Trimming and Validation of Illumina Short Reads Using Trimmomatic, Trinity Assembly, and Assessment of RNA-Seq Data

Next-generation sequencing (NGS) technologies can generate billions of reads in a single sequencing run. However, with such high-throughput comes quality issues which have to be addressed before undertaking downstream analysis. Quality control on short reads is usually performed at default settings due to a lack of in-depth understanding of a particular software's parameters and their effect if changed on the output. Here we demonstrate how to optimize read trimming using Trimmomatic. We highlight the benefits of trimming by comparing the quality of transcripts assembled using trimmed and untrimmed reads.

Benefits of merging paired-end reads before pre-processing environmental metagenomics data

BACKGROUND

High throughput sequencing of environmental DNA has applications in biodiversity monitoring, taxa abundance estimation, understanding the dynamics of community ecology, and marine species studies and conservation. Environmental DNA, especially, marine eDNA, has a fast degradation rate. Aside from the good quality reads, the data could have a significant number of reads that fall slightly below the default PHRED quality threshold of 30 on sequencing. For quality control, trimming methods are employed, which generally precede the merging of the read pairs. However, in the case of eDNA, a significant percentage of reads within the acceptable quality score range are also dropped.

METHODS

To infer the ideal merge tool that is sensitive to eDNA, two Hiseq paired-end eDNA datasets were utilized to study the merging by the tools - FLASH (Fast Length Adjustment of SHort reads), PANDAseq, COPE, BBMerge, and VSEARCH without preprocessing. We assessed these tools on the following parameters: Time taken to process, the quality, and the number of merged reads. Trimmomatic, a widely-used preprocessing tool, was also assessed by preprocessing the datasets at different parameters for the two approaches of preprocessing: Sliding Window and Maximum Information. The preprocessed read pairs were then merged using the ideal merge tool identified earlier.

RESULTS

FLASH is the most efficient merge tool balancing data conservation, quality of reads, and processing time. We compared Trimmomatic's two quality trimming options with increasing strictness with FLASH's direct merge. The raw reads processed with Trimmomatic then merged, yielded a significant drop in reads compared to the direct merge. An average of 29% of reads was dropped when directly merged with FLASH. Maximum Information option resulted in 30.7% to 68.05% read loss with lowest and highest stringency parameters, respectively. The Sliding Window approach conserves approximately 10% more reads at a PHRED score of 25 set as the threshold for a window of size 4. The lowered PHRED cut off conserves about 50% of the reads that could potentially be informative. We noted no significant reduction of data while optimizing the number of reads read in a window with the ideal quality (Q) score.

CONCLUSIONS

Losing reads can negatively impact the downstream processing of the environmental data, especially for sequence alignment studies. The quality trim-first-merge-later approach can significantly decrease the number of reads conserved. However, direct merging of pair-end reads using FLASH conserved more than 60% of the reads. Therefore, direct merging of the paired-end reads can prevent potential removal of informative reads that do not comply by the trimming tool's strict checks. FLASH to be an efficient tool in conserving reads while carrying out quality trimming in moderation. Overall, our results show that merging paired-end reads of eDNA data before trimming can conserve more reads.

Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

A Galaxy-based bioinformatics pipeline for optimised, streamlined microsatellite development from Illumina next-generation sequencing data

Microsatellites are useful tools for ecologists and conservationist biologists, but are taxa-specific and traditionally expensive and time-consuming to develop. New methods using next-generation sequencing (NGS) have reduced these problems, but the plethora of software available for processing NGS data may cause confusion and difficulty for researchers new to the field of bioinformatics. We developed a bioinformatics pipeline for microsatellite development from Illumina paired-end sequences, which is packaged in the open-source bioinformatics tool Galaxy. This optimises and streamlines the design of a microsatellite panel and provides a user-friendly graphical user interface. The pipeline utilises existing programs along with our own novel program and wrappers to: quality-filter and trim reads (Trimmomatic); generate sequence quality reports (FastQC); identify potentially-amplifiable microsatellite loci (Pal_finder); design primers (Primer3); assemble pairs of reads to enhance marker amplification success rates (PANDAseq); and filter optimal loci (Pal_filter). The complete pipeline is freely available for use via a pre-configured Galaxy instance, accessible at https://palfinder.ls.manchester.ac.uk.

Analysis of Genotyping-by-Sequencing (GBS) Data

The development of genotyping-by-sequencing (GBS) to rapidly detect nucleotide variation at the whole genome level, in many individuals simultaneously, has provided a transformative genetic profiling technique. GBS can be carried out in species with or without reference genome sequences yields huge amounts of potentially informative data. One limitation with the approach is the paucity of tools to transform the raw data into a format that can be easily interrogated at the genetic level. In this chapter we describe bioinformatics tools developed to address this shortfall together with experimental design considerations to fully leverage the power of GBS for genetic analysis.