Motivation: Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc. Results: Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format. Availability and implementation: CrossMap is written in Python and C. Source code and a comprehensive user’s manual are freely available at: http://crossmap.sourceforge.net/. Contact:Kocher.JeanPierre@mayo.edu or wang.liguo@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. Result: We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). Conclusion: TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis.

Mutations in SPOP, the gene encoding a component of the E3 ubiquitin ligase complex, impair ubiquitination-dependent degradation of BRD2, BRD3 and BRD4 proteins and result in activation of ATK–mTORC1 signaling and resistance to BET inhibitors. Pharmacological blockade of AKT represents a viable strategy to restore the sensitivity of SPOP-mutant prostate tumors to BET inhibitors. These results, together with findings by Dai et al. and Janouskova et al., uncover a new nongenetic mechanism of resistance to BET inhibition involving cancer-type-specific mutations in SPOP, and support the evaluation of SPOP mutation status to inform the administration of BET inhibitors in the clinic. Bromodomain and extraterminal domain (BET) protein inhibitors are emerging as promising anticancer therapies. The gene encoding the E3 ubiquitin ligase substrate-binding adaptor speckle-type POZ protein (SPOP) is the most frequently mutated in primary prostate cancer. Here we demonstrate that wild-type SPOP binds to and induces ubiquitination and proteasomal degradation of BET proteins (BRD2, BRD3 and BRD4) by recognizing a degron motif common among them. In contrast, prostate cancer–associated SPOP mutants show impaired binding to BET proteins, resulting in decreased proteasomal degradation and accumulation of these proteins in prostate cancer cell lines and patient specimens and causing resistance to BET inhibitors. Transcriptome and BRD4 cistrome analyses reveal enhanced expression of the GTPase RAC1 and cholesterol-biosynthesis-associated genes together with activation of AKT–mTORC1 signaling as a consequence of BRD4 stabilization. Our data show that resistance to BET inhibitors in SPOP-mutant prostate cancer can be overcome by combination with AKT inhibitors and further support the evaluation of SPOP mutations as biomarkers to guide BET-inhibitor-oriented therapy in patients with prostate cancer.

Tumor tissues are chronically exposed to hypoxia owing to aberrant vascularity. Lipid droplet (LD) accumulation is a hallmark of hypoxic cancer cells, yet how LDs form and function during hypoxia remains poorly understood. Herein, we report that in various cancer cells upon oxygen deprivation, HIF-1 activation down-modulates LD catabolism mediated by adipose triglyceride lipase (ATGL), the key enzyme for intracellular lipolysis. Proteomics and functional analyses identified hypoxia-inducible gene 2 (HIG2), a HIF-1 target, as a new inhibitor of ATGL. Knockout of HIG2 enhanced LD breakdown and fatty acid (FA) oxidation, leading to increased ROS production and apoptosis in hypoxic cancer cells as well as impaired growth of tumor xenografts. All of these effects were reversed by co-ablation of ATGL. Thus, by inhibiting ATGL, HIG2 acts downstream of HIF-1 to sequester FAs in LDs away from the mitochondrial pathways for oxidation and ROS generation, thereby sustaining cancer cell survival in hypoxia.

Motivation: The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results: Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability and implementation: LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information: Supplementary data are available at Bioinformatics online.

Hepatocellular carcinoma (HCC) is one of the leading cause of cancer death in the world. Fructose-1,6-biphosphatase (FBP1), a rate-limiting enzyme in gluconeogenesis, has been identified recently as a tumor suppressor in HCC and other cancer types. In this study, we demonstrated that the tripartite motif-containing protein 28 (TRIM28) binds directly to and promotes FBP1 for ubiquitination and degradation. MAGE-A3 and MAGE-C2, which are known to be overexpressed in HCC, can enhance TRIM28-dependent degradation of FBP1 by forming ubiquitin ligase complexes with TRIM28. We further showed that expression of TRIM28 increased glucose consumption and lactate production by promoting FBP1 degradation in HCC cells and that FBP1 is a key mediator of TRIM28-induced HCC growth in culture and in mice. Moreover, we demonstrated that FBP1 and TRIM28 protein levels inversely correlated in HCC patient specimens. Finally, we showed that the proteasome inhibitor bortezomib mitigated the Warburg effect by inhibiting FBP1 degradation in HCC. Collectively, our findings not only identify oncogenic MAGE-TRIM28 complex-mediated proteasome degradation of FBP1 as a key mechanism underlying downregulation of FBP1 proteins in HCC, but also reveal that MAGE-TRIM28-regulated reprogramming of cancer cell metabolism and HCC tumorigenesis is mediated, at least in part, through FBP1 degradation.