ALBERT

Description: Motivation The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis , where a large number of mutations instead occur simultaneously . A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction , which allows us to overcome some of the limitations of the original signature. Results We apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion. Availability and implementation An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac . Contact loesper@carleton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites. Results Tested on four different datasets, CCmiR predicted miRNA target sites with a high recall and a reasonable precision, and identified known and new cooperative and competitive miRNAs supported by literature. Compared with three state-of-the-art computational methods, CCmiR had a higher recall and a higher precision. Availability and implementation CCmiR is freely available at http://hulab.ucf.edu/research/projects/miRNA/CCmiR . Contact xiaoman@mail.ucf.edu or haihu@cs.ucf.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions ( HiCnv ), (ii) to call inter-chromosomal translocations and their boundaries ( HiCtrans ) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities ( AveSim ) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line ( r  = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100–300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C. Availability and implementation CNV calling: https://github.com/ay-lab/HiCnv , Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim . Contact ferhatay@lji.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Availability and implementation Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove . Contact hechua@ntu.edu.sg or assourav@ntu.edu.sg

Description: Summary High-throughput screening of the host transcriptional response to various viral infections provides a wealth of data, but utilization of microarray and next generation sequencing (NGS) data for analysis can be difficult. The Ho st T ranscriptional R esponse D ata B ase (HoTResDB), allows visitors to access already processed microarray and NGS data from non-human primate models of viral hemorrhagic fever to better understand the host transcriptional response. Availability HoTResDB is freely available at http://hotresdb.bu.edu Contact jhconnor@bu.edu

Description: Motivation Structural variation, including large deletions, duplications, inversions, translocations and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (∼5 to 10) DNA molecules ∼50 Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. Results We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in an individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification—including two recent methods that also analyze linked-reads—on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes. Availability and implementation Software is available at compbio.cs.brown.edu/software . Contact braphael@princeton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient’s mutational profile into signatures but also establish the accuracy of such decomposition. Results We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. Availability and implementation All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation . Contact wojtowda@ncbi.nlm.nih.gov or przytyck@ncbi.nlm.nih.gov Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ 1 -norm or its variants to induce sparsity. The ℓ 0 -norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. Results In this paper, we propose the truncated ℓ 1 -norm penalized SCCA to improve the performance and effectiveness of the ℓ 1 -norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ . It can avoid the time intensive parameter tuning if given a reasonable small τ . Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. Availability and implementation The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/ . Contact dulei@nwpu.edu.cn or shenli@iu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling. Results Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor–ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor–ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor–ligand complexes. Availability http://qasdom.eimb.ru . Contact alexei.adzhubei@eimb.ru. Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/ . Contact andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.