bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-pass measurement of gain and emission
Authors: Blood Peter 
Published: November 2015

Abstract
The most direct measurement of gain is given by the fractional increase in energy with distance as light makes a single pass through the medium. Amplified spontaneous emission is observed over a single pass, usually by having absorbing regions or low-reflectivity mirrors at one end of the structure. Measurements can be made on laser structures, but not while operating as a laser. The intensity of light from the end of the structure is measured as the amplifying length is varied, so these are known as ‘variable stripe length’ methods. The chapter begins with a short survey of the development of this approach, followed by a detailed account of the electrically pumped ‘segmented contact’ or ‘multi-section’ method, much of which is relevant to other realisations of the experiment, such as optical excitation. Calibration using the population inversion factor gives the radiative current and internal efficiency.
Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments
Authors: Fox Gearóid , Sievers Fabian , Higgins Desmond G. 
Published: November 2015

Abstract
Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data.
Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins.
Availability and implementation: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz.
Contact: des.higgins@ucd.ie
Supplementary information: Supplementary data are available at Bioinformatics online.
rHAT: fast alignment of noisy long reads with regional hashing
Authors: Liu Bo , Guan Dengfeng , Teng Mingxiang , Wang Yadong 
Published: November 2015

Abstract
Motivation: Single Molecule Real-Time (SMRT) sequencing has been widely applied in cutting-edge genomic studies. However, it is still an expensive task to align the noisy long SMRT reads to reference genome by state-of-the-art aligners, which is becoming a bottleneck in applications with SMRT sequencing. Novel approach is on demand for improving the efficiency and effectiveness of SMRT read alignment.
Results: We propose Regional Hashing-based Alignment Tool (rHAT), a seed-and-extension-based read alignment approach specifically designed for noisy long reads. rHAT indexes reference genome by regional hash table (RHT), a hash table-based index which describes the short tokens within local windows of reference genome. In the seeding phase, rHAT utilizes RHT for efficiently calculating the occurrences of short token matches between partial read and local genomic windows to find highly possible candidate sites. In the extension phase, a sparse dynamic programming-based heuristic approach is used for reducing the cost of aligning read to the candidate sites. By benchmarking on the real and simulated datasets from various prokaryote and eukaryote genomes, we demonstrated that rHAT can effectively align SMRT reads with outstanding throughput.
Availability and implementation: rHAT is implemented in C++; the source code is available at https://github.com/HIT-Bioinformatics/rHAT.
Contact: ydwang@hit.edu.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
KMAD: knowledge-based multiple sequence alignment for intrinsically disordered proteins
Authors: Lange Joanna , Wyrwicz Lucjan S. , Vriend Gert 
Published: November 2015

Abstract
Summary: Intrinsically disordered proteins (IDPs) lack tertiary structure and thus differ from globular proteins in terms of their sequence–structure–function relations. IDPs have lower sequence conservation, different types of active sites and a different distribution of functionally important regions, which altogether make their multiple sequence alignment (MSA) difficult. The KMAD MSA software has been written specifically for the alignment and annotation of IDPs. It augments the substitution matrix with knowledge about post-translational modifications, functional domains and short linear motifs.
Results: MSAs produced with KMAD describe well-conserved features among IDPs, tend to agree well with biological intuition, and are a good basis for designing new experiments to shed light on this large, understudied class of proteins.
Availability and implementation: KMAD web server is accessible at http://www.cmbi.ru.nl/kmad/. A standalone version is freely available.
Contact: vriend@cmbi.ru.nl
BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts
Authors: Yang Kun , Stracquadanio Giovanni , Luo Jingchuan , Boeke Jef D. , Bader Joel S. 
Published: November 2015

Abstract
Summary: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment.
Availability and implementation: BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder.
Contact: joel.bader@jhu.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins
Authors: Heffernan Rhys , Dehzangi Abdollah , Lyons James , Paliwal Kuldip , Sharma Alok , Wang Jihua , Sattar Abdul , Zhou Yaoqi , Yang Yuedong 
Published: November 2015

AbstractMotivation: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ.Results: This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction.Availability and implementation: The method is available at http://sparks-lab.org.Contact: yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.
MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction
Authors: Li Nan , Ainsworth Richard I. , Wu Meixin , Ding Bo , Wang Wei 
Published: November 2015

Abstract
Motivation: MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency.
Availability and implementation: The program is available at http://wanglab.ucsd.edu/MIEC-SVM.
Contact: wei-wang@ucsd.edu
Supplementary information : Supplementary data available at Bioinformatics online.
PAPA: a flexible tool for identifying pleiotropic pathways using genome-wide association study summaries
Authors: Wen Yan , Wang Wenyu , Guo Xiong , Zhang Feng 
Published: November 2015

Abstract
Summary: Pleiotropy is common in the genetic architectures of complex diseases. To the best of our knowledge, no analysis tool has been developed for identifying pleiotropic pathways using multiple genome-wide association study (GWAS) summaries by now. Here, we present PAPA, a flexible tool for pleiotropic pathway analysis utilizing GWAS summary results. The performance of PAPA was validated using publicly available GWAS summaries of body mass index and waist-hip ratio of the GIANT datasets. PAPA identified a set of pleiotropic pathways, which have been demonstrated to be involved in the development of obesity.
Availability and implementation : PAPA program, document and illustrative example are available at http://sourceforge.net/projects/papav1/files/.
Contact : fzhxjtu@mail.xjtu.edu.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
Uncovering multiloci-ordering by algebraic property of Laplacian matrix and its Fiedler vector
Authors: Cheon Mookyung , Kim Choongrak , Chang Iksoo 
Published: November 2015

AbstractMotivation: The loci-ordering, based on two-point recombination fractions for a pair of loci, is the most important step in constructing a reliable and fine genetic map.Results: Using the concept from complex graph theory, here we propose a Laplacian ordering approach which uncovers the loci-ordering of multiloci simultaneously. The algebraic property for a Fiedler vector of a Laplacian matrix, constructed from the recombination fraction of the loci-ordering for 26 loci of barley chromosome IV, 846 loci of Arabidopsisthaliana and 1903 loci of Malus domestica, together with the variable threshold uncovers their loci-orders. It offers an alternative yet robust approach for ordering multiloci.Availability and implementation : Source code program with data set is available as supplementary data and also in a software category of the website (http://biophysics.dgist.ac.kr)Contact: crkim@pusan.ac.kr or iksoochang@dgist.ac.kr.Supplementary information: Supplementary data are available at Bioinformatics online.
Optimal seed solver: optimizing seed selection in read mapping
Authors: Xin Hongyi , Nahar Sunny , Zhu Richard , Emmons John , Pekhimenko Gennady , Kingsford Carl , Alkan Can , Mutlu Onur 
Published: November 2015

Abstract
Motivation: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the ability of the mapper in selecting less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds.
Results: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-base-pair read in O(x×L) operations on average and in O(x×L2) operations in the worst case, while generating a maximum of O(L2) seed frequency database lookups. We compare OSS against four state-of-the-art seed selection schemes and observe that OSS provides a 3-fold reduction in average seed frequency over the best previous seed selection optimizations.
Availability and implementation: We provide an implementation of the Optimal Seed Solver in C++ at: https://github.com/CMU-SAFARI/Optimal-Seed-Solver
Contact: hxin@cmu.edu, calkan@cs.bilkent.edu.tr or onur@cmu.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Metabolic network-guided binning of metagenomic sequence fragments
Authors: Biggs Matthew B. , Papin Jason A. 
Published: November 2015

Abstract
Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health.
Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks.
Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/.
Contact: papin@virginia.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Optimal design of gene knockout experiments for gene regulatory network inference
Authors: Ud-Dean S. M. Minhaz , Gunawan Rudiyanto 
Published: November 2015

Abstract
Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference.
Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates.
Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality.
Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE.
Contact: rudi.gunawan@chem.ethz.ch
Supplementary information: Supplementary data are available at Bioinformatics online.
CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis
Authors: Vrahatis Aristidis G. , Dimitrakopoulou Konstantina , Balomenos Panos , Tsakalidis Athanasios K. , Bezerianos Anastasios 
Published: November 2015

Abstract
Motivation: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific ‘active parts’ of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical ‘themes’—in the form of enriched biologically relevant microRNA-mediated subpathways—that determine the functionality of signaling networks across time.
Results: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways.
Availability and implementation: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/.
Contact: tassos.bezerianos@nus.edu.sg.
Supplementary information: Supplementary data are available at Bioinformatics online.
Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences
Authors: Audain Enrique , Ramos Yassel , Hermjakob Henning , Flower Darren R. , Perez-Riverol Yasset 
Published: November 2015

Abstract
Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.
Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction.
Contact: yperez@ebi.ac.uk
Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR.
Supplementary information: Supplementary data are available at Bioinformatics online.
TCGA2STAT: simple TCGA data access for integrated statistical analysis in R
Authors: Wan Ying-Wooi , Allen Genevera I. , Liu Zhandong 
Published: November 2015

Abstract
Motivation: Massive amounts of high-throughput genomics data profiled from tumor samples were made publicly available by the Cancer Genome Atlas (TCGA).
Results: We have developed an open source software package, TCGA2STAT, to obtain the TCGA data, wrangle it, and pre-process it into a format ready for multivariate and integrated statistical analysis in the R environment. In a user-friendly format with one single function call, our package downloads and fully processes the desired TCGA data to be seamlessly integrated into a computational analysis pipeline. No further technical or biological knowledge is needed to utilize our software, thus making TCGA data easily accessible to data scientists without specific domain knowledge.
Availability and implementation: TCGA2STAT is available from the https://cran.r-project.org/web/packages/TCGA2STAT/index.html.
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: zhandong.liu@bcm.edu
Improving health care proxy documentation using a web-based interview through a patient portal
Authors: Bajracharya Adarsha S , Crotty Bradley H , Kowaloff Hollis B , Safran Charles , Slack Warner V 
Published: November 2015

Objective Health care proxy (HCP) documentation is suboptimal. To improve rates of proxy selection and documentation, we sought to develop and evaluate a web-based interview to guide patients in their selection, and to capture their choices in their electronic health record (EHR).
Methods We developed and implemented a HCP interview within the patient portal of a large academic health system. We analyzed the experience, together with demographic and clinical factors, of the first 200 patients who used the portal to complete the interview. We invited users to comment about their experience and analyzed their comments using established qualitative methods.
Results From January 20, 2015 to March 13, 2015, 139 of the 200 patients who completed the interview submitted their HCP information for their clinician to review in the EHR. These patients had a median age of 57 years (Inter Quartile Range (IQR) 45–67) and most were healthy. The 99 patients who did not previously have HCP information in their EHR were more likely to complete and then submit their information than the 101 patients who previously had a proxy in their health record (odds ratio 2.4, P = .005). Qualitative analysis identified several ways in which the portal-based interview reminded, encouraged, and facilitated patients to complete their HCP.
Conclusions Patients found our online interview convenient and helpful in facilitating selection and documentation of an HCP. Our study demonstrates that a web-based interview to collect and share a patient’s HCP information is both feasible and useful.