Measuring Gene Expression With Next Generation Sequencing Technology

Thesis advisor: Gabor MarthWhile a PhD student in Dr. Gabor Marth's laboratory, I have had primary responsibility for two projects focused on using RNA-Seq to measure differential gene expression. In the first project we used RNA-Seq to identify differentially expressed genes in four yeast species and I analyzed the findings in terms of the evolution of gene expression. In this experiment, gene expression was measured using two biological replicates of each species of yeast. While we had several interesting biological findings, during the analysis we dealt with several statistical issues that were caused by the experiment's low number of replicates. The cost of sequencing has decreased rapidly since this experiment's design and many of these statistical issues can now practically be avoided by sequencing a greater number of samples. However, there is little guidance in the literature as to how to intelligently design an RNA-Seq experiment in terms of the number of replicates that are required and how deeply each replicate must be sequenced. My second project, therefore, was to develop Scotty, a web-based program that allows users to perform power analysis for RNA-Seq experiments. The yeast project resulted in a highly accessed first author publication in BMC Genomics in 2011. I have structured my dissertation as follows: The first chapter, entitled General Issues in RNA-Seq, is intended to synthesize the themes and issues of RNA-Seq statistical analysis that were common to both papers. In this section, I have discussed the main findings from the two papers as they relate to analyzing RNA-Seq data. Like the Scotty application, this section is designed to be "used" by wet-lab biologists who have a limited background in statistics. While some background in statistics would be required to fully understand the following chapters, the essence of this background can be gained by reading this first chapter. The second and third chapters contain the two papers that resulted from the two RNA-Seq projects. Each chapter contains both the original manuscript and original supplementary methods and data section. Finally, I include brief summaries of my contributions to the two papers on which I was a middle author. The first was a functional analysis of the genomic regions affected by mobile element insertions as a part of Chip Stewart's paper with the 1000 Genome Consortium. This paper was published in Plos Genetics. The second was a cluster analysis of microarray gene expression in Toxoplasma gondii, which was included as part of Alexander Lorestani et al.'s paper, Targeted proteomic dissection of Toxoplasma cytoskeleton sub-compartments using MORN1. This paper is currently under review. The yeast project was a collaborative effort between Jesse Gray, Michael Springer, and Allen Costa at Harvard Medical School, Jeffery Chuang here at Boston College, and members of the Marth lab. Jesse Gray conceived of the project. While I wrote the draft for the manuscript, many people, particularly Gabor Marth, provided substantial guidance on the actual text. I conceived of and implemented Scotty and wrote its manuscript with only editorial assistance from my co-authors. I produced all figures for the two manuscripts. Chip Stewart provided extensive guidance and mentorship to me on all aspects of my statistical analyses for both projects.Thesis (PhD) — Boston College, 2012.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Biology

Systematic comparison of monoclonal versus polyclonal antibodies for mapping histone modifications by ChIP-seq.

BackgroundThe robustness of ChIP-seq datasets is highly dependent upon the antibodies used. Currently, polyclonal antibodies are the standard despite several limitations: They are non-renewable, vary in performance between lots and need to be validated with each new lot. In contrast, monoclonal antibody lots are renewable and provide consistent performance. To increase ChIP-seq standardization, we investigated whether monoclonal antibodies could replace polyclonal antibodies. We compared monoclonal antibodies that target five key histone modifications (H3K4me1, H3K4me3, H3K9me3, H3K27ac and H3K27me3) to their polyclonal counterparts in both human and mouse cells.ResultsOverall performance was highly similar for four monoclonal/polyclonal pairs, including when we used two distinct lots of the same monoclonal antibody. In contrast, the binding patterns for H3K27ac differed substantially between polyclonal and monoclonal antibodies. However, this was most likely due to the distinct immunogen used rather than the clonality of the antibody.ConclusionsAltogether, we found that monoclonal antibodies as a class perform equivalently to polyclonal antibodies for the detection of histone post-translational modifications in both human and mouse. Accordingly, we recommend the use of monoclonal antibodies in ChIP-seq experiments

Expression divergence measured by transcriptome sequencing of four yeast species

<p>Abstract</p> <p>Background</p> <p>The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial.</p> <p>Results</p> <p>We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of <it>Saccharomyces </it>species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence (R²= 0.90).</p> <p>Conclusion</p> <p>We provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.</p> <p>GEO Accession Number: GSE32679</p

Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples

RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and confirmed this with actual degraded samples. RNase H can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development

Simultaneous generation of many RNA-seq libraries in a single reaction

Although RNA-seq is a powerful tool, the considerable time and cost associated with library construction has limited its utilization for various applications. RNAtag-Seq, an approach to generate multiple RNA-seq libraries in a single reaction, lowers time and cost per sample, and it produces data on prokaryotic and eukaryotic samples that are comparable to those generated by traditional strand-specific RNA-seq approaches

Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples

We have developed a robust RNA sequencing method for generating complete de novo assemblies with intra-host variant calls of Lassa and Ebola virus genomes in clinical and biological samples. Our method uses targeted RNase H-based digestion to remove contaminating poly(rA) carrier and ribosomal RNA. This depletion step improves both the quality of data and quantity of informative reads in unbiased total RNA sequencing libraries. We have also developed a hybrid-selection protocol to further enrich the viral content of sequencing libraries. These protocols have enabled rapid deep sequencing of both Lassa and Ebola virus and are broadly applicable to other viral genomics studies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0519-7) contains supplementary material, which is available to authorized users

Comparative analysis of RNA sequencing methods for degraded or low-input samples

available in PMC 2014 January 01RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.National Institutes of Health (U.S.) (Pioneer Award DP1-OD003958-01)National Human Genome Research Institute (U.S.) (NHGRI) 1P01HG005062-01)National Human Genome Research Institute (U.S.) (NHGRI Center of Excellence in Genome Science Award 1P50HG006193-01)Howard Hughes Medical Institute (Investigator)Merkin Family Foundation for Stem Cell ResearchBroad Institute of MIT and Harvard (Klarman Cell Observatory)National Human Genome Research Institute (U.S.) (NHGRI grant HG03067)Fonds voor Wetenschappelijk Onderzoek--Vlaandere

Upper limits on the strength of periodic gravitational waves from PSR J1939+2134

The first science run of the LIGO and GEO gravitational wave detectors presented the opportunity to test methods of searching for gravitational waves from known pulsars. Here we present new direct upper limits on the strength of waves from the pulsar PSR J1939+2134 using two independent analysis methods, one in the frequency domain using frequentist statistics and one in the time domain using Bayesian inference. Both methods show that the strain amplitude at Earth from this pulsar is less than a few times $10^{-22}$.Comment: 7 pages, 1 figure, to appear in the Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 200

Improving the sensitivity to gravitational-wave sources by modifying the input-output optics of advanced interferometers

We study frequency dependent (FD) input-output schemes for signal-recycling interferometers, the baseline design of Advanced LIGO and the current configuration of GEO 600. Complementary to a recent proposal by Harms et al. to use FD input squeezing and ordinary homodyne detection, we explore a scheme which uses ordinary squeezed vacuum, but FD readout. Both schemes, which are sub-optimal among all possible input-output schemes, provide a global noise suppression by the power squeeze factor, while being realizable by using detuned Fabry-Perot cavities as input/output filters. At high frequencies, the two schemes are shown to be equivalent, while at low frequencies our scheme gives better performance than that of Harms et al., and is nearly fully optimal. We then study the sensitivity improvement achievable by these schemes in Advanced LIGO era (with 30-m filter cavities and current estimates of filter-mirror losses and thermal noise), for neutron star binary inspirals, and for narrowband GW sources such as low-mass X-ray binaries and known radio pulsars. Optical losses are shown to be a major obstacle for the actual implementation of these techniques in Advanced LIGO. On time scales of third-generation interferometers, like EURO/LIGO-III (~2012), with kilometer-scale filter cavities, a signal-recycling interferometer with the FD readout scheme explored in this paper can have performances comparable to existing proposals. [abridged]Comment: Figs. 9 and 12 corrected; Appendix added for narrowband data analysi

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure