Mining the NCBI Influenza Sequence Database: adaptive grouping of BLAST results using precalculated neighbor indexing

In silico integrative analysis for the characterization of LYT1 a unique protein of Trypanosoma cruzi

Trypanosoma rangeli is the most similar organism to Trypanosoma cruzi. They share distribution areas, hosts, and some vectors. However, there are key differences between them; the first lacks a multiplicative form in the host and does not cause disease, while the second is the etiological agent of the American tripanosomiasis, a tropical disease that still does not have an effective vaccine nor treatment. Aiming to reveal the differences in their gene expression patterns in each life cycle form, the comparison of expression profiles was made parting from the ESTs available in TriTrypDB. We verified that there are no genes unique to T. rangeli in the ESTs. Astonishingly, we determined that T. cruzi has a single copy gene called LYT1, which has no similarity to any other protein of any organism on Earth. LYT1 is involved in invasion, motility, and cell cycle, making it an attractive vaccine target. After its identification, using immunoinformatics programs, we found multiple potential B- and T-cell epitopes in this protein, which is also rich in intrinsically disordered regions. Additionally, an approximation of the 3 D structure was predicted where the B-cell epitopes were located to assess their solvent access. We propose that its particular structural conformation confers the flexibility required for the interactions with multiple proteins, which in part may be performed through N-myristoylation sites. Given its important role in the infectiveness of T. cruzi and its antigenic potential, we highlight the need for future studies focused on its molecular and immunological in vivo characterization.Communicated by Ramaswamy H. Sarma.

Treetrimmer: a method for phylogenetic dataset size reduction

Background

With rapid advances in genome sequencing and bioinformatics, it is now possible to generate phylogenetic trees containing thousands of operational taxonomic units (OTUs) from a wide range of organisms. However, use of rigorous tree-building methods on such large datasets is prohibitive and manual ‘pruning’ of sequence alignments is time consuming and raises concerns over reproducibility. There is a need for bioinformatic tools with which to objectively carry out such pruning procedures.

Findings

Here we present ‘TreeTrimmer’, a bioinformatics procedure that removes unnecessary redundancy in large phylogenetic datasets, alleviating the size effect on more rigorous downstream analyses. The method identifies and removes user-defined ‘redundant’ sequences, e.g., orthologous sequences from closely related organisms and ‘recently’ evolved lineage-specific paralogs. Representative OTUs are retained for more rigorous re-analysis.

Conclusions

TreeTrimmer reduces the OTU density of phylogenetic trees without sacrificing taxonomic diversity while retaining the original tree topology, thereby speeding up downstream computer-intensive analyses, e.g., Bayesian and maximum likelihood tree reconstructions, in a reproducible fashion.

Tree Pruner: An efficient tool for selecting data from a biased genetic database

Background

Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses.

Results

We developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner session, the editing actions are implemented in the dataset.

Currently, Tree Pruner is implemented on the Influenza Research Database (IRD). The data management capabilities of the IRD allow the user to store a pruned dataset for additional pruning or for subsequent analysis. Tree Pruner can be easily adapted for use with other organisms.

Conclusions

Tree Pruner is an efficient, manual tool for selecting a high-quality dataset with desired evolutionary properties from a biased database of genetic sequences. It offers an important alternative to automated approaches to the same goal, by providing the user with a dynamic, visual guide to the ongoing selection process and ultimate control over the contents (and therefore quality) of the dataset.