Motivation: Identifying piwi-interacting RNAs (piRNAs) of non-model organisms is a difficult and unsolved problem because piRNAs lack conservative secondary structure motifs and sequence homology in different species.Results: In this article, a k-mer scheme is proposed to identify piRNA sequences, relying on the training sets from non-piRNA and piRNA sequences of five model species sequenced: rat, mouse, human, fruit fly and nematode. Compared with the existing ‘static’ scheme based on the position-specific base usage, our novel ‘dynamic’ algorithm performs much better with a precision of over 90% and a sensitivity of over 60%, and the precision is verified by 5-fold cross-validation in these species. To test its validity, we use the algorithm to identify piRNAs of the migratory locust based on 603 607 deep-sequenced small RNA sequences. Totally, 87 536 piRNAs of the locust are predicted, and 4426 of them matched with existing locust transposons. The transcriptional difference between solitary and gregarious locusts was described. We also revisit the position-specific base usage of piRNAs and find the conservation in the end of piRNAs. Therefore, the method we developed can be used to identify piRNAs of non-model organisms without complete genome sequences.Availability: The web server for implementing the algorithm and the software code are freely available to the academic community at http://59.79.168.90/piRNA/index.php.Contact: lkang@ioz.ac.cnSupplementary information: Supplementary data are available at Bioinformatics online.

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.Predicting drug response of a patient based on various genetic information is a fundamental problem in current research of precision medicine. It is known that drug metabolism varies among patients, and some patients will respond faster to drugs than others 1 . Drug sensitivity is a lower threshold to achieve the normal pharmacological action of a drug 2 . Accurate prediction of drug response is very important for disease therapy and safety of patients. However, the biological mechanisms underlying the heterogeneity of individual drug response remain elusive. Recent studies have suggested that various cancer genomic markers are highly associated with anti-cancer drug sensitivity, and patients have been benefited from the drugs related to these biomarkers in clinical trials. For example, the usage of drugs targeting drug response-associated (DRA) fusion gene BCR-ABL in chronic myeloid leukemia 3 and gene BRAF in melanoma 4 have substantially improved the survival rate of patients. Thus, identification of DRA signatures has become an important task in personalized medicine development.With the advent of multiple high throughput technologies, it is now practical to measure the panomics (including transcriptome, metabolome, epigenome, etc.) at a reasonable cost 5 . The rich information in panomic data provides enormous opportunities to systematically identify DRA biomarkers. For example, expressions of ATP binding cassette transporter (ABC) genes are found to be highly correlated with the response of cytotoxic drugs in cancer cell lines through an analysis of 48 known ABC transporters in 60 diverse cancer cell lines with the treatment of 1,429 anti-cancer drugs 6 . Garnett et al. performed a systematic analysis on 639 h...

Oxidized phospholipids (oxPAPC) induce endothelial dysfunction and atherosclerosis. Here we show that oxPAPC induce a gene network regulating serine-glycine metabolism with the mitochondrial methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) as a causal regulator using integrative network modeling and Bayesian network analysis in human aortic endothelial cells. The cluster is activated in human plaque material and by atherogenic lipoproteins isolated from plasma of patients with coronary artery disease (CAD). Single nucleotide polymorphisms (SNPs) within the MTHFD2-controlled cluster associate with CAD. The MTHFD2-controlled cluster redirects metabolism to glycine synthesis to replenish purine nucleotides. Since endothelial cells secrete purines in response to oxPAPC, the MTHFD2-controlled response maintains endothelial ATP. Accordingly, MTHFD2-dependent glycine synthesis is a prerequisite for angiogenesis. Thus, we propose that endothelial cells undergo MTHFD2-mediated reprogramming toward serine-glycine and mitochondrial one-carbon metabolism to compensate for the loss of ATP in response to oxPAPC during atherosclerosis.