A statistical framework for eQTL mapping using RNA-seq data

RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.     

Harnessing the MinION: An example of how to establish long‐read sequencing in a laboratory using challenging plant tissue from Eucalyptus pauciflora

Long‐read sequencing technologies are transforming our ability to assemble highly complex genomes. Realizing their full potential is critically reliant on extracting high‐quality, high‐molecular‐weight (HMW) DNA from the organisms of interest. This is especially the case for the portable MinION sequencer which enables all laboratories to undertake their own genome sequencing projects, due to its low entry cost and minimal spatial footprint. One challenge of the MinION is that each group has to independently establish effective protocols for using the instrument, which can be time‐consuming and costly. Here, we present a workflow and protocols that enabled us to establish MinION sequencing in our own laboratories, based on optimizing DNA extraction from a challenging plant tissue as a case study. Following the workflow illustrated, we were able to reliably and repeatedly obtain >6.5 Gb of long‐read sequencing data with a mean read length of 13 kb and an N50 of 26 kb. Our protocols are open source and can be performed in any laboratory without special equipment. We also illustrate some more elaborate workflows which can increase mean and average read lengths if this is desired. We envision that our workflow for establishing MinION sequencing, including the illustration of potential pitfalls and suggestions of how to adapt it to other tissue types, will be useful to others who plan to establish long‐read sequencing in their own laboratories.     

The effects of read length,quality and quantity on microsatellite discovery and primer development: from Illumina to PacBio

The advent of next‐generation sequencing (NGS) technologies has transformed the way microsatellites are isolated for ecological and evolutionary investigations. Recent attempts to employ NGS for microsatellite discovery have used the 454, Illumina, and Ion Torrent platforms, but other methods including single‐molecule real‐time DNA sequencing (Pacific Biosciences or PacBio) remain viable alternatives. We outline a workflow from sequence quality control to microsatellite marker validation in three plant species using PacBio circular consensus sequencing (CCS). We then evaluate the performance of PacBio CCS in comparison with other NGS platforms for microsatellite isolation, through simulations that focus on variations in read length, read quantity and sequencing error rate. Although quality control of CCS reads reduced microsatellite yield by around 50%, hundreds of microsatellite loci that are expected to have improved conversion efficiency to functional markers were retrieved for each species. The simulations quantitatively validate the advantages of long reads and emphasize the detrimental effects of sequencing errors on NGS‐enabled microsatellite development. In view of the continuing improvement in read length on NGS platforms, sequence quality and the corresponding strategies of quality control will become the primary factors to consider for effective microsatellite isolation. Among current options, PacBio CCS may be optimal for rapid, small‐scale microsatellite development due to its flexibility in scaling sequencing effort, while platforms such as Illumina MiSeq will provide cost‐efficient solutions for multispecies microsatellite projects.     

piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER

With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction.     

Quantitative assessment of tear production: A review of methods and utility in dry eye drug discovery

The successful development of a therapeutic agent targeting treatment of dry eye syndrome necessitates the demonstration of drug efficacy for both sign and symptom endpoints. As numerous therapeutic strategies incorporate a secretagogue function into their overall mechanism of action, the quantitative assessment of tear production serves as a logical endpoint to anchor “sign” efficacy. Although several methods including the Schirmer, the phenol red thread and tear clearance tests exist, their utility in clinical evaluations of novel therapeutics is unclear. The purpose of this review is to summarize findings and conclusions describing the performance of each of these tests so as to gain insight into which, if any, is most applicable for use in discovering new dry eye therapeutics.     

Diet preferences based on sequence read count: The role of species interaction in tissue bias correction

High-throughput sequencing and metabarcoding techniques provide a unique opportunity to study predator–prey relationships. However, in animal dietary preference studies, how to properly correct tissue bias within the sequence read count and the role of interactions between co-occurring species in metabarcoding mixtures remain largely unknown. In this study, we propose two categories of tissue bias correction indices: sequence read count number per unit tissue (SCN) and its ratio form (SCN ratio). By constructing plant mock communities with different numbers of co-occurring species in metabarcoding mixtures and conducting feeding trails on captive sika deer (Cervus nippon), we demonstrate the features of the SCN and SCN ratio, evaluate their correction effects and assess the role of species interactions during tissue bias correction. Tissue differences between species are defined as the differential ability to generate sequence counts. Our study suggests that pure tissue differences among species without a species interaction is not an optimal correction index for many biomes with limited tissue differences among species. Species interactions in mixtures may amplify tissue differences, which is beneficial for tissue bias correction. However, caution must be taken because varied species interactions among communities may increase the risk of worse correction. Correction effects based on the SCN and SCN ratio are comparable, but the SCN is less influenced by control species than the SCN ratio. Based on our study, several suggestions are provided for future animal diet studies or other high-throughput sequencing studies containing tissue bias.