Epitachophoresis is a novel versatile total nucleic acid extraction method.

Epitachophoresis is a novel next generation extraction system capable of isolating DNA and RNA simultaneously from clinically relevant samples. Here we build on the versatility of Epitachophoresis by extracting diverse nucleic acids ranging in lengths (20 nt-290 Kbp). The quality of extracted miRNA, mRNA and gDNA was assessed by downstream Next-Generation Sequencing.

Author Correction: Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

ConcatSeq: A method for increasing throughput of single molecule sequencing by concatenating short DNA fragments.

Single molecule sequencing (SMS) platforms enable base sequences to be read directly from individual strands of DNA in real-time. Though capable of long read lengths, SMS platforms currently suffer from low throughput compared to competing short-read sequencing technologies. Here, we present a novel strategy for sequencing library preparation, dubbed ConcatSeq, which increases the throughput of SMS platforms by generating long concatenated templates from pools of short DNA molecules. We demonstrate adaptation of this technique to two target enrichment workflows, commonly used for oncology applications, and feasibility using PacBio single molecule real-time (SMRT) technology. Our approach is capable of increasing the sequencing throughput of the PacBio RSII platform by more than five-fold, while maintaining the ability to correctly call allele frequencies of known single nucleotide variants. ConcatSeq provides a versatile new sample preparation tool for long-read sequencing technologies.

LongISLND: in silico sequencing of lengthy and noisy datatypes.

LongISLND is a software package designed to simulate sequencing data according to the characteristics of third generation, single-molecule sequencing technologies. The general software architecture is easily extendable, as demonstrated by the emulation of Pacific Biosciences (PacBio) multi-pass sequencing with P5 and P6 chemistries, producing data in FASTQ, H5, and the latest PacBio BAM format. We demonstrate its utility by downstream processing with consensus building and variant calling. AVAILABILITY AND IMPLEMENTATION: LongISLND is implemented in Java and available at http://bioinform.github.io/longislnd CONTACT: hugo.lam@roche.comSupplementary information: Supplementary data are available at Bioinformatics online.

ResBoost: characterizing and predicting catalytic residues in enzymes.

BACKGROUND: Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. RESULTS: We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). CONCLUSION: ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA.