The lack of clear predictors of prostate cancer progression leads to subjective decision-making regarding courses of treatment. The identification of new biomarkers that are predictive of recurrence after radical prostatectomy would advance the field of prostate cancer treatment. Disclosed are miRNAs that can be used as molecular biomarkers to detect or predict the progression of prostate cancer and to adjust a treatment plan accordingly. Furthermore, kits are included for the detection of these miRNAs.

A log of molecular events experienced by a cell and timing indicators for those events are stored in existing polynucleotides through a process of creating a double strand break (“DSB”) in a polynucleotide and inserting a new polynucleotide sequence by repairing the DSB with homology directed repair (“HDR”). The presence, order, and number of new polynucleotide sequences provides a log of events and timing of those events. Cellular mechanisms for creating the DSB and/or repairing with HDR are regulated by intra- or extra-cellular signals. When the log is created in the DNA of a cell, the changes may be heritably passed to subsequent generations of the cell. A correlation between the cellular signals and sequence of inserted HDR templates allows for identification of events and the timing experienced by the cell.

Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.

Methods for making a synthetic gene are provided. The methods find use in optimizing a candidate gene nucleic acid sequence for expression in a selected target expression system. The method identifies stable or retained sequences in the candidate gene nucleic acid sequence, identifies disallowed sequences, develops a statistical model based on a whole genome, a partial genome, or transcriptome sequences of the target expression system, generates an optimized candidate gene nucleic acid sequence for use in the target expression system, and makes a synthetic gene comprising the optimized candidate gene nucleic acid sequence. The method allows for optimization of the candidate gene nucleic acid sequence without removing certain stable or retained sequences. Rather, the activity of these sites is positionally modulated or inactivated through upstream and downstream modifications of codons and/or sequence patterns.

Methods and kits for diagnosing and/or treating a lower respiratory infection in a subject include obtaining a biological sample from the subject; detecting RNA expression levels of one or more biomarkers in the biological sample and comparing the expression levels of the one or more three biomarkers to at least one invariant control marker wherein an increase or decrease in the level of expression of the one or more biomarkers as compared to the at least one invariant control marker is indicative of a lower respiratory infection.

The present invention relates to methods for the analysis of nucleic acids present in biological samples, and more specifically to normalize a high resolution melt curve to assist in the identification of one or more properties of the nucleic acids. The present invention provides methods and systems that incorporate a background identification algorithm according to invention principles using raw melt curve data to identify reactions that are unrelated actual DNA melt reactions. Furthermore, a web-based application for analyzing experimental data is provided. The raw experimental data obtained from a variety of instruments is processed and analyzed on a server and presented to a user through a user interface (UI).

The present disclosure provides a method for enriching for multiple genomic regions using a first bait set that selectively hybridizes to a first set of genomic regions of a nucleic acid sample and a second bait set that selectively hybridizes to a second set of genomic regions of the nucleic acid sample. These bait set panels can selectively enrich for one or more nucleosome-associated regions of a genome, said nucleosome-associated regions comprising genomic regions having one or more genomic base positions with differential nucleosomal occupancy, wherein the differential nucleosomal occupancy is characteristic of a cell or tissue type of origin or disease state.

A method of identifying co-expressed coding and noncoding genes is disclosed. The method may include receiving genetic sequences, mapping the genetic sequences to known coding and noncoding genes, correlating the mapped genes, and generating a co-expression network. A system for generating a co-expression network and providing the co-expression network to a user on a display is disclosed. The system may include a memory, one or more processors, one or more databases, and a display.

Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Methods of evaluating or providing a clonal profile of a subject interval, e.g., a subgenomic interval, or an expressed subgenomic interval (or of a cell containing the same), in a subject, are disclosed.