bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting horizontal gene transfer by mapping sequencing reads across species boundaries
Authors: Trappe Kathrin , Marschall Tobias , Renard Bernhard Y. 
Published: September 2016

Abstract

Motivation
Horizontal gene transfer (HGT) is a fundamental mechanism that enables organisms such as bacteria to directly transfer genetic material between distant species. This way, bacteria can acquire new traits such as antibiotic resistance or pathogenic toxins. Current bioinformatics approaches focus on the detection of past HGT events by exploring phylogenetic trees or genome composition inconsistencies. However, these techniques normally require the availability of finished and fully annotated genomes and of sufficiently large deviations that allow detection and are thus not widely applicable. Especially in outbreak scenarios with HGT-mediated emergence of new pathogens, like the enterohemorrhagic Escherichia coli outbreak in Germany 2011, there is need for fast and precise HGT detection. Next-generation sequencing (NGS) technologies facilitate rapid analysis of unknown pathogens but, to the best of our knowledge, so far no approach detects HGTs directly from NGS reads.

Results
We present Daisy, a novel mapping-based tool for HGT detection. Daisy determines HGT boundaries with split-read mapping and evaluates candidate regions relying on read pair and coverage information. Daisy successfully detects HGT regions with base pair resolution in both simulated and real data, and outperforms alternative approaches using a genome assembly of the reads. We see our approach as a powerful complement for a comprehensive analysis of HGT in the context of NGS data.

Availability and Implementation
Daisy is freely available from http://github.com/ktrappe/daisy.

Contact
renardb@rki.de

Supplementary information
Supplementary data are available at Bioinformatics online.
Pathway-based approach using hierarchical components of collapsed rare variants
Authors: Lee Sungyoung , Choi Sungkyoung , Kim Young Jin , Kim Bong-Jo ,   T2d-Genes Consortium, Hwang Heungsun , Park Taesung 
Published: September 2016

AbstractMotivationTo address ‘missing heritability’ issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem.ResultsThrough simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study.Availability and ImplementationAn implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/.Contacttspark@stats.snu.ac.krSupplementary informationSupplementary data are available at Bioinformatics online.
Snowball: strain aware gene assembly of metagenomes
Authors: Gregor I. , Schönhuth A. , McHardy A. C. 
Published: September 2016

Abstract

Motivation
Gene assembly is an important step in functional analysis of shotgun metagenomic data. Nonetheless, strain aware assembly remains a challenging task, as current assembly tools often fail to distinguish among strain variants or require closely related reference genomes of the studied species to be available.

Results
We have developed Snowball, a novel strain aware gene assembler for shotgun metagenomic data that does not require closely related reference genomes to be available. It uses profile hidden Markov models (HMMs) of gene domains of interest to guide the assembly. Our assembler performs gene assembly of individual gene domains based on read overlaps and error correction using read quality scores at the same time, which results in very low per-base error rates.

Availability and Implementation
The software runs on a user-defined number of processor cores in parallel, runs on a standard laptop and is available under the GPL 3.0 license for installation under Linux or OS X at https://github.com/hzi-bifo/snowball.

Contact
AMC14@helmholtz-hzi.de,a.schoenhuth@cwi.nl

Supplementary information
Supplementary data are available at Bioinformatics online.
Estimating real cell size distribution from cross-section microscopy imaging
Authors: Lenz Michael , Roumans Nadia JT , Vink Roel G , van Baak Marleen A , Mariman Edwin CM , Arts Ilja CW , de Kok Theo M , Ertaylan Gökhan 
Published: September 2016

Abstract

Motivation
Microscopy imaging is an essential tool for medical diagnosis and molecular biology. It is particularly useful for extracting information about disease states, tissue heterogeneity and cell specific parameters such as cell type or cell size from biological specimens. However, the information obtained from the images is likely to be subjected to sampling and observational bias with respect to the underlying cell size/type distributions.

Results
We present an algorithm, Estimate Tissue Cell Size/Type Distribution (EstiTiCS), for the adjustment of the underestimation of the number of small cells and the size of measured cells while accounting for the section thickness independent of the tissue type. We introduce the sources of bias under different tissue distributions and their effect on the measured values with simulation experiments. Furthermore, we demonstrate our method on histological sections of paraffin-embedded adipose tissue sample images from 57 people from a dietary intervention study. This data consists of measured cell size and its distribution over the dietary intervention period at four time points. Adjusting for the bias with EstiTiCS results in a closer fit to the true/expected adipocyte size distribution with earlier studies. Therefore, we conclude that our method is suitable as the final step in estimating the tissue wide cell type/size distribution from microscopy imaging pipeline.

Availability and Implementation
Source code and its documentation are available at https://github.com/michaelLenz/EstiTiCS. The whole pipeline of our method is implemented in R and makes use of the ‘nloptr’ package. Adipose tissue data used for this study are available on request.

Contact
Michael.Lenz@Maastrichtuniversity.nl, Gokhan.Ertaylan@Maastrichtuniversity.nl
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration
Authors: Le Van Thanh , van Leeuwen Matthijs , Carolina Fierro Ana , De Maeyer Dries , Van den Eynden Jimmy , Verbeke Lieven , De Raedt Luc , Marchal Kathleen , Nijssen Siegfried 
Published: September 2016

Abstract

Motivation
Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates` mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering.

Results
We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters. We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss.

Availability and Implementation
The implementation is available at: https://github.com/rankmatrixfactorisation/SRF.

Contact
kathleen.marchal@intec.ugent.be, siegfried.nijssen@cs.kuleuven.be

Supplementary information
Supplementary data are available at Bioinformatics online.
Causality modeling for directed disease network
Authors: Bang Sunjoo , Kim Jae-Hoon , Shin Hyunjung 
Published: September 2016

Abstract

Motivation
Causality between two diseases is valuable information as subsidiary information for medicine which is intended for prevention, diagnostics and treatment. Conventional cohort-centric researches are able to obtain very objective results, however, they demands costly experimental expense and long period of time. Recently, data source to clarify causality has been diversified: available information includes gene, protein, metabolic pathway and clinical information. By taking full advantage of those pieces of diverse information, we may extract causalities between diseases, alternatively to cohort-centric researches.

Method
In this article, we propose a new approach to define causality between diseases. In order to find causality, three different networks were constructed step by step. Each step has different data sources and different analytical methods, and the prior step sifts causality information to the next step. In the first step, a network defines association between diseases by utilizing disease–gene relations. And then, potential causalities of disease pairs are defined as a network by using prevalence and comorbidity information from clinical results. Finally, disease causalities are confirmed by a network defined from metabolic pathways.

Results
The proposed method is applied to data which is collected from database such as MeSH, OMIM, HuDiNe, KEGG and PubMed. The experimental results indicated that disease causality that we found is 19 times higher than that of random guessing. The resulting pairs of causal-effected diseases are validated on medical literatures.

Availability and Implementation
http://www.alphaminers.net

Contact
shin@ajou.ac.kr

Supplementary information
Supplementary data are available at Bioinformatics online.
Simulated linear test applied to quantitative proteomics
Authors: Pham TV , Jimenez CR 
Published: September 2016

Abstract

Motivation
Omics studies aim to find significant changes due to biological or functional perturbation. However, gene and protein expression profiling experiments contain inherent technical variation. In discovery proteomics studies where the number of samples is typically small, technical variation plays an important role because it contributes considerably to the observed variation. Previous methods place both technical and biological variations in tightly integrated mathematical models that are difficult to adapt for different technological platforms. Our aim is to derive a statistical framework that allows the inclusion of a wide range of technical variability.

Results
We introduce a new method called the simulated linear test, or the s-test, that is easy to implement and easy to adapt for different models of technical variation. It generates virtual data points from the observed values according to a pre-defined technical distribution and subsequently employs linear modeling for significance analysis. We demonstrate the flexibility of the proposed approach by deriving a new significance test for quantitative discovery proteomics for which missing values have been a major issue for traditional methods such as the t-test. We evaluate the result on two label-free (phospho) proteomics datasets based on ion-intensity quantitation.

Availability and Implementation
Available at http://www.oncoproteomics.nl/software/stest.html.

Contact
t.pham@vumc.nl
PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins
Authors: Tsirigos Konstantinos D , Elofsson Arne , Bagos Pantelis G 
Published: September 2016

Abstract

Motivation
The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments.

Results
The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower.

Availability and Implementation
The method, along with all datasets used, is freely available for academic users at http://www.compgen.org/tools/PRED-TMBB2.

Contact
pbagos@compgen.org
TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types
Authors: Aben Nanne , Vis Daniel J , Michaut Magali , Wessels Lodewyk FA 
Published: September 2016

Abstract

Motivation
Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways.

Results
To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression.

Availability and Implementation
TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem).

Contact
m.michaut@nki.nl or l.wessels@nki.nl

Supplementary information
Supplementary data are available at Bioinformatics online.
Optimization of signal-to-noise ratio for efficient microarray probe design
Authors: Matveeva Olga V. , Nechipurenko Yury D. , Riabenko Evgeniy , Ragan Chikako , Nazipova Nafisa N. , Ogurtsov Aleksey Y. , Shabalina Svetlana A. 
Published: September 2016

Abstract
Motivation: Target-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis.
Results: In this study, we defined hybridization specificity as a ratio between oligo target-specific hybridization and oligo genome-wide cross-hybridization. A microarray database, derived from the Genomic Comparison Hybridization (GCH) experiment and performed using the Affymetrix platform, contains two different types of probes. The first type of oligo-probes does not have a specific target on the genome and their hybridization signals are derived from genome-wide cross-hybridization alone. The second type includes oligonucleotides that have a specific target on the genomic DNA and their signals are derived from specific and cross-hybridization components combined together in a total signal. A comparative analysis of hybridization specificity of oligo-probes, as well as their nucleotide sequences and thermodynamic features was performed on the database. The comparison has revealed that hybridization specificity was negatively affected by low stability of the fully-paired oligo-target duplex, stable probe self-folding, G-rich content, including GGG motifs, low sequence complexity and nucleotide composition symmetry.
Conclusion: Filtering out the probes with defined ‘negative’ characteristics significantly increases specific hybridization and dramatically decreasing genome-wide cross-hybridization. Selected oligo-probes have two times higher hybridization specificity on average, compared to the probes that were filtered from the analysis by applying suggested cutoff thresholds to the described parameters. A new approach for efficient oligo-probe design is described in our study.
Contact: shabalin@ncbi.nlm.nih.gov or olga.matveeva@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
PanTools: representation, storage and exploration of pan-genomic data
Authors: Sheikhizadeh Siavash , Schranz M. Eric , Akdel Mehmet , de Ridder Dick , Smit Sandra 
Published: September 2016

Abstract

Motivation
Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability.

Results
We introduce a generalized De Bruijn graph as a pan-genome representation, as well as an online algorithm to construct it. This representation is stored in a Neo4j graph database, which makes our approach scalable to large eukaryotic genomes. Besides the construction algorithm, our software package, called PanTools, currently provides functionality for annotating pan-genomes, adding sequences, grouping genes, retrieving gene sequences or genomic regions, reconstructing genomes and comparing and querying pan-genomes. We demonstrate the performance of the tool using datasets of 62 E. coli genomes, 93 yeast genomes and 19 Arabidopsis thaliana genomes.

Availability and Implementation
The Java implementation of PanTools is publicly available at http://www.bif.wur.nl.

Contact
sandra.smit@wur.nl
Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation
Authors: Traynard Pauline , Fauré Adrien , Fages François , Thieffry Denis 
Published: September 2016

Abstract

Motivation
Understanding the temporal behaviour of biological regulatory networks requires the integration of molecular information into a formal model. However, the analysis of model dynamics faces a combinatorial explosion as the number of regulatory components and interactions increases.

Results
We use model-checking techniques to verify sophisticated dynamical properties resulting from the model regulatory structure in the absence of kinetic assumption. We demonstrate the power of this approach by analysing a logical model of the molecular network controlling mammalian cell cycle. This approach enables a systematic analysis of model properties, the delineation of model limitations, and the assessment of various refinements and extensions based on recent experimental observations. The resulting logical model accounts for the main irreversible transitions between cell cycle phases, the sequential activation of cyclins, and the inhibitory role of Skp2, and further emphasizes the multifunctional role for the cell cycle inhibitor Rb.

Availability and Implementation
The original and revised mammalian cell cycle models are available in the model repository associated with the public modelling software GINsim (http://ginsim.org/node/189).

Contact
thieffry@ens.fr

Supplementary information
Supplementary data are available at Bioinformatics online.