The Identification of Discriminating Patterns from 16S rRNA Gene to Generate Signature for Bacillus Genus

Mapping Microbial Capacities for Bioremediation: Genes to Genomics

Bioremediation is a process wherein the decontamination strategies are designed so that a site could achieve the environmental abiotic and biotic parameters close to its baseline. In the process, the driving force is the available microbial genetic degradative capabilities, which are supported by required nutrients so that the desired expression of these capabilities could be exploited in favour of removal of pollutants. With genomics tools not only the available abilities could be estimated but their dynamic performance could also be established. These tools are now playing important role in bioprocess optimization, which not only derive the bio-stimulation plans but also could suggest possible genetic bio-augmentation options.

matK-QR classifier: a patterns based approach for plant species identification

Background

DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK (matK) and ribulose-1, 5-bisphosphate carboxylase (rbcL) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification.

Methods

In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset.

Results

Due to the higher discrimination success obtained with the matK as compared to the rbcL, we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK-QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species.

Conclusions

This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK-QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0120-6) contains supplementary material, which is available to authorized users.

Insights in Waste Management Bioprocesses Using Genomic Tools

Microbial capacities drive waste stabilization and resource recovery in environmental friendly processes. Depending on the composition of waste, a stress-mediated selection process ensures a scenario that generates a specific enrichment of microbial community. These communities dynamically change over a period of time while keeping the performance through the required utilization capacities. Depending on the environmental conditions, these communities select the appropriate partners so as to maintain the desired functional capacities. However, the complexities of these organizations are difficult to study. Individual member ratios and sharing of genetic intelligence collectively decide the enrichment and survival of these communities. The next-generation sequencing options with the depth of structure and function analysis have emerged as a tool that could provide the finer details of the underlying bioprocesses associated and shared in environmental niches. These tools can help in identification of the key biochemical events and monitoring of expression of associated phenotypes that will support the operation and maintenance of waste management systems. In this chapter, we link genomic tools with process optimization and/or management, which could be applied for decision making and/or upscaling. This review describes both, the aerobic and anaerobic, options of waste utilization process with the microbial community functioning as flocs, granules, or biofilms. There are a number of challenges involved in harnessing the microbial community intelligence with associated functional plasticity for efficient extension of microbial capacities for resource recycling and waste management. Mismanaged wastes could lead to undesired genotypes such as antibiotic/multidrug-resistant microbes.