Post-transcriptional regulation of RNAs is critical to the diverse range of cellular processes. The volume of functional genomic data focusing on post-transcriptional regulation logics continues to grow in recent years. In the current database version, POSTAR2 (http://lulab.life.tsinghua.edu.cn/postar), we included the following new features and data: updated ∼500 CLIP-seq datasets (∼1200 CLIP-seq datasets in total) from six species, including human, mouse, fly, worm, Arabidopsis and yeast; added a new module 'Translatome', which is derived from Ribo-seq datasets and contains ∼36 million open reading frames (ORFs) in the genomes from the six species; updated and unified post-transcriptional regulation and variation data. Finally, we improved web interfaces for searching and visualizing protein-RNA interactions with multi-layer information. Meanwhile, we also merged our CLIPdb database into POSTAR2. POSTAR2 will help researchers investigate the post-transcriptional regulatory logics coordinated by RNA-binding proteins and translational landscape of cellular RNAs.

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

Translation is dynamically regulated during cell development and stress response. In order to detect actively translated open reading frames (ORFs) and dynamic cellular translation events, we have developed a computational method, RiboWave, to process ribosome profiling data. RiboWave utilizes wavelet transform to denoise the original signal by extracting 3-nt periodicity of ribosomes and precisely locate their footprint denoted as Periodic Footprint P-site (PF P-site). Such high-resolution footprint is found to capture the full track of actively elongating ribosomes, from which translational landscape can be explicitly characterized. We compare RiboWave with several published methods, like RiboTaper, ORFscore and RibORF, and found that RiboWave outperforms them in both accuracy and usage when defining actively translated ORFs. Moreover, we show that PF P-site derived by RiboWave shows superior performance in characterizing the dynamics and complexity of cellular translatome by accurately estimating the abundance of protein levels, assessing differential translation and identifying dynamic translation frameshift.

Sufficient codominant genetic markers are needed for various genetic investigations in alfalfa since the species is an outcrossing autotetraploid. With the newly developed next generation sequencing technology, a large amount of transcribed sequences of alfalfa have been generated and are available for identifying SSR markers by data mining. A total of 54,278 alfalfa non-redundant unigenes were assembled through the Illumina HiSeqTM 2000 sequencing technology. Based on 3,903 unigene sequences, 4,493 SSRs were identified. Tri-nucleotide repeats (56.71%) were the most abundant motif class while AG/CT (21.7%), AGG/CCT (19.8%), AAC/GTT (10.3%), ATC/ATG (8.8%), and ACC/GGT (6.3%) were the subsequent top five nucleotide repeat motifs. Eight hundred and thirty- seven EST-SSR primer pairs were successfully designed. Of these, 527 (63%) primer pairs yielded clear and scored PCR products and 372 (70.6%) exhibited polymorphisms. High transferability was observed for ssp falcata at 99.2% (523) and 71.7% (378) in M. truncatula. In addition, 313 of 527 SSR marker sequences were in silico mapped onto the eight M. truncatula chromosomes. Thirty-six polymorphic SSR primer pairs were used in the genetic relatedness analysis of 30 Chinese alfalfa cultivated accessions generating a total of 199 scored alleles. The mean observed heterozygosity and polymorphic information content were 0.767 and 0.635, respectively. The codominant markers not only enriched the current resources of molecular markers in alfalfa, but also would facilitate targeted investigations in marker-trait association, QTL mapping, and genetic diversity analysis in alfalfa.