BackgroundSemiochemical is a generic term used for a chemical substance that influences the behaviour of an organism. It is a common term used in the field of chemical ecology to encompass pheromones, allomones, kairomones, attractants and repellents. Insects have mastered the art of using semiochemicals as communication signals and rely on them to find mates, host or habitat. This dependency of insects on semiochemicals has allowed chemical ecologists to develop environment friendly pest management strategies. However, discovering semiochemicals is a laborious process that involves a plethora of behavioural and analytical techniques, making it expansively time consuming. Recently, reverse chemical ecology approach using odorant binding proteins (OBPs) as target for elucidating behaviourally active compounds is gaining eminence. In this scenario, we describe a “computational reverse chemical ecology” approach for rapid screening of potential semiochemicals.ResultsWe illustrate the high prediction accuracy of our computational method. We screened 25 semiochemicals for their binding potential to a GOBP of B. dorsalis using molecular docking (in silico) and molecular dynamics. Parallely, compounds were subjected to fluorescent quenching assays (Experimental). The correlation between in silico and experimental data were significant (r2 = 0.9408; P < 0.0001). Further, predicted compounds were subjected to behavioral bioassays and were found to be highly attractive to insects.ConclusionsThe present study provides a unique methodology for rapid screening and predicting behaviorally active semiochemicals. This methodology may be developed as a viable approach for prospecting active semiochemicals for pest control, which otherwise is a laborious process.

BackgroundWith the advent of paired-end high throughput sequencing, it is now possible to identify various types of structural variation on a genome-wide scale. Although many methods have been proposed for structural variation detection, most do not provide precise boundaries for identified variants. In this paper, we propose a new method, Distribution Based detection of Duplication Boundaries (DB2), for accurate detection of tandem duplication breakpoints, an important class of structural variation, with high precision and recall.ResultsOur computational experiments on simulated data show that DB2 outperforms state-of-the-art methods in terms of finding breakpoints of tandem duplications, with a higher positive predictive value (precision) in calling the duplications’ presence. In particular, DB2’s prediction of tandem duplications is correct 99% of the time even for very noisy data, while narrowing down the space of possible breakpoints within a margin of 15 to 20 bps on the average. Most of the existing methods provide boundaries in ranges that extend to hundreds of bases with lower precision values. Our method is also highly robust to varying properties of the sequencing library and to the sizes of the tandem duplications, as shown by its stable precision, recall and mean boundary mismatch performance. We demonstrate our method’s efficacy using both simulated paired-end reads, and those generated from a melanoma sample and two ovarian cancer samples. Newly discovered tandem duplications are validated using PCR and Sanger sequencing.ConclusionsOur method, DB2, uses discordantly aligned reads, taking into account the distribution of fragment length to predict tandem duplications along with their breakpoints on a donor genome. The proposed method fine tunes the breakpoint calls by applying a novel probabilistic framework that incorporates the empirical fragment length distribution to score each feasible breakpoint. DB2 is implemented in Java programming language and is freely available at http://mendel.gene.cwru.edu/laframboiselab/software.php.

BackgroundB-thalassaemia and sickle cell disease (SCD) are two of the most common monogenic diseases that are found in many populations worldwide. In both disorders the clinical severity is highly variable, with the persistence of fetal haemoglobin (HbF) being one of the major ameliorating factors. HbF levels are affected by, amongst other factors, single nucleotide polymorphisms (SNPs) at the BCL11A gene and the HBS1L-MYB intergenic region, which are located outside the β-globin locus. For this reason, we developed two multiplex assays that allow the genotyping of SNPs at these two genomic regions which have been shown to be associated with variable HbF levels in different populations.ResultsTwo multiplex assays based on the SNaPshot minisequencing approach were developed. The two assays can be used to simultaneous genotype twelve SNPs at the BCL11A gene and sixteen SNPs at HBS1L-MYB intergenic region which were shown to modify HbF levels. The different genotypes can be determined based on the position and the fluorescent colour of the peaks in a single electropherogram. DNA sequencing and restriction fragment length polymorphism (PCR-RFLP) assays were used to verify genotyping results obtained by SNaPshot minisequencing.ConclusionsIn summary, we propose two multiplex assays based on the SNaPshot minisequencing approach for the simultaneous identification of SNPs located at the BCL11A gene and HBS1L-MYB intergenic region which have an effect on HbF levels. The assays can be easily applied for accurate, time and cost efficient genotyping of the selected SNPs in various populations.

BackgroundB-thalassaemia and sickle cell disease (SCD) are two of the most common monogenic diseases that are found in many populations worldwide. In both disorders the clinical severity is highly variable, with the persistence of fetal haemoglobin (HbF) being one of the major ameliorating factors. HbF levels are affected by, amongst other factors, single nucleotide polymorphisms (SNPs) at the BCL11A gene and the HBS1L-MYB intergenic region, which are located outside the β-globin locus. For this reason, we developed two multiplex assays that allow the genotyping of SNPs at these two genomic regions which have been shown to be associated with variable HbF levels in different populations.ResultsTwo multiplex assays based on the SNaPshot minisequencing approach were developed. The two assays can be used to simultaneous genotype twelve SNPs at the BCL11A gene and sixteen SNPs at HBS1L-MYB intergenic region which were shown to modify HbF levels. The different genotypes can be determined based on the position and the fluorescent colour of the peaks in a single electropherogram. DNA sequencing and restriction fragment length polymorphism (PCR-RFLP) assays were used to verify genotyping results obtained by SNaPshot minisequencing.ConclusionsIn summary, we propose two multiplex assays based on the SNaPshot minisequencing approach for the simultaneous identification of SNPs located at the BCL11A gene and HBS1L-MYB intergenic region which have an effect on HbF levels. The assays can be easily applied for accurate, time and cost efficient genotyping of the selected SNPs in various populations.

BackgroundNext-generation DNA sequencing (NGS) technologies have made huge impacts in many fields of biological research, but especially in evolutionary biology. One area where NGS has shown potential is for high-throughput sequencing of complete mtDNA genomes (of humans and other animals). Despite the increasing use of NGS technologies and a better appreciation of their importance in answering biological questions, there remain significant obstacles to the successful implementation of NGS-based projects, especially for new users.ResultsHere we present an ‘A to Z’ protocol for obtaining complete human mitochondrial (mtDNA) genomes – from DNA extraction to consensus sequence. Although designed for use on humans, this protocol could also be used to sequence small, organellar genomes from other species, and also nuclear loci. This protocol includes DNA extraction, PCR amplification, fragmentation of PCR products, barcoding of fragments, sequencing using the 454 GS FLX platform, and a complete bioinformatics pipeline (primer removal, reference-based mapping, output of coverage plots and SNP calling).ConclusionsAll steps in this protocol are designed to be straightforward to implement, especially for researchers who are undertaking next-generation sequencing for the first time. The molecular steps are scalable to large numbers (hundreds) of individuals and all steps post-DNA extraction can be carried out in 96-well plate format. Also, the protocol has been assembled so that individual ‘modules’ can be swapped out to suit available resources.

BackgroundDevelopment of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment.ResultsIn this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites.ConclusionsGiven its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at ‘http://bioinf.uab.cat/antibactr’.