionary studies. While the systematic assignments of domains by human experts exist (CATH and SCOP), the introduction of high throughput technologies for structure determination threatens to overwhelm expert approaches. A variety of algorithmic methods have been developed to expedite this process, allowing almost instant structural decomposition into domains. The performance of algorithmic methods can approach 85% agreement on the number of domains with the consensus reached by experts. However, each algorithm takes a somewhat different conceptual approach, each with unique strengths and weaknesses. Currently there is no simple way to automatically compare assignments from different structure-based domain assignment methods, thereby providing a comprehensive understanding of possible structure partitioning as well as providing some insight into the tendencies of particular algorithms. Most importantly, a consensus assignment drawn from multiple assignment methods can provide a singular and presumably more accurate view.     

A novel method for high accuracy sumoylation site prediction from protein sequences

Background

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome [1] would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.

Results

To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.

Conclusion

We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.     

Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

Background  

The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.     

Outcome prediction based on microarray analysis: a critical perspective on methods

Background  

Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.     

A simulation study comparing supertree and combined analysis methods using SMIDGen

Background  

Supertree methods comprise one approach to reconstructing large molecular phylogenies given multi-marker datasets: trees are estimated on each marker and then combined into a tree (the "supertree") on the entire set of taxa. Supertrees can be constructed using various algorithmic techniques, with the most common being matrix representation with parsimony (MRP). When the data allow, the competing approach is a combined analysis (also known as a "supermatrix" or "total evidence" approach) whereby the different sequence data matrices for each of the different subsets of taxa are concatenated into a single supermatrix, and a tree is estimated on that supermatrix.     

Phylogenetic analysis of the true water bugs (Insecta: Hemiptera: Heteroptera: Nepomorpha): evidence from mitochondrial genomes

Background  

The true water bugs are grouped in infraorder Nepomorpha (Insecta: Hemiptera: Heteroptera) and are of great economic importance. The phylogenetic relationships within Nepomorpha and the taxonomic hierarchies of Pleoidea and Aphelocheiroidea are uncertain. Most of the previous studies were based on morphological characters without algorithmic assessment. In the latest study, the molecular markers employed in phylogenetic analyses were partial sequences of 16S rDNA and 18S rDNA with a total length about 1 kb. Up to now, no mitochondrial genome of the true water bugs has been sequenced, which is one of the largest data sets that could be compared across animal taxa. In this study we analyzed the unresolved problems in Nepomorpha using evidence from mitochondrial genomes.     

Estimating the size of the solution space of metabolic networks

Background  

Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.     

An algorithmic approach to diagnose haematolymphoid neoplasms in effusion by combining morphology,immunohistochemistry and molecular cytogenetics

Objective

There are limited studies of cytology diagnosis of haematopoietic and lymphoid tumours in serosal effusion except for occasional case reports. We would like to demonstrate an algorithmic approach for accurate diagnosis, especially in patients without previous history.

Methods

We reviewed 36 cases of lymphoma diagnosed in serosal effusion following an algorithmic approach. Suspected tumour cells were classified into small, intermediate and large sizes and two characteristic forms of plasmacytoid and Reed Sternberg‐like on smears (step 1), followed by utilising panels of immunohistochemical markers and Epstein‐Barr encoding region in situ hybridisation on cell blocks (step 2). A panel of CD3, CD20 and Ki‐67 formed the basic workup, followed by pertinent batteries of immunostaining. Molecular tests were applied in 22 selected cases by fluorescence in situ hybridisation (step 3).

Results

There were 15 diffuse large B‐cell lymphomas; 12 plasma cell myelomas; two mantle cell lymphomas; one anaplastic large cell lymphoma ALK +; one small lymphocytic lymphoma; one plasmablastic lymphoma; one peripheral T‐cell lymphoma, not otherwise specified, one extranodal NK/T‐cell lymphoma, nasal type and two T‐cell lymphoblastic lymphomas. 14 cases with previous history had complete concordance in immunophenotype between cytology and histology. Another 14 cases were primarily diagnosed in patients with initial symptom of effusion based on immunophenotyping and cytogenetic test in selected cases. Eight cases were diagnosed based on morphology alone.

Conclusion

An algorithmic approach based on morphology and immunohistochemistry is the key to making an accurate diagnosis of haematopoietic and lymphoid tumours in effusion. A molecular test is also important for confirmation and prognostic prediction. We reviewed 36 haematolymphoid neoplasms diagnosed in effusion including 14 cases primarily diagnosed in patients without previous history following an algorithmic approach by combining morphology, immunohistochemistry and molecular cytogenetics.