Transcriptome-wide identification of single-stranded RNA binding proteins.

RNA-protein interactions are precisely regulated by RNA secondary structures in various biological processes. Large-scale identification of proteins that interact with particular RNA structure is important to the RBPome. Herein, a kethoxal assisted single-stranded RNA interactome capture (KASRIC) strategy was developed to globally identify single-stranded RNA binding proteins (ssRBPs).

Highly Robust Full-Length Protein Sequencing.

Accurate full-length sequencing of a purified unknown protein is still challenging nowadays due to the error-prone mass-spectrometry (MS)-based methods. identified peptide sequence largely contain errors, undermining the accuracy of assembly. Bias on the detectability of the peptides also makes low-coverage regions, resulting in gaps.

Misassembly of long reads undermines de novo-assembled ethnicity-specific genomes: validation in a Chinese Han population.

An ethnicity is characterized by genomic fragments, single nucleotide polymorphisms (SNPs), and structural variations specific to it. However, the widely used 'standard human reference genome' GRCh37/38 is based on Caucasians. Therefore, de novo-assembled reference genomes for specific ethnicities would have advantages for genetics and precision medicine applications, especially with the long-read sequencing techniques that facilitate genome assembly.

Optimal Settings of Mass Spectrometry Open Search Strategy for Higher Confidence.

In most proteome mass spectrometry experiments, more than half of the mass spectra cannot be identified, mainly because of various modifications. The open search strategy allows for a larger precursor tolerance to utilize more spectra, especially those with post-translational modifications; however, thorough quality control based on independent information is lacking. Here, we used the "Suspicious Discovery Rate (SDR)" based on translatome sequencing (RNC-seq) as an independent source to reference the proteome open search results in steady-state cells.

Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing.

Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here we demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq).

FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy.