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Szulkin R.,Karolinska Institutet | Szulkin R.,Center for Family and Community Medicine | Holmberg E.,Gothenburg University | Stattin P.,Umea University | And 6 more authors.
Prostate | Year: 2012

BACKGROUND Currently used prognostic markers are limited in their ability to accurately predict disease progression among patients with localized prostate cancer. We examined 23 reported prostate cancer susceptibility variants for association with disease progression. METHODS Disease progression was explored among 4,673 Swedish patients treated for clinically localized prostate cancer between 1997 and 2002. Prostate cancer progression was defined according to primary treatment as a composed event reflecting termination of deferred treatment, biochemical recurrence, local progression, or presence of distant metastasis. Association between single variants, and all variants combined, were performed in Cox regression analysis assuming both log-additive and co-dominant genetic models. RESULTS Three of the 23 genetic variants explored were nominally associated with prostate cancer progression; rs9364554 (P = 0.041) on chromosome 6q25 and rs10896449 (P = 0.029) on chromosome 11q13 among patients treated with curative intent; and rs4054823 (P = 0.008) on chromosome 17p12 among patients on surveillance. However, none of these associations remained statistically significant after correction for multiple testing. The combined effect of all susceptibility variants was not associated with prostate cancer progression neither among patients receiving treatment with curative intent (P = 0.14) nor among patients on surveillance (P = 0.92). CONCLUSIONS We observed no evidence for an association between any of 23 established prostate cancer genetic risk variants and disease progression. Accumulating evidence suggests separate genetic components for initiation and progression of prostate cancer. Future studies systematically searching for genetic risk variants associated with prostate cancer progression and prognosis are warranted. Copyright © 2011 Wiley Periodicals, Inc.


Forslund K.,University of Stockholm | Sonnhammer E.L.L.,University of Stockholm | Sonnhammer E.L.L.,Swedish cience Research Center
Methods in Molecular Biology | Year: 2012

This chapter reviews the current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this directly impacts which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multidomain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). © 2012 Springer Science+Business Media, LLC.


Sonnhammer E.L.L.,Stockholm Bioinformatics Center | Sonnhammer E.L.L.,Swedish cience Research Center | Sonnhammer E.L.L.,University of Stockholm | Gabaldon T.,Center for Genomic Regulation | And 10 more authors.
Bioinformatics | Year: 2014

Given the rapid increase of species with a sequenced genome, the need to identify orthologous genes between them has emerged as a central bioinformatics task. Many different methods exist for orthology detection, which makes it difficult to decide which one to choose for a particular application. Here, we review the latest developments and issues in the orthology field, and summarize the most recent results reported at the third 'Quest for Orthologs' meeting. We focus on community efforts such as the adoption of reference proteomes, standard file formats and benchmarking. Progress in these areas is good, and they are already beneficial to both orthology consumers and providers. However, a major current issue is that the massive increase in complete proteomes poses computational challenges to many of the ortholog database providers, as most orthology inference algorithms scale at least quadratically with the number of proteomes. The Quest for Orthologs consortium is an open community with a number of working groups that join efforts to enhance various aspects of orthology analysis, such as defining standard formats and datasets, documenting community resources and benchmarking. © The Author 2014. Published by Oxford University Press.


Guala D.,Stockholm Bioinformatics Center | Guala D.,University of Stockholm | Sjolund E.,Stockholm Bioinformatics Center | Sjolund E.,University of Stockholm | And 3 more authors.
Bioinformatics | Year: 2014

MaxLink, a guilt-by-association network search algorithm, has been made available as a web resource and a stand-alone version. Based on a user-supplied list of query genes, MaxLink identifies and ranks genes that are tightly linked to the query list. This functionality can be used to predict potential disease genes from an initial set of genes with known association to a disease. The original algorithm, used to identify and rank novel genes potentially involved in cancer, has been updated to use a more statistically sound method for selection of candidate genes and made applicable to other areas than cancer. The algorithm has also been made faster by re-implementation in C++, and the Web site uses FunCoup 3.0 as the underlying network.Availability and implementation: MaxLink is freely available at http://maxlink.sbc.su.se both as a web service and a stand-alone application for download. © 2014 The Author.


Schmitt T.,Stockholm Bioinformatics Center | Schmitt T.,University of Stockholm | Ogris C.,Stockholm Bioinformatics Center | Ogris C.,University of Stockholm | And 3 more authors.
Nucleic Acids Research | Year: 2014

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction. © 2013 The Author(s). Published by Oxford University Press.

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