Bioinformatics Bioinformatics Analysis Phylogenetic analysis

Diversity Analysis

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Diversity analysis investigates questions such as “how many species are in a sample?” and “how similar are these two samples?”. The diversity in a single sample is called alpha diversity, and the diversity (differences or similarities) between two samples is called beta diversity.

The first studies into determining the number of groups in a data set focus on automatic stopping rules for hierarchical agglomeration techniques. A stopping rule shows where to stop merging objects to determine the number of groups found by the scheme. Comprehensive simulation tests of 30 of these criteria show not only a clear, best set of indices but also a wide variety of performances, and conclude unsurprisingly that the performance of each criterion is data set dependent.

Predictive arguments for determining the number of clusters in a data set are becoming more popular, as they can be explained in terms of model complexity.

The input data used for diversity analysis is an OTU table. A tree for the OTUs is also needed for UniFrac beta diversity analysis. We can visualize diversity using octave plots. Most standard diversity metrics are difficult to interpret or invalid for NGS OTUs.

The study on genetic diversity is critical to success in plant breeding, as it provides information on the quantum of genetic divergence, which serves as a platform for specific breeding objectives. Parental combinations likely to create segregating progenies with maximum genetic potential for further selection, designing introgression program, and selection of parental combinations toward maximization of heterosis are dependent on diversity analysis. Genetic diversity may be assessed using different marker systems, which encompasses morphological, biochemical, and molecular (DNA) markers. With recent advances in genomics research, DNA markers assume much more significance. Using different marker systems, genetic diversity in crop plants may be accessed at species level, at the population level, among germplasm accessions, at an individual genotype level like among pure lines or clones, etc. Large arrays of statistical packages are available to conduct diversity analysis. In recent past, functional diversity is being assayed using gene and EST-based markers. 

Focusing on the analysis of species survey data, tree diversity analysis provides a comprehensive review of the methods that are most often used in recent diversity and community ecology literature including:

  • Species accumulation curves for site-based and individual-based species accumulation, including a new technique for exact calculation of site-based species accumulation.
  • Description of appropriate methods for investigating differences in diversity and evenness such as Rényi diversity profiles.
  • Modern regression methods of generalized linear models and generalized additive models that are often appropriate for investigating patterns of species occurrence and species counts.
  • Methods of ordination for investigating community structure and the influence of environmental characteristics, including recent methods such as distance-based redundancy analysis and constrained analysis of principal coordinates.

With several microbes discovered and rediscovered, there is a growing need to understand their lineage, origin, and occurrence. Given the microbial diversity and its importance in global impact, it would be interesting to explore the microbial resources to better disseminate the phenotyping, epidemiological investigations, screening, and metagenomics. 

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We are providing “Diversity Analysis” service to our researchers and seekers to relate bioinformatics tools to mention from phylogenetic taxa, sequence and structural relationship, evolutionary mechanisms, horizontal gene transfer, and importantly functional genomics. 

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