PEPIS: A Pipeline for Estimating Epistatic Effects in Quantitative Trait Locus Mapping and Genome-Wide Association Studies

<div><p>The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the ‘missing heritability,’ which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at <a href="http://bioinfo.noble.org/PolyGenic_QTL/" target="_blank">http://bioinfo.noble.org/PolyGenic_QTL/</a>.</p></div

Summary of parallel strategy in the PEPIS for increasing analysis speed.

<p>Summary of parallel strategy in the PEPIS for increasing analysis speed.</p

Pie charts illustrating the polygenic structure of the quantitative traits of rice.

<p>(A) YIELD, (B) KGW, (C) GRAIN, and (D) TILLER.</p

Plot of main-effect LRT results for quantitative traits with the markers/bins distributed across the complete rice genome.

<p>(A) YIELD, (B) KGW, (C) GRAIN, and (D) TILLER. Dashed lines distinguish the 12 chromosomes and corresponding marker/bin numbers for the complete rice genome.</p

PEPIS user interfaces.

<p>(A) Data submission and (B) Results return.</p

Two-dimensional illustration of epistatic-effect LRTs for quantitative traits with marker/bin pairs distributed across the complete rice genome.

<p>(A)YIELD, (B) KGW, (C) GRAIN, and (D) TILLER.</p

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in <i>Arabidopsis thaliana</i>

<div><p>Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive. In this study, we performed a comprehensive analysis of mature precursor miRNA (pre-miRNA) sequences from <i>Arabidopsis</i> to understand their features and regulatory targets. The influence of isomiR terminal heterogeneity in target binding was examined comprehensively. Our comprehensive analyses suggested a novel computational strategy that utilizes miRNA and its isomiRs to enhance the accuracy of their regulatory target prediction in <i>Arabidopsis</i>. A few targets are shared by several members of isomiRs; however, this phenomenon was not typical. Gene Ontology (GO) enrichment analysis showed that commonly targeted mRNAs were enriched for certain GO terms. Moreover, comparison of these commonly targeted genes with validated targets from published data demonstrated that the validated targets are bound by most isomiRs and not only the canonical miRNA. Furthermore, the biological role of isomiRs in target cleavage was supported by degradome data. Incorporating this finding, we predicted potential target genes of several miRNAs and confirmed them by experimental assays. This study proposes a novel strategy to improve the accuracy of predicting miRNA targets through combined use of miRNA with its isomiRs.</p></div

A first step in understanding an invasive weed through its genes: an EST analysis of invasive -1

<p><b>Copyright information:</b></p><p>Taken from "A first step in understanding an invasive weed through its genes: an EST analysis of invasive "</p><p>http://www.biomedcentral.com/1471-2229/7/25</p><p>BMC Plant Biology 2007;7():25-25.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1890287.</p><p></p>racterized genes from (Hean), (Pecr) fungal elicited desaturases (ELI), and with sterol desaturasase from as an outgroup. CENT_UG_00643, CENT_UG_00475, and CENT_UG_00098 cluster with the characterized acetylenase from , distinct from the remaining Δ12 desaturases (Del12), suggesting potential for acetylenase activity

A first step in understanding an invasive weed through its genes: an EST analysis of invasive -0

<p><b>Copyright information:</b></p><p>Taken from "A first step in understanding an invasive weed through its genes: an EST analysis of invasive "</p><p>http://www.biomedcentral.com/1471-2229/7/25</p><p>BMC Plant Biology 2007;7():25-25.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1890287.</p><p></p> of . Five thousand ESTs were sequenced from the 5' end (Agencourt biosciences), and assembled into 4,423 contigs, or 'unigenes.' Unigenes were translated in all frames and the resulting amino acid sequences were used as BLAST queries. Top BLAST hits provided annotation and functional categorization (gene ontology assignment) for each unigene. Not all unigenes were able to be annotated by GO programs. Computational analysis was done using the PLAN database (Noble Foundation)

Analysis of tall fescue ESTs representing different abiotic stresses, tissue types and developmental stages-2

Af, SD1: Young seedling, FSS: Field stressed shoot, HSS: Heat stressed shoot, TFM: Floral meristem, RT1: Greenhouse grown root, ST1: Field grown stem, DR1: Drought stressed root. The number of ESTs forming a TC in each cDNA library is presented in coloured rows. The correlation map resulting from clustering of the TCs is given at the left. The dendrogram on top illustrates the relationship between the cDNA libraries/plant organs analyzed. Nine clusters (A to I) are indicated on the right, with number of TCs included in each cluster.<p><b>Copyright information:</b></p><p>Taken from "Analysis of tall fescue ESTs representing different abiotic stresses, tissue types and developmental stages"</p><p>http://www.biomedcentral.com/1471-2229/8/27</p><p>BMC Plant Biology 2008;8():27-27.</p><p>Published online 4 Mar 2008</p><p>PMCID:PMC2323379.</p><p></p