A comparative study of RNA-seq analysis strategies.

Three principal approaches have been proposed for inferring the set of transcripts expressed in RNA samples using RNA-seq. The simplest approach uses curated annotations, which assumes the transcripts in a sample are a subset of the transcripts listed in a curated database. A more ambitious method involves aligning reads to a reference genome and using the alignments to infer the transcript structures, possibly with the aid of a curated transcript database. The most challenging approach is to assemble reads into putative transcripts de novo without the aid of reference data. We have systematically assessed the properties of these three approaches through a simulation study. We have found that the sensitivity of computational transcript set estimation is severely limited. Computational approaches (both genome-guided and de novo assembly) produce a large number of artefacts, which are assigned large expression estimates and absorb a substantial proportion of the signal when performing expression analysis. The approach using curated annotations shows good expression correlation even when the annotations are incomplete. Furthermore, any incorrect transcripts present in a curated set do not absorb much signal, so it is preferable to have a curation set with high sensitivity than high precision. Software to simulate transcript sets, expression values and sequence reads under a wider range of parameter values and to compare sensitivity, precision and signal-to-noise ratios of different methods is freely available online (https://github.com/boboppie/RSSS) and can be expanded by interested parties to include methods other than the exemplars presented in this article.This work was supported by the Wellcome Trust (WT097679); the Cambridge Biomedical Research Centre; Cancer Research UK (C14303/A10825) and the Medical Research Council (G1002319).This is the final version of the article. It was first available from Oxford University Press via http://dx.doi.org/10.1093/bib/bbv00

A comprehensive evaluation of alignment algorithms in the context of RNA-seq.

Transcriptome sequencing (RNA-Seq) overcomes limitations of previously used RNA quantification methods and provides one experimental framework for both high-throughput characterization and quantification of transcripts at the nucleotide level. The first step and a major challenge in the analysis of such experiments is the mapping of sequencing reads to a transcriptomic origin including the identification of splicing events. In recent years, a large number of such mapping algorithms have been developed, all of which have in common that they require algorithms for aligning a vast number of reads to genomic or transcriptomic sequences. Although the FM-index based aligner Bowtie has become a de facto standard within mapping pipelines, a much larger number of possible alignment algorithms have been developed also including other variants of FM-index based aligners. Accordingly, developers and users of RNA-seq mapping pipelines have the choice among a large number of available alignment algorithms. To provide guidance in the choice of alignment algorithms for these purposes, we evaluated the performance of 14 widely used alignment programs from three different algorithmic classes: algorithms using either hashing of the reference transcriptome, hashing of reads, or a compressed FM-index representation of the genome. Here, special emphasis was placed on both precision and recall and the performance for different read lengths and numbers of mismatches and indels in a read. Our results clearly showed the significant reduction in memory footprint and runtime provided by FM-index based aligners at a precision and recall comparable to the best hash table based aligners. Furthermore, the recently developed Bowtie 2 alignment algorithm shows a remarkable tolerance to both sequencing errors and indels, thus, essentially making hash-based aligners obsolete

Comparative transcriptomics reveals key differences in the response to milk oligosaccharides of infant gut-associated bifidobacteria.

Breast milk enhances the predominance of Bifidobacterium species in the infant gut, probably due to its large concentration of human milk oligosaccharides (HMO). Here we screened infant-gut isolates of Bifidobacterium longum subsp. infantis and Bifidobacterium bifidum using individual HMO, and compared the global transcriptomes of representative isolates on major HMO by RNA-seq. While B. infantis displayed homogeneous HMO-utilization patterns, B. bifidum were more diverse and some strains did not use fucosyllactose (FL) or sialyllactose (SL). Transcriptomes of B. bifidum SC555 and B. infantis ATCC 15697 showed that utilization of pooled HMO is similar to neutral HMO, while transcriptomes for growth on FL were more similar to lactose than HMO in B. bifidum. Genes linked to HMO-utilization were upregulated by neutral HMO and SL, but not by FL in both species. In contrast, FL induced the expression of alternative gene clusters in B. infantis. Results also suggest that B. bifidum SC555 does not utilize fucose or sialic acid from HMO. Surprisingly, expression of orthologous genes differed between both bifidobacteria even when grown on identical substrates. This study highlights two major strategies found in Bifidobacterium species to process HMO, and presents detailed information on the close relationship between HMO and infant-gut bifidobacteria

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. Result: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

A new reference genome assembly for the microcrustacean Daphnia pulex

Comparing genomes of closely related genotypes from populations with distinct demographic histories can help reveal the impact of effective population size on genome evolution. For this purpose, we present a high quality genome assembly of Daphnia pulex (PA42), and compare this with the first sequenced genome of this species (TCO), which was derived from an isolate from a population with >90% reduction in nucleotide diversity. PA42 has numerous similarities to TCO at the gene level, with an average amino acid sequence identity of 98.8 and >60% of orthologous proteins identical. Nonetheless, there is a highly elevated number of genes in the TCO genome annotation, with similar to 7000 excess genes appearing to be false positives. This view is supported by the high GC content, lack of introns, and short length of these suspicious gene annotations. Consistent with the view that reduced effective population size can facilitate the accumulation of slightly deleterious genomic features, we observe more proliferation of transposable elements (TEs) and a higher frequency of gained introns in the TCO genome

Capturing the ‘ome’ : the expanding molecular toolbox for RNA and DNA library construction

All sequencing experiments and most functional genomics screens rely on the generation of libraries to comprehensively capture pools of targeted sequences. In the past decade especially, driven by the progress in the field of massively parallel sequencing, numerous studies have comprehensively assessed the impact of particular manipulations on library complexity and quality, and characterized the activities and specificities of several key enzymes used in library construction. Fortunately, careful protocol design and reagent choice can substantially mitigate many of these biases, and enable reliable representation of sequences in libraries. This review aims to guide the reader through the vast expanse of literature on the subject to promote informed library generation, independent of the application