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Vectoral and alignment-free approaches to biological sequence representation have been explored in bioinformatics to efficiently handle big data. Even so, most current methods involve sequence comparisons via alignment-based heuristics and fail when applied to the analysis of large data sets. Here, we present "Spaced Words Projection (SWeeP)", a method for representing biological sequences using relatively small vectors while preserving intersequence comparability. SWeeP uses spaced-words by scanning the sequences and generating indices to create a higher-dimensional vector that is later projected onto a smaller randomly oriented orthonormal base. We constructed phylogenetic trees for all organisms with mitochondrial and bacterial protein data in the NCBI database. SWeeP quickly built complete and accurate trees for these organisms with low computational cost. We compared SWeeP to other alignment-free methods and Sweep was 10 to 100 times quicker than the other techniques. A tool to build SWeeP vectors is available at https://sourceforge.net/projects/spacedwordsprojection/.

Background: Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials., Results: Here we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS 3 G), a method to clustering aiming of losing less biological information in the processes of generation groups. The strategy developed in our algorithm is optimized to be more astringent which reflects increase in accuracy and sensitivity in the generation of clusters in a wide range of similarity. RAFTS 3 G is the better choice compared to three main methods when the user wants more reliable result even ignoring the ideal threshold to clustering., Conclusion: In general, RAFTS 3 G is able to group up to millions of biological sequences into large datasets, which is a remarkable option of efficiency in clustering. RAFTS 3 G compared to other "standard-gold" methods in the clustering of large biological data maintains the balance between the reduction of biological information redundancy and the creation of consistent groups. We bring the binary search concept applied to grouped sequences which shows maintaining sensitivity/accuracy relation and up to minimize the time of data generated with RAFTS 3 G process.

Background: The fast reduction of prices of DNA sequencing allowed rapid accumulation of genome data. However, the process of obtaining complete genome sequences is still very time consuming and labor demanding. In addition, data produced from various sequencing technologies or alternative assemblies remain underexplored to improve assembly of incomplete genome sequences., Findings: We have developed FGAP, a tool for closing gaps of draft genome sequences that takes advantage of different datasets. FGAP uses BLAST to align multiple contigs against a draft genome assembly aiming to find sequences that overlap gaps. The algorithm selects the best sequence to fill and eliminate the gap., Conclusions: FGAP reduced the number of gaps by 78% in an E. coli draft genome assembly using two different sequencing technologies, Illumina and 454. Using PacBio long reads, 98% of gaps were solved. In human chromosome 14 assemblies, FGAP reduced the number of gaps by 35%. All the inserted sequences were validated with a reference genome using QUAST. The source code and a web tool are available at http://www.bioinfo.ufpr.br/fgap/.