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NucVoter: A Voting Algorithm for Reliable Nucleosome Prediction Using Next-Generation Sequencing Data.

ABSTRACT: Nucleosomes, which consist of DNA wrapped around histone octamers, are dynamic, and their structure, including their location, size, and occupancy, can be transformed. Nucleosomes can regulate gene expression by controlling the DNA accessibility of proteins. Using next-generation sequencing techniques along with such laboratory methods as micrococcal nuclease digestion, predicting the genomic locations of nucleosomes is possible. However, the true locations of nucleosomes are unknown, and it is difficult to determine their exact locations using next-generation sequencing data. This paper proposes a novel voting algorithm, NucVoter, for the reliable prediction of nucleosome locations. Multiple models verify the consensus areas in which nucleosomes are placed by the model with the highest priority. NucVoter significantly improves the performance of nucleosome prediction.

SUBMITTER: Byeon B

PROVIDER: S-EPMC4393064 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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NucVoter: A Voting Algorithm for Reliable Nucleosome Prediction Using Next-Generation Sequencing Data.

Byeon Boseon B

ISRN bioinformatics 20131107

Nucleosomes, which consist of DNA wrapped around histone octamers, are dynamic, and their structure, including their location, size, and occupancy, can be transformed. Nucleosomes can regulate gene expression by controlling the DNA accessibility of proteins. Using next-generation sequencing techniques along with such laboratory methods as micrococcal nuclease digestion, predicting the genomic locations of nucleosomes is possible. However, the true locations of nucleosomes are unknown, and it is ...[more]

PMID: 25937943

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Project description:BackgroundThe sequencing depth provided by high-throughput sequencing technologies has allowed a rise in the number of de novo sequenced genomes that could potentially be closed without further sequencing. However, genome scaffolding and closure require costly human supervision that often results in genomes being published as drafts. A number of automatic scaffolders were recently released, which improved the global quality of genomes published in the last few years. Yet, none of them reach the efficiency of manual scaffolding.ResultsHere, we present an innovative semi-automatic scaffolder that additionally helps with chimerae resolution and generates valuable contig maps and outputs for manual improvement of the automatic scaffolding. This software was tested on the newly sequenced marine cyanobacterium Synechococcus sp. WH8103 as well as two reference datasets used in previous studies, Rhodobacter sphaeroides and Homo sapiens chromosome 14 (http://gage.cbcb.umd.edu/). The quality of resulting scaffolds was compared to that of three other stand-alone scaffolders: SSPACE, SOPRA and SCARPA. For all three model organisms, WiseScaffolder produced better results than other scaffolders in terms of contiguity statistics (number of genome fragments, N50, LG50, etc.) and, in the case of WH8103, the reliability of the scaffolds was confirmed by whole genome alignment against a closely related reference genome. We also propose an efficient computer-assisted strategy for manual improvement of the scaffolding, using outputs generated by WiseScaffolder, as well as for genome finishing that in our hands led to the circularization of the WH8103 genome.ConclusionAltogether, WiseScaffolder proved more efficient than three other scaffolders for both prokaryotic and eukaryotic genomes and is thus likely applicable to most genome projects. The scaffolding pipeline described here should be of particular interest to biologists wishing to take advantage of the high added value of complete genomes.

Project description:Accurate normalization of the gene expression assays, using housekeeping genes (HKGs), is critically necessary. To do so, selection of a proper set of HKGs for a specific experiment is of great importance. Despite many studies, there is no consensus about the suitable set of HKGs for implementing in the quantitative real-time PCR analyses of chicken tissues. A limited number of HKGs have been widely used. However, wide utilization of a little number of HKGs for all tissues is challenging. The emergence of high-throughput gene expression RNA-seq data has enabled the simultaneous comparison of the stability of multiple HKGs. Therefore, employing the average coefficient of variations of at least three datasets per tissue, we sorted all reliably expressed genes (REGs; with FPKM ≥ 1 in at least one sample) and introduced the top 10 most suitable and stable reference genes for each of the 16 chicken tissues. We evaluated the consistency of the results of five tissues using the same methodology on other datasets. Furthermore, we assessed 96 previously widely used HKGs (WU-HKGs) in order to challenge the accuracy of the previous studies. The New Tuxedo software suite was used for the main analyses. The results revealed novel, different sets of reference genes for each of the tissues with 17 common genes among the top 10 genes lists of 16 tissues. The results did disprove the suitability of WU-HKGs such as Actb, Ldha, Scd, B2m, and Hprt1 for any of the tissues examined. On the contrary, a total of 6, 13, 14, 23, and 32 validated housekeeping genes (V-HKGs) were discovered as the most stable and suitable reference genes for muscle, spleen, liver, heart, and kidney tissues, respectively. Although we identified a few new HKGs usable for multiple tissues, the selection of suitable HKGs is required to be tissue specific. The newly introduced reference genes from the present study, despite lacking experimental validation, will be able to contribute to the more accurate normalization for future expression analysis of chicken genes.

Dataset Information

NucVoter: A Voting Algorithm for Reliable Nucleosome Prediction Using Next-Generation Sequencing Data.

Publications

NucVoter: A Voting Algorithm for Reliable Nucleosome Prediction Using Next-Generation Sequencing Data.

Similar Datasets

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