scholarly journals MixMir: microRNA motif discovery from gene expression data using mixed linear models

2014 ◽  
Vol 42 (17) ◽  
pp. e135-e135 ◽  
Author(s):  
Liyang Diao ◽  
Antoine Marcais ◽  
Scott Norton ◽  
Kevin C. Chen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Linear Models ◽  
Expression Data ◽  
Mixed Linear Models

scholarly journals MixMir: microRNA motif discovery from gene expression data using mixed linear models

2014 ◽  
Author(s):  
LIYANG Diao ◽  
Antoine Marcais ◽  
Scott Norton ◽  
Kevin C. Chen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Linear Models ◽  
Developmental Stages ◽  
Sequence Similarity ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Mixed Linear Models

MicroRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. MiRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3' UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miREDUCE, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in Dicer knockout CD4+ T-cells, as well as protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

scholarly journals SArKS: de novo discovery of gene expression regulatory motifs and domains by suffix array kernel smoothing

2017 ◽  
Author(s):  
Dennis Wylie ◽  
Hans A. Hofmann ◽  
Boris V. Zemelman
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Kernel Smoothing ◽  
De Novo ◽  
Supplementary Information ◽  
Spatial Proximity ◽  
Regulatory Sequences ◽  
Expression Data

AbstractMotivationWe set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score—fold-change, test-statistic, p-value—comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies nonparametric kernel smoothing to uncover promoter motifs that correlate with elevated differential expression scores. SArKS detects motifs by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.ResultsWe applied SArKS to published gene expression data representing distinct neocortical neuron classes in M. musculus and interneuron developmental states in H. sapiens. When benchmarked against several existing algorithms for correlative motif discovery using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.Availabilityhttps://github.com/denniscwylie/[email protected] informationappended to document.

scholarly journals IntLIM: integration using linear models of metabolomics and gene expression data

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Jalal K. Siddiqui ◽  
Elizabeth Baskin ◽  
Mingrui Liu ◽  
Carmen Z. Cantemir-Stone ◽  
Bofei Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Linear Models ◽  
Expression Data
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