Improvement of base-calling in multilane automated DNA sequencing by use of electrophoretic calibration standards, data linearization, and trace alignment

A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy

Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. In addition, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged, in part, because of the lack of systematic methods to define drug off-target effects (OTE) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent cell-cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Local alignment of two-base encoded DNA sequence

Background

DNA sequence comparison is based on optimal local alignment of two sequences using a similarity score. However, some new DNA sequencing technologies do not directly measure the base sequence, but rather an encoded form, such as the two-base encoding considered here. In order to compare such data to a reference sequence, the data must be decoded into sequence. The decoding is deterministic, but the possibility of measurement errors requires searching among all possible error modes and resulting alignments to achieve an optimal balance of fewer errors versus greater sequence similarity.

Results

We present an extension of the standard dynamic programming method for local alignment, which simultaneously decodes the data and performs the alignment, maximizing a similarity score based on a weighted combination of errors and edits, and allowing an affine gap penalty. We also present simulations that demonstrate the performance characteristics of our two base encoded alignment method and contrast those with standard DNA sequence alignment under the same conditions.

Conclusion

The new local alignment algorithm for two-base encoded data has substantial power to properly detect and correct measurement errors while identifying underlying sequence variants, and facilitating genome re-sequencing efforts based on this form of sequence data.