Background: High-throughput automated sequencing has enabled an exponential growth rate of sequencing data. This requires increasing sequence quality and reliability in order to avoid database contamination with artefactual sequences. The arrival of pyrosequencing enhances this problem and necessitates customisable pre-processing algorithms. Results: SeqTrim has been implemented both as a Web and as a standalone command line application. Already-published and newly-designed algorithms have been included to identify sequence inserts, to remove low quality, vector, adaptor, low complexity and contaminant sequences, and to detect chimeric reads. The availability of several input and output formats allows its inclusion in sequence processing workflows. Due to its specific algorithms, SeqTrim outperforms other pre-processors implemented as Web services or standalone applications. It performs equally well with sequences from EST libraries, SSH libraries, genomic DNA libraries and pyrosequencing reads and does not lead to over-trimming. Conclusions: SeqTrim is an efficient pipeline designed for pre-processing of any type of sequence read, including next-generation sequencing. It is easily configurable and provides a friendly interface that allows users to know what happened with sequences at every pre-processing stage, and to verify pre-processing of an individual sequence if desired. The recommended pipeline reveals more information about each sequence than previously described pre-processors and can discard more sequencing or experimental artefacts.

The latest shifts in technology have brought about new kinds of users who occasionally access unfamiliar systems in new scenarios. This way of use should not request any learning curve. There have been many attempts to help this kind of users: agents, floating help, tooltips, direct video demonstrations, etc., elements that support the appealing direct manipulation style (DM), but add indeed an extra layer of supporting material over it. In this paper, we describe an analysegoals-guided Interaction, GGI, as the basis for a different approach. We do not expect the user to understand or use a metaphor but guide them throughout the whole interaction process. This help system is integrated in the interface. The user will be closely guided on the “what to do” and on the “how to do,” always according to their current goal. This no-previous-knowledge-required way of interaction shortens significantly the learning curve. In order to support this proposal, a complete study based on user testing is performed. The results show that, for non-expert occasional users, GGI is more efficient in time and less error prone than the classic Direct Manipulation. Participants also showed a strong preference for GGI. This research offers an alternative way of interaction for designers and developers of human-centric interactive systems who have occasional users as their main target users.

This work analyzes the evolutionary consequences of different aggregation levels of species distribution with an Evolutionary Cellular Automaton (ECA). We have found that in habitats with the same carrying capacity, aggregated distributions preserve smaller populations than do uniform distributions, i.e., they are less efficient. Nonetheless, we have also found that aggregated distributions, among other factors, can help the evolutionary stability of some biological interactions, such as predator–prey interactions, despite their granting less individual fitness. Besides, the competitive exclusion principle does not usually stand in populations with aggregated distribution. We have applied ECA to study the effects of aggregated distribution in two notorious cases: in the so-called paradox of the plankton and in gregarious animals. In doing so, we intend to ratify long-established ecological knowledge explaining these phenomena from a new perspective. In the first case, due to aggregate distribution, large aggregations of digital organisms mimicking very abundant planktonic species, leave large patches or oceanic areas free for other less competitive organisms, which mimic rare species, to prosper. In this case, we can see how effects, such as ecological drift and the small portion, act simultaneously. In the second case of aggregation, the aggregate distribution of gregarious animals could be explained under specialized predator–prey interactions and interdemic competition. Thus, digital organisms that imitate predators reduce the competitive capacity of their prey, destabilizing their competitiveness against other species. The specialized predator also goes extinct if the prey goes extinct by natural selection. Predators that have an aggregate distribution compensate the prey and thus avoid exclusion. This way there are more predator-free patches in which the prey can prosper. However, by granting greater colonization capacity to its prey, the predator loses competitiveness. Therefore, it is a multilevel selection event in which group adaptation grows to the detriment of the predator as an individual.

This paper presents an Evolutionary Cellular Automaton (ECA) that simulates the evolutionary dynamics of biological interactions by manipulating strategies of dispersion and associations between digital organisms. The parameterization of the different types of interaction and distribution strategies using configuration files generates easily interpretable results. In that respect, ECA is an effective instrument for measuring the effects of relative adaptive advantages and a good resource for studying natural selection. Although ECA works effectively in obtaining the expected results from most well-known biological interactions, some unexpected effects were observed. For example, organisms uniformly distributed in fragmented habitats do not favor eusociality, and mutualism evolved from parasitism simply by varying phenotypic flexibility. Finally, we have verified that natural selection represents a cost for the emergence of sex by destabilizing the stable evolutionary strategy of the 1:1 sex ratio after generating randomly different distributions in each generation.