Automated differential expression analysis of rna sequencing data

#The present disclosure relates to differential expression analysis of ribonucleic acid sequencing (RNA-Seq) data, and in particular to an automated workflow for differential expression analysis of RNA-Seq data.

#RNA-Seq data may be used to identify, analyze, and quantify the expression of a particular gene at a certain moment in time and under certain experimental conditions. RNA-Seq utilizes one or more next generation sequencing platforms, allowing rapid analysis of various sized genomes compared to previous sequencing technologies. Typically, RNA-Seq consists of some or all of identifying a biological sample of interest that has been subjected to one or more experimental conditions, isolating RNA therefrom, obtaining RNA reads, aligning the RNA reads to a transcriptome (e.g., of a transcriptome library), and performing various downstream analyses, such as differential expression analysis.

#Differential expression analysis using RNA-Seq data is a methodology employed to evaluate how biological organisms (e.g., microorganisms and other biological organisms, including any prokaryotes and eukaryotes) respond to changes in conditions. For example, such analysis may be used for evaluating how a microorganism responds to changes in concentration of a given compound within its environment by exposing the same microorganism to various concentrations of the compound, with all other variables remaining constant or controlled. Each selected concentration may additionally be tested in replicates (e.g., duplicates, triplicates, and the like) to control for natural variability in the testing and the microorganisms themselves. In response to the experimental conditions, the microorganisms may respond by transcribing different genes or different gene levels (i.e., intensity or quantity), which results in different proteins or protein levels operating in the microorganism. Accordingly, RNA-Seq data comprised of sequenced RNA reads (i.e., messenger RNA (mRNA) transcripts or reverse transcribed cDNA) may be used to identify which gene(s) and how much of said gene(s) is expressed in the presence of a given condition by differential expression analysis.

#The following figures are included to illustrate certain aspects of the embodiments, and should not be viewed as exclusive embodiments. The subject matter disclosed is capable of considerable modifications, alterations, combinations, and equivalents in form and function, as will occur to those skilled in the art and having the benefit of this disclosure.

#FIG. 1 is a schematic flowchart demonstrating one or more aspects of the parallel analysis and combination pairing of the RNA-Seq differential expression analysis workflows of the present disclosure.

#FIG. 2 is a schematic flowchart demonstrating one or more aspects of the parallel analysis and combination pairing of the RNA-Seq differential expression analysis workflows of the present disclosure.

#The present disclosure relates to differential expression analysis of ribonucleic acid sequencing (RNA-Seq) data, and in particular to an automated workflow for differential expression analysis of RNA-Seq data. More particularly, the automated workflow for differential expression analysis of RNA-Seq data described herein permits analysis of pairs of any number of RNA-Seq reads subjected to multiple experimental conditions, as well as any potential replicates tested thereof.

#The RNA-Seq differential expression analysis workflows (also referred to herein simply as “RNA-Seq workflow(s)”) disclosed herein allow rapid differential gene expression analysis of large RNA-Seq read datasets, automatically adapting to the dataset including any number of input files (e.g., files comprising specific RNA-Seq reads), any number of replicates, and any number and type of conditions. The workflows are parallelized, such that certain operations are independently performed and, thereafter, a combination pair of experimental conditions is subsequently used in parallel for differential expression analysis. Accordingly, unlike traditional RNA-Seq differential expression analysis workflows, every combination pair of RNA-Seq reads may be analyzed for differential expression, thereby permitting a more robust review of the gene expression and the biological organisms' reaction to one or more experimental conditions, as described in greater detail below. Moreover, the workflow of the present disclosure may be run automatically with minimal user input, whereas traditional RNA-Seq differential expression analysis workflows require each operation (e.g., alignment, quantification, and the like) to be performed manually in sequence (i.e., from a command line or potentially a graphical user interface). Accordingly, the RNA-Seq differential expression analysis workflows described herein are streamlined and permit rapid and effective (e.g., without error) differential expression analysis across all experimental condition pairs of large datasets. The workflow automatically identify and aggregate all relevant data for differential expression analysis and process it though the steps of the workflow.

#Moreover, the RNA-Seq differential expression analysis workflows of the present disclosure are user-friendly and adaptable, such that minimal user input is required and the workflow can be easily adapted to the user's needs, including handling the large datasets aforementioned (e.g., multiple experimental conditions and/or replicates) and automatically adapting to changes in the datasets. Such changes may include, for example, changes stemming from user configuration options and/or changes in the specific input data (e.g., the RNA-Seq read data). Changes that stem from user configuration options may include, but are not limited to, changes to the selection of a particular tool/algorithm for analysis in the workflow (e.g., for alignment), changes to the computational options of such tools/algorithms (e.g., to assess sensitivity to changes of algorithms or parameters thereof), changes to the input data (e.g., user input RNA-Seq data), and the like, and any combination thereof. The dynamic quality of the workflows of the present disclosure automatically links the various tools/algorithms/parameters together to perform the RNA-Seq differential expression analysis. For example, the workflows described herein standardize the use of various tools (e.g., alignment tool, quantification tool, and the like, as described herein) such that each of the tools can be used together with ease (i.e., the tools are made compatible via the workflow). Changes to the input data for analysis by one or more workflows described herein may include, for example, selection of particular input files, RNA sequencing runs, experimental conditions, replicate numbers, and the like, and any combination thereof. The workflows described herein automatically and dynamically adapts to any such changes because the workflows are designed to recognize sets of organism files, conditions, and replicates based on a general nomenclature pattern, as described herein, and which may be designed and adapted based on user preference. Example nomenclature may include, for instance, regular expressions on files of interest (e.g., each example condition in a file of interest is prefixed with the term “condition_”). As such, the workflows described herein are able to identify all of the relevant files, such as the relevant experimental conditions, to be considered and the corresponding RNA-Seq read files and apply all relevant analytical tools (i.e., computing steps) selected for the particular workflow.