BACKGROUND: Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. RESULTS: We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S . CONCLUSIONS: Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

UNLABELLED: The search for short words that are absent in the genome of one or more organisms (neverwords, also known as nullomers) is attracting growing interest because of the impact they may have in recent molecular biology applications. keeSeek is able to find absent sequences with primer-like features, which can be used as unique labels for exogenously inserted DNA fragments to recover their exact position into the genome using PCR techniques. The main differences with respect to previously developed tools for neverwords generation are (i) calculation of the distance from the reference genome, in terms of number of mismatches, and selection of the most distant sequences that will have a low probability to anneal unspecifically; (ii) application of a series of filters to discard candidates not suitable to be used as PCR primers. KeeSeek has been implemented in C++ and CUDA (Compute Unified Device Architecture) to work in a General-Purpose Computing on Graphics Processing Units (GPGPU) environment. AVAILABILITY AND IMPLEMENTATION: Freely available under the Q Public License at http://www.medcomp.medicina.unipd.it/main_site/doku.php?id=keeseek.

BACKGROUND: In the last decade, Next-Generation Sequencing technologies have been extensively applied to quantitative transcriptomics, making RNA sequencing a valuable alternative to microarrays for measuring and comparing gene transcription levels. Although several methods have been proposed to provide an unbiased estimate of transcript abundances through data normalization, all of them are based on an initial count of the total number of reads mapping on each transcript. This procedure, in principle robust to random noise, is actually error-prone if reads are not uniformly distributed along sequences, as happens indeed due to sequencing errors and ambiguity in read mapping. Here we propose a new approach, called maxcounts, to quantify the expression assigned to an exon as the maximum of its per-base counts, and we assess its performance in comparison with the standard approach described above, which considers the total number of reads aligned to an exon. The two measures are compared using multiple data sets and considering several evaluation criteria: independence from gene-specific covariates, such as exon length and GC-content, accuracy and precision in the quantification of true concentrations and robustness of measurements to variations of alignments quality. RESULTS: Both measures show high accuracy and low dependency on GC-content. However, maxcounts expression quantification is less biased towards long exons with respect to the standard approach. Moreover, it shows lower technical variability at low expressions and is more robust to variations in the quality of alignments. CONCLUSIONS: In summary, we confirm that counts computed with the standard approach depend on the length of the feature they are summarized on, and are sensitive to the non-uniform distribution of reads along transcripts. On the opposite, maxcounts are robust to biases due to the non-uniformity distribution of reads and are characterized by a lower technical variability. Hence, we propose maxcounts as an alternative approach for quantitative RNA-sequencing applications.

Next-generation sequencing technologies have fostered an unprecedented proliferation of high-throughput sequencing projects and a concomitant development of novel algorithms for the assembly of short reads. In this context, an important issue is the need of a careful assessment of the accuracy of the assembly process. Here, we review the efficiency of a panel of assemblers, specifically designed to handle data from GS FLX 454 platform, on three bacterial data sets with different characteristics in terms of reads coverage and repeats content. Our aim is to investigate their strengths and weaknesses in the reconstruction of the reference genomes. In our benchmarking, we assess assemblers' performance, quantifying and characterizing assembly gaps and errors, and evaluating their ability to solve complex genomic regions containing repeats. The final goal of this analysis is to highlight pros and cons of each method, in order to provide the final user with general criteria for the right choice of the appropriate assembly strategy, depending on the specific needs. A further aspect we have explored is the relationship between coverage of a sequencing project and quality of the obtained results. The final outcome suggests that, for a good tradeoff between costs and results, the planned genome coverage of an experiment should not exceed 20-30 ×.