Towards Improved Assessment of Functional Similarity in Large-Scale Screens: A Study on Indel Length

Quantitative prediction of the effect of genetic variation using hidden Markov models

Background

With the development of sequencing technologies, more and more sequence variants are available for investigation. Different classes of variants in the human genome have been identified, including single nucleotide substitutions, insertion and deletion, and large structural variations such as duplications and deletions. Insertion and deletion (indel) variants comprise a major proportion of human genetic variation. However, little is known about their effects on humans. The absence of understanding is largely due to the lack of both biological data and computational resources.

Results

This paper presents a new indel functional prediction method HMMvar based on HMM profiles, which capture the conservation information in sequences. The results demonstrate that a scoring strategy based on HMM profiles can achieve good performance in identifying deleterious or neutral variants for different data sets, and can predict the protein functional effects of both single and multiple mutations.

Conclusions

This paper proposed a quantitative prediction method, HMMvar, to predict the effect of genetic variation using hidden Markov models. The HMM based pipeline program implementing the method HMMvar is freely available at https://bioinformatics.cs.vt.edu/zhanglab/hmm.

The evolution of phenylpropanoid metabolism in the green lineage

Phenolic secondary metabolites are only produced by plants wherein they play important roles in both biotic and abiotic defense in seed plants as well as being potentially important bioactive compounds with both nutritional and medicinal benefits reported for animals and humans as a consequence of their potent antioxidant activity. During the long evolutionary period in which plants have adapted to the environmental niches in which they exist (and especially during the evolution of land plants from their aquatic algal ancestors), several strategies such as gene duplication and convergent evolution have contributed to the evolution of this pathway. In this respect, diversity and redundancy of several key genes of phenolic secondary metabolism such as polyketide synthases, cytochrome P450s, Fe(2+)/2-oxoglutarate-dependent dioxygenases and UDP-glycosyltransferases have played an essential role. Recent technical developments allowing affordable whole genome sequencing as well as a better inventory of species-by-species chemical diversity have resulted in a dramatic increase in the number of tools we have to assess how these pathways evolved. In parallel, reverse genetics combined with detailed molecular phenotyping is allowing us to elucidate the functional importance of individual genes and metabolites and by this means to provide further mechanistic insight into their biological roles. In this review, phenolic metabolite-related gene sequences (for a total of 65 gene families including shikimate biosynthetic genes) are compared across 23 independent species, and the phenolic metabolic complement of various plant species are compared with one another, in attempt to better understand the evolution of diversity in this crucial pathway.

Speeding up the Consensus Clustering methodology for microarray data analysis

Background

The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up.

Results

Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by Consensus. We have also experimented with the use of Consensus and FC in conjunction with NMF (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although NMF is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about NMF, shedding further light on its merits and limitations.

Conclusions

In summary, FC with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures.