RNAseq Analysis

Formula Syntax in RNAseq Packages like DESeq2 or edgeR

Popular RNAseq packages often use the formula notation in R. For example, the DESeq package uses it in the design parameter, whereas edgeR creates its design matrix by expanding a formula with “model.matrix”. The formula syntax seems to confuse many users of these libraries.

Rnaseq.work - Current APIs and Design Decisions

As mentioned in an earlier post, I have been working on a R library for RNAseq data analysis. The goal of this library is to provide clean, easy-to-remember functions for data analysis. In this post, I will describe the functional options chosen for the rna_visualize function for plotting of data. I will also discuss the design and coding challenges encountered during this implementation.

Rnaseq.work - Plotting Functions in RNAseq-related Packages

Rnaseq.work - A Package with Clean APIs for Statistical Analysis of RNAseq Data

Over the last couple of months, I have been working on and off on a new R package for statistical analysis of RNAseq data. A number of popular and excellent packages (e.g. edgeR, DEseq, DEseq2, limma-voom, sleuth, etc.) exist to solve this problem, and they all use different mathematical methods to find statistically significant genes.

Live Online Class - RNAseq Data Analysis using R

If you like to use R for RNAseq data analysis, please join our online class on Dec 1/8/15 at 10AM-1PM Pacific time. This module is designed for those from biology background.

GRASS for Rapid Reannotation of RNAseq Data

Many exciting papers/preprints on RNAseq came out over the last few months. Among them, a recently posted preprint solves an important problem - improving annotations based on new RNAseq data. There were other papers on quantification, compression and search, and we like to cover them in the next few posts.

SuperTranscript - a reference for analysis and visualization of the transcriptome

Abstract: Transcriptomes are tremendously diverse and highly dynamic; visualizing and analysing this complexity is a major challenge. Here we present superTranscript, a single linear representation for each gene. SuperTranscripts contain all unique exonic sequence, built from any combination of transcripts, including reference assemblies, de novo assemblies and long-read sequencing. Our approach enables visualization of transcript structure and provides increased power to detect differential isoform usage.

Lior Pachter's Zika Paper

Lynn Yi, Harlod Pimentel and Lior Pachter published a new RNAseq paper that our readers will definitely find interesting. In this paper, the authors showcase the new RNAseq technologies Pachterlab has been developing over the last few years. We covered those components (e.g Kallisto, Sleuth) in earlier posts, but here you can see a biological application to get new insights from already published data.

Trimming of sequence reads alters RNA-Seq gene expression estimates

Boiler: Lossy compression of RNA-seq alignments using coverage vectors

They lost half of the DATA !

Unicorn-barbecue party -

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA- seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

RapMap - Today's Must Read Paper

Rob Patro, the senior author of the paper, also developed Sailfish.

An opinionated guide to the proper care and feeding of your transcriptome

Characterizing transcriptomes in both model and non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. Each step may be accomplished in one of several different ways, using different software packages, each producing different results. This analytical complexity begs the question – Which method(s) are optimal? Using reference and non-reference based evaluative methods, I propose a set of guidelines that aim to standardize and facilitate the process of transcriptome assembly. These recommendations include the generation of between 20 million and 40 million sequencing reads from single individual where possible, error correction of reads, gentle quality trimming, assembly filtering using Transrate and/or gene expression, annotation using dammit, and appropriate reporting. These recommendations have been extensively benchmarked and applied to publicly available transcriptomes, resulting in improvements in both content and contiguity. To facilitate the implementation of the proposed standardized methods, I have released a set of version controlled open-sourced code, The Oyster River Protocol for Transcriptome Assembly, available at http://oyster- river-protocol.rtfd.org/.

Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data


An opinionated guide to the proper care and feeding of your transcriptome

tximport: import and summarize transcript-level estimates for gene-level analysis


Timecourse analysis with Sleuth

An extremely interesting application of RNA-sequencing analysis is to study samples over a time series. This allows you to identify patterns of expression over some response to a stimuli or developmental progression.

Accurate, fast, and model-aware transcript expression quantification with Salmon

RapMap - Rob Patro's Reimplements Kallisto's Pseudoalignment Code

If software license is the only thing that stops you from using wonderful Kallisto algorithm/program, maybe this github code can help. As another advantage, it comes with GPL license (could be BSD if not for Jellyfish dependence) and you can build your code on top of it by using RapMap as a module. Pseudoalignment is a powerful lightweight concept and we can expect more applications to use this module.

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