bluuebunny/crossref_metadata_2025 huggingface dataset:
A Huge Infected Popliteal Cyst Dissecting into Gastrocnemius Mimicking Calf Abscess
Authors: Leong-Pan Hung , Yuen-Fai Leung , Archie Lo Bill , Tony Chau Kwok-Fung
Published: June 2015

We report a case of a chronic huge popliteal cyst, which dissected into the gastrocnemius without rupture and was complicated with acute primary infection mimicking calf abscess upon presentation. To the best of our knowledge, it is the largest popliteal cyst with intramuscular dissection reported in the literature. It is noteworthy that infected popliteal cyst is a rare differential diagnosis of calf abscess.
Biliary stenting for unresectable cholangiocarcinoma: A population‐based study of long‐term outcomes and hospital costs in Taiwan
Authors: Yang Ling‐Chun , Shi Hon‐Yi , Huang Jian‐Wei , Lee King‐Teh
Published: June 2015

Few studies have compared percutaneous biliary stenting (PBS) and endoscopic biliary stenting (EBS) in terms of long‐term effects on cholangiocarcinoma (CC), and few have systematically evaluated outcome associations in Taiwan. This study aimed to compare long‐term outcomes between two treatments for unresectable CC: PBS and EBS. After propensity score matching (PSM) to reduce the effect of selection bias, 1002 CC patients were included in this analysis: 501 in the PBS group and 501 in the EBS group. The Kaplan‐Meier method was used to construct the survival curve for all CC patients, and the Cox proportional hazards model was used for multivariate assessment of outcome predictors. After PSM, group comparisons revealed a significantly longer length of stay in the PBS group compared to the EBS group (25 days vs. 19 days, respectively; p < 0.001). Hospital costs were also significantly higher in the PBS group than in the EBS group (US$126,575 vs. US$89,326, respectively; p < 0.001). The median survival time was 3.7 months in all CC patients, 3.5 months in the PBS group, and 4.0 months in the EBS group. The 1‐year, 3‐year, and 5‐year survival rates were 17.6%, 6.1%, and 3.2% in all CC patients; 16.6%, 4.8%, and 3.2% in the PBS group; and 18.6%, 7.27%, and 3% in the EBS group, respectively. The most important predictor of survival is extrahepatic CC. Medical professionals and healthcare providers should carefully consider the use of EBS for initial treatment of obstructive jaundice in patients with unresectable CC.
Src family kinases differentially influence glioma growth and motility
Authors: Lewis-Tuffin Laura J. , Feathers Ryan , Hari Priya , Durand Nisha , Li Zhimin , Rodriguez Fausto J. , Bakken Katie , Carlson Brett , Schroeder Mark , Sarkaria Jann N. , Anastasiadis Panos Z.
Published: June 2015

Src‐family kinase (SFK) signaling impacts multiple tumor‐related properties, particularly in the context of the brain tumor glioblastoma. Consequently, the pan‐SFK inhibitor dasatinib has emerged as a therapeutic strategy, despite physiologic limitations to its effectiveness in the brain. We investigated the importance of individual SFKs (Src, Fyn, Yes, and Lyn) to glioma tumor biology by knocking down individual SFK expression both in culture (LN229, SF767, GBM8) and orthotopic xenograft (GBM8) contexts. We evaluated the effects of these knockdowns on tumor cell proliferation, migration, and motility‐related signaling in culture, as well as overall survival in the orthotopic xenograft model. The four SFKs differed significantly in their importance to these properties. In culture, Src, Fyn, and Yes knockdown generally reduced growth and migration and altered motility‐related phosphorylation patterns while Lyn knockdown did so to a lesser extent. However the details of these effects varied significantly depending on the cell line: in no case were conclusions about the role of a particular SFK applicable to all of the measures or all of the cell types examined. In the orthotopic xenograft model, mice implanted with non‐target or Src or Fyn knockdown cells showed no differences in survival. In contrast, mice implanted with Yes knockdown cells had longer survival, associated with reduced tumor cell proliferation. Those implanted with Lyn knockdown cells had shorter survival, associated with higher overall tumor burden. Together, our results suggest that Yes signaling directly affects tumor cell biology in a pro‐tumorigenic manner, while Lyn signaling affects interactions between tumor cells and the microenvironment in an anti‐tumor manner. In the context of therapeutic targeting of SFKs, these results suggest that pan‐SFK inhibitors may not produce the intended therapeutic benefit when Lyn is present.
Earth's Future: Now Indexed in Web of Science
Authors: van der Pluijm Ben
Published: June 2015

One of AGU's newest journals is highly rated.
Initiative to Assist Developing Nations with Climate Resilience
Authors: Showstack Randy
Published: June 2015

The Climate Services for Resilient Development partnership plans to provide expertise and data to help developing nations be less vulnerable.
Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure
Authors: Mimno David , Blei David M. , Engelhardt Barbara E.
Published: June 2015

Significance

Bayesian models, including admixture models, are a powerful framework for articulating complex assumptions about large-scale genetic data; such models are widely used to explore data or to study population-level statistics of interest. However, we assume that a Bayesian model does not oversimplify the complexities in the data, to the point of invalidating our analyses. Here, we develop and study procedures for quantitatively evaluating admixture models of genetic data. Using four large genetic studies, we demonstrate that model checking should be an important part of the modern genetic data analysis pipeline. Our methods help to support inferences drawn from recovered population structure, to protect scientists from being misled by a misspecified model class, and to point scientists toward useful model extensions.
On Marginal‐Risk Behavior
Authors: Chambers Robert G.
Published: June 2015

This paper examines marginal‐risk behavior in a decision‐theoretic setting that is sufficiently general to accommodate any decision‐making model representable by a certainty equivalent. The analysis shows that risk aversion in its usual sense is a special case of marginal‐risk aversion associated with mean‐preserving changes in gambles. Thus, marginal‐risk behavior and risk behavior are not inherently different. Rather, they manifest similar behavior evaluated at different gambles. The paradox of marginal‐risk‐loving risk averters arises from mixing definitions of an “increase in risk.”
Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments
Authors: Yamanishi Yoshihiro , Tabei Yasuo , Kotera Masaaki
Published: June 2015

Motivation: Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized.

Results: In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework. We proposed a novel chemical graph alignment algorithm, which we called PACHA (Pairwise Chemical Aligner), to detect the regioisomer-sensitive connectivities between the aligned substructures of two compounds. Unlike other existing graph alignment methods, PACHA can efficiently detect only one common subgraph between two compounds. Our results show that the proposed method outperforms previous descriptor-based methods or existing graph alignment-based methods in the enzymatic reaction-likeness prediction for isomer-enriched reactions. It is also useful for reaction annotation that assigns potential reaction characteristics such as EC (Enzyme Commission) numbers and PIERO (Enzymatic Reaction Ontology for Partial Information) terms to substrate–product pairs. Finally, we conducted a comprehensive enzymatic reaction-likeness prediction for all possible uncharacterized compound pairs, suggesting potential metabolic pathways for newly predicted substrate–product pairs.

Contact:  maskot@bio.titech.ac.jp
A generic methodological framework for studying single cell motility in high-throughput time-lapse data
Authors: Schoenauer Sebag Alice , Plancade Sandra , Raulet-Tomkiewicz Céline , Barouki Robert , Vert Jean-Philippe , Walter Thomas
Published: June 2015

Motivation: Motility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.

Results: The primary contribution of this article is to present Motility study Integrated Workflow (MotIW), a generic workflow for the study of single cell motility in high-throughput time-lapse screening data. It is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.

Availability and implementation: Python code and examples are available online (http://cbio.ensmp.fr/∼aschoenauer/motiw.html)

Contact:  thomas.walter@mines-paristech.fr

Supplementary information:  Supplementary data are available at Bioinformatics online.
De novo meta-assembly of ultra-deep sequencing data
Authors: Mirebrahim Hamid , Close Timothy J. , Lonardi Stefano
Published: June 2015

We introduce a new divide and conquer approach to deal with the problem of de novo genome assembly in the presence of ultra-deep sequencing data (i.e. coverage of 1000x or higher). Our proposed meta-assembler Slicembler partitions the input data into optimal-sized ‘slices’ and uses a standard assembly tool (e.g. Velvet, SPAdes, IDBA_UD and Ray) to assemble each slice individually. Slicembler uses majority voting among the individual assemblies to identify long contigs that can be merged to the consensus assembly. To improve its efficiency, Slicembler uses a generalized suffix tree to identify these frequent contigs (or fraction thereof). Extensive experimental results on real ultra-deep sequencing data (8000x coverage) and simulated data show that Slicembler significantly improves the quality of the assembly compared with the performance of the base assembler. In fact, most of the times, Slicembler generates error-free assemblies. We also show that Slicembler is much more resistant against high sequencing error rate than the base assembler.

Availability and implementation: Slicembler can be accessed at http://slicembler.cs.ucr.edu/.

Contact:  hamid.mirebrahim@email.ucr.edu
Exploring the structure and function of temporal networks with dynamic graphlets
Authors: Hulovatyy Y. , Chen H. , Milenković T.
Published: June 2015

Motivation: With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.

Results: We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging.

Availability and implementation:  http://www.nd.edu/∼cone/DG.

Contact:  tmilenko@nd.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
A hierarchical Bayesian model for flexible module discovery in three-way time-series data
Authors: Amar David , Yekutieli Daniel , Maron-Katz Adi , Hendler Talma , Shamir Ron
Published: June 2015

Motivation: Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g. genes × patients), this is often done via clustering or biclustering. More recently, studies monitoring patients over time have added another dimension. Analysis is much more challenging in this case, especially when time measurements are not synchronized. New methods that can analyze three-way data are thus needed.

Results: We present a new algorithm for finding coherent and flexible modules in three-way data. Our method can identify both core modules that appear in multiple patients and patient-specific augmentations of these core modules that contain additional genes. Our algorithm is based on a hierarchical Bayesian data model and Gibbs sampling. The algorithm outperforms extant methods on simulated and on real data. The method successfully dissected key components of septic shock response from time series measurements of gene expression. Detected patient-specific module augmentations were informative for disease outcome. In analyzing brain functional magnetic resonance imaging time series of subjects at rest, it detected the pertinent brain regions involved.

Availability and implementation: R code and data are available at http://acgt.cs.tau.ac.il/twigs/.

Contact:  rshamir@tau.ac.il

Supplementary information  :  Supplementary data are available at Bioinformatics online.
Comparing genomes with rearrangements and segmental duplications
Authors: Shao Mingfu , Moret Bernard M.E.
Published: June 2015

Motivation: Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability.

Results: We study the comparison of two genomes under a model including general rearrangements (through double-cut-and-join) and segmental duplications. We formulate the comparison as an optimization problem and describe an exact algorithm to solve it by using an integer linear program. We also devise a sufficient condition and an efficient algorithm to identify optimal substructures, which can simplify the problem while preserving optimality. Using the optimal substructures with the integer linear program (ILP) formulation yields a practical and exact algorithm to solve the problem. We then apply our algorithm to assign in-paralogs and orthologs (a necessary step in handling duplications) and compare its performance with that of the state-of-the-art method MSOAR, using both simulations and real data. On simulated datasets, our method outperforms MSOAR by a significant margin, and on five well-annotated species, MSOAR achieves high accuracy, yet our method performs slightly better on each of the 10 pairwise comparisons.

Availability and implementation:  http://lcbb.epfl.ch/softwares/coser.

Contact:  mingfu.shao@epfl.ch or bernard.moret@epfl.ch
Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses
Authors: Park Danny S. , Brown Brielin , Eng Celeste , Huntsman Scott , Hu Donglei , Torgerson Dara G. , Burchard Esteban G. , Zaitlen Noah
Published: June 2015

Motivation: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global ‘best guess’ reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations.

Results: We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of ‘best guess’ reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing.

Availability and implementation: Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix.

Contact:  noah.zaitlen@ucsf.edu
Cypiripi: exact genotyping of CYP2D6 using high-throughput sequencing data
Authors: Numanagić Ibrahim , Malikić Salem , Pratt Victoria M. , Skaar Todd C. , Flockhart David A. , Sahinalp S. Cenk
Published: June 2015

Motivation:  CYP2D6 is highly polymorphic gene which encodes the (CYP2D6) enzyme, involved in the metabolism of 20–25% of all clinically prescribed drugs and other xenobiotics in the human body. CYP2D6 genotyping is recommended prior to treatment decisions involving one or more of the numerous drugs sensitive to CYP2D6 allelic composition. In this context, high-throughput sequencing (HTS) technologies provide a promising time-efficient and cost-effective alternative to currently used genotyping techniques. To achieve accurate interpretation of HTS data, however, one needs to overcome several obstacles such as high sequence similarity and genetic recombinations between CYP2D6 and evolutionarily related pseudogenes CYP2D7 and CYP2D8, high copy number variation among individuals and short read lengths generated by HTS technologies.

Results: In this work, we present the first algorithm to computationally infer CYP2D6 genotype at basepair resolution from HTS data. Our algorithm is able to resolve complex genotypes, including alleles that are the products of duplication, deletion and fusion events involving CYP2D6 and its evolutionarily related cousin CYP2D7. Through extensive experiments using simulated and real datasets, we show that our algorithm accurately solves this important problem with potential clinical implications.

Availability and implementation: Cypiripi is available at http://sfu-compbio.github.io/cypiripi.

Contact:  cenk@sfu.ca.
Inferring models of multiscale copy number evolution for single-tumor phylogenetics
Authors: Chowdhury Salim Akhter , Gertz E. Michael , Wangsa Darawalee , Heselmeyer-Haddad Kerstin , Ried Thomas , Schäffer Alejandro A. , Schwartz Russell
Published: June 2015

Motivation: Phylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models.

Results: We propose a framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. We propose a new algorithm for identification of most parsimonious combinations of single gene and single chromosome events. We extend it via dynamic programming to include genome duplications. We implement an expectation maximization (EM)-like method to estimate mutation-specific and tumor-specific event rates concurrently with tree reconstruction. Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival.

Availability and implementation: Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

Contact:  russells@andrew.cmu.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes
Authors: Mirarab Siavash , Warnow Tandy
Published: June 2015

Motivation: The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed ‘bipartitions’. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent.

Results: We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL’s running time is O(n2k|X|2), and ASTRAL-II’s running time is O(nk|X|2), where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space.

Availability and implementation: ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/.

Contact:  smirarab@gmail.com

Supplementary information:  Supplementary data are available at Bioinformatics online.
Large-scale model quality assessment for improving protein tertiary structure prediction
Authors: Cao Renzhi , Bhattacharya Debswapna , Adhikari Badri , Li Jilong , Cheng Jianlin
Published: June 2015

Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.

Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling.

Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/.

Contact: chengji@missouri.edu
MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms
Authors: Zickmann Franziska , Renard Bernhard Y.
Published: June 2015

Summary: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes.

Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/.

Contact:  renardb@rki.de
MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence
Authors: Liu Ke , Peng Shengwen , Wu Junqiu , Zhai Chengxiang , Mamitsuka Hiroshi , Zhu Shanfeng
Published: June 2015

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.

Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ‘learning to rank’. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.

Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI.

Availability and implementation: The software is available upon request.

Contact:  zhusf@fudan.edu.cn
IgRepertoireConstructor: a novel algorithm for antibody repertoire construction and immunoproteogenomics analysis
Authors: Safonova Yana , Bonissone Stefano , Kurpilyansky Eugene , Starostina Ekaterina , Lapidus Alla , Stinson Jeremy , DePalatis Laura , Sandoval Wendy , Lill Jennie , Pevzner Pavel A.
Published: June 2015

The analysis of concentrations of circulating antibodies in serum (antibody repertoire) is a fundamental, yet poorly studied, problem in immunoinformatics. The two current approaches to the analysis of antibody repertoires [next generation sequencing (NGS) and mass spectrometry (MS)] present difficult computational challenges since antibodies are not directly encoded in the germline but are extensively diversified by somatic recombination and hypermutations. Therefore, the protein database required for the interpretation of spectra from circulating antibodies is custom for each individual. Although such a database can be constructed via NGS, the reads generated by NGS are error-prone and even a single nucleotide error precludes identification of a peptide by the standard proteomics tools. Here, we present the IgRepertoireConstructor algorithm that performs error-correction of immunosequencing reads and uses mass spectra to validate the constructed antibody repertoires.

Availability and implementation: IgRepertoireConstructor is open source and freely available as a C++ and Python program running on all Unix-compatible platforms. The source code is available from http://bioinf.spbau.ru/igtools.

Contact: ppevzner@ucsd.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Inferring parental genomic ancestries using pooled semi-Markov processes
Authors: Zou James Y. , Halperin Eran , Burchard Esteban , Sankararaman Sriram
Published: June 2015

Motivation: A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parent’s genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns.

Results: We model the problem of parental genomic ancestry inference as a pooled semi-Markov process. We develop a general mathematical framework for pooled semi-Markov processes and construct efficient inference algorithms for these models. Applying our inference algorithm to genotype data from 231 Mexican trios and 258 Puerto Rican trios where we have the true genomic ancestry of each parent, we demonstrate that our method accurately infers parameters of the semi-Markov processes and parents’ genomic ancestries. We additionally validated the method on simulations. Our model of pooled semi-Markov process and inference algorithms may be of independent interest in other settings in genomics and machine learning.

Contact:  jazo@microsoft.com
Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins
Authors: Soltan Ghoraie Laleh , Burkowski Forbes , Zhu Mu
Published: June 2015

Motivation: Inferring structural dependencies among a protein’s side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.

Results: We developed a novel extension of canonical correlation analysis (CCA), which we call ‘kernelized partial CCA’ (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family.

Availability and implementation:  https://github.com/lsgh/ismb15

Contact:  lsoltang@uwaterloo.ca
Finding optimal interaction interface alignments between biological complexes
Authors: Cui Xuefeng , Naveed Hammad , Gao Xin
Published: June 2015

Motivation: Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which are keys to understanding their evolutionary histories and functions. Although various structure alignment methods have been developed to successfully access the similarities of protein structures or certain types of interaction interfaces, existing alignment tools cannot directly align arbitrary types of interfaces formed by protein, DNA or RNA molecules. Specifically, they require a ‘blackbox preprocessing’ to standardize interface types and chain identifiers. Yet their performance is limited and sometimes unsatisfactory.

Results: Here we introduce a novel method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures. Our method uses sequentially remote fragments to search for the optimal superimposition. The optimal residue matching problem is then formulated as a maximum weighted bipartite matching problem to detect the optimal sequence order-independent alignment. Benchmark evaluation on all non-redundant protein–DNA complexes in PDB shows significant performance improvement of our method over TM-align and iAlign (with the ‘blackbox preprocessing’). Two case studies where our method discovers, for the first time, structural similarities between two pairs of functionally related protein–DNA complexes are presented. We further demonstrate the power of our method on detecting structural similarities between a protein–protein complex and a protein–RNA complex, which is biologically known as a protein–RNA mimicry case.

Availability and implementation: The PROSTA-inter web-server is publicly available at http://www.cbrc.kaust.edu.sa/prosta/.

Contact:  xin.gao@kaust.edu.sa
Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery
Authors: Speicher Nora K. , Pfeifer Nico
Published: June 2015

Motivation: Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, are mainly based on individual data types. However, the analysis of multidimensional patient data involving the measurements of various molecular features could reveal intrinsic characteristics of the tumor. Large-scale projects accumulate this kind of data for various cancer types, but we still lack the computational methods to reliably integrate this information in a meaningful manner. Therefore, we apply and extend current multiple kernel learning for dimensionality reduction approaches. On the one hand, we add a regularization term to avoid overfitting during the optimization procedure, and on the other hand, we show that one can even use several kernels per data type and thereby alleviate the user from having to choose the best kernel functions and kernel parameters for each data type beforehand.

Results: We have identified biologically meaningful subgroups for five different cancer types. Survival analysis has revealed significant differences between the survival times of the identified subtypes, with P values comparable or even better than state-of-the-art methods. Moreover, our resulting subtypes reflect combined patterns from the different data sources, and we demonstrate that input kernel matrices with only little information have less impact on the integrated kernel matrix. Our subtypes show different responses to specific therapies, which could eventually assist in treatment decision making.

Availability and implementation: An executable is available upon request.

Contact:  nora@mpi-inf.mpg.de or npfeifer@mpi-inf.mpg.de
Phenome-driven disease genetics prediction toward drug discovery
Authors: Chen Yang , Li Li , Zhang Guo-Qiang , Xu Rong
Published: June 2015

Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data.

Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e−4) and 81.3% (P < e−12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery.

Availability and implementation: nlp.case.edu/public/data/DMN

Contact:  rxx@case.edu
MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types
Authors: Kim Yoo-Ah , Cho Dong-Yeon , Dao Phuong , Przytycka Teresa M.
Published: June 2015

Motivation: The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated.

Results: We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks.

Contact:  przytyck@ncbi.nlm.nih.gov

Supplementary information:  Supplementary data are available at Bioinformatics online.
Deconvolving molecular signatures of interactions between microbial colonies
Authors: Harn Y.-C. , Powers M. J. , Shank E. A. , Jojic V.
Published: June 2015

Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret.

Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies.

Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project.

Contact:  vjojic@cs.unc.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival
Authors: Schissler A. Grant , Gardeux Vincent , Li Qike , Achour Ikbel , Li Haiquan , Piegorsch Walter W. , Lussier Yves A.
Published: June 2015

Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).

Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences).

Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).

Availability and implementation:  http://www.lussierlab.net/publications/N-of-1-pathways.

Contact:  yves@email.arizona.edu or piegorsch@math.arizona.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
FERAL: network-based classifier with application to breast cancer outcome prediction
Authors: Allahyar Amin , de Ridder Jeroen
Published: June 2015

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.

Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.

Availability and implementation: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.

Contact:  j.deridder@tudelft.nl

Supplementary information:  Supplementary data are available at Bioinformatics online.