A two-step site and mRNA-level model for predicting microRNA targets

<p>Abstract</p> <p>Background</p> <p>Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. To address this possible fault, we developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs.</p> <p>Results</p> <p>Benchmark experiments showed that this two-step model generally had a higher overall performance than other established miRNA target prediction algorithms and that the model was especially suited to identify true miRNA targets among genes that all contain conserved target sites.</p> <p>Conclusions</p> <p>This improved performance could partly be explained by the model not relying on conservation when predicting targets. The critical factors for the model's performance, however, were mRNA-level features that characterized the number and strength of individual target sites within the mRNA. The model is available for online predictions or as pre-computed predictions on the human genome <url>http://tare.medisin.ntnu.no/mirna_target</url>.</p

Motif kernel generated by genetic programming improves remote homology and fold detection

BACKGROUND: Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences. RESULTS: We introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of remote homology detection. CONCLUSION: The GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better description of the similarities and differences between different folds than do previous motif-based methods

Inferring causative variants in microRNA target sites

MicroRNAs (miRNAs) regulate genes post transcription by pairing with messenger RNA (mRNA). Variants such as single nucleotide polymorphisms (SNPs) in miRNA regulatory regions might result in altered protein levels and disease. Genome-wide association studies (GWAS) aim at identifying genomic regions that contain variants associated with disease, but lack tools for finding causative variants. We present a computational tool that can help identifying SNPs associated with diseases, by focusing on SNPs affecting miRNA-regulation of genes. The tool predicts the effects of SNPs in miRNA target sites and uses linkage disequilibrium to map these miRNA-related variants to SNPs of interest in GWAS. We compared our predicted SNP effects in miRNA target sites with measured SNP effects from allelic imbalance sequencing. Our predictions fit measured effects better than effects based on differences in free energy or differences of TargetScan context scores. We also used our tool to analyse data from published breast cancer and Parkinson's disease GWAS and significant trait-associated SNPs from the NHGRI GWAS Catalog. A database of predicted SNP effects is available at http://www.bigr.medisin.ntnu.no/mirsnpscore/. The database is based on haplotype data from the CEU HapMap population and miRNAs from miRBase 16.0

Genome-wide hydroxymethylation profiles in liver of female Nile tilapia with distinct growth performance

The mechanisms underlying the fast genome evolution that occurs during animal domestication are poorly understood. Here, we present a genome-wide epigenetic dataset that quantifies DNA hydroxymethylation at single nucleotide resolution among full-sib Nile tilapia (Oreochromis niloticus) with distinct growth performance. In total, we obtained 355 million, 75 bp reads from 5 large- and 5 small-sized fish on an Illumina NextSeq500 platform. We identified several growth-related genes to be differentially hydroxymethylated, especially within gene bodies and promoters. Previously, we proposed that DNA hydroxymethylation greatly affects the earliest responses to adaptation and potentially drives genome evolution through its targeted enrichment and elevated nucleotide transversion rates. This dataset can be analysed in various contexts (e.g., epigenetics, evolution and growth) and compared to other epigenomic datasets in the future, namely DNA methylation and histone modifications. With forthcoming advancements in genome research, this hydroxymethylation dataset will also contribute to better understand the epigenetic regulation of key genomic features, such as cis-regulatory and transposable elements.publishedVersio

Major gene expression changes and epigenetic remodelling in Nile tilapia muscle after just one generation of domestication

The historically recent domestication of fishes has been essential to meet the protein demands of a growing human population. Selection for traits of interest during domestication is a complex process whose epigenetic basis is poorly understood. Cytosine hydroxymethylation is increasingly recognized as an important DNA modification involved in epigenetic regulation. In the present study, we investigated if hydroxymethylation plays a role in fish domestication and demonstrated for the first time at a genome-wide level and single nucleotide resolution that the muscle hydroxymethylome changes after a single generation of Nile tilapia (Oreochromis niloticus, Linnaeus) domestication. The overall decrease in hydroxymethylcytosine levels was accompanied by the downregulation of 2015 genes in fish reared in captivity compared to their wild progenitors. In contrast, several myogenic and metabolic genes that can affect growth potential were upregulated. There were 126 differentially hydroxymethylated cytosines between groups, which were not due to genetic variation; they were associated with genes involved in immune-, growth- and neuronal-related pathways. Taken together, our data unveil a new role for DNA hydroxymethylation in epigenetic regulation of fish domestication with impact in aquaculture and implications in artificial selection, environmental adaptation and genome evolution

Predicting non-coding RNA genes in Escherichia coli with boosted genetic programming

Several methods exist for predicting non-coding RNA (ncRNA) genes in Escherichia coli (E.coli). In addition to about sixty known ncRNA genes excluding tRNAs and rRNAs, various methods have predicted more than thousand ncRNA genes, but only 95 of these candidates were confirmed by more than one study. Here, we introduce a new method that uses automatic discovery of sequence patterns to predict ncRNA genes. The method predicts 135 novel candidates. In addition, the method predicts 152 genes that overlap with predictions in the literature. We test sixteen predictions experimentally, and show that twelve of these are actual ncRNA transcripts. Six of the twelve verified candidates were novel predictions. The relatively high confirmation rate indicates that many of the untested novel predictions are also ncRNAs, and we therefore speculate that E.coli contains more ncRNA genes than previously estimated

Penerapan Pembelajaran Kooperatif Tipe Jigsaw Dalam Meningkatkan Motivasi Dan Hasil Belajar IPA Pada Siswa Kelas VII Semester II SMP Negeri 2 Pulokulon

Permasalahan pokok yang akan dipecahkan lewat Penelitian Tindakan Kelas ini adalah: apakah penerapan model pembelajaran kooperatif tipe jigsaw dapat meningkatkan hasil belajar IPA. Tujuannya untuk meningkatkan motivasi dan hasil belajar siswa dalam mata pelajaran IPA..Penelitian ini merupakan tindakan guru untuk memperbaiki hasil belajar siswa kelas VII SMP Negeri 2 Pulokulon Semester 2 Tahun Pelajaran 2013/2004, dan pelakunya adalah guru IPA. Penelitian dilakukan dalam 2 siklus dan meliputi 4 tahapan, yaitu perencanaan, tindakan,pengamatan dan refleksi.Hasil penelitian menunjukkan bahwa dari keseluruhan siklus yang telah dilakukan motivasi dan perolehan nilai siswa kelas VII SMP Negeri 2 Pulokulon Semester 2 Tahun Pelajaran 2013/2004 mengalami peningkatan dari satu siklus ke siklus berikutnya. Jadi secara keseluruhan siklus yang telah dilakukan, penerapan model pembelajaran kooperatif tipe jigsaw dapat meningkatkan motivasi dan hasil belajar siswa dalam mata pelajaran IPA