ALBERT

Description: Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues. Results: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. Availability and implementation: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/ . Contact : aoli@ustc.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. 〉3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Residue–residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize. Results: We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redundant polytopic proteins using a strict leave-one-out cross-validation protocol, MemBrain achieves an average accuracy of 62%, which is 12.5% higher than the current best method from the literature. When applied to 13 recently solved G protein-coupled receptors, the MemBrain contact predictions helped increase the TM-score of the I-TASSER models by 37% in the transmembrane region. The number of foldable cases (TM-score 〉0.5) increased by 100%, where all G protein-coupled receptor templates and homologous templates with sequence identity 〉30% were excluded. These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins. Availability: www.csbio.sjtu.edu.cn/bioinf/MemBrain/ Contact: hbshen@sjtu.edu.cn or zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: T cell activation is a well-established model for studying cellular responses to exogenous stimulation. Using strand-specific RNA-seq, we observed that intron retention is prevalent in polyadenylated transcripts in resting CD4 + T cells and is significantly reduced upon T cell activation. Several lines of evidence suggest that intron-retained transcripts are less stable than fully spliced transcripts. Strikingly, the decrease in intron retention (IR) levels correlate with the increase in steady-state mRNA levels. Further, the majority of the genes upregulated in activated T cells are accompanied by a significant reduction in IR. Of these 1583 genes, 185 genes are predominantly regulated at the IR level, and highly enriched in the proteasome pathway, which is essential for proper T cell proliferation and cytokine release. These observations were corroborated in both human and mouse CD4 + T cells. Our study revealed a novel post-transcriptional regulatory mechanism that may potentially contribute to coordinated and/or quick cellular responses to extracellular stimuli such as an acute infection.

Description: Aims Although stem CO 2 efflux is critical to ecosystem carbon and energy balance and its feedback to future climate change, little information is available on stem CO 2 efflux and its responses to temperature, especially in subtropical China. This study aims to (i) evaluate the temporal and spatial variations of stem CO 2 efflux of three species, including oak ( Quercus acutissima Carr.), masson pine ( Pinus massoniana Lamb.) and loblolly pine ( Pinus taeda Linn.) in subtropical China and (ii) analyze the temperature sensitivity of stem CO 2 efflux in the three species based on 2-year field measurements. Methods We measured stem CO 2 efflux and stem temperature (at 3 cm depth) of the three species using the horizontally oriented soil chamber technique from September 2008 to August 2010. We also conducted a 24-h measurement to examine the diurnal variation of stem CO 2 efflux in three consecutive days in April 2009. Important findings The temporal dynamics of stem CO 2 efflux followed the change of the stem temperature in a 3-cm depth with a bell-shaped curve in the three species. Stem temperature explained 77–85% of the seasonal variations of stem CO 2 efflux over the entire study period in the three species. The temperature sensitivity ( Q 10 ) of stem CO 2 efflux was obviously different among the three species with higher Q 10 value found in oak (2.24) and lower values in the coniferous species (1.76 and 1.63). Our results also showed that the Q 10 values of stem CO 2 efflux in all the three species were lower in the growing season than that in the non-growing season, indicating that the growth and maintenance respiration had different temperature responses. Moreover, we found that the temperature-normalized stem CO 2 efflux ( R 10 ) changed greatly between the growing and non-growing seasons in oak and masson pine, but not in loblolly pine. Additionally, we also found that in the non-growing season, the principal factor responsible for the spatial variation of stem CO 2 efflux among the 15 sampling trees was sapwood volume, whereas in the growing season, stem CO 2 efflux was closely related to annual dry-matter production in the three subtropical species.

Description: Tumors are characterized by properties of genetic instability, heterogeneity, and significant oligoclonality. Elucidating this intratumoral heterogeneity is challenging but important. In this study, we propose a framework, BubbleTree, to characterize the tumor clonality using next generation sequencing (NGS) data. BubbleTree simultaneously elucidates the complexity of a tumor biopsy, estimating cancerous cell purity, tumor ploidy, allele-specific copy number, and clonality and represents this in an intuitive graph. We further developed a three-step heuristic method to automate the interpretation of the BubbleTree graph, using a divide-and-conquer strategy. In this study, we demonstrated the performance of BubbleTree with comparisons to similar commonly used tools such as THetA2, ABSOLUTE, AbsCN-seq and ASCAT, using both simulated and patient-derived data. BubbleTree outperformed these tools, particularly in identifying tumor subclonal populations and polyploidy. We further demonstrated BubbleTree's utility in tracking clonality changes from patients’ primary to metastatic tumor and dating somatic single nucleotide and copy number variants along the tumor clonal evolution. Overall, the BubbleTree graph and corresponding model is a powerful approach to provide a comprehensive spectrum of the heterogeneous tumor karyotype in human tumors. BubbleTree is R-based and freely available to the research community ( https://www.bioconductor.org/packages/release/bioc/html/BubbleTree.html ).

Description: Several genome-wide association studies (GWAS) have demonstrated the association between genetic variants in the major histocompatibility complex (MHC) region and chronic hepatitis B (CHB) virus infection, but it is still unknown about the disease-causing loci and potential mechanisms owing to the complicated linkage disequilibrium for this region. To systematically characterize the MHC variations in relation to the CHB infection, we fine mapped the MHC region on our existing GWAS data with SNP2HLA taken the Pan-Asian panel as reference and finally identified four independent associations. The HLA-DPβ1 amino acid positions 84–87, which drove the effect of reported single nucleotide polymorphisms rs9277535 and rs3077, showed the most significant association (OR = 0.65, P = 2.03 x 10 –8 ). The Leu-15 of HLA-C, conferring the effect of rs3130542, increased the risk of CHB infection independently (OR = 1.61, P = 3.42 x 10 –7 ). The HLA-DRβ1*13 , in perfect LD with glutamic at site 71, and rs400488, an expression quantitative trait locus for HLA-J , were newly identified to be associated with CHB infection independently (OR = 1.84, P = 3.84 x 10 –9 ; OR = 0.28, P = 6.27 x 10 –7 , respectively). HLA-DPβ1 positions 84–87 and HLA-DRβ1 position 71 implicated the P1 and P4 in the antigen-binding groove, whereas HLA-C position 15 affected the signal peptide. These four independent loci together can explain ~6% of the phenotypic variance for CHB infection, accounting for 72.94% of that explained by known genetic variations. We fine mapped the MHC region and identified four loci that independently drove the chronic HBV infection. The results provided a deeper understanding of the GWAS signals and identified additional susceptibility loci which were missed in previous association studies.

Description: Chronic inflammation impairs metabolic homeostasis and is intimately correlated with the pathogenesis of type 2 diabetes. The pro-inflammatory cytokine IFN- is an integral part of the metabolic inflammation circuit and contributes significantly to metabolic dysfunction. The underlying mechanism, however, remains largely unknown. In the present study, we report that IFN- disrupts the expression of genes key to cellular metabolism and energy expenditure by repressing the expression and activity of SIRT1 at the transcription level. Further analysis reveals that IFN- requires class II transactivator (CIITA) to repress SIRT1 transcription. CIITA, once induced by IFN-, is recruited to the SIRT1 promoter by hypermethylated in cancer 1 (HIC1) and promotes down-regulation of SIRT1 transcription via active deacetylation of core histones surrounding the SIRT1 proximal promoter. Silencing CIITA or HIC1 restores SIRT1 activity and expression of metabolic genes in skeletal muscle cells challenged with IFN-. Therefore, our data delineate an IFN-/HIC1/CIITA axis that contributes to metabolic dysfunction by suppressing SIRT1 transcription in skeletal muscle cells and as such shed new light on the development of novel therapeutic strategies against type 2 diabetes.

Description: 〈span〉〈div〉Summary〈/div〉Dispersion curve inversion is one of the core components of Rayleigh wave surveys, which mainly include linear and nonlinear inversion theoretical systems. Damped least squares (DLS) is the most mature and commonly used method of linear optimization, but it relies heavily on more accurate initial models, otherwise it can easily fall into a local minimum or can even result in an incorrect inversion. As a representative method of nonlinear optimization, genetic algorithm (GA) may be more feasible to obtain a global optimal solution for the geophysical inversion in theory. However, the GA algorithm is less stable, as well as less efficient in the later period of the inversion. In the past, the above two systems have been used independently to perform inversion processing. Faced with complex seismic geological conditions, they often display poor adaptability and lack balance between speed and accuracy. For this reason, we made a reasonable and effective improvement to the generation of the initial population and the coding of the classic GA to overcome the time-consuming and memory-intensive computational issues. We redefined the selection and crossover function to prevent the ‘premature convergence’ phenomenon in genetic iterations. Simultaneously, the DLS and the steepest descent method (SD) are embedded in the GA inversion process to linearly optimize the dominant individuals in the population (i.e. local extrema) in the local space to guide the population to move quickly and stably advance towards the global optimal direction. Next, a robust DLS inversion is used to obtain the final S-wave velocity model using the adaptive GA inversion results as the input velocity model. The model and actual dataset processing results show that our proposed nested joint inversion can combine the advantages of linear and nonlinear inversion that can effectively suppress the multi-solution problem of the dispersion curve inversion and significantly improve the inversion efficiency and accuracy.〈/span〉

Description: Motivation: There is a long-term interest in the challenging task of finding translocated and mislocated cancer biomarker proteins. Bioimages of subcellular protein distribution are new data sources which have attracted much attention in recent years because of their intuitive and detailed descriptions of protein distribution. However, automated methods in large-scale biomarker screening suffer significantly from the lack of subcellular location annotations for bioimages from cancer tissues. The transfer prediction idea of applying models trained on normal tissue proteins to predict the subcellular locations of cancerous ones is arbitrary because the protein distribution patterns may differ in normal and cancerous states. Results: We developed a new semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy. Our approach enables us to selectively use the low-quality images in normal states to expand the training sample space and provides a general way for dealing with the small size of annotated images used together with large unannotated ones. Experiments demonstrate that the new semi-supervised protocol can result in improved accuracy and sensitivity of subcellular location difference detection. Availability and implementation: The data and code are available at: www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/ . Contact: hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.