RNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based ...
Read More »Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts
Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling which limit their scope and generality. Researchers from UC Berkely and Stanford University have developed a novel method that departs from standard analysis pipelines, comparing and clustering ...
Read More »(Post) Graduate Course ‘The Power of RNA-Seq’
Date: February 10th-12th, 2016 Location: Room PC95 (first floor), RADIX building (107), Wageningen Campus, Droevendaalsesteeg 1, Wageningen, the Netherlands Directions: directions (check also Map Wageningen UR Campus) Language: English Group size: maximum of 35 participants Credits: 0.8 ECTS Registration and ...
Read More »htsint – a Python library for sequencing pipelines that combines data through gene set generation
Sequencing technologies provide a wealth of details in terms of genes, expression, splice variants, polymorphisms, and other features. A standard for sequencing analysis pipelines is to put genomic or transcriptomic features into a context of known functional information, but the ...
Read More »RNA extraction method, read length and sequencing layout (single-end versus paired-end) contribute strongly to variation between RNA-Seq samples
Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library ...
Read More »OrthoClust: an orthology-based network framework for clustering data across multiple species
Increasingly, high-dimensional genomics data are becoming available for many organisms. Now, researchers from Yale University have developed OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing ...
Read More »BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles
Non-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. However the vast majority of ncRNAs still have no functional annotation. One prominent approach for putative function assignment is clustering of transcripts ...
Read More »piClust: A density based piRNA clustering algorithm
Piwi-interacting RNAs (piRNAs) are recently discovered, endogenous small non-coding RNAs. piRNAs protect the genome from invasive transposable elements (TE) and sustain integrity of the genome in germ cell lineages. Small RNA-sequencing data can be used to detect piRNA activations in ...
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