On the Effectiveness of Rebuilding RNA Secondary Structures from Sequence Chunks

Despite the computing power of emerging technolo-gies, predicting long RNA secondary structures with thermodynamics-based methods is still infeasible, espe-cially if the structures include complex motifs such as pseu-doknots. This paper presents preliminary results on rebuilding RNA secondary structures by an extensive and systematic sampling of nucleotide chunks. The rebuilding approach merges the significant motifs found in the secondary struc-tures of the single chunks. The extensive sampling and pre-diction of nucleotide chunks are supported by grid tech-nology as part of the RNAVLab functionality. Significant motifs are identified in the chunk secondary structures and merged in a single structure based on their recurrences an

A Dynamic Programming Algorithm for Finding the Optimal Segmentation of an RNA Sequence in Secondary Structure Predictions

Abstract In this paper, we present a dynamic programming algorithm that runs in polynomial time and allows us to achieve the optimal, non-overlapping segmentation of a long RNA sequence into segments (chunks). The secondary structure of each chunk is predicted independently, then combined with the structures predicted for the other chunks, to generate a complete secondary structure prediction that is thus a combination of local energy minima. The proposed approach not only is more efficient and accurate than other traditionally used methods that are based on global energy minimizations, but it also allows scientists to overcome computing and storage constraints when trying to predict the secondary structure of long RNA sequences

SEC31A-ALK Fusion Gene in Lung Adenocarcinoma

Anaplastic lymphoma kinase (ALK) fusion is a common mechanism underlying pathogenesis of non-small cell lung carcinoma (NSCLC) where these rearrangements represent important diagnostic and therapeutic targets. In this study, we found a new ALK fusion gene, SEC31A-ALK, in lung carcinoma from a 53-year-old Korean man. The conjoined region in the fusion transcript was generated by the fusion of SEC31A exon 21 and ALK exon 20 by genomic rearrangement, which contributed to generation of an intact, in-frame open reading frame. SEC31A-ALK encodes a predicted fusion protein of 1438 amino acids comprising the WD40 domain of SEC31A at the N-terminus and ALK kinase domain at the C-terminus. FISH studies suggested that SEC31A-ALK was generated by an unbalanced genomic rearrangement associated with loss of the 3'end of SEC31A. This is the first report of SEC31A-ALK fusion transcript in clinical NSCLC, which could be a novel diagnostic and therapeutic target for patients with NSCLC.status: publishe

Optimized PCR Conditions and Increased shRNA Fold Representation Improve Reproducibility of Pooled shRNA Screens

<div><p>RNAi screening using pooled shRNA libraries is a valuable tool for identifying genetic regulators of biological processes. However, for a successful pooled shRNA screen, it is imperative to thoroughly optimize experimental conditions to obtain reproducible data. Here we performed viability screens with a library of ∼10 000 shRNAs at two different fold representations (100- and 500-fold at transduction) and report the reproducibility of shRNA abundance changes between screening replicates determined by microarray and next generation sequencing analyses. We show that the technical reproducibility between PCR replicates from a pooled screen can be drastically improved by ensuring that PCR amplification steps are kept within the exponential phase and by using an amount of genomic DNA input in the reaction that maintains the average template copies per shRNA used during library transduction. Using these optimized PCR conditions, we then show that higher reproducibility of biological replicates is obtained by both microarray and next generation sequencing when screening with higher average shRNA fold representation. shRNAs that change abundance reproducibly in biological replicates (primary hits) are identified from screens performed with both 100- and 500-fold shRNA representation, however a higher percentage of primary hit overlap between screening replicates is obtained from 500-fold shRNA representation screens. While strong hits with larger changes in relative abundance were generally identified in both screens, hits with smaller changes were identified only in the screens performed with the higher shRNA fold representation at transduction.</p> </div

Effect of fold representation of shRNA at transduction on HEK293T viability screen reproducibility using NGS analysis.

<p>Viability screens performed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042341#pone-0042341-g003" target="_blank">Figure 3</a> analyzed by NGS. Scatter plot of log₁₀(T₁/T₀) of the biological replicates of the S100 (A) and S500 (B) screens are shown with Pearson correlation values indicated in the corner of each plot. Primary hits (shRNA that passed fold change criteria of (T₁/T₀) greater than two and FDR rate of ≤0.05 in both screening (biological) replicates) are depicted in red. Signal (log₂Mean Counts) for S100 (C) and S500 (D) screens are plotted as a function of log ratio (log ₂(T₁/T₀)). Primary hits are color coded with hits identified in both S100 and S500 screens (red), hits identified in the S100 screen only (blue) and hits identified in the S500 screen only (green). The complete data set is presented in the small insert.</p

Identification of the exponential phase during PCR amplification of barcode sequences.

<p>A. Schematic of the strategy used to identify the transition point from exponential to linear PCR amplification. gDNA isolated from HEK293T cells transduced with the pooled shRNA library were amplified in replicate PCR reactions. A replicate reaction was stopped at each cycle from 15 to 27 cycles. Subsequently, PCR products were used as templates for SYBR qPCR reactions using nested primers targeting a common sequence (outside of the barcode region) to examine the ΔC_q between cycles. B. Difference of C_q obtained in the qPCR on diluted amplicons from every cycle of the Phusion HS II polymerase PCR reaction (C_qN+1−C_qN) as a function of the Phusion PCR cycle number (N). C. Gel analysis of the PCR product generated from amplification cycles 22 to 25. Sizes of DNA bands in DNA marker (lane M) are indicated on the left.</p