Gregor Mendel (1822-1884) is almost unanimously recognized as the founder of modern genetics. However, long ago, a shadow of doubt was cast on his integrity by another eminent scientist, the statistician and geneticist, Sir Ronald Fisher (1890?1962), who questioned the honesty of the data that form the core of Mendel's work. This issue, nowadays called the Mendel-Fisher controversy, can be traced back to 1911, when Fisher first presented his doubts about Mendel's results, though he only published a paper with his analysis of Mendel's data in 1936. A large number of papers have been published about this controversy culminating with the publication in 2008 of a book (Franklin et al., Ending the Mendel-Fisher controversy) aiming at ending the issue, definitely rehabilitating Mendel image. However, quoting from Franklin et al., the issue of the too good to be true aspect of Mendel's data found by Fisher still stands. We have submitted Mendel data and Fisher's statistical analysis to extensive computations and simulations attempting to discover an hidden explanation or hint that could help finding an answer to the questions: is Fisher right or wrong, and if Fisher is right is there any reasonable explanation for the too good to be true, other than deliberate fraud? In this talk some results of this investigation and the conclusions obtained will be presented.

Gregor Mendel (1822-1884) is almost unanimously recognized as the founder of modern genetics. However, long ago, a shadow of doubt was cast on his integrity by another eminent scientist, the statistician and geneticist, Sir Ronald Fisher (1890?1962), who questioned the honesty of the data that form the core of Mendel's work. This issue, nowadays called the Mendel-Fisher controversy, can be traced back to 1911, when Fisher first presented his doubts about Mendel's results, though he only published a paper with his analysis of Mendel's data in 1936. A large number of papers have been published about this controversy culminating with the publication in 2008 of a book (Franklin et al., Ending the Mendel-Fisher controversy) aiming at ending the issue, definitely rehabilitating Mendel image. However, quoting from Franklin et al., the issue of the too good to be true aspect of Mendel's data found by Fisher still stands. We have submitted Mendel data and Fisher's statistical analysis to extensive computations and simulations attempting to discover an hidden explanation or hint that could help finding an answer to the questions: is Fisher right or wrong, and if Fisher is right is there any reasonable explanation for the too good to be true, other than deliberate fraud? In this talk some results of this investigation and the conclusions obtained will be presented.

Gregor Mendel (1822-1884) is almost unanimously recognized as the founder of modern genetics. However, long ago, a shadow of doubt was cast on his integrity by another eminent scientist, the statistician and geneticist, Sir Ronald Fisher (1890?1962), who questioned the honesty of the data that form the core of Mendel's work. This issue, nowadays called the Mendel-Fisher controversy, can be traced back to 1911, when Fisher first presented his doubts about Mendel's results, though he only published a paper with his analysis of Mendel's data in 1936. A large number of papers have been published about this controversy culminating with the publication in 2008 of a book (Franklin et al., Ending the Mendel-Fisher controversy) aiming at ending the issue, definitely rehabilitating Mendel image. However, quoting from Franklin et al., the issue of the too good to be true aspect of Mendel's data found by Fisher still stands. We have submitted Mendel data and Fisher's statistical analysis to extensive computations and simulations attempting to discover an hidden explanation or hint that could help finding an answer to the questions: is Fisher right or wrong, and if Fisher is right is there any reasonable explanation for the too good to be true, other than deliberate fraud? In this talk some results of this investigation and the conclusions obtained will be presented.

Gregor Mendel (1822-1884) is almost unanimously recognized as the founder of modern genetics. However, long ago, a shadow of doubt was cast on his integrity by another eminent scientist, the statistician and geneticist, Sir Ronald Fisher (1890?1962), who questioned the honesty of the data that form the core of Mendel's work. This issue, nowadays called the Mendel-Fisher controversy, can be traced back to 1911, when Fisher first presented his doubts about Mendel's results, though he only published a paper with his analysis of Mendel's data in 1936. A large number of papers have been published about this controversy culminating with the publication in 2008 of a book (Franklin et al., Ending the Mendel-Fisher controversy) aiming at ending the issue, definitely rehabilitating Mendel image. However, quoting from Franklin et al., the issue of the too good to be true aspect of Mendel's data found by Fisher still stands. We have submitted Mendel data and Fisher's statistical analysis to extensive computations and simulations attempting to discover an hidden explanation or hint that could help finding an answer to the questions: is Fisher right or wrong, and if Fisher is right is there any reasonable explanation for the too good to be true, other than deliberate fraud? In this talk some results of this investigation and the conclusions obtained will be presented.

There are currently more than 600 bacterial species and 28 vertebrate species, ranging from primates to fishes, for which we know (nearly) their entire DNA sequences. These number will continue to increase rapidly over the next few years. Comparing these genome sequences has emerged as one of the most important areas of computational biology. For example, one way to predict functional portions of the human genome is to search among related genomes for sequences that appear to be remarkably similar due to purifying selection. I will discuss and demonstrate some of the methods and tools for such an approach, as well as some of the challenges and unsolved problems.

We do not have a semantic web as such yet and instead have a collection of semantic web technologies. These technologies have recently started to deliver on their promise of an interoperable world particularly in data driven initiatives that integrate data management with its analysis. In this presentation we will describe our own travails with identifying and putting to use data driven representations of biomolecular repositories for biomarker studies. The examples will include user-driven “incubation” of data models using a software prototype that manages their representation as dyadic predicates (s3db.org). This solution falls into the generic W3C’s Resource Description Framework (RDF). As this prototype is used by other groups as the server-side partner of client-side computational statistics applications we find that cloud computing presents a better solution for deployment of web data services. The different models of cloud computing will be briefly overviewed in the life sciences context.

The study of gene regulatory networks, as well as other biological networks, have recently yield an increase on the number and detail of available models describing specific intracellular processes. The study of these models by means of analysis and simulation tools leads to innumerous predictions representing the possible behaviours of the system. In order to validate these predictions one must confront them with experimental data. Performed manually, this comparison may prove to be impracticable and prone to errors, leading to a growing need for an automatic and scalable method to perform this task. The formal verification field provides powerful methods to deal with the analysis of large models. Some issues still need to be addressed tough, in order to achieve a good integration of formal verification tools in the modeling practice in systems biology. We present the CTRL temporal logic which is powerful enough to express biological properties (multistability and oscillations), as well as an implementation of a set of temporal logic patterns for systems biology, and its integration with the GNA modeling tool. We also propose a web-server based architecture to integrate modeling and simulation tools with model-checking tools and its application to the analysis of the carbon starvation response model in E. coli.

This talk provides an introductory overview to DNA sequencing, as well as to the algorithms and architectures used for sequence alignment. The presentation will start with a brief introduction to the DNA sequencing process. Afterwards, a description of the optimal and heuristic algorithms for sequence alignment will be presented, as well as the data structures that usually support them. Special attention will be put on approximate string matching algorithms, due to the considerable speedup that may be obtained by using this type of search. Finally, some tools available for biological sequence comparison and for DNA re-sequencing will be presented, as well as some of the hardware structures used to further speed up the alignment process.

The aim of this work is to benchmark scoring functions used by Bayesian network learning algorithms in the context of classification. We considered both information-theoretic scores, such as LL, AIC, BIC/MDL, NML and MIT, and Bayesian scores, such as K2, BD, BDe and BDeu. We tested the scores in a classification task by learning the optimal TAN classifier with benchmark datasets. We conclude that, in general, information-theoretic scores perform better than Bayesian scores.

In this seminar I will be honoured to present myself to the local community and talk about the joys of cities with beautiful riverside landscapes. Incidentally, I might be caught talking about my research interests concerning the characterisation of conserved functional gene modules from heterogeneous high throughput data.