Differences in sequencing technologies improve the retrieval of anammox bacterial genome from metagenomes.

BACKGROUND: Sequencing technologies have different biases, in single-genome sequencing and metagenomic sequencing; these can significantly affect ORFs recovery and the population distribution of a metagenome. In this paper we investigate how well different technologies represent information related to a considered organism of interest in a metagenome, and whether it is beneficial to combine information obtained using different technologies. We analyze comparatively three metagenomic datasets acquired from a sample containing the anammox bacterium Candidatus 'Brocadia fulgida' (B. fulgida). These datasets were obtained using Roche 454 FLX and Sanger sequencing with two different libraries (shotgun and fosmid). RESULTS: In each dataset, the abundance of the reads annotated to B. fulgida was much lower than the abundance expected from available cell count information. This was due to the overrepresentation of GC-richer organisms, as shown by GC-content distribution of the reads. Nevertheless, by considering the union of B. fulgida reads over the three datasets, the number of B. fulgida ORFs recovered for at least 80% of their length was twice the amount recovered by the best technology. Indeed, while taxonomic distributions of reads in the three datasets were similar, the respective sets of B. fulgida ORFs recovered for a large part of their length were highly different, and depth of coverage patterns of 454 and Sanger were dissimilar. CONCLUSIONS: Precautions should be sought in order to prevent the overrepresentation of GC-rich microbes in the datasets. This overrepresentation and the consistency of the taxonomic distributions of reads obtained with different sequencing technologies suggests that, in general, abundance biases might be mainly due to other steps of the sequencing protocols. Results show that biases against organisms of interest could be compensated combining different sequencing technologies, due to the differences of their genome-level sequencing biases even if the species was present in not very different abundances in the metagenomes.

MTR: taxonomic annotation of short metagenomic reads using clustering at multiple taxonomic ranks.

MOTIVATION: Metagenomics is a recent field of biology that studies microbial communities by analyzing their genomic content directly sequenced from the environment. A metagenomic dataset consists of many short DNA or RNA fragments called reads. One interesting problem in metagenomic data analysis is the discovery of the taxonomic composition of a given dataset. A simple method for this task, called the Lowest Common Ancestor (LCA), is employed in state-of-the-art computational tools for metagenomic data analysis of very short reads (about 100 bp). However LCA has two main drawbacks: it possibly assigns many reads to high taxonomic ranks and it discards a high number of reads. RESULTS: We present MTR, a new method for tackling these drawbacks using clustering at Multiple Taxonomic Ranks. Unlike LCA, which processes the reads one-by-one, MTR exploits information shared by reads. Specifically, MTR consists of two main phases. First, for each taxonomic rank, a collection of potential clusters of reads is generated, and each potential cluster is associated to a taxon at that rank. Next, a small number of clusters is selected at each rank using a combinatorial optimization algorithm. The effectiveness of the resulting method is tested on a large number of simulated and real-life metagenomes. Results of experiments show that MTR improves on LCA by discarding a significantly smaller number of reads and by assigning much more reads at lower taxonomic ranks. Moreover, MTR provides a more faithful taxonomic characterization of the metagenome population distribution. AVAILABILITY: Matlab and C++ source codes of the method available at http://cs.ru.nl/gori/software/MTR.tar.gz.

Toward high-throughput, multicriteria protein-structure comparison and analysis.

Protein-structure comparison (PSC) is an essential component of biomedical research as it impacts on, e.g., drug design, molecular docking, protein folding and structure prediction algorithms as well as being essential to the assessment of these predictions. Each of these applications, as well as many others where molecular comparison plays an important role, requires a different notion of similarity that naturally lead to the multicriteria PSC (MC-PSC) problem. Protein (Structure) Comparison, Knowledge, Similarity, and Information (ProCKSI) (www.procksi.org) provides algorithmic solutions for the MC-PSC problem by means of an enhanced structural comparison that relies on the principled application of information fusion to similarity assessments derived from multiple comparison methods. Current MC-PSC works well for moderately sized datasets and it is time consuming as it provides public service to multiple users. Many of the structural bioinformatics applications mentioned above would benefit from the ability to perform, for a dedicated user, thousands or tens of thousands of comparisons through multiple methods in real time, a capacity beyond our current technology. In this paper, we take a key step into that direction by means of a high-throughput distributed reimplementation of ProCKSI for very large datasets. The core of the proposed framework lies in the design of an innovative distributed algorithm that runs on each compute node in a cluster/grid environment to perform structure comparison of a given subset of input structures using some of the most popular PSC methods [e.g., universal similarity metric (USM), maximum contact map overlap (MaxCMO), fast alignment and search tool (FAST), distance alignment (DaliLite), combinatorial extension (CE), template modeling alignment (TMAlign)]. We follow this with a procedure of distributed consensus building. Thus, the new algorithms proposed here achieve ProCKSI's similarity assessment quality but with a fraction of the time required by it. Our results show that the proposed distributed method can be used efficiently to compare: 1) a particular protein against a very large protein structures dataset (target-against-all comparison), and 2) a particular very large-scale dataset against itself or against another very large-scale dataset (all-against-all comparison). We conclude the paper by enumerating some of the outstanding challenges for real-time MC-PSC.