SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Whole genome and exome sequencing are contributing to the extraordinary progress in the study of human genetic variants. In this fast developing field, appropriate and easily accessible tools are required to facilitate data analysis. RESULTS: Here we describe QueryOR, a web platform suitable for searching among known candidate genes as well as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive, flexible and easy to use. Instead of being designed on specific datasets, it works on a general XML schema specifying formats and criteria of each data source. Thanks to this flexibility, new criteria can be easily added for future expansion. Currently, up to 70 user-selectable criteria are available, including a wide range of gene and variant features. Moreover, rather than progressively discarding variants taking one criterion at a time, the prioritization is achieved by a global positive selection process that considers all transcript isoforms, thus producing reliable results. QueryOR is easy to use and its intuitive interface allows to handle different kinds of inheritance as well as features related to sharing variants in different patients. QueryOR is suitable for investigating single patients, families or cohorts. CONCLUSIONS: QueryOR is a comprehensive and flexible web platform eligible for an easy user-driven variant prioritization. It is freely available for academic institutions at http://queryor.cribi.unipd.it/ .

SUMMARY: The sequencing of bisulfite-treated DNA (Bi-Seq) is becoming a gold standard for methylation studies. The mapping of Bi-Seq reads is complex and requires special alignment algorithms. This problem is particularly relevant for SOLiD color space, where the bisulfite conversion C/T changes two adjacent colors into 16 possible combinations. Here, we present an algorithm that efficiently aligns Bi-Seq reads obtained either from SOLiD or Illumina. An accompanying methylation-caller program creates a genomic view of methylated and unmethylated Cs on both DNA strands. AVAILABILITY AND IMPLEMENTATION: The algorithm has been implemented as an option of the program PASS, freely available at http://pass.cribi.unipd.it. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Standard DNA alignment programs are inadequate to manage the data produced by new generation DNA sequencers. To answer this problem, we developed PASS with the objective of improving execution time and sensitivity when compared with other available programs. PASS performs fast gapped and ungapped alignments of short DNA sequences onto a reference DNA, typically a genomic sequence. It is designed to handle a huge amount of reads such as those generated by Solexa, SOLiD or 454 technologies. The algorithm is based on a data structure that holds in RAM the index of the genomic positions of 'seed' words (typically 11 and 12 bases) as well as an index of the precomputed scores of short words (typically seven and eight bases) aligned against each other. After building the genomic index, the program scans every query sequence performing three steps: (1) it finds matching seed words in the genome; (2) for every match checks the precomputed alignment of the short flanking regions; (3) if passes step 2, then it performs an exact dynamic alignment of a narrow region around the match. The performance of the program is very striking both for sensitivity and speed. For instance, gap alignment is achieved hundreds of times faster than BLAST and several times faster than SOAP, especially when gaps are allowed. Furthermore, PASS has a higher sensitivity when compared with the other available programs. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at http://pass.cribi.unipd.it, implemented in C++and supported on Linux and Windows.

BACKGROUND: Vibrionaceae represent a significant portion of the cultivable heterotrophic sea bacteria; they strongly affect nutrient cycling and some species are devastating pathogens. In this work we propose an improved phylogenetic profile analysis on 14 Vibrionaceae genomes, to study the evolution of this family on the basis of gene content. The phylogenetic profile is based on the observation that genes involved in the same process (e.g. metabolic pathway or structural complex) tend to be concurrently present or absent within different genomes. This allows the prediction of hypothetical functions on the basis of a shared phylogenetic profiles. Moreover this approach is useful to identify putative laterally transferred elements on the basis of their presence on distantly phylogenetically related bacteria. RESULTS: Vibrionaceae ORFs were aligned against all the available bacterial proteomes. Phylogenetic profile is defined as an array of distances, based on aminoacid substitution matrixes, from single genes to all their orthologues. Final phylogenetic profiles, derived from non-redundant list of all ORFs, was defined as the median of all the profiles belonging to the cluster. The resulting phylogenetic profiles matrix contains gene clusters on the rows and organisms on the columns. Cluster analysis identified groups of "core genes" with a widespread high similarity across all the organisms and several clusters that contain genes homologous only to a limited set of organisms. On each of these clusters, COG class enrichment has been calculated. The analysis reveals that clusters of core genes have the highest number of enriched classes, while the others are enriched just for few of them like DNA replication, recombination and repair. CONCLUSION: We found that mobile elements have heterogeneous profiles not only across the entire set of organisms, but also within Vibrionaceae; this confirms their great influence on bacteria evolution even inside the same family. Furthermore, several hypothetical proteins highly correlate with mobile elements profiles suggesting a possible horizontal transfer mechanism for the evolution of these genes. Finally, we suggested the putative role of some ORFs having an unknown function on the basis of their phylogenetic profile similarity to well characterized genes.

MOTIVATION: DNA repeats are a common feature of most genomic sequences. Their de novo identification is still difficult despite being a crucial step in genomic analysis and oligonucleotides design. Several efficient algorithms based on word counting are available, but too short words decrease specificity while long words decrease sensitivity, particularly in degenerated repeats. RESULTS: The Repeat Analysis Program (RAP) is based on a new word-counting algorithm optimized for high resolution repeat identification using gapped words. Many different overlapping gapped words can be counted at the same genomic position, thus producing a better signal than the single ungapped word. This results in better specificity both in terms of low-frequency detection, being able to identify sequences repeated only once, and highly divergent detection, producing a generally high score in most intron sequences. AVAILABILITY: The program is freely available for non-profit organizations, upon request to the authors. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The program has been tested on the Caenorhabditis elegans genome using word lengths of 12, 14 and 16 bases. The full analysis has been implemented in the UCSC Genome Browser and is accessible at http://genome.cribi.unipd.it.