bluuebunny/crossref_metadata_2025_split huggingface dataset:
XGSA: A statistical method for cross-species gene set analysis
Authors: Djordjevic Djordje , Kusumi Kenro , Ho Joshua W K 
Published: September 2016

Abstract

Motivation
Gene set analysis is a powerful tool for determining whether an experimentally derived set of genes is statistically significantly enriched for genes in other pre-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene sets. Current gene set analysis methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species gene set analysis.

Results
In this study, we show that not accounting for the complex homology structure when comparing gene sets in two species can lead to false positive discoveries, especially when comparing gene sets that have complex gene homology relationships. To overcome this bias, we propose a straightforward statistical approach, called XGSA, that explicitly takes the cross-species homology mapping into consideration when doing gene set analysis. Simulation experiments confirm that XGSA can avoid false positive discoveries, while maintaining good statistical power compared to other ad hoc approaches for cross-species gene set analysis. We further demonstrate the effectiveness of XGSA with two real-life case studies that aim to discover conserved or species-specific molecular pathways involved in social challenge and vertebrate appendage regeneration.

Availability and Implementation
The R source code for XGSA is available under a GNU General Public License at http://github.com/VCCRI/XGSA

Contact
jho@victorchang.edu.au
Complementary feature selection from alternative splicing events and gene expression for phenotype prediction
Authors: Labuzzetta Charles J , Antonio Margaret L , Watson Patricia M , Wilson Robert C , Laboissonniere Lauren A , Trimarchi Jeffrey M , Genc Baris , Ozdinler P Hande , Watson Dennis K , Anderson Paul E 
Published: September 2016

Abstract

Motivation
A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from alternative splicing, may provide a novel and complementary set of transcripts for phenotype prediction. In contrast to gene expression, the number of isoforms increases significantly due to numerous alternative splicing patterns, resulting in a prioritization problem for many machine learning algorithms. This study identifies the empirically optimal methods of transcript quantification, feature engineering and filtering steps using phenotype prediction accuracy as a metric. At the same time, the complementary nature of gene and isoform data is analyzed and the feasibility of identifying isoforms as biomarker candidates is examined.

Results
Isoform features are complementary to gene features, providing non-redundant information and enhanced predictive power when prioritized and filtered. A univariate filtering algorithm, which selects up to the N highest ranking features for phenotype prediction is described and evaluated in this study. An empirical comparison of pipelines for isoform quantification is reported by performing cross-validation prediction tests with datasets from human non-small cell lung cancer (NSCLC) patients, human patients with chronic obstructive pulmonary disease (COPD) and amyotrophic lateral sclerosis (ALS) transgenic mice, each including samples of diseased and non-diseased phenotypes.

Availability and Implementation
https://github.com/clabuzze/Phenotype-Prediction-Pipeline.git

Contact
clabuzze@iastate.edu, antoniom@bc.edu, watsondk@musc.edu, andersonpe2@cofc.edu
Mutual enrichment in aggregated ranked lists with applications to gene expression regulation
Authors: Cohn-Alperovich Dalia , Rabner Alona , Kifer Ilona , Mandel-Gutfreund Yael , Yakhini Zohar 
Published: September 2016

Abstract

Motivation
It is often the case in biological measurement data that results are given as a ranked list of quantities—for example, differential expression (DE) of genes as inferred from microarrays or RNA-seq. Recent years brought considerable progress in statistical tools for enrichment analysis in ranked lists. Several tools are now available that allow users to break the fixed set paradigm in assessing statistical enrichment of sets of genes. Continuing with the example, these tools identify factors that may be associated with measured differential expression. A drawback of existing tools is their focus on identifying single factors associated with the observed or measured ranks, failing to address relationships between these factors. For example, a scenario in which genes targeted by multiple miRNAs play a central role in the DE signal but the effect of each single miRNA is too subtle to be detected, as shown in our results.

Results
We propose statistical and algorithmic approaches for selecting a sub-collection of factors that can be aggregated into one ranked list that is heuristically most associated with an input ranked list (pivot). We examine performance on simulated data and apply our approach to cancer datasets. We find small sub-collections of miRNA that are statistically associated with gene DE in several types of cancer, suggesting miRNA cooperativity in driving disease related processes. Many of our findings are consistent with known roles of miRNAs in cancer, while others suggest previously unknown roles for certain miRNAs.

Availability and Implementation
Code and instructions for our algorithmic framework, MULSEA, are in: https://github.com/YakhiniGroup/MULSEA.

Contact
dalia.cohn@gmail.com

Supplementary information
Supplementary data are available at Bioinformatics online.
The Network Library: a framework to rapidly integrate network biology resources
Authors: Summer Georg , Kelder Thomas , Radonjic Marijana , van Bilsen Marc , Wopereis Suzan , Heymans Stephane 
Published: September 2016

AbstractMotivationMuch of the biological knowledge accumulated over the last decades is stored in different databases governed by various organizations and institutes. Integrating and connecting these vast knowledge repositories is an extremely useful method to support life sciences research and help formulate novel hypotheses.ResultsWe developed the Network Library (NL), a framework and toolset to rapidly integrate different knowledge sources to build a network biology resource that matches a specific research question. As a use-case we explore the interactions of genes related to heart failure with miRNAs and diseases through the integration of 6 databases.Availability and ImplementationThe NL is open-source, developed in Java and available on Github (https://github.com/gsummer).Contactgeorg.summer@gmail.com
GTRAC: fast retrieval from compressed collections of genomic variants
Authors: Tatwawadi Kedar , Hernaez Mikel , Ochoa Idoia , Weissman Tsachy 
Published: September 2016

Abstract

Motivation
The dramatic decrease in the cost of sequencing has resulted in the generation of huge amounts of genomic data, as evidenced by projects such as the UK10K and the Million Veteran Project, with the number of sequenced genomes ranging in the order of 10 K to 1 M. Due to the large redundancies among genomic sequences of individuals from the same species, most of the medical research deals with the variants in the sequences as compared with a reference sequence, rather than with the complete genomic sequences. Consequently, millions of genomes represented as variants are stored in databases. These databases are constantly updated and queried to extract information such as the common variants among individuals or groups of individuals. Previous algorithms for compression of this type of databases lack efficient random access capabilities, rendering querying the database for particular variants and/or individuals extremely inefficient, to the point where compression is often relinquished altogether.

Results
We present a new algorithm for this task, called GTRAC, that achieves significant compression ratios while allowing fast random access over the compressed database. For example, GTRAC is able to compress a Homo sapiens dataset containing 1092 samples in 1.1 GB (compression ratio of 160), while allowing for decompression of specific samples in less than a second and decompression of specific variants in 17 ms. GTRAC uses and adapts techniques from information theory, such as a specialized Lempel-Ziv compressor, and tailored succinct data structures.

Availability and Implementation
The GTRAC algorithm is available for download at: https://github.com/kedartatwawadi/GTRAC

Contact
kedart@stanford.edu

Supplementary information
Supplementary data are available at Bioinformatics online.
Combining dependentP-values with an empirical adaptation of Brown’s method
Authors: Poole William , Gibbs David L , Shmulevich Ilya , Bernard Brady , Knijnenburg Theo A 
Published: September 2016

AbstractMotivationCombining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here, we discuss an empirical adaptation of Brown’s method (an extension of Fisher’s method) for combining dependent P-values which is appropriate for the large and correlated datasets found in high-throughput biology.ResultsWe show that the Empirical Brown’s method (EBM) outperforms Fisher’s method as well as alternative approaches for combining dependent P-values using both noisy simulated data and gene expression data from The Cancer Genome Atlas.Availability and ImplementationThe Empirical Brown’s method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvalues UsingEBM. The R code is also available as a Bioconductor package from https://www.bioconductor.org/packages/devel/bioc/html/EmpiricalBrownsMethod.html.ContactTheo.Knijnenburg@systemsbiology.orgSupplementary informationSupplementary data are available at Bioinformatics online.
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields
Authors: Wang Sheng , Ma Jianzhu , Xu Jinbo 
Published: September 2016

Abstract

Motivation
Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it challenging to accurately predict IDRs. Most IDR prediction methods use sequence profile to improve accuracy, which prevents its application to proteome-wide prediction since it is time-consuming to generate sequence profiles. On the other hand, the methods without using sequence profile fare much worse than using sequence profile.

Method
This article formulates IDR prediction as a sequence labeling problem and employs a new machine learning method called Deep Convolutional Neural Fields (DeepCNF) to solve it. DeepCNF is an integration of deep convolutional neural networks (DCNN) and conditional random fields (CRF); it can model not only complex sequence–structure relationship in a hierarchical manner, but also correlation among adjacent residues. To deal with highly imbalanced order/disorder ratio, instead of training DeepCNF by widely used maximum-likelihood, we develop a novel approach to train it by maximizing area under the ROC curve (AUC), which is an unbiased measure for class-imbalanced data.

Results
Our experimental results show that our IDR prediction method AUCpreD outperforms existing popular disorder predictors. More importantly, AUCpreD works very well even without sequence profile, comparing favorably to or even outperforming many methods using sequence profile. Therefore, our method works for proteome-wide disorder prediction while yielding similar or better accuracy than the others.

Availability and Implementation
http://raptorx2.uchicago.edu/StructurePropertyPred/predict/

Contact
wangsheng@uchicago.edu, jinboxu@gmail.com

Supplementary information
Supplementary data are available at Bioinformatics online.
PeakXus: comprehensive transcription factor binding site discovery from ChIP-Nexus and ChIP-Exo experiments
Authors: Hartonen Tuomo , Sahu Biswajyoti , Dave Kashyap , Kivioja Teemu , Taipale Jussi 
Published: September 2016

Abstract

Motivation
Transcription factor (TF) binding can be studied accurately in vivo with ChIP-exo and ChIP-Nexus experiments. Only fraction of TF binding mechanisms are yet fully understood and accurate knowledge of binding locations and patterns of TFs is key to understanding binding that is not explained by simple positional weight matrix models. ChIP-exo/Nexus experiments can also offer insight on the effect of single nucleotide polymorphism (SNP) at TF binding sites on expression of the target genes. This is an important mechanism of action for disease-causing SNPs at non-coding genomic regions.

Results
We describe a peak caller PeakXus that is specifically designed to leverage the increased resolution of ChIP-exo/Nexus and developed with the aim of making as few assumptions of the data as possible to allow discoveries of novel binding patterns. We apply PeakXus to ChIP-Nexus and ChIP-exo experiments performed both in Homo sapiens and in Drosophila melanogaster cell lines. We show that PeakXus consistently finds more peaks overlapping with a TF-specific recognition sequence than published methods. As an application example we demonstrate how PeakXus can be coupled with unique molecular identifiers (UMIs) to measure the effect of a SNP overlapping with a TF binding site on the in vivo binding of the TF.

Availability and Implementation
Source code of PeakXus is available at https://github.com/hartonen/PeakXus

Contact
tuomo.hartonen@helsinki.fi or jussi.taipale@ki.se
Characterizing leader sequences of CRISPR loci
Authors: Alkhnbashi Omer S , Shah Shiraz A , Garrett Roger A , Saunders Sita J , Costa Fabrizio , Backofen Rolf 
Published: September 2016

Abstract

Motivation
The CRISPR-Cas system is an adaptive immune system in many archaea and bacteria, which provides resistance against invading genetic elements. The first phase of CRISPR-Cas immunity is called adaptation, in which small DNA fragments are excised from genetic elements and are inserted into a CRISPR array generally adjacent to its so called leader sequence at one end of the array. It has been shown that transcription initiation and adaptation signals of the CRISPR array are located within the leader. However, apart from promoters, there is very little knowledge of sequence or structural motifs or their possible functions. Leader properties have mainly been characterized through transcriptional initiation data from single organisms but large-scale characterization of leaders has remained challenging due to their low level of sequence conservation.

Results
We developed a method to successfully detect leader sequences by focusing on the consensus repeat of the adjacent CRISPR array and weak upstream conservation signals. We applied our tool to the analysis of a comprehensive genomic database and identified several characteristic properties of leader sequences specific to archaea and bacteria, ranging from distinctive sizes to preferential indel localization. CRISPRleader provides a full annotation of the CRISPR array, its strand orientation as well as conserved core leader boundaries that can be uploaded to any genome browser. In addition, it outputs reader-friendly HTML pages for conserved leader clusters from our database.

Availability and Implementation
CRISPRleader and multiple sequence alignments for all 195 leader clusters are available at http://www.bioinf.uni-freiburg.de/Software/CRISPRleader/.

Contact
costa@informatik.uni-freiburg.de or backofen@informatik.uni-freiburg.de

Supplementary information
Supplementary data are available at Bioinformatics online.
Improve homology search sensitivity of PacBio data by correcting frameshifts
Authors: Du Nan , Sun Yanni 
Published: September 2016

Abstract

Motivation
Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than secondary generation sequencing technologies such as Illumina. The long read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and identify gene isoforms with higher accuracy in transcriptomic sequencing. However, PacBio data has high sequencing error rate and most of the errors are insertion or deletion errors. During alignment-based homology search, insertion or deletion errors in genes will cause frameshifts and may only lead to marginal alignment scores and short alignments. As a result, it is hard to distinguish true alignments from random alignments and the ambiguity will incur errors in structural and functional annotation. Existing frameshift correction tools are designed for data with much lower error rate and are not optimized for PacBio data. As an increasing number of groups are using SMRT, there is an urgent need for dedicated homology search tools for PacBio data.

Results
In this work, we introduce Frame-Pro, a profile homology search tool for PacBio reads. Our tool corrects sequencing errors and also outputs the profile alignments of the corrected sequences against characterized protein families. We applied our tool to both simulated and real PacBio data. The results showed that our method enables more sensitive homology search, especially for PacBio data sets of low sequencing coverage. In addition, we can correct more errors when comparing with a popular error correction tool that does not rely on hybrid sequencing.

Availability and Implementation
The source code is freely available at https://sourceforge.net/projects/frame-pro/.

Contact
yannisun@msu.edu
Fast genotyping of known SNPs through approximate k-mer matching
Authors: Shajii Ariya , Yorukoglu Deniz , William Yu Yun , Berger Bonnie 
Published: September 2016

Abstract

Motivation
As the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS).

Results
We introduce lightweight assignment of variant alleles (LAVA), an NGS-based genotyping algorithm for a given set of SNP loci, which takes advantage of the fact that approximate matching of mid-size k-mers (with k = 32) can typically uniquely identify loci in the human genome without full read alignment. LAVA accurately calls the vast majority of SNPs in dbSNP and Affymetrix’s Genome-Wide Human SNP Array 6.0 up to about an order of magnitude faster than standard NGS genotyping pipelines. For Affymetrix SNPs, LAVA has significantly higher SNP calling accuracy than existing pipelines while using as low as ∼5 GB of RAM. As such, LAVA represents a scalable computational method for population-level genotyping studies as well as a flexible NGS-based replacement for SNP arrays.

Availability and Implementation
LAVA software is available at http://lava.csail.mit.edu.

Contact
bab@mit.edu

Supplementary information
Supplementary data are available at Bioinformatics online.
Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach
Authors: Koutroumpas Konstantinos , Ballarini Paolo , Votsi Irene , Cournède Paul-Henry 
Published: September 2016

AbstractMotivationLikelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC–SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels.ResultsIn this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC–SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC–SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data.Availability and ImplementationPython scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software.Contactkonstantinos.koutroumpas@ecp.fr
Overwintering Stages of Pseudosclerotia Development for Monilinia vaccinii-corymbosi, Causal Agent of Mummy Berry Disease on Highbush Blueberry in North America
Authors: Florence Jade , Pscheidt Jay W. 
Published: September 2016

Mummy berry, caused by Monilinia vaccinii-corymbosi, is an economically important disease of blueberries throughout North America. Pseudosclerotia develop apothecia and produce primary inoculum in coordination with floral bud break. Terminology for M. vaccinii-corymbosi pseudosclerotial overwintering stages is inconsistent throughout the literature, and the progression from development to sporulation has never been cohesively and sequentially outlined. A developmental classification scheme was derived from existing terminology and observations of naturally-conditioned pseudosclerotia over a two-year period in Corvallis, OR. The proposed terminology for pseudosclerotial overwintering stages include maturation, dormancy, germination, emergence, differentiation, and carpogenic germination. The standardization of these terms will allow researchers to better communicate regarding this pathogen. Accepted for publication 19 July 2016. Published 15 August 2016.
A Standardized Inoculation Protocol to Test Wheat Cultivars for Reaction to Head Blast Caused by Magnaporthe oryzae (Triticum pathotype)
Authors: Cruz C. D. , Bockus W. W. , Stack J. P. , Valent B. , Maciel J. N. , Peterson G. L. 
Published: September 2016

Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae, poses a significant threat to wheat production worldwide. Because this pathotype does not occur in the United States, it is important to prepare for its possible introduction. As part of this preparation, over 500 U.S. wheat cultivars were tested for susceptibility or resistance to head blast. Inoculations were conducted under biosafety level 3 conditions. However, the protocols to phenotype wheat cultivars vary among researchers, and head blast inoculation protocols need to be standardized so that results can be easily interpreted and shared internationally. The protocol presented, based on several years' experience, is recommended for common use to facilitate direct comparison of disease phenotyping results among researchers. Accepted for publication 12 August 2016.