BayesMotif: de novo protein sorting motif discovery from impure datasets

Background Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. Methods We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Results Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. Conclusion We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of PWM (position weight matrix) motif model

Consequential considerations when mapping tRNA fragments

We examine several of the choices that went into the design of tDRmapper, a recently reported tool for identifying transfer RNA (tRNA) fragments in deep sequencing data, evaluate them in the context of currently available knowledge, and discuss their potential impact on the output that the tool generates

SEARCHPATTOOL: a new method for mining the most specific frequent patterns for binding sites with application to prokaryotic DNA sequences

<p>Abstract</p> <p>Background</p> <p>Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limited to short ones or impose some constraints on the pattern type. Many patterns for binding sites in prokaryotic species are not well characterized but are known to be large, between 16–30 base pairs (bp) and contain at least 2 conserved bases. The length of prokaryotic species promoters (about 400 bp) and our interest in studying a small set of genes that could be a cluster of co-regulated genes from microarray experiments led to the development of a new exhaustive algorithm targeting these large patterns.</p> <p>Results</p> <p>We present Searchpattool, a new method to search for and select the most specific (conservative) frequent patterns. This method does not impose restrictions on the density or the structure of the pattern. The best patterns (motifs) are selected using several statistics, including a new application of a z-score based on the number of matching sequences. We compared Searchpattool against other well known algorithms on a <it>Bacillus subtilis </it>group of 14 input sequences and found that in our experiments Searchpattool always performed the best based on performance scores.</p> <p>Conclusion</p> <p>Searchpattool is a new method for pattern discovery relative to transcription factor binding sites for species or genes with short promoters. It outputs the most specific significant patterns and helps the biologist to choose the best candidates.</p

Motif-All: discovering all phosphorylation motifs

Background: Phosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques. Results: We describe an algorithm called Motif-All that is able to efficiently identify all statistically significant motifs. The proposed method explores a support constraint to reduce search space and avoid generating random artifacts. As the number of phosphorylated peptides are far less than that of unphosphorylated ones, we divide the mining process into two stages: The first step generates candidates from the set of phosphorylated sequences using only support constraint and the second step tests the statistical significance of each candidate using the odds ratio derived from the whole data set. Experimental results on real data show that Motif-All outperforms current algorithms in terms of both effectiveness and efficiency. Conclusions: Motif-All is a useful tool for discovering statistically significant phosphorylation motifs. Source codes and data sets are available at: http://bioinformatics.ust.hk/MotifAll.rar

Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data

Background: MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown. Methodology: We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters. Conclusion: miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex. © 2009 Corcoran et al

Characterisation of Inactivation Domains and Evolutionary Strata in Human X Chromosome through Markov Segmentation

Markov segmentation is a method of identifying compositionally different subsequences in a given symbolic sequence. We have applied this technique to the DNA sequence of the human X chromosome to analyze its compositional structure. The human X chromosome is known to have acquired DNA through distinct evolutionary events and is believed to be composed of five evolutionary strata. In addition, in female mammals all copies of X chromosome in excess of one are transcriptionally inactivated. The location of a gene is correlated with its ability to undergo inactivation, but correlations between evolutionary strata and inactivation domains are less clear. Our analysis provides an accurate estimate of the location of stratum boundaries and gives a high–resolution map of compositionally different regions on the X chromosome. This leads to the identification of a novel stratum, as well as segments wherein a group of genes either undergo inactivation or escape inactivation in toto. We identify oligomers that appear to be unique to inactivation domains alone

MetaMine – A tool to detect and analyse gene patterns in their environmental context

Background Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes. Results MetaMine is an interactive data mining tool which enables the detection of gene patterns in an environmental context. The gene pattern search starts with a user defined environmentally interesting key gene. With this gene a BLAST search is carried out against the Microbial Ecological Genomics DataBase (MEGDB) containing marine genomic and metagenomic sequences. This is followed by the determination of all neighbouring genes within a given distance and a search for functionally equivalent genes. In the final step a set of common genes present in a defined number of distinct genomes is determined. The gene patterns found are associated with their individual pattern instances describing gene order and directions. They are presented together with information about the sample and the habitat. MetaMine is implemented in Java and provided as a client/server application with a user-friendly graphical user interface. The system was evaluated with environmentally relevant genes related to the methane-cycle and carbon monoxide oxidation. Conclusion MetaMine offers a targeted, semi-automatic search for gene patterns based on expert input. The graphical user interface of MetaMine provides a user-friendly overview of the computed gene patterns for further inspection in an ecological context. Prevailing biological processes associated with a key gene can be used to infer new annotations and shape hypotheses to guide further analyses. The use-cases demonstrate that meaningful gene patterns can be quickly detected using MetaMine

The miR-17/92 cluster: a comprehensive update on its genomics, genetics, functions and increasingly important and numerous roles in health and disease.

The miR-17/92 cluster is among the best-studied microRNA clusters. Interest in the cluster and its members has been increasing steadily and the number of publications has grown exponentially since its discovery with more than 1000 articles published in 2012 alone. Originally found to be involved in tumorigenesis, research work in recent years has uncovered unexpected roles for its members in a wide variety of settings that include normal development, immune diseases, cardiovascular diseases, neurodegenerative diseases and aging. In light of its ever-increasing importance and ever-widening regulatory roles, we review here the latest body of knowledge on the cluster\u27s involvement in health and disease as well as provide a novel perspective on the full spectrum of protein-coding and non-coding transcripts that are likely regulated by its members