Roary: Rapid large-scale prokaryote pan genome analysis

Andrew J Page, Carla A Cummins, Martin Hunt, Vanessa K Wong, Sandra Reuter, Matthew T. G. Holden, Maria Fookes, Jacqueline A Keane, Julian Parkhill
doi: http://dx.doi.org/10.1101/019315

A typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and dispensable accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.

Controlling False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

David M Rocke , Luyao Ruan , J. Jared Gossett , Blythe Durbin-Johnson , Sharon Aviran
doi: http://dx.doi.org/10.1101/018739

We review existing methods for the analysis of RNA-Seq data and place them in a common framework of a sequence of tasks that are usually part of the process. We show that many existing methods produce large numbers of false positives in cases where the null hypothesis is true by construction and where actual data from RNA-Seq studies are used, as opposed to simulations that make specific assumptions about the nature of the data. We show that some of those mathematical assumptions about the data likely are one of the causes of the false positives, and define a general structure that is not apparently subject to these problems. The best performance was shown by limma-voom and by some simple methods composed of easily understandable steps.

Fine-mapping cellular QTLs with RASQUAL and ATAC-seq

Natsuhiko Kumasaka , Andrew Knights , Daniel Gaffney
doi: http://dx.doi.org/10.1101/018788

When cellular traits are measured using high-throughput DNA sequencing quantitative trait loci (QTLs) manifest at two levels: population level differences between individuals and allelic differences between cis-haplotypes within individuals. We present RASQUAL (Robust Allele Specific QUAntitation and quality controL), a novel statistical approach for association mapping that integrates genetic effects and robust modelling of biases in next generation sequencing (NGS) data within a single, probabilistic framework. RASQUAL substantially improves causal variant localisation and sensitivity of association detection over existing methods in RNA-seq, DNaseI-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximise association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,706 independent caQTLs (FDR 10%) and illustrate how RASQUAL’s improved causal variant localisation provides powerful information for fine-mapping disease-associated variants. We also map “multipeak” caQTLs, identical genetic associations found across multiple, independent open chromatin regions and illustrate how genetic signals in ATAC-seq data can be used to link distal regulatory elements with gene promoters. Our results highlight how joint modelling of population and allele-specific genetic signals can improve functional interpretation of noncoding variation.