Near-optimal RNA-Seq quantification

We present a novel approach to RNA-Seq quantification that is near optimal in speed and accuracy. Software implementing the approach, called kallisto, can be used to analyze 30 million unaligned paired-end RNA-Seq reads in less than 5 minutes on a standard laptop computer while providing results as accurate as those of the best existing tools. This removes a major computational bottleneck in RNA-Seq analysis.Comment: - Added some results (paralog analysis, allele specific expression analysis, alignment comparison, accuracy analysis with TPMs) - Switched bootstrap analysis to human sample from SEQC-MAQCIII - Provided link to a snakefile that allows for reproducibility of all results and figures in the pape

Advancing transcriptome platforms

During the last decade of years, remarkable technological innovations have emerged that allow the direct or indirect determination of the transcriptome at unprecedented scale and speed. Studies using these methods have already altered our view of the extent and complexity of transcript profiling, which has advanced from one-gene-at-a-time to a holistic view of the genome. Here, we outline the major technical advances in transcriptome characterization, including the most popular used hybridization-based platform, the well accepted tag-based sequencing platform, and the recently developed RNA-Seq (RNA sequencing) based platform. Importantly, these next-generation technologies revolutionize assessing the entire transcriptome via the recent RNA-Seq technology

Gene-to-metabolite network for biosynthesis of lignans in MeJA-elicited Isatis indigotica hairy root cultures.

Root and leaf tissue of Isatis indigotica shows notable anti-viral efficacy, and are widely used as "Banlangen" and "Daqingye" in traditional Chinese medicine. The plants' pharmacological activity is attributed to phenylpropanoids, especially a group of lignan metabolites. However, the biosynthesis of lignans in I. indigotica remains opaque. This study describes the discovery and analysis of biosynthetic genes and AP2/ERF-type transcription factors involved in lignan biosynthesis in I. indigotica. MeJA treatment revealed differential expression of three genes involved in phenylpropanoid backbone biosynthesis (IiPAL, IiC4H, Ii4CL), five genes involved in lignan biosynthesis (IiCAD, IiC3H, IiCCR, IiDIR, and IiPLR), and 112 putative AP2/ERF transcription factors. In addition, four intermediates of lariciresinol biosynthesis were found to be induced. Based on these results, a canonical correlation analysis using Pearson's correlation coefficient was performed to construct gene-to-metabolite networks and identify putative key genes and rate-limiting reactions in lignan biosynthesis. Over-expression of IiC3H, identified as a key pathway gene, was used for metabolic engineering of I. indigotica hairy roots, and resulted in an increase in lariciresinol production. These findings illustrate the utility of canonical correlation analysis for the discovery and metabolic engineering of key metabolic genes in plants

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Evidence of neutral transcriptome evolution in plants

The transcriptome of an organism is its set of gene transcripts (mRNAs) at a defined spatial and temporal locus. Because gene expression is affected markedly by environmental and developmental perturbations, it is widely assumed that transcriptome divergence among taxa represents adaptive phenotypic selection. This assumption has been challenged by neutral theories which propose that stochastic processes drive transcriptome evolution. To test for evidence of neutral transcriptome evolution in plants, we quantified 18 494 gene transcripts in nonsenescent leaves of 14 taxa of Brassicaceae using robust cross-species transcriptomics which includes a two-step physical and in silicobased normalization procedure based on DNA similarity among taxa. Transcriptome divergence correlates positively with evolutionary distance between taxa and with variation in gene expression among samples. Results are similar for pseudogenes and chloroplast genes evolving at different rates. Remarkably, variation in transcript abundance among root-cell samples correlates positively with transcriptome divergence among root tissues and among taxa. Because neutral processes affect transcriptome evolution in plants, many differences in gene expression among or within taxa may be nonfunctional, reflecting ancestral plasticity and founder effects. Appropriate null models are required when comparing transcriptomes in space and time