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Background: Next-generation transcriptome sequencing (RNA-Seq) has become the standard practice for studying gene splicing, mutations and changes in gene expression to obtain valuable, accurate biological conclusions. However, obtaining good sequencing coverage and depth to study these is impeded by the difficulties of obtaining high quality total RNA with minimal genomic DNA contamination. With this in mind, we evaluated the performance of Phenol-free total RNA purification kit (Amresco) in comparison with TRI Reagent (MRC) and RNeasy Mini (Qiagen) for the extraction of total RNA of Pseudomonas aeruginosa which was grown in glucose-supplemented (control) and polyethylene- supplemented (growth-limiting condition) minimal medium. All three extraction methods were coupled with an in-house DNase I treatment before the yield, integrity and size distribution of the purified RNA were assessed. RNA samples extracted with the best extraction kit were then sequenced using the Illumina HiSeq 2000 platform. Results: TRI Reagent gave the lowest yield enriched with small RNAs (sRNAs), while RNeasy gave moderate yield of good quality RNA with trace amounts of sRNAs. The Phenol-free kit, on the other hand, gave the highest yield and the best quality RNA (RIN value of 9.85 ± 0.3) with good amounts of sRNAs. Subsequent bioinformatic analysis of the sequencing data revealed that 5435 coding genes, 452 sRNAs and 7 potential novel intergenic sRNAs were detected, indicating excellent sequencing coverage across RNA size ranges. In addition, detection of low abundance transcripts and consistency of their expression profiles across replicates from the same conditions demonstrated the reproducibility of the RNA extraction technique. Conclusions: Amresco's Phenol-free Total RNA purification kit coupled with DNase I treatment yielded the highest quality RNAs containing good ratios of high and low molecular weight transcripts with minimal genomic DNA. These RNA extracts gave excellent non-biased sequencing coverage useful for comprehensive total transcriptome sequencing and analysis. Furthermore, our findings would be useful for those interested in studying both coding and noncoding RNAs from precious bacterial samples cultivated in growth-limiting condition, in a single sequencing run. [ABSTRACT FROM AUTHOR]

This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.