Fast search of sequences with complex symbol correlations using profile context-sensitive HMMS and pre-screening filters

Recently, profile context-sensitive HMMs (profile-csHMMs) have been proposed which are very effective in modeling the common patterns and motifs in related symbol sequences. Profile-csHMMs are capable of representing long-range correlations between distant symbols, even when these correlations are entangled in a complicated manner. This makes profile-csHMMs an useful tool in computational biology, especially in modeling noncoding RNAs (ncRNAs) and finding new ncRNA genes. However, a profile-csHMM based search is quite slow, hence not practical for searching a large database. In this paper, we propose a practical scheme for making the search speed significantly faster without any degradation in the prediction accuracy. The proposed method utilizes a pre-screening filter based on a profile-HMM, which filters out most sequences that will not be predicted as a match by the original profile-csHMM. Experimental results show that the proposed approach can make the search speed eighty times faster

Non-Coding RNA Sequencing of Equine Endometrium During Maternal Recognition of Pregnancy.

Maternal recognition of pregnancy (MRP) in the mare is not well defined. In a non-pregnant mare, prostaglandin F2α (PGF) is released on day 14 post-ovulation (PO) to cause luteal regression, resulting in loss of progesterone production. Equine MRP occurs prior to day 14 to halt PGF production. Studies have failed to identify a gene candidate for MRP, so attention has turned to small, non-coding RNAs. The objective of this study was to evaluate small RNA (<200 nucleotides) content in endometrium during MRP. Mares were used in a cross-over design with each having a pregnant and non-mated cycle. Each mare was randomly assigned to collection day 11 or 13 PO (n = 3/day) and endometrial biopsies were obtained. Total RNA was isolated and sequencing libraries were prepared using a small RNA library preparation kit and sequenced on a HiSeq 2000. EquCab3 was used as the reference genome and DESeq2 was used for statistical analysis. On day 11, 419 ncRNAs, representing miRNA, snRNA, snoRNA, scaRNA, and vaultRNA, were different between pregnancy statuses, but none on day 13. Equine endometrial ncRNAs with unknown structure and function were also identified. This study is the first to describe ncRNA transcriptome in equine endometrium. Identifying targets of these ncRNAs could lead to determining MRP

A Genetic Algorithms Approach to Non-coding RNA Gene Searches

A genetic algorithm is proposed as an alternative to the traditional linear programming method for scoring covariance models in non-coding RNA (ncRNA) gene searches. The standard method is guaranteed to find the best score, but it is too slow for general use. The observation that most of the search space investigated by the linear programming method does not even remotely resemble any observed sequence in real sequence data can be used to motivate the use of genetic algorithms (GAs) to quickly reject regions of the search space. A search space with many local minima makes gradient decent an unattractive alternative. It is shown that a fixed-length representation for alignment of two sequences taken from the protein threading literature can be adapted for use with covariance models

Computation Intelligence Method to Find Generic Non-Coding RNA Search Models

Fairly effective methods exist for finding new noncoding RNA genes using search models based on known families of ncRNA genes (for example covariance models). However, these models only find new members of the existing families and are not useful in finding potential members of novel ncRNA families. Other problems with family-specific search include large processing requirements, ambiguity in defining which sequences form a family and lack of sufficient numbers of known sequences to properly estimate model parameters. An ncRNA search model is proposed which includes a collection of non-overlapping RNA hairpin structure covariance models. The hairpin models are chosen from a hairpin-model list compiled from many families in the Rfam non-coding RNA families database. The specific hairpin models included and the overall score threshold for the search model is determined through the use of a genetic algorithm

Accelerated Non-Coding RNA Searches with Covariance Model Approximations

Covariance models (CMs) are a very sensitive tool for finding non-coding RNA (ncRNA) genes in DNA sequence data. However, CMs are extremely slow. One reason why CMs are so slow is that they allow all possible combinations of insertions and deletions relative to the consensus model even though the vast majority of these are never seen in practice. In this paper we examine reduction in the number of states in covariance models. A simplified CM with reduced states which can be scored much faster is introduced. A comparison of the results of a full CM versus a reduced-state model found using a genetic algorithm is given for the let7 ncRNA family

Covariance Searches for ncRNA Gene Finding

The use of covariance models for non-coding RNA gene finding is extremely powerful and also extremely computationally demanding. A major reason for the high computational burden of this algorithm is that the search proceeds through every possible start position in the database and every possible sequence length between zero and a user-defined maximum length at every one of these start positions. Furthermore, for every start position and sequence length, all possible combinations of insertions and deletions leading to the given sequence length are searched. It has been previously shown that a large portion of this search space is nowhere near any database match observed in practice and that the search space can be limited significantly with little change in expected search results. In this work a different approach is taken in which the space of starting positions, sequence lengths, and insertion/deletion patterns is searched using a genetic algorithm

Fast Structural Similarity Search of Noncoding RNAs Based on Matched Filtering of Stem Patterns

Many noncoding RNAs (ncRNAs) have characteristic secondary structures that give rise to complicated base correlations in their primary sequences. Therefore, when performing an RNA similarity search to find new members of a ncRNA family, we need a statistical model - such as the profile- csHMM or the covariance model (CM) - that can effectively describe the correlations between distant bases. However, these models are computationally expensive, making the resulting RNA search very slow. To overcome this problem, various prescreening methods have been proposed that first use a simpler model to scan the database and filter out the dissimilar regions. Only the remaining regions that bear some similarity are passed to a more complex model for closer inspection. It has been shown that the prescreening approach can make the search speed significantly faster at no (or a slight) loss of prediction accuracy. In this paper, we propose a novel prescreening method based on matched filtering of stem patterns. Unlike many existing methods, the proposed method can prescreen the database solely based on structural similarity. The proposed method can handle RNAs with arbitrary secondary structures, and it can be easily incorporated into various search methods that use different statistical models. Furthermore, the proposed approach has a low computational cost, yet very effective for prescreening, as will be demonstrated in the paper

RNA Search with Decision Trees and Partial Covariance Models

The use of partial covariance models to search for RNA family members in genomic sequence databases is explored. The partial models are formed from contiguous subranges of the overall RNA family multiple alignment columns. A binary decision-tree framework is presented for choosing the order to apply the partial models and the score thresholds on which to make the decisions. The decision trees are chosen to minimize computation time subject to the constraint that all of the training sequences are passed to the full covariance model for final evaluation. Computational intelligence methods are suggested to select the decision tree since the tree can be quite complex and there is no obvious method to build the tree in these cases. Experimental results from seven RNA families shows execution times of 0.066-0.268 relative to using the full covariance model alone. Tests on the full sets of known sequences for each family show that at least 95 percent of these sequences are found for two families and 100 percent for five others. Since the full covariance model is run on all sequences accepted by the partial model decision tree, the false alarm rate is at least as low as that of the full model alone

A Computational Pipeline for High- Throughput Discovery of cis-Regulatory Noncoding RNA in Prokaryotes

Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair–level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth